what is data classification in research

• Database management

Data is central to most every element of modern business -- employees and leaders alike need reliable data to make daily decisions and plan strategically. This guide to explores risks to data and explains the best practices to keep it secure throughout its lifecycle.

Data classification.

• Cameron Hashemi-Pour, Site Editor
• Garry Kranz
• Laura Fitzgibbons

What is data classification?

Data classification is the process of organizing data into categories that make it easy to retrieve, sort and store for future use. A well-planned data classification system makes essential data easy to find and retrieve. This can be of particular importance for risk management , legal discovery and regulatory compliance .

Written procedures and guidelines for data classification policies should define what categories and criteria the organization will use to classify data. They also specify the roles and responsibilities of employees within the organization regarding data stewardship .

Once a data classification scheme is created, security standards should be identified that specify appropriate data handling practices for each category. Storage standards that define the data's lifecycle requirements must be addressed as well.

What is the purpose of data classification?

Systematic classification of data helps organizations manipulate, track and analyze individual pieces of data. Data professionals often have a specific goal when categorizing data. The goal affects the approach they take and the classification levels and definitions they use.

This article is part of

What is data security? The ultimate guide

• Which also includes:
• The importance of data security in the enterprise
• 5 data security challenges enterprises face today
• How to create a data security policy, with template

Some common business goals for data classification projects include the following:

• Confidentiality. A classification system can help safeguard highly sensitive data, such as customers' personally identifiable information ( PII ), including credit card numbers, Social Security numbers and other vulnerable data types. Establishing a classification system helps an organization focus on confidentiality and security policy requirements, such as user permissions and encryption .
• Data integrity. A system that focuses on data integrity requires more storage resources and more sophisticated user permissions and access control.
• Data availability. Addressing information security and integrity makes it easier to know what data can be shared with specific users.

Why data classification is important

Data classification is an important part of data lifecycle management that specifies which standard category or grouping a data object should be assigned to. Once sorted, data classification can help ensure an organization adheres to its data handling guidelines, and to local, state and federal compliance regulations, such as the Health Insurance Portability and Accountability Act, or HIPAA , and the Federal Information Processing Standard that the National Institute of Standards and Technology oversees. Companies in highly regulated industries often implement data classification processes or workflows to aid in compliance audit and data discovery processes.

Data classification is typically used to categorize structured data, but it is especially important when applied to unstructured data. Unstructured data lacks clear labels, so classification makes this data more usable and easier to search or query. Data categorization also helps identify duplicate copies of data. Eliminating redundant data contributes to efficient use of storage and maximizes data security measures.

Common data classification steps

Not all data needs to be classified. In some cases, it isn't necessary to retain data, so destroying it is the prudent course of action . Understanding why data needs to be classified is an important part of the process.

Steps involved in developing a comprehensive set of policies to govern data include the following:

• Gather information. At the start of a data categorization project, organizations must identify and inspect the data that needs to be retained and classified or reclassified. It's important to know where it resides, how valuable it is, how many copies exist and who has access to it.
• Develop a framework. Data scientists and other stakeholders collaborate to develop a framework within which to organize the data, including assigning metadata or other tags to the information. This approach enables machines and software to instantly identify the groups and categories to which a data object belongs. Any information about the data, from file type to character units to size of data packets, can be used to sort and organize data into searchable, sortable categories.
• Apply standards. Companies must ensure their data classification strategy conforms to their internal data protection and handling practices, and reflects industry standards and customer expectations. Unauthorized disclosure of sensitive information , such as protected health information or biometric data, could be a breach of protocol and, in some countries, a crime. To enforce proper protocols and protect against data breaches, the data must be categorized and sorted according to its degree of data sensitivity.
• Process data. This step ensures that items in a database can be identified and sorted according to the established data classification framework.

Types of data classification

Standard data classification levels or categories include the following:

• Public information . Public data in this category is typically maintained by state institutions and subject to disclosure as related to certain laws. For example, aggregated information about a population or different agencies' activities and disclosures fall into this category.
• Confidential information. Confidential data might have legal restrictions in place regarding the way it's handled. There might be other consequences related to how confidential data is handled. Information documenting how a company's product is made or configured would be considered confidential information.
• Sensitive information. This data is any restricted data stored or handled by government or other institutions that have authorization or authentication requirements and other rules associated with its use. An organization's nonpublic financial information would fall within this category. All PII is considered sensitive information.
• Personal information. PII is protected by law and must be handled according to certain protocols. An example would be a person's Social Security number.

Examples of data classification

A number of different category lists can be applied to the information in a system. These lists of qualifications are also known as data classification schemes. For example, one way to classify data's level of sensitivity might include classes such as secret , confidential , business use only and public .

An organization might also use a system that classifies information based on the type of content in files, looking for certain common characteristics. For example, context-based classification examines applications, users, geographic location and creator info. User classification is based on what an end user chooses to create, edit and review.

Data classification and data parsing

In computer programming, file parsing is a method of splitting data packets into smaller subpackets that are easier to move, manipulate, categorize and sort. Different parsing styles determine how a system incorporates information. For instance, dates are split up by day, month or year, and words might be separated by spaces.

Some standard approaches to data classification using parsing include the following:

• Manual intervals. With manual intervals, a person reviews the entire data set and enters class breaks by observing where they make the most sense. This is a fine system for smaller data sets, but it can prove problematic for larger collections of information.
• Defined intervals. Defined intervals specify a number of characters to include in a packet. For example, information might be broken into smaller packets every three units.
• Equal intervals. Equal intervals divide a data set into a specified number of groups, distributing the amount of data evenly across the groups.
• Quantiles. Using quantiles involves setting a number of data values allowed per class type.
• Natural breaks. A program determines where changes in the data occur and uses those indicators as a way of determining where to break up the data.
• Geometric intervals. For geometric intervals, the same number of units is allowed per class category.
• Standard deviation intervals. The standard deviation of a data entry is determined by the degree to which its attributes differ from the norm. There are set number values to show each entry's deviations.
• Custom ranges. Users create and set custom ranges. They can change them at any point.

Tools used for data classification

Various tools are used in data classification, including databases, data management systems and business intelligence software. Some examples of BI software tools that help simplify data classification include Databox, Google Looker Studio and SAP Lumira.

Developers and data scientists use these tools to pull specific kinds of data to complete classification tasks faster. Other methods can be used to assist in applying data classification. For example, a regular expression is an equation used to quickly pull data that fits a certain category, making it easier to categorize all information that falls within those particular parameters.

Benefits of data classification

Data classification methods are useful to an organization for multiple reasons:

• Security and confidentiality. Using data classification helps organizations maintain the security, confidentiality and integrity of their data. Data that's labeled as more sensitive will have stronger security measures applied to it.
• Reducing costs. Classification also helps companies avoid paying increasing data storage costs. Storing data volumes that are excessive, unorganized and not likely to be accessed in their native states is expensive and can be a liability .
• Compliance. Various federal, state and local compliance standards can be met more easily when data is organized according to levels of sensitivity.
• Ease of access. Data that pertains to a specific scenario can be more easily found and queried with labels that reflect its content or metadata.

How does data classification help with compliance and security?

Data classification that's conducted with enough specificity ensures an organization pinpoints which data sets are public, confidential, sensitive and why. Classification lets an organization apply the proper security tools, such as encryption, access controls or data loss prevention , to ensure that restricted data isn't accessible to the wrong audiences and can't be tampered with. Additionally, classification ensures a trail documenting how data is used.

For unstructured data in particular, data classification makes it less vulnerable to breaches. For example, merchants and other businesses that accept credit cards are expected to comply with the data classification and other Payment Card Industry's Data Security Standards . PCI DSS is a set of 12 security requirements aimed at safeguarding customer financial information.

Data classification and the General Data Protection Regulation

The European Union (EU) adopted the General Data Protection Regulation ( GDPR ) in 2016. The GDPR is a set of international guidelines created to help ensure that companies and institutions handle confidential and sensitive data carefully and respectfully. The regulation went into effect in early 2018. It's made up of seven guiding principles: fairness, limited scope, minimized data, accuracy, storage limitations, rights and integrity. The GDPR prescribes stiff penalties for not complying with these standards.

Implementing methodical data classification is a necessity to comply with the many parts of GDPR. It requires organizations handling data on EU citizens to assign specific security control levels to it to prevent unauthorized access or disclosure. Classifying data helps data security teams identify data that requires anonymization or encryption.

Another aspect of GDPR that requires effective data classification is that it gives individuals the right to access, change and delete their personal data. Data classification makes it possible for companies to quickly retrieve such data and fulfill a person's specific request.

What is data reclassification?

To keep data classification systems as efficient as possible, it's important for an organization to continuously update the classification systems it uses. It might be necessary to reassign the values, ranges and outputs of these systems to more effectively meet the organization's evolving classification goals. There are a number of reasons why a business would need to engage in reclassification, including ensuring accuracy, mitigating risks, addressing security and cybersecurity concerns, and complying with local, state and federal regulations.

Implementing a policy to codify periodic reviews of data classification is a sound strategy to achieve this. Employees or managers delegated with data ownership can work with security and compliance officers to develop and enforce such a policy. It should address both internal changes and evolving compliance standards that would warrant data reclassification. It should also introduce new data categories as needed.

Data governance is important for organizations using data as part of their business. Find out more about data governance and how it lowers data risk , ensuring data is consistent, trustworthy and not misused.

Continue Reading About data classification

• Use data classification to protect data, aid backup compliance
• Data classification tools: What they do and who makes them
• Data classification: What it is and why you need it
• Data analytics pipeline best practices: Data classification
• How to build a data protection policy, with template

Related Terms

Dig deeper on database management.

ABC classification

mandatory access control (MAC)

supervised learning

Guard information in cloud with a data classification policy

AR and VR data visualizations offer a new perspective to capture patterns and trends in complex data sets that traditional data ...

Data discovery can use sampling, profiling, visualizations or data mining to extract insights from data. Choose from 10 of the ...

Data professionals can use LLMs for data and predictive analytics work. Still, the analysis of large amounts of textual and ...

Compare Datadog vs. New Relic capabilities including alerts, log management, incident management and more. Learn which tool is ...

Many organizations struggle to manage their vast collection of AWS accounts, but Control Tower can help. The service automates ...

There are several important variables within the Amazon EKS pricing model. Dig into the numbers to ensure you deploy the service ...

Paper and unstructured PDFs need more help to be ingested into and findable within enterprise knowledge repositories. Enter ...

SharePoint 2019 and SharePoint Online have different customization capabilities, payment models and more. Organizations must ...

As strict privacy laws challenge organizations, information governance is the answer. This quiz can help business leaders test ...

With its Cerner acquisition, Oracle sets its sights on creating a national, anonymized patient database -- a road filled with ...

Oracle plans to acquire Cerner in a deal valued at about $30B. The second-largest EHR vendor in the U.S. could inject new life ...

The Supreme Court ruled 6-2 that Java APIs used in Android phones are not subject to American copyright law, ending a ...

As SAP pushes its clean core methodology for S/4HANA Cloud environments, the partners who customized legacy SAP systems will need...

Two executive board members will depart SAP in a move that the company says is both to streamline the structure of the board and ...

Sophia Mendelsohn talks about SAP's ambitions to both set an example of sustainability and be an enabler of it with products such...

This is a potential security issue, you are being redirected to https://csrc.nist.gov .

You have JavaScript disabled. This site requires JavaScript to be enabled for complete site functionality.

An official website of the United States government

Here’s how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock Locked padlock icon ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

NIST IR 8496 (Initial Public Draft)

Data classification concepts and considerations for improving data collection.

    Documentation     Topics

Date Published: November 15, 2023 Comments Due: January 9, 2024 (public comment period is CLOSED) Email Questions to: [email protected]

William Newhouse (NIST) , Murugiah Souppaya (NIST) , John Kent (MITRE) , Kenneth Sandlin (MITRE) , Karen Scarfone (Scarfone Cybersecurity)

Announcement

Data classification is the process an organization uses to characterize its data assets using persistent labels so those assets can be managed properly. Data classification is vital for protecting an organization’s data at scale because it enables application of cybersecurity and privacy protection requirements to the organization’s data assets. This publication defines basic terminology and explains fundamental concepts in data classification so there is a common language for all to use. It can also help organizations improve the quality and efficiency of their data protection approaches by becoming more aware of data classification considerations and taking them into account in business and mission use cases, such as secure data sharing, compliance reporting and monitoring, zero-trust architecture, and large language models.

Submit Comments

  The public comment period for the draft is open until 11:59 p.m. EST on Tuesday, January 9, 2024 . Visit the NCCoE Data Classification project page for a copy of the draft and comment form.

  Join the Community of Interest

 To receive the latest project news and updates, consider joining the NCCoE Data Classification Community of Interest. You can sign-up to become a COI member via the webform here .

Control Families

Media Protection ; Risk Assessment

Documentation

Publication: https://doi.org/10.6028/NIST.IR.8496.ipd Download URL

Supplemental Material: Submit comments Project homepage

Document History: 11/15/23: IR 8496 (Draft)

media protection , privacy , privacy controls , security controls , zero trust

big data , storage

cybersecurity education , enterprise , small & medium business

How to Classify Research Data

Appropriately protecting research data is a fundamental obligation warranted by the research community's underlying commitments to:.

• the providers and sources of the data,
• uphold the efficacy of the campus' research mission, and
• to prevent financial or reputational damages to the University.

To protect research data appropriately and effectively, researchers must understand and carry out their responsibilities related to data security.  The first step towards that goal is to identify the appropriate data classification, which defines the necessary security control requirements for protecting research data.

Why should research data be classified?

Researchers must securely protect research data when:

• The data elements pose a risk of exposing the identity of the research participants.
• The risk of exposure includes personal medical or financial information, social security or driver's license numbers, or other highly sensitive information that could require notification to the affected research participants in the event of a breach.
• A data usage agreement (DUA) from the data provider explicitly stipulates the related security control requirements.

Researchers also must meet campus security policies:

• To provide baseline protection of the research data that corresponds to the protection level classification, regardless of an existing DUA.
• To act as responsible members of the campus computing community by protecting endpoint and server devices from compromise that could affect other members of campus.

And at a basic level, researchers should avoid a costly security incident that could delay or distract from their research goals by protecting data appropriately.

A relevant example of this last point occurred recently on campus.  Ransomware infected a researcher's workstation and spread to the department's network file-share drive, encrypting files containing over 20 years of research project data, with little hope of retrieving the encryption key except by paying the ransom.

This disaster was averted by restoring the files from a recent backup, a good example of security preparedness.  Proper security logging also helped to rule out any incidents of illicit access to personally identifiable information.  Without such logging, the department may have been responsible for costly notification regarding potential identity fraud to research subjects.  Additional security safeguards based upon campus policies, when implemented appropriately, could have prevented this incident or stopped it from spreading.

How is research data classified?

The UC Berkeley Data Classification Standard is a framework for assessing data sensitivity, measured by the adverse business impact a breach of the data would have upon the campus.  The following protection levels reflect the basic principle that as the risk associated with the research data increases, more exacting security requirements must be implemented.

Steps for classifying research data

The following steps provide a guideline for the considerations necessary to determine the data classification protection level for research data.  Answer the following questions:

Protection Level Requirements

Based on the data protection levels defined in the Data Classification Standard, the Minimum Security Standard for Electronic Information (MSSEI) policy identifies the security protections required to safeguard the data.

The MSSEI requirements include the Minimum Security Standard for Networked Devices (MSSND), which is a mandatory set of protections for all endpoint devices that utilize campus network services and is required for all protection level data classes.

These basic requirements, such as keeping the operating system and productivity software programs up-to-date, and running current malware detection tools, go a long way towards protecting the campus from security incidents such as the ransomware example cited above.

Following is an overview of the basic requirements for each of the protection level data classes:

For the classification of UC P2/3 or UC P4 data, please contact the Research Data Management Program and/or the  Information Security Office (ISO)  for assistance with how to apply the MSSEI requirements to research data, and for help with planning the implementation of the requirements.

Additional Resources

• Research Data Classification Questionnaire
• ISO CPHS Assessment Service
• Research topic page

New Atlan Named a Leader in The Forrester Wave™: Enterprise Data Catalogs, Q3 2024. Read Full Report Learn More

The Forrester Wave™: Enterprise Data Catalogs for DataOps, Q2 2022

• Document hundreds of tables on autopilot
• Explore data with natural language
• Ask any question about your data stack to your personal AI copilot.

Start integrating with Atlan on the go

The role of active metadata in the modern data stack

A deep dive into the 10 data trends you should know

May 24, 2022

May 10, 2023

Feb 02, 2022

Join over 5k data leaders from companies like Amazon, Apple, and Spotify who subscribe to our weekly newsletter.

Best practices for building a collaborative data culture

Data Classification: Definition, Types, Examples, Tools & More!

Share this article

Ever wondered how your sensitive data is organized and safeguarded? Data classification tackles the chaos by categorizing based on sensitivity, preventing breaches and ensuring confidentiality.

Without it, are you willing to risk data breaches, compliance lapses, and compromised privacy? Proper classification is the key to secure handling, storage, and meeting regulatory standards.

Modern data problems require modern solutions - Try Atlan, the data catalog of choice for forward-looking data teams! 👉 Book your demo today

In this article, we will delve deep into what data classification actually is, exploring its benefits, types, tools, and more.

Ready? Let’s begin!

Table of contents #

• What is data classification?
• 5 Key benefits of data classification
• 4 Main types of data classification
• 3 Common methods of data classification
• Data classification standards and policies
• 5 Data classification challenges
• Top 5 Data classification tools for you
• How to implement data classification in organizations
• To summarize
• What is data classification: Related articles

What is data classification? #

Data classification is a process used in information technology and data management to categorize data so that it can be used and protected more effectively. It involves assigning a level of sensitivity to different types of data, based on the potential impact to an organization or individual if that data were accessed, altered, or lost.

For example , certain data might be personal or sensitive, requiring strict handling due to privacy concerns, while other data might be less sensitive and thus subject to fewer restrictions. This process not only involves the identification and labeling of data but also requires understanding the implications of different data types, including how their exposure or misuse could impact the organization or individuals.

By classifying data, organizations can prioritize their security resources, comply with legal and regulatory requirements, and optimize their data management strategies. The main goal of this process is to assess and determine how different types of data should be handled, stored, and protected.

In other words, it is to ensure that data is used effectively and responsibly, balancing the need for accessibility and usability with the imperative of security and compliance.

5 Key benefits of data classification #

Data classification is a critical process in managing and protecting an organization’s data. It brings numerous benefits, from enhancing security to ensuring compliance with various regulations.

Here are five key benefits of data classification:

• Improved data security
• Enhanced compliance with regulations
• Efficient data management
• Risk management and mitigation
• Increased awareness and responsibility

Let’s understand each benefit in detail.

1. Improved data security #

By classifying data, organizations can determine which data is most sensitive and requires the highest level of protection. This allows them to apply robust security measures like encryption and access controls specifically to the data that needs it most, reducing the risk of data breaches and unauthorized access.

This targeted approach to security is more effective and efficient than applying the same level of security to all data indiscriminately.

2. Enhanced compliance with regulations #

Many industries are subject to regulations that dictate how certain types of data must be handled. Data classification helps organizations identify which data falls under these regulations and ensure they are complying with legal standards.

This is particularly important for sensitive data like personal health information or financial records, which are often subject to stringent privacy laws.

3. Efficient data management #

Classifying data streamlines data management by organizing data according to its importance and sensitivity.

This organization makes it easier to locate and retrieve data when needed, improving operational efficiency. It also assists in data retention policies, ensuring that data is not kept longer than necessary, thereby reducing storage costs and simplifying data management.

4. Risk management and mitigation #

Data classification plays a vital role in risk management by identifying which data is most valuable and at risk.

With this knowledge, organizations can prioritize their resources and focus their risk mitigation strategies on the most critical data, reducing the potential impact of data-related incidents.

5. Increased awareness and responsibility #

When data is clearly classified, it raises awareness among employees about the different types of data they handle and the corresponding security protocols. This heightened awareness fosters a culture of responsibility and security, as employees are more likely to follow best practices for handling sensitive information.

In summary, data classification not only enhances the security and efficiency of data handling but also ensures regulatory compliance, aids in risk management, and cultivates a more security-conscious organizational culture. These benefits are essential for any organization looking to safeguard its data in today’s digital landscape.

4 Main types of data classification #

Data classification is typically divided into several types, each tailored to the specific needs and risks associated with the data. These types help organizations in managing data security, compliance, and accessibility.

Here are four main types of data classification:

• Confidential

Let’s understand each example sequentially.

1. Public #

Public data is information that can be freely accessed and distributed without any risk of harm to the organization or individuals. This type typically includes information that is already in the public domain or intended for public dissemination, such as press releases, job postings, or marketing materials.

The main focus with public data is on ensuring its accuracy and integrity, rather than its security.

2. Internal #

Internal data is information that is not sensitive but is intended for use within the organization. This might include internal policies, training materials, or certain types of operational data.

While the unauthorized disclosure of internal data is not likely to cause significant harm, it’s still managed with a basic level of security to maintain organizational privacy and efficiency.

3. Confidential #

Confidential data is sensitive information that could cause harm or provide unfair advantage if disclosed. This category often includes trade secrets, customer information, and certain financial records.

Access to confidential data is typically restricted within the organization to those with a legitimate need to know, and it is protected with higher security measures, such as encryption and strict access controls.

4. Restricted #

Restricted data is the most sensitive category, requiring the highest level of security. This type of data could cause significant harm or legal issues if accessed by unauthorized persons.

Examples include personally identifiable information (PII), health records, or sensitive government data. Organizations handling restricted data must adhere to strict regulatory and compliance requirements, employing advanced security measures and strict access controls to safeguard this information.

In short, these data classification types allow organizations to allocate their security resources effectively, ensuring that each type of data is protected in accordance with its sensitivity and the potential impact of its compromise.

3 Common methods of data classification #

When it comes to sorting and securing your company’s information, there are a few ways to go about it. Think of data classification as organizing your business files into different cabinets for easy access and protection. Let’s explore the main ways you can do this:

• Manual data classification
• Automated data classification
• Hybrid approaches

Let us understand these methods in brief.

1. Manual data classification #

Manual classification is like sorting through your papers and deciding where they should go based on what they contain. It requires someone to look at each piece of information and decide how sensitive it is. If a document has personal details about customers, it might be marked as confidential.

This method gives a personal touch and can be very accurate since it relies on human judgment. However, it can take a lot of time, especially if your business has tons of data to go through. It’s also possible for mistakes to happen because, well, we’re all human.

2. Automated data classification #

Automated classification uses software to scan through your data and organize it. It’s like having a super-smart assistant who can quickly file everything correctly without getting tired. This method is fast and can handle a lot of data at once.

The software can look for certain words or patterns that help it decide how sensitive the data is. For example, it can identify and protect any document that contains a credit card number. The downside? It might not catch everything since it follows a set of rules and doesn’t understand context as we do.

3. Hybrid approaches #

A hybrid approach combines the best of both worlds. Here, you’d use software to sort through the bulk of your data, but you’d also have people check over the results or make decisions on the trickier cases.

It’s like having a machine do the heavy lifting while a skilled worker does the fine-tuning. This method can save time and reduce errors, making sure that your data is sorted accurately and efficiently.

How you classify your data will depend on the size of your business, the type of information you handle, and how much time and resources you have. Each method has its benefits, but the most important thing is that you do classify your data. It helps keep your business’s information safe and well-organized, which is key to operating smoothly and maintaining your customers’ trust.

Data classification standards and policies #

When you’re running a business, it’s crucial to keep your information organized and safe. Think of data classification as sorting your data into different groups, much like how you might sort files into folders.

This helps you manage who can see and use your business’s information. Some data might be open for everyone, while other information is strictly for certain eyes only. To do this effectively, there are rules and best practices you should follow.

Overview of industry standards #

Imagine you have a set of instructions that tell you the best way to sort and protect your data. These instructions are known as industry standards. One of the most recognized sets of instructions is called ISO/IEC 27001.

This standard gives you a plan to manage your data securely, covering everything from how to start, what to do, and how to check if you’re doing it right. It’s like a quality seal that tells others you take data safety seriously, which can be great for your business’s reputation.

Government and regulatory policies impacting data classification #

Different places have different rules about how data should be handled. Governments create these rules to make sure personal and sensitive information is not misused or gets into the wrong hands.

For example, in the European Union, there’s a rule known as GDPR , which is all about protecting personal data. It’s important to know the rules that apply to your business because not following them can lead to big fines and harm your business’s trustworthiness.

Company-specific policies and best practices #

Your company should have its own set of rules for how to handle data. These are policies you create that fit your business’s specific needs and comply with the broader rules we just talked about.

For example, you might decide that only senior managers can access customer financial information. You should write these rules down, train your team on them, and check regularly to make sure everyone’s following them. It’s also smart to look at what has worked well for other businesses and consider adopting similar practices.

Remember, sorting and protecting your data isn’t just about following rules; it’s about being responsible and earning trust. A solid approach to data classification helps you run your business smoothly and keeps your customers’ and employees’ information safe.

5 Data classification challenges #

In the world of business, keeping your data in order is like organizing a library. It sounds straightforward until you face the real-life challenges. Let’s talk about some of the common hurdles you might encounter when classifying your data. They are:

• Handling unstructured data
• Balancing security with access
• Keeping up with laws and regulations
• The human factor
• Technology can be a double-edged sword

Let us understand these challenges in detail.

1. Handling unstructured data #

Think about all the emails, documents, and other files your business creates daily. They don’t come in a neat package, making them hard to sort through. This is what we call ‘unstructured data’, and it’s a bit like trying to organize a pile of papers without any labels.

It’s tricky because this kind of data doesn’t follow a set format, and it’s tough to categorize using simple rules.

2. Balancing security with access #

You want to keep your data safe, but you also need the right people to access it when necessary. It’s a delicate balance. Locking away data too tightly might hinder your team’s ability to do their jobs, but being too lax could lead to security risks.

It’s like keeping a door locked but making sure those who need to can still get the key.

3. Keeping up with laws and regulations #

Laws about data privacy are always changing, and they can vary from place to place. Staying on top of these can be as complex as trying to keep up with the latest fashion trends — as soon as you think you’ve got it, it changes again.

You have to make sure that your data classification aligns with these laws to avoid hefty fines and protect your customers’ privacy.

4. The human factor #

Even the best plans can go awry if the people involved don’t follow through. Training your team to understand and correctly apply data classification can be as challenging as teaching someone a new language. And just like language learning, it requires consistent practice and reinforcement.

5. Technology can be a double-edged sword #

On one hand, technology offers tools that can sort and classify data almost like magic. On the other hand, these tools can be costly, complex, and sometimes they might not work with the other systems you already have in place.

It’s a bit like finding a piece of puzzle that doesn’t fit into your puzzle board.

Addressing these challenges may not be easy, but it’s vital for the health and efficiency of your business. Think of data classification as the foundation of a building. Get it right, and you’ll have a stable base for your business operations to grow and thrive.

Top 5 data classification tools for you #

Data classification tools are essential for organizations to effectively categorize and manage their data. These tools vary in their features and capabilities, but they all aim to enhance data security, ensure compliance, and streamline data management.

Here’s a look at five top data classification tools:

1. Varonis Data Classification Engine #

Varonis Data Classification Engine specializes in identifying sensitive information across an organization’s digital assets. It uses sophisticated algorithms to locate, classify, and tag particularly sensitive or regulated data, such as personal information.

Its strength lies in providing detailed visibility into where sensitive data is stored and how it is utilized, aiding in compliance and security enhancement.

2. Titus Classification Suite #

Titus Classification Suite offers a combination of manual and automated data classification capabilities. It integrates well with other security systems to enhance data loss prevention and ensure compliance with organizational policies.

Its user-friendly approach allows data creators to classify data at the point of creation, making it a versatile tool for organizations.

3. Symantec Data Loss Prevention #

Symantec Data Loss Prevention is a comprehensive solution that extends beyond classification to offer strong data protection. It employs a variety of detection techniques to identify sensitive data across different environments, including the cloud.

This tool is particularly effective in preventing data breaches and ensuring regulatory compliance.

4. Microsoft Azure Information Protection #

Microsoft Azure Information Protection is a cloud-based solution designed for classifying, labeling, and protecting data. It works well within the Microsoft ecosystem, offering seamless integration with Office 365 and other Microsoft products.

The tool is user-friendly and provides robust encryption capabilities, making it a good choice for organizations using Microsoft services.

5. Boldon James Classifier #

Boldon James Classifier is known for its customizable approach, allowing organizations to tailor their classification schema and policies. It focuses on user-centric manual classification, integrating into commonly used applications like Microsoft Office.

This flexibility makes it a strong option for organizations looking for a personalized data classification solution.

Each of these tools brings unique strengths to the table, helping organizations manage their data more securely and efficiently, while also staying compliant with various regulatory requirements.

How to implement data classification in organizations? #

When you’re running a business, you’ve got a lot of information flowing through your company. Some of this information might be things you’re okay with sharing, like your business hours or the services you offer.

But some information is sensitive and should be kept under wraps, like customer details or your secret sauce recipe. This is where data classification comes in. It’s like deciding which documents go into a locked cabinet and which can be left on the desk for anyone to see.

Steps to develop a data classification policy #

Developing a data classification policy is a crucial step for any organization that handles sensitive or confidential information. This policy helps in categorizing data based on its sensitivity and lays out guidelines for its protection and handling. Here are the steps to develop a comprehensive data classification policy:

• Define the purpose and scope
• Identify stakeholders
• Data inventory and categorization
• Data classification criteria
• Data handling guidelines
• Access control and authentication
• Data labeling and marking
• Training and awareness
• Incident response plan
• Regular audits and reviews
• Legal and compliance considerations
• Documentation and communication
• Feedback mechanism
• Enforcement and consequences
• Periodic updates

Let’s look at each of the steps in detail:

1. Define the purpose and scope

Start by clearly defining the purpose of the data classification policy. Determine the scope of the policy by specifying the types of data it will cover. This may include customer data, financial records, intellectual property, or any other sensitive information relevant to your organization.

2. Identify stakeholders

Identify the key stakeholders involved in data management and security within your organization. This may include IT personnel, legal experts, data owners, and senior management. Involve them in the policy development process to ensure a well-rounded perspective.

3. Data inventory and categorization

Conduct a thorough inventory of all data assets in your organization. Categorize data into different classes or levels based on its sensitivity. Common classifications include public, internal, confidential, and highly confidential.

4. Data classification criteria

Develop clear criteria for each data classification level. For example, confidential data might be defined as information that, if compromised, could have a severe impact on the organization, while public data is information that can be freely shared.

5. Data handling guidelines

Specify how each classification level should be handled, stored, transmitted, and disposed of. Include encryption requirements, access controls, and retention policies. Ensure that these guidelines align with legal and regulatory requirements.

6. Access control and authentication

Define who has access to each classification level and how access is granted and revoked. Implement authentication mechanisms like strong passwords, multi-factor authentication, and role-based access control to ensure data security.

7. Data labeling and marking

Establish a consistent labeling and marking system for data assets. This helps employees easily identify the classification level of data they are working with. Labels can be physical (e.g., on paper documents) or digital (e.g., in file headers).

8. Training and awareness

Develop a training program to educate employees about the data classification policy. Make sure all staff members understand their responsibilities in handling data according to its classification level. Regularly update training materials to reflect changes in the policy.

9. Incident response plan

Create an incident response plan that outlines the steps to be taken in case of a data breach or unauthorized access to sensitive information. Define roles and responsibilities for incident response team members.

10. Regular audits and reviews

Establish a schedule for regular audits and reviews of the data classification policy and its implementation. This ensures that the policy remains up-to-date and effective in protecting sensitive information.

11. Legal and compliance considerations

Ensure that the data classification policy aligns with relevant laws and regulations, such as GDPR, HIPAA , or industry-specific standards. Involve legal experts to review and validate the policy from a compliance standpoint.

12. Documentation and communication

Document the data classification policy in a clear and easily accessible format. Communicate the policy to all employees and regularly remind them of their obligations regarding data classification and security.

13. Feedback mechanism

Establish a feedback mechanism to allow employees to report concerns or suggest improvements to the policy. Encourage a culture of continuous improvement in data security.

14. Enforcement and consequences

Clearly outline the consequences of violating the data classification policy. This may include disciplinary actions or legal consequences. Ensure that enforcement is consistent and fair.

15. Periodic updates

Recognize that data classification needs may evolve over time. Periodically review and update the policy to adapt to changing business needs, technology advancements, and emerging threats.

By following these steps, an organization can develop a robust data classification policy that not only protects sensitive information but also fosters a culture of data security and compliance throughout the organization. Regular monitoring and adaptation are key to ensuring the policy remains effective in the face of evolving data-related challenges .

Training and awareness for employees #

Your data classification policy won’t do much good if your team doesn’t know about it or understand it. Training is crucial. The following steps can help you better train the employees and generate awareness among them:

• Regular training sessions
• Resources and materials
• Create a culture of security

Let’s look at them below:

Hold workshops or sessions that teach your team about the different types of data, the categories you’ve set, and the importance of following these rules.

Give your employees cheat sheets, guides, or posters that they can refer to when they’re not sure about something.

Encourage your employees to take data security seriously. Make it part of your business’s culture.

Monitoring and enforcing compliance #

Once your policy is in place and your team is trained, you need to make sure everyone’s following the rules. Follow the below aspects to ensure for monitoring and ensuring compliance:

• Regular checks
• Technology helps
• Act on issues
• Update your policy

Have regular reviews where you check if the information is being handled correctly. This could be a quick look at recent documents or a more formal audit.

Use software that can help you keep an eye on your data. This software can alert you if someone’s not following the policy.

If you find that someone isn’t following the policy, act on it. This could mean more training or even disciplinary action if needed.

Keep in mind that your business will change and grow, and so will the types of data you deal with. Your policy should be a living document that gets updated regularly.

Think of data classification as a way to make sure your business’s information is only seen by the right eyes. It’s about keeping things orderly and safe, which is something any business owner or decision-maker should care about. By setting clear rules, training your team, and making sure those rules are followed, you can protect your business and your customers.

To summarize #

Data classification is a crucial step for businesses to protect and efficiently manage information. It organizes data based on sensitivity, ensuring compliance and security. While it presents challenges, including adherence to complex standards and handling diverse data types, the right technology can streamline and simplify the process.

Public, internal, confidential, and restricted are the four main types of data classification. And the benefits of data classification include improved data security, enhanced compliance with regulations, efficient data management, risk management and mitigation, increased awareness and responsibility.

For business leaders, embracing data classification is not optional but essential. It’s a continuous commitment to data integrity and a proactive stance on information security.

Implementing and maintaining a data classification system secures a company’s most valuable assets and instills a culture of awareness, safeguarding the business’s future in an increasingly data-driven world.

What is data classification: Related articles #

• Data Classification and Tagging: How to Marie Kondo Your Data Catalog and Spark Joy
• Data Governance vs Data Classification: 5 Key Differences
• Data Catalog and Data Governance: How Do They Complement?
• Agile Data Governance Model: Concept, Importance, Components, and Best Practices
• Active Data Governance: What It Is and How to Get Started
• Data Privacy vs Data Security: How & Why They Aren’t Same?
• Data Management 101: Four Things Every Human of Data Should Know

Data Classification: What It Is and How to Implement It

What Is Data Classification?

Simply put, data classification is the process of categorizing files, databases and other content into logical groupings according to their content. For example, a data classification process might distinguish between public information and various types of sensitive data, as well as identify information that is subject to regulatory mandates like the GDPR , HIPAA or the  California Privacy Rights Act ( CPRA ).

Data classification is therefore vital to both data security and  compliance , especially for organizations that store large volumes of sensitive or protected data. Classifying data also improves user productivity and decision-making, and reduces storage and maintenance costs by empowering you to eliminate unneeded data.

In this article, you will learn more about the purpose and benefits of data classification, the steps in the data protection process, best practices, and tips for getting a program approved. Finally, you’ll get a guide to help you determine the best solution for your organization.  

Data Classification Software from Netwrix

Types of Data Classification

At a high level, most organizations use a basic strategy to classify data: They manually organize data into folders and subfolders based on their contents. For instance, mortgage applications might be sorted into the Finance category, while offer letters may fall under Human Resources. Windows and other operating systems even come with some basic categories, like Music, Videos and Documents.

However, this is not what the term “data classification” refers to in the world of data security . Rather, data classification means to categorize data based on its sensitivity, which is indicated by who should be permitted to access and use the data. For example, categories might include Top Secret and Confidential for data that needs to be restricted to specific audiences, and Public for information that can be shared freely.  

Here are some examples of sensitivity-based classification schemas:

Example Commercial Classification

The data classification schemes used by private organizations typically have three or four levels, such as this one:

• Public : Data that can be freely disclosed, such as your company’s contact information and browser cookie policy
• Proprietary: Information that is private but has low sensitivity, such as organizational processes
• Confidential: Data that has higher security requirements, like competitor research. vendor contracts and employee reviews
• Sensitive : Highly sensitive data whose disclosure could disrupt operations or put the organization at financial or legal risk, such as intellectual property, bespoke applications or healthcare records.

Example Government Classification 

Government agencies often use the following levels when classifying data:

• Top Secret : Cryptologic and communications intelligence
• Secret : Select military plans
• Confidential: Data indicating the strength of ground forces
• Sensitive unclassified (or “CUI”) : Data tagged “For Official Use Only”
• Unclassified : Data that may be publicly released with authorization

Data Classification Process

The data classification process comprises the following steps:

Step 1. Categorize the Data

The first step in the data classification process is to determine what type of information a piece of data is. To automate this process, organizations can specify specific words and phrases to look for, as well as define  regular expressions to find data that follows a certain pattern, such as credit card numbers or medical procedure codes.

Step 2. Label the Data

Once a piece of data has been categorized, It’s important to record that decision for future use. There are several ways to do this:

• Tagging — Another options is to place a digital tag on each file, such as the tags offered by Microsoft Office. Users can search for content based on these tags, and they can be also used by security tools such as data loss prevention (DLP) solutions. 
• Extended f ile m etadata — Many modern collaboration platforms can add metadata to content without changing the file itself. For instance, SharePoint, Box, Dropbox and Google Drive can add metadata to a file to improve searchability and classification.

Step 3. Repeat

It’s important to remember that data classification is not a once-and-done process. Not only is new data constantly being created and collected, but existing data can change classification due to new contractual obligations and modifications to internal policies or legal mandates.  

Benefits of Data Classification

Understanding what types of data you’re storing and where brings many benefits, including improved data security and  regulatory compliance .

Data Security

Classifying your data improves data security by enabling you to:

• Prioritize your security efforts and apply appropriate security controls based on data sensitivity.
• More easily understand who can access, modify or delete certain types of data.
• Improve risk management processes by providing insights like the potential business impact of a breach or ransomware attack.

Regulatory Compliance

Data classification can identify data that is subject to various compliance regulations so you can protect it as required and pass audits. Here’s how data classification can help you meet  common compliance standards :

• GDPR : Data classification helps you uphold the rights of data subjects, including fulfilling  data subject access request s by quickly retrieving documents that contain a given individual’s data.
• HIPAA : Accurately storing health records helps you implement security controls for proper data protection.
• ISO 27001 :  Classifying information according to value and sensitivity helps you meet requirements for preventing unauthorized disclosure or modification.
• NIST SP 800-53 : Categorizing data helps federal agencies properly structure and manage their IT systems.
• PCI DSS : Sensitivity data classification helps you identify and secure payment card information.
• CMMC: US government contactors can establish control over both personal sensitive data and CUI.

Data Classification for Compliance: Looking at the Nuances 

Other Benefits

In addition, a solid data discovery and data classification system can:

• Enable faster and more accurate legal discovery.
• Improve user productivity and decision-making through more effective search.
• Reduce data maintenance and storage costs by identifying duplicate and stale data.

Tips for Justifying a Data Classification Policy

In addition to outlining the data security , compliance and other benefits of data classification, here are some tips to get support for implementing your program.

Demonstrate Current Risk

The most compelling way to secure funding for a data classification program is a demo. Pick one of your data repositories, such as SharePoint, and scan it with a data classification tool. Most likely, it’ll pinpoint loads of sensitive data that needs to be tagged and properly secured. Be sure to show how many individuals have access to the data — and how many of them should not have that access. 

Quantify Potential Damage

Try to quantify the damage that the organization could suffer if an adversary used a compromised account to steal data that should have been out of reach or to deploy ransomware to encrypt it.

Also list any compliance regulations the current situation might be violating, and the penalties that could be levied.

Show Additional Benefits

Classifying data can enhance the value of existing investments, like data loss prevention and user and entity behavior analytics (UEBA) tools, by identifying the most critical files to protect.

Data classification can also accelerate high-profile programs like cloud migration. Indeed, one of the biggest hindrances to cloud adoption is the fear of losing control of sensitive data. But if your files are classified, it is easy to ensure that critical content remains in secure locations.

Present a Comprehensive Data Classification Policy

Having a detailed  data classification policy helps demonstrate that the project is not just worthwhile, but clearly thought out and ready to implement. Effective classification policies should:

• Use language and formatting that is clear and simple.
• Explain the purpose and scope of the data classification process.
• Detail an appropriate number of classification levels (often 3–5), with unambiguous criteria that are generic enough to apply to different data sets.
• Identify roles and responsibilities, including points of contact for clarification.
• Include a history of revisions.

Data Classification Policy Template

How to Select a Data Classification Solution

To find the best data classification solution for your organization, be sure to look for the following capabilities:

• Automation : It’s essential to choose a solution that automates the work of classifying data at the time of creation — as well as classifying all the organization has already amassed, which can be terabytes of data.
• Compound term search : This feature improves the accuracy of determining whether a given file falls into a particular category, minimizing both false positives and false negatives.
• Index:  It’s important to be able to identify sensitive terms without re-crawling the data.
• Flexible taxonomy manager:  Your organization can start with out-of-the-box taxonomies, but you will soon want to add and modify terms and rules, so look for a solution that makes the task easy.
• Workflows : It’s extremely helpful to have a solution that can take specific actions automatically based on a document’s classification. For example, if sensitive data is discovered on a public share, the solution could immediately move it to a secure quarantine area.
• Breadth of coverage:  Be sure the solution supports all your data sources, including structured and unstructured data in the cloud and on premises.

Conclusion: Is Data Classification Worth the Effort?

Given that an estimated  33 billion data records will be stolen in 2023, organizations are eager to improve data security. And with data privacy regulations packing steep penalties, they cannot afford to neglect compliance.

But how can you even begin to protect your most sensitive data if you don’t know where it is?  And how can you get the most value from your current security tools if they can’t tell what’s inside your files?

Data classification is a foundational technology that helps you strengthen both security and compliance. Moreover, it can improve user productivity and effectiveness, speed initiatives like cloud migration, and reduce data management and storage costs. By choosing the right data classification solution, you can gain a wealth of benefits without disrupting your operations.

How Can Netwrix Help?

The Netwrix Data classification software will help you lock down critical data. But that’s not all. In addition, it empowers you to:

• Focus your security efforts on truly sensitive data.
• Ensure high-accuracy classification results with our unique compound term processing and statistical analysis technology.
• Protect sensitive files by automatically moving them to a safe area and removing permissions from global access groups.
• Embed classification information right into the files to improve the accuracy of your DLP or IRM products and streamline data management tasks.
• Reduce the cost and effort associated with the flow of DSAR requests.

To experience all advantages of Netwrix Data classification software , please visit this page .

What is the purpose of data classification?  

Data classification sorts data into categories based on its value and sensitivity.

Why is data classification important, and what benefits does it offer?

Data classification helps you improve data security and regulatory compliance. You can prioritize your protection efforts, improve user productivity and decision-making, and reduce costs by eliminating unneeded data to free up storage.

What are common data classification levels? 

Data is often classified as Public, Proprietary, Confidential or Sensitive.

What software should I use for data classification? 

Look for  data classification software that:

• Uses compound word search to ensure accurate classification
• Has an index to find sensitive terms without re-crawling your data stores
• Includes a flexible taxonomy manager that empowers you to customize your classification parameters
• Provides workflows to automate processes such as moving sensitive data from public shares
• Supports both on-premises and cloud content sources, including structured and unstructured data

Who is responsible for data classification in an organization? 

Organizations typically designate a security and risk manager, a data protection manager, a compliance committee, or a similar entity.

Before you go, grab this Data Protection Policy template, which you can freely adapt to meet your organization’s unique security and compliance requirements.

• Data Security

What is Data Classification? Definition, Levels & Examples

Data Classification is simply the process of organizing data based on a set of pre-defined categories. Since organizations have limited resources, it is important for them to know exactly where their most sensitive data is located, in order to be able to allocate those resources in the most effective manner.

Data Classification Definition

Data classification is the process of categorizing data based on its confidentiality in order to determine the level of access that should be granted to it and the level of protection it requires against unauthorized access or disclosure. The classification of data can be based on factors such as the type of data, its value, the level of risk of its exposure, and any applicable regulatory requirements. The purpose of data classification is to provide a framework for data management and security that enables organizations to identify and protect their most valuable and sensitive data assets.

Data Classification Reasons and Benefits

There are many reasons/benefits why organizations choose to classify their data, which are as follows;

• Data classification helps ensure sensitive information is properly protected
• It allows organizations to prioritize resources based on the value of the data
• Data classification can help with regulatory compliance by making it easier to respond to subject access request (SARs)
• It enables more effective data sharing and collaboration within an organization
• Proper data classification can reduce the risk of data breaches or leaks
• It can aid in disaster recovery and business continuity planning
• Classification can help organizations determine appropriate levels of access and control for different types of data
• Classification allows for better data management and organization
• It can support more accurate reporting and analysis of data
• Data classification can help organizations save time and resources by focusing efforts on the most important data.

Types of Data Classification

One common classification is based on sensitivity or confidentiality. In this approach, data is classified as public, internal, confidential, or highly confidential. Public data is non-sensitive information that can be openly shared. Internal data is restricted to an organization and accessible only to authorized personnel. Confidential data requires a higher level of protection due to its sensitive nature, such as customer details or financial records. Highly confidential data includes trade secrets or classified information, which demands the highest level of security.

Another classification type is based on data content. It involves categorizing data according to its characteristics or attributes. For instance, data can be classified as text, images, audio, video, or numerical data. This classification helps in understanding the nature of the data and determining appropriate storage and processing techniques.

Temporal data classification is used to organize data based on time-related properties. Time-based classifications include historical data, current data, or forecasted data. Historical data refers to past records, while current data represents real-time information. Forecasted data, on the other hand, involves predicting future trends based on historical or current data.

Data classification can also be based on the purpose or usage of the data. Examples include reference data, transactional data, or analytical data. Reference data provides a framework for other data and includes things like country codes or product catalogs. Transactional data captures the details of specific business transactions. Analytical data, on the other hand, is used for analysis and decision-making, often derived from multiple sources. Learn more about Data Classification types .

Data Classification Levels

Data classification involves assigning levels of classification to data based on its sensitivity and confidentiality. These levels help determine the appropriate handling, storage, and access controls for the data. Here are the different levels of data classification commonly used:

• Unclassified : This is the lowest level of data classification. Unclassified data contains information that is non-sensitive and can be freely shared or accessed without any restrictions. It does not pose any risk if disclosed or accessed by unauthorized individuals.
• Confidential : The confidential level is used for data that requires protection due to its sensitive nature. It includes information that, if disclosed or accessed without authorization, could harm individuals or organizations. Access to confidential data is restricted to authorized personnel who have a legitimate need to know.
• Secret : Secret data classification is used for highly sensitive information that, if compromised, could cause significant damage to national security or an organization’s operations. Access to secret data is strictly controlled, and only individuals with appropriate security clearance and a need-to-know basis can access it.
• Top Secret : This is the highest level of data classification. Top secret data contains information that, if disclosed, could cause severe damage to national security or critical infrastructure. It is heavily protected and access is limited to a select few individuals with the highest security clearances.
• Special Categories : In some cases, additional special categories may be defined to address specific types of sensitive data. These categories could include sensitive personal information, financial data, health records, or legal information. Each special category may have its own set of access controls and protection requirements.

Data classification levels ensure that data is handled and protected according to its sensitivity. Organizations and governments define their specific classification levels and associated security protocols based on their unique requirements and the nature of the data they handle. Implementing appropriate data classification helps safeguard sensitive information and maintain data integrity and confidentiality.

Data Classification Examples

Here are some examples of data classification:

Personal Identifiable Information (PII) : This classification includes data that can identify an individual, such as names, addresses, social security numbers, or phone numbers. It is classified as sensitive and requires strict protection to prevent identity theft or privacy breaches.

Financial Data : Financial data classification encompasses information related to financial transactions, banking details, credit card information, or income records. It requires a high level of confidentiality and security to prevent financial fraud or unauthorized access.

Medical Records : Medical data classification involves healthcare-related information, including patient medical history, diagnoses, treatment plans, or test results. It falls under strict privacy regulations, such as the Health Insurance Portability and Accountability Act (HIPAA), and requires strong safeguards to protect patient privacy.

Intellectual Property : This classification includes trade secrets, patents, copyrights, or proprietary information that belongs to a company or individual. Intellectual property data requires stringent protection to maintain its competitive advantage and prevent unauthorized use or theft.

Government Classified Information : Government data classification involves sensitive information related to national security, defense, or intelligence. It includes classified documents, plans, or strategic information that must be protected from unauthorized disclosure to maintain the integrity and security of the nation.

These are just a few examples of data classification categories. Organizations and industries may have their own specific classifications based on their unique needs and compliance requirements. Data classification ensures that appropriate security measures and access controls are implemented based on the sensitivity and confidentiality of the data.

Data Classification Process

The process of data classification will vary based on the organization’s objectives, but there are certain common practices that can lead to successful outcomes. Below are some best practices to consider:

1. Define the objectives of the data classification process

• Identify the in-scope systems for the initial classification phase
• Determine the applicable compliance regulations
• Consider other business objectives such as risk mitigation, storage optimization, and analytics.

2. Categorize data types

• Identify data created/collected by your organization
• Distinguish proprietary data from public data
• Identify all regulated data, such as that covered by GDPR, HIPAA or CCPA.

3. Establish classification levels

• Determine the number of classification levels needed
• Document each level and provide examples (use a classification matrix)
• Train users to classify data if manual classification is required.

4. Define the automated classification process

• Define the prioritization criteria for discovering sensitive data
• Establish the frequency of classification, and resources required to automate the process.

5. Define categories and classification criteria

• Establish high-level categories and provide examples
• Define or enable applicable classification patterns and labels
• Establish a process for validating both user classified and automated results.

6. Define outcomes and usage of classified data

• Document risk mitigation steps and automated policies
• Determine analysis processes for classification results
• Establish expected outcomes from analytics.

7. Monitor and maintain your classification process

• Develop a workflow to classify new or updated data
• Review and update the classification process if necessary due to changes in business or regulatory requirements.

Data Classification Best Practices

Here are some best practices to consider:

• Define Data Classification Policies : Develop clear and comprehensive data classification policies that outline the criteria, levels, and procedures for classifying data. These policies should align with industry best practices and regulatory requirements.
• Involve Stakeholders : Engage key stakeholders, such as data owners, IT personnel, legal teams, and security professionals, in the data classification process. Collaborative input helps ensure a holistic and accurate classification of data.
• Educate Employees : Conduct regular training and awareness programs to educate employees about data classification principles, their roles and responsibilities, and the importance of protecting classified data. This helps promote a culture of data security within the organization.
• Automate Classification : Leverage technology and data classification tools to automate the classification process. These tools use various techniques, such as pattern matching, keyword analysis, or machine learning algorithms, to classify data accurately and efficiently.
• Assign Data Owners : Assign data owners or custodians responsible for classifying, managing, and protecting data within their respective domains. Data owners should have a clear understanding of the classification policies and should regularly review and update data classifications as needed.
• Implement Access Controls : Apply access controls based on the data classification levels. Limit access to classified data to authorized personnel with a need-to-know basis. Use strong authentication mechanisms, role-based access controls, and encryption to protect data.
• Regularly Review and Update Classifications : Conduct periodic reviews to ensure data classifications are accurate and up to date. Data classification should be a dynamic process that adapts to changes in data sensitivity, regulatory requirements, or organizational needs.
• Monitor and Audit Data Access : Implement robust monitoring and auditing mechanisms to track data access, usage, and modifications. Regularly review audit logs to identify any unauthorized access attempts or policy violations.
• Data Retention and Disposal : Establish clear policies for data retention and disposal. Determine the appropriate retention periods for each classification level and ensure secure data destruction when data is no longer needed.
• Continuously Improve : Continuously evaluate and improve data classification practices based on feedback, industry trends, and emerging technologies. Stay updated with evolving data privacy and security regulations to ensure compliance.

By following these best practices, organizations can enhance their data protection efforts, reduce risks, and ensure that data is properly classified and secured throughout its lifecycle.

How Lepide Helps with Data Classification

As data breaches continue to make the headlines, and Governments across the globe implement their own data privacy laws, the importance of data classification cannot be overstated. The Lepide Data Security Platform plays a crucial role in this process. It facilitates the discovery and classification of various types of data across a wide range of platforms, including both cloud-based and on-premise servers. Below are some of the main features/benefits that our Data Classification software provides.

Sensitive Data Discovery – Pre-defined schemas can be used to locate unstructured sensitive data across all data repositories, on-premise or cloud-based, which can be aligned it with compliance mandates like HIPAA, SOX, PCI, GDPR, CCPA, and more.

Incremental Scanning – Our solution scans various file formats like word and text documents, PDF files, and Excel spreadsheets to discover sensitive data. Data can be classified incrementally during creation and modification, ensuring a fast, scalable, and reliable process.

More context to classified data – Our software provides information about sensitive data location, access, and usage, enabling organizations to apply appropriate access controls.

Real-time threat detection – Our software can automatically identify and respond to hazardous user behavior in real-time, and provide reports and alerts on how users interact with sensitive/regulated data.

Reduction in False Positives – The Lepide software leverages proximity scanning to discover patterns that add context, ensuring accurate predictions of sensitive data and avoiding false positives.

Better Access Governance – Our data classification solution enables companies to manage access to sensitive information, restrict excessive permissions, for better data access governance (DAG).

Prioritization Based on Risk – Our solution assesses the level of risk associated with content, categorizes it, and assigns scores. Identifying important data enables organizations to concentrate on it and implement effective access control and activity monitoring.

If you’d like to see how the Lepide Data Security Platform can help you discover and classify your sensitive data, schedule a demo with one of our engineers or start your free trial today.

Phil joined Lepide in 2016 after spending most of his career in B2B marketing roles for global organizations. Over the years, Phil has strived to create a brand that is consistent, fun and in keeping with what it’s like to do business with Lepide. Phil leads a large team of marketing professionals that share a common goal; to make Lepide a dominant force in the industry.

By submitting the form you agree to the terms in our privacy policy .

• Privacy Policy

• Skip to Content
• Skip to Main Navigation
• Skip to Search

Indiana University Indiana University IU

• Institutional data
• Use and transmissions
• Encryption assistance
• Searching & Inventorying
• Disposal, wiping, & shredding
• Sharing and disclosing
• Restricted Data
• University-Internal Data
• Public Data
• Data Stewards
• Data Managers
• Record in a Meeting Containing PHI
• Secure research data
• Classify research data
• Select a storage service
• Request a Third-Party Assessment
• Share IU data with a third party external to the university
• Hire consultants that will access IU data
• Request classification of a new storage platform
• Purchase a product that will collect or store IU data
• Add a new application to IU’s inventory
• Access reports and dashboards
• Get access to enterprise systems
• Manage event data at IU
• Privacy Notice Generator
• Storage Management Tool
• Institutional Data Standards Checklist
• Data Sharing & Handling
• Data Classification Matrix
• Institutional Storage Request Form
• Acceptable Use Agreement
• Data Protection and Privacy Tutorial
• FERPA Tutorial
• HRMS Data Use Tutorial
• HIPAA Training
• Managing Permissions for Cloud Storage Owners

IU Data Management

• How do I...?

How do I determine the classification of research data?

This guidance is designed to help researchers determine the classification of their research data. Data classification is a necessary first step in choosing appropriate storage options, purchasing new software or hardware, and using external services or infrastructure for research data.

Which environment should I use to store my institutional data?

Why and when is research data considered institutional data.

According to policies DM-01 and UA-05 , research data is considered institutional data , unless the data are generated or collected under an agreement that assigns ownership to the sponsor. A common example of this exception is sponsor-initiated clinical trials. Other common scenarios include consulting contracts under which the third party does not give up their ownership of the data.

The following is a list of common, but not exclusive, scenarios in which research data are considered institutional data.

• IU is managing the award or contract for the project generating the data in question.
• IU has other ethical or legal obligations (i.e., IRB, animal care and use, biosafety, etc.) with respect to the project or data in question.
• Research data are being generated, collected, analyzed by faculty or staff in their role at IU.

In general, research data is considered institutional data when IU has legal and/or ethical obligations with regards to the associated award, project, or the data itself. If external (public domain, open, or licensed) data are reused in the conduct of research at IU, the external data must be handled according to the same guidance used for institutional data to ensure the integrity of the data and research, but IU does not assert ownership.

Guidance for classifying research data

Step 1 – review existing data classifications.

See the Data Sharing & Handling (DSH) Tool to see how common research data elements* are classified.

If the DSH Tool does not provide the classification level for all data in your research project, proceed to Step 2.

Step 2 – Identify relevant data regulations

Consider the following two questions:

Question 1: Do any data elements or variables fall under one or more of the following categories of protected data?

• Health Information Portability & Accountability Act (HIPAA)
• Personally Identifiable Information (PII) for human participants in research
• Endangered Species Act
• Related to patent application

Action : If you answered yes, your data are considered critical . Proceed to Step 4.

Question 2: Do any data elements or variables fall under one or more of the following categories of protected data?

• Family Educational Rights & Privacy Act (FERPA)
• Export Control regulations
• European Union General Data Protection Regulation (GDPR)
• Mental health and other health related data that is not subject to HIPAA
• Related to a commercial product or service
• Non-standard contractual requirements - The contract or agreement with the sponsor/vendor requires IU to handle the data in ways that deviate from or exceed our usual security measures.
• Controlled Unclassified Information (CUI)

Action : If you answered yes, your data may be considered critical . Proceed to Step 3.

Step 3 – Get help from the experts

Due to the complexities of local, state, federal, and international regulations, the classification of data is not always obvious. If you answered yes to Question 2 above, contact the appropriate office(s) listed below to get a final determination on the data classification.

• FERPA > Contact Data Steward for Student Data ( [email protected] )
• Export Control > Contact the IU Export Control Office ( [email protected] )
• EU GDPR > GDPR Working Group ( [email protected] )
• Mental health and other health related data not subject to HIPAA > Contact the Health Data Steward ( [email protected] )
• Commercial product or service > Contact an Innovation & Commercialization Manager
• Specific contractual requirements > Contact SecureMyResearch
• Controlled Unclassified Information (CUI) > Contact SecureMyResearch

Step 4 – Manage your research data appropriately for its classification

When your dataset includes any data elements that are classified as critical, you must handle (collect, store, manage, analyze, etc.) the entire dataset as critical data. When feasible, store the critical data in a different system than the less sensitive data. For example, avoid storing PII with other data by creating unique participant identifiers that are recorded in a separate file. Ensure that the PII is stored in one of the approved locations for critical data.

How do I manage critical research data?

• Choose Secure Storage @ IU
• Use Secure Storage responsibly (Guidance for Google & Microsoft )
• Secure your entire workflow (Get help from SecureMyResearch )
• See the Critical Data Guide for more tips

Key Resources

• Indiana Data Protection laws
• International & Federal data protection laws
• GDPR Guidance at IU
• HRPP Policy - Research data management

Site Navigation

• Data Classification
• How Do I...?

University Data Management Council

• Phone: (812) 555-5555
• Email: [email protected]

• Data Center
• Applications
• Open Source

Datamation content and product recommendations are editorially independent. We may make money when you click on links to our partners. Learn More .

Data classification is a component of the data management process in which data is categorized based on various characteristics to reinforce data security, aid regulatory compliance, and enable efficient data management. Data classification helps companies comply with regulations, cut costs, manage risks, and maintain data integrity.

This process typically includes identifying and categorizing data types and implementing security measures accordingly. Generally, data management teams and executives or IT professionals must work together to classify data and ensure its alignment with business policies.

Despite its technical nature, understanding how to perform data classification is a must for organizations, as it is a key element of a comprehensive data governance strategy.

Table of Contents

What Is Data Classification?

Data classification entails organizing data into categories based on content, sensitivity, and importance to promote efficient data use and protection, simplifying locating and retrieving information. It also involves tagging data to make it easier to search and track, reducing duplications and cutting storage and backup costs.

Data classification is also a foundational process for risk mitigation that encompasses both structured and unstructured data analyses. It gives valuable insights into user-generated sensitive information and helps organizations answer essential questions about their data, thereby shaping their risk mitigation strategies and governance policies.

How Does Data Classification Work?

Your organization can establish a robust data classification system that improves data management , supports compliance efforts, and strengthens data security by working through a series of seven steps to identify, categorize, label, control access to, encrypt, manage, and audit data throughout its entire lifecycle.

Data Identification

Data identification includes recognizing and distinguishing the different types of enterprise data for classification. The goal is to gain insights into such specifics as source, format, and purpose for accurate data classification based on the relevance of the data to your business operations and objectives.

As part of a solid data management strategy, an extensive data classification policy is necessary during the identification process.

Data Categorization

This stage builds on the insights from data identification, grouping data based on predefined criteria. It requires a systematic classification process according to factors such as content, sensitivity, and significance. The idea is to create a structured framework for efficient data management and control.

Labeling is an important aspect of data classification, where identified and categorized data is assigned specific tags or labels. These labels serve as markers to signify the data’s nature, criticality, or purpose. Through this process, each piece of information receives a clear identifier, indicating its classification level and guiding subsequent handling procedures.

Access Control

After data is labeled and categorized, you roll out measures to limit who gets access to it. These access controls help make sure that only the right people or systems can connect with specific data sets, keeping information secure.

Data Encryption

Encryption adds an extra layer of security to access controls, especially for confidential and restricted information. It ensures that even if someone gains access, the data remains unreadable without the right decryption keys. Encryption can protect sensitive data during storage, transmission, and processing, safeguarding digital assets in accordance with stringent security protocols.

Retention Policies and Enforcement 

The next step is implementing a methodical approach to managing data throughout its lifecycle. You must establish guidelines on how long varied types of information should be retained to comply with regulatory requirements. By enforcing retention policies, your business can fine-tune data management, mitigate risks associated with unnecessary data storage, and maintain a compliant data environment.

Monitoring and Auditing 

After enforcing your retention policies, you must actively track and evaluate how individuals or systems access and use your data. Keep tabs on who interacts with your information and how to safeguard against unauthorized access and find ways you can continuously upgrade your data management practices.

In this step, following data classification trends becomes particularly important—as new types of data emerge and regulations evolve, your monitoring and auditing strategies should adapt accordingly. For instance, the rise of artificial intelligence in data classification can be leveraged to increase the accuracy of your audits. Similarly, changes in data protection laws should be reflected in your compliance checks.

Data Classification Types

Data classification types serve as distinct labels for various categories of information, guiding how each should be handled, accessed, and protected within the organizational ecosystem. THe following are the seven key types of data classification:

• Public Data: Information intended for public sharing that does not endanger the organization if it is disclosed—government publications, for example.
• Internal Data: Data intended for internal use within the company, typically not intended for public disclosure but not highly sensitive—employee information, for example.
• Confidential Data: Sensitive information requiring a higher level of protection—disclosure may have adverse effects on the organization—internal investigations, for example.
• Restricted Data: Strictly-regulated data, limited to specific individuals or departments due to its sensitivity—trade agreements and contracts, for example.
• Private Data: Personal information about individuals, subject to privacy regulations and needing careful handling to prevent unauthorized access—contact information, for example.
• Critical Data: Sensitive information vital to the organization’s operations. Its exposure could result in serious repercussions—company infrastructure and system configurations, for example.
• Regulatory Data: Information that must adhere to specific regulations and compliance standards, necessitating careful management and protection—patient health records, for example.

Data Classification Techniques

Many organizations use multiple techniques for data classification. Choosing a technique is not a one-size-fits-all approach but a strategic decision influenced by the unique details of the data you’re working with. Some organizations even combine different techniques to create a comprehensive data classification strategy to suit their complex needs.

Rule-Based Classification

Rule-based classification, as the name suggests, calls for creating a set of rules to categorize data into distinct groups or classes. These rules are derived from analyzing data characteristics and attributes, and serve as decision criteria for assigning data to particular categories.

This technique is commonly used in industries where clear and interpretable decision-making is imperative, like credit scoring in financial institutions and patient risk stratification in healthcare organizations.

Data Labeling

This technique is fundamental practice in data classification, and many organizations use metadata or descriptive tags to indicate data characteristics or categories. Data labeling aids in maintaining organized datasets and is commonly used in conjunction with other classification techniques.

Data labeling is valuable in training machine learning models, offering labeled examples for algorithms to learn and generalize patterns. In addition, this data classification technique is used in the healthcare industry for annotating medical images and detecting specific features or anomalies.

Machine Learning Classification

Machine learning (ML)-based classification uses algorithms and statistical models to allow systems to learn and make predictions or without being explicitly programmed. This technique is quickly gaining popularity, especially in larger organizations dealing with vast and complex datasets that may be challenging to define manually.

ML algorithms analyze patterns and characteristics within large datasets to automatically categorize and label data into predefined classes or categories, saving time and effort while increasing precision over time.

Global industries, including international e-commerce and marketing corporations, apply this classification technique in big data environments. It allows them to automatically segment customers based on their behavior, preferences, and interactions with products or services.

Content-Based Classification

This technique organizes data according to its inherent features and characteristics, as well as historical interactions. It is used to make personalized recommendations, improving user experience and engagement across platforms by delivering content suggestions tailored to individual preferences and needs.

Streaming services use content-based classification to recommend movies or songs to users based on the genre, actors, or musicians they have previously enjoyed.

User-Based Classification

User-based classification, also called collaborative filtering, is a data classification technique that recommends items or content to users based on the selections and behaviors of other users with similar tastes. It enhances personalization by leveraging the collective preferences of a community of users.

This technique is common in recommendation systems within social media platforms, e-commerce industries, and streaming services.

Advantages of Data Classification

Data classification brings numerous advantages that contribute to a resilient and well-managed data environment, addressing both security concerns and regulatory requirements while optimizing operational processes:

• Heightened Security and Data Protection: Classifying data by sensitivity and importance lets you customize security measures, including access controls, encryption, and retention policies. This ensures the highest level of protection for sensitive information.
• Risk Mitigation and Regulatory Compliance: Systematically categorizing data lets you determine potential risks associated with different types of information, helping ensure your business adheres to data privacy regulations and avoids penalties, legal consequences, and reputational damage.
• Efficient Resource Allocation: Data classification gives you confidence that sensitive data receives the necessary resources for safe storage and retrieval, optimizing overall system performance. The process also reduces redundancy, streamlining backup processes and minimizing unnecessary resource usage.
• Tailored Access Controls and Privacy Compliance: Individuals or systems only get access to the data relevant to their roles, ensuring a need-to-know basis with tailored access controls from data classification. You can apply specific privacy measures to particular data categories, aligning your business practices with privacy standards.
• Improved Incident Response and Data Lifecycle Management: Data classification presents a roadmap for handling data, helping you find the most sensitive data and prioritize a response in the event of a data breach. Also, understanding data category sensitivity helps in applying controls, retention policies, and disposal methods.

Disadvantages of Data Classification

While data classification brings numerous benefits, it’s important to note that its implementation isn’t without potential challenges:

• Complex Implementation: Deploying a comprehensive data classification system involves defining criteria, rules, and ensuring consistency across diverse datasets. It requires thorough planning, understanding of business requirements, and potential integration with existing systems.
• Costs: Initial setup and integration costs associated with data classification can be substantial, including investments in data classification software and training programs—maintaining a data classification system may also require additional resources in terms of technology, personnel, and ongoing monitoring efforts.
• Ongoing Maintenance: Regular updates and maintenance are needed to make sure that process remains effective and aligned with changing business needs, industry regulations, and emerging data types.
• Misclassification Risks: Mistakenly categorized information, either intentionally or unintentionally, can result in inadequate protection for important data or unnecessary security measures for non-sensitive data. This could lead to data breaches, compromised security, and issues in trying to meet regulatory compliance.

Data Classification Use Cases and Examples

Data classification is a widely adopted practice in several industries, offering a systematized approach to organizing and securing information based on its attributes. It is instrumental in addressing industry-specific challenges and optimizing information security.

Financial Institutions

Banks and financial institutions use data classification to manage, categorize, and protect vast volumes of data, including transactions, customer details, and market trends. The process helps detect and prevent fraudulent activities, maintaining strict adherence to regulatory frameworks—particularly anti-money laundering (AML) regulations—and safeguarding sensitive customer information.

The classified data serves as a structured input for data mining processes, too. By applying data mining techniques to the classified data, these organizations can uncover hidden patterns, predict future trends, and make informed decisions, elevating their services and operations. An example of this is the HSBC Nudge app , which evaluates the customer’s account, determines trends in their spending habits, and sends regular, targeted digital “nudges” to make people aware of their spending.

Healthcare Organizations

Hospitals, clinics, and healthcare organizations classify patient records, medical history, and other health-related information as protected health information (PHI). As a result, they can protect sensitive patient data in compliance with the Health Insurance Portability and Accountability Act (HIPAA) regulations. Healthcare institutions that deal with PHI, such as Cleveland Clinic and UnitedHealth Group , rely on data classification to identify, label, and secure PHI.

E-Commerce Platforms

E-commerce platforms classify customer data based on purchase history, preferences, and demographics to create targeted marketing campaigns, recommend personalized products, and give customers a positive experience—ultimately driving sales and customer loyalty.

Amazon and eBay use data classification to organize and understand customer preferences and shopping behaviors. This equips them to offer personalized product suggestions and take customer service experiences to the next level.

Technology Companies

Technology companies classify their intellectual property, such as software code, patents, and trade secrets. This helps them apply strict access controls, safeguard valuable assets, and prevent unauthorized use or disclosure of their newest innovations.

Intel employs a data classification system to categorize its products for export control. This system plays a major role in safeguarding the intellectual property associated with its products.

Frequently Asked Questions (FAQs)

Why is data classification important.

Data classification is important because it enables your organization to strategically identify and secure the most critical data. It promotes operational efficiency by supporting robust data analytics, security systems, and streamlined data lifecycle management. It also facilitates adherence to data handling guidelines and regulatory mandates like HIPAA, which is required for businesses in regulated sectors.

Is Data Classification Required?

The requirement for data classification varies depending on your organization, data type, regulations, and risk tolerance. The entire process is a proactive approach to safeguarding information and maintaining efficiency.

In some industries, regulatory bodies mandate data protection and privacy measures, such as General Data Protection Regulation (GDPR) or HIPAA. These regulations obligate organizations handling sensitive data, such as financial information, intellectual property, or personal identifiable details, to classify and protect sensitive information.

But even without regulations, many organizations adopt data classification as a best practice to manage data and reduce data breach impacts.

Bottom Line: Data Classification Is Important

Data classification is of utmost importance as it can help your organization allocate resources strategically and ensure high-value data security. It bolsters data management, decision-making, regulatory compliance, and sensitive information protection.

Data classification has several types, and each type demands a tailored approach. Not all data is created equal, and recognizing the differences is key. By acknowledging distinctions, you can implement appropriate security measures, access controls, and retention policies for every category.

Choose the right data classification technique according to the nature and goals of your business and leverage data classification matrices and tools to accurately categorize your enterprise data.

Data is a valuable business asset, and how you classify and manage it can significantly impact your business’s success. So, invest time and resources in data classification – it’s a decision that will pay dividends in the long run.

Read our buyer’s guide on the top-rated data classification software tools to find out which products we rated most highly and how they compare against enterprise data classification requirements criteria.

Subscribe to Data Insider

Learn the latest news and best practices about data science, big data analytics, artificial intelligence, data security, and more.

Similar articles

8 best data analytics tools: gain data-driven advantage in 2024, common data visualization examples: transform numbers into narratives, what is data management a guide to systems, processes, and tools, get the free newsletter.

Subscribe to Data Insider for top news, trends & analysis

Latest Articles

Exploring multi-tenant architecture: a..., 8 best data analytics..., common data visualization examples:..., what is data management....

Figure 1: All-important data security role of data classification.

Regardless of the number of compliance mandates an organization must follow, embracing data classification is essential. Implementing data discovery as a best practice can significantly enhance security in a targeted and efficient manner. By understanding the sensitive data within their ecosystem and categorizing it accordingly, organizations can allocate resources more effectively and prioritize security measures accordingly.

Data classification not only aids in compliance efforts but also plays a crucial role in preventing security breaches. By identifying and protecting sensitive data, organizations can mitigate the risks of unauthorized access and potential breaches, avoiding the negative consequences of compromised security. Embracing data classification and utilizing discovery techniques is a proactive step toward safeguarding valuable information and ensuring the integrity and trustworthiness of an organization’s data assets.

‍What Are Some Data Classification Examples?

Several types of data must be classified for effective data security, as these types are considered sensitive and require protection from unauthorized access, theft, or loss.

• Personal identifiable information (PII) includes data that can be used to identify an individual, such as full name, Social Security number, driver's license number, or passport number.
• Financial information refers to data related to financial transactions and accounts, such as credit card numbers, bank account numbers, and investment information.
• Confidential business information involves proprietary data that gives a company a competitive advantage, such as trade secrets, business plans, and market research.
• Health information is data related to a person's health status and medical history, such as diagnoses, treatment plans, and prescription information.
• Intellectual property includes data related to patents, trademarks, copyrights, and trade secrets.
• Government information is classified or restricted by government agencies, such as national security information, law enforcement records, and classified military information.
• Employee Information: This includes data related to employees, such as payroll information, job performance evaluations, and disciplinary records.

These are just a few examples of the classification data vital for better data security. The specific data types that must be classified will vary based on the security requirements of the organization. The goal of data classification, however, remains centered on understanding the level of sensitivity of data and determining the appropriate security measures needed to protect it.

‍Figure 2: Regulating bodies for at-a-glance understanding of data compliance focus

Data classification determines the appropriate security measures needed to protect data from unauthorized access, theft, or loss. As such, it informs many practices in data security.

Risk Assessment

Data classification is used to identify the most critical assets and prioritize protecting sensitive data. This helps organizations to focus their cybersecurity efforts on the areas that require the most attention.

Access Control

Data classification helps organizations to determine who should have access to sensitive data and what level of access they should have. For example, highly sensitive data may only be accessible by a small group of authorized personnel, while less sensitive data may be accessible by a wider group of employees.

Data Encryption

Data classification helps organizations determine which data requires encryption and the necessary level of encryption. For example, some highly sensitive data might require encryption both at rest and in transit, while less sensitive data may only need to be encrypted at rest.

Data Backup and Recovery

Data classification helps organizations determine which data needs to be backed up and how often. For example, highly sensitive data may need to be backed up daily and stored in secure off-site locations, while less sensitive data may only need to be backed up weekly.

Data classification is also used to ensure compliance with data protection regulations such as the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), or the Payment Card Industry Data Security Standard (PCI DSS). These regulations often require organizations to implement specific security measures for protecting sensitive data, and data classification is the first step in determining which data falls into this category.

What are the types of data classification?

What are some data classification examples, what is data privacy compliance.

Data privacy compliance refers to an organization's adherence to laws, regulations, and industry standards governing the collection, storage, processing, and sharing of personal and sensitive data.

Compliance requirements vary depending on the jurisdiction, sector, and type of data involved, with examples including the General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA), and

What is GDPR compliance?

GDPR compliance refers to an organization's adherence to the European Union's General Data Protection Regulation, a comprehensive data privacy law that came into effect in May 2018. The regulation applies to any organization that processes the personal data of EU residents, regardless of its geographical location.

GDPR compliance involves implementing data protection measures such as data minimization, encryption, and pseudonymization, as well as ensuring that data subjects' rights, including the right to access, rectification, and erasure, are respected. Organizations must also conduct data protection impact assessments, appoint a Data Protection Officer if required, and report data breaches within 72 hours.

What are HIPAA regulations?

HIPAA regulations refer to the Health Insurance Portability and Accountability Act, a U.S. federal law that establishes standards for protecting the privacy and security of patients' health information. The regulations consist of the Privacy Rule, which governs the use and disclosure of protected health information (PHI), and the Security Rule, which sets specific requirements for safeguarding the confidentiality, integrity, and availability of electronic PHI.

Organizations handling PHI, such as healthcare providers and their business associates, must implement administrative, physical, and technical safeguards, as well as ensure proper training and risk management practices to achieve HIPAA compliance.

Related Content

Protecting information consistently across the enterprise means having the right people to align the information security program with business and technology strategy.

Detect malware and prevent inadvertent or malicious exposure of sensitive data with predefined data profiles and data patterns.

Learn how organizations achieve centralized visibility across cloud environments to remediate vulnerabilities and eliminate threats.

Many organizations don’t have enough visibility of critical data types such as personal identifiable information. This becomes problematic when facing audits and prioritizing data ...

Get the latest news, invites to events, and threat alerts

By submitting this form, you agree to our Terms of Use and acknowledge our Privacy Statement .

Products and Services

• Network Security Platform
• CLOUD DELIVERED SECURITY SERVICES
• Advanced Threat Prevention
• DNS Security
• Data Loss Prevention
• IoT Security
• Next-Generation Firewalls
• Hardware Firewalls
• Strata Cloud Manager
• SECURE ACCESS SERVICE EDGE
• Prisma Access
• Prisma SD-WAN
• Autonomous Digital Experience Management
• Cloud Access Security Broker
• Zero Trust Network Access
• Code to Cloud Platform
• Prisma Cloud
• AI-Driven Security Operations Platform
• Cortex XSOAR
• Cortex Xpanse
• Cortex XSIAM
• External Attack Surface Protection
• Security Automation
• Threat Prevention, Detection & Response
• Threat Intel and Incident Response Services
• Proactive Assessments
• Incident Response
• Transform Your Security Strategy
• Discover Threat Intelligence
• Corporate Responsiblity
• Investor Relations

Popular Links

• Communities
• Content Library
• Event Center
• Manage Email Preferences
• Products A-Z
• Product Certifications
• Report a Vulnerability
• Do Not Sell or Share My Personal Information

• Threat Geek Blog
• Services & Support
• Fidelis Network®
• Fidelis Endpoint®
• Fidelis Deception®
• Active Directory Intercept™
• Network Data Loss Prevention
• Server Secure™
• Cloud Secure™
• Container Secure™
• Information Technology
• Tribal & Gaming
• Education Center
• Customer Success
• Whitepapers
• August 9, 2024

How to Classify, Protect, and Control Your Data: The Ultimate Guide to Data Classification

Table of contents.

In our digital world, data fuels businesses. This power brings huge responsibility. Cyber threats are real and present dangers. One data breach can destroy a company causing money problems and long-lasting harm to its name. These breaches cost a lot – $4.45 million on average in 2023. This shows we need strong protection right away.  

Data classification forms the base of this protection. When you grasp and use good data classification methods, you can guard your most important asset: your data .

Let’s look at how to change data from a weak spot into a strong point.

What is Data Classification?

Sorting data into groups based on type, content, and metadata helps companies understand their information better. This allows them to reduce risks and follow data governance policies effectively. 

For example, a hospital may need to look at patient records with specific health problems for research purposes. A bank may also need to identify transactions associated with suspicious activities for compliance purposes. 

Data classification standards and tools let companies find information that matters to them. It can help to show where your most valuable data sits or what types of sensitive data your users make most often.  

By organizing data correctly, you can improve your organization’s security and compliance efforts.

Why is Data Classification Important?

With only 54% of companies knowing where they keep their sensitive data , calls for the need for a strong data classification policy. Knowing what data classification means helps protect important information from being lost , follow rules, and handle risks.  

Protect Sensitive Information

Data classification is critical in information protection. Much data goes unsorted and unidentified within organizations, and we refer to this as dark data. This brings out the importance of a solid data classification policy.  

Properly classifying data will be able to protect the confidential information of any business from unwanted eyeballs but also from possible data breaches . Using the appropriate sensitive data classification methods ensures the protection of data depending on the level of sensitivity, thus reducing the risks.

Compliance with Regulations

Classification of data helps companies to apply the laws. Laws, such as the GDPR, require that companies attain certain data classification standards. 

Understanding data classification and using data categorization helps companies stay legal and avoid fines. This involves using examples and a matrix to organize data according to the law.

Risk Management

D ata classification helps organizations assess and manage risks based on the types of data . This process supports applying the right security measures to reduce threats. Using data classification tools is important for effective risk management in cyber security.

Types of Data Classification

Here is a view of the main types of data classification and their characteristics:

• Public Data: This refers to data with no implied ownership and is freely available in the public domain. It does not require protection from unauthorized access but requires protection against unauthorized modification or destruction. Data classification examples for public data include market research data available without restriction on access or usage. 
• Internal Data: Information only for use by organization insiders, like memos, emails, and company rules. The categorization of internal data protects it and prevents moderate harm in case of its unauthorized disclosure. The data classification process for internal data uses reasonable security measures proportionate to the level of sensitivity. 
• Confidential Data: This includes sensitive information like employee reviews and vendor contracts. Deserve high protection so that this category may not be accessed by any unauthorized personnel to avoid potential damage. The methods of sensitive data classification treat this category under very strict security measures to avoid its exposure. 
• Restricted Data: This includes the most confidential and sensitive information, such as PHI and government-classified data. The data classification matrix has considered restricted data to be of the highest order of security with controlled access, for its unauthorized disclosure or change can cause substantial damage.  

With respect to the healthcare sector , HIPAA (health insurance portability and accountability act) rules for classification mandate that organizations classify restricted data by sensitivity and potential impact if compromised.  

This data classification policy shall ensure that such data has protection according to its critical nature and potential impact.

Data Classification Levels

Here’s a look at the main types of data classification levels showing why they matter and what protections they need:

1. High Sensitivity Data : This covers information that could lead to dire results for a company or people if it gets exposed. This kind of data needs tight access limits and safeguards because of how crucial it is and what the law requires, including GDPR data classification and other rules.  

Data classification examples of sensitive info are money-related files, ideas protected by law, and login details. Putting strong data security classification steps in place is key to stop people who shouldn’t see this data from getting to it and to follow the rules.  

2. Medium Sensitivity Data: This data is meant for internal use and, while it needs protection, its exposure wouldn’t be disastrous. Examples include non-confidential internal emails and documents, or blueprints for buildings in the works. 

The data classification process for medium sensitivity data involves using sensible security measures to guard against unauthorized access while keeping it usable for internal needs. Good data classification methods make sure this data is protected without slowing down the organization’s work.

3. Low Sensitivity Data : This group includes information meant for the public and doesn’t need tight protection. Some examples are public website content, job listings, and blog posts.   

To classify data at this level makes sure people can access it but can’t change it without permission. Using a data classification matrix helps companies sort and safeguard data based on how sensitive it is and how it’s meant to be used. 

Having a clear data classification policy is important for organizing and protecting different types of data. This policy should use manual and automated techniques to ensure accuracy and efficiency. 

Properly classifying data helps align security measures with the sensitivity of the information. This ultimately safeguards company assets and ensures compliance with regulations. 

This process should use both manual and automated techniques to ensure accuracy and efficiency. Properly classifying data helps to align security measures with the sensitivity of the information, ultimately safeguarding company assets and complying with regulations. 

Data Classification Examples

Here are some typical data classification examples that show different kinds of sensitive data and their classification levels :  

• PII (Identifiable Information): This data type includes info that can identify a person, like names social security numbers, addresses, and birth dates. Keeping PII safe is key to protect privacy and follow rules. The classification levels for PII give it a high sensitivity rating because unauthorized access or misuse could cause serious harm.
• PHI (Protected Health Information): PHI includes medical records, health insurance info, and biometric identifiers. Keeping PHI safe is key to follow rules like HIPAA and keep patient info private. The data classification tagging for PHI marks it as sensitive, so it needs strong security to stop unauthorized access and keep data accurate.
• PCI (Payment Card Information): PCI covers details linked to payment cards, like credit card numbers, names of cardholders when cards expire, and security codes. Companies must safeguard this data to stop money fraud and follow rules such as PCI DSS. The classification levels for PCI give it a high sensitivity rating, which means it needs tough security steps like encoding and limits on who can access it.

Protecting patient health data is crucial for healthcare providers in the U.S. HIPAA rules require tough security to stop data leaks and keep patient information private.  

The Data Classification Process

Here’s a look at the data classification process , including key ideas and terms:

• Set the aims of the data classification process.
• Pick out systems for initial classification.
• Make sure you follow rules like GDPR data classification.
• Find data types (like customer lists, money records, PHI).
• Tell the difference between company data and public data.
• Spot data that laws control such as GDPR or CCPA data.
• Choose how many classification levels you need.
• Write down each level with data classification examples.
• Teach staff how to classify data by manually if that's the plan.
• Create and use a full set of rules to classify data.
• Make sure everyone in the company classifies data the same way and follows the rules.
• Use both manual-sorting and computer-sorting to classify data.
• Sort by hand for data that needs a careful, case-by-case look.
• Let computers sort large amounts of data to keep things uniform.
• Add safety steps based on how you've grouped your data.
• Use coding, limits on who can see what, and regular checks for weak spots to protect important data.
• Write down steps to reduce risks and set up automatic rules.
• Use analytics on classification results to make better decisions.
• Set up a regular process to classify new or updated info.
• Check and update the classification process often.

Methods for Classification of Data

There are basically two methods for classifying data with respect to its sensitivity and importance: manual classification and automated classification.

1. Manual Classification

Manual classification is the process where a human makes a judgement about data to be classified against predetermined criteria. The following are the key aspects:  

• Data Classification Tagging: Data gets tagged as sensitive, like PII, PHI, and PCI.  
• Compliance: This is useful in meeting specific compliance regulations, such as the GDPR data classification.  
• Examples: Applied for legal documents, sensitive business information, other forms of critical data.

2. Automatic Classification

Automatic classification uses technology to classify data quickly and consistently. The key aspects of this are:

• Efficiency: It quickly processes huge volumes of data.  
• Consistency: Fewer human errors and uniformity in data security classification.  
• Tools: Leverage data classification tools with appropriate algorithms that support scalable, accurate classification.

Data Classification and Compliance

Data classification facilitates compliance with data protection regulations, such as the General Data Protection Regulation, the Health Insurance Portability and Accountability Act, or the Payment Card Industry Data Security Standard.   

The majority of these regulations impose certain security measures within organizations on the protection of sensitive data , and data classification is a step that enables an organization to determine which data falls into the category.  

For instance, the Cloud Security Alliance requests features like data type, jurisdiction, context, legal constraints, and sensitivity; its part, PCI DSS, does not require origin or domicile tags.  

Let’s see how you can create your Data Classification Policy: 

• Data categorization: Know who the data was created by or who owns the data and which organizational unit can bring the most context to the data.  
• Data classification process: Define at what frequency classification will take place, types of suitable data classification, and technical means for data classification tagging.  
• Regulatory compliance: Check the applicable regulations (e.g., GDPR, PCI DSS) also, what risks are in place in case of no compliance.

Data Classification Challenges

Following are some data classification challenges that are often faced by organizations and that may bring inefficiency in managing and protecting data.  

• Finding Data and Location : Identification of sensitive data within an organization is typically constrained because of organizational silos and a variety of data storage systems.  
• Manual Classification: Manual classification of data is time-consuming, prone to errors, and labor-intensive.  
• Inconsistency: Incoherent methods and criteria of classification prevent adequate protection of organizational data.  
• Cost and Resource Constraints: A holistic program of data classification may involve substantial investments in technology, people, and time.  
• Compliance Complexity: The compliance landscape is increasingly complex with evolving data privacy regulations, and the need for maintaining accurate classification.  
• Organizational Resistance: Resistance faced from employees in implementing data classification initiatives and attempting to change organizational behavior towards a data protection culture.  
• Data Ownership and Responsibility: Lack of clear ownership and responsibility for data gives a way to confusion and can expose data.

Best Practices in Data Classification

Organizations need to follow the best practices in data classification to overcome and optimize the related challenges in data classification:

• Automated, Real-Time Classification : Utilize available data classification tools to automate and make data classification easier. This also extends to real-time scanning and tagging of data classification based on predefined parameters.  
• Commit to Data Classification : Get management approval in order to emphasize that data classification is a must across the corporation. This commitment creates a culture in which data security classification and protection will be of main concern.  
• Establish a Culture of Compliance with Data Privacy : Train employees on their roles regarding the classification of data and protection of sensitive information. Regular training keeps privacy and security awareness part of everyday operations.  
• Collaborate with IT and Business Units : Collaborate with information technology and business teams in creating a standardized data classification framework. Such collaboration ensures consistency of advice, guidance, and approval through the process of data classification.  
• Minimize storage of any excess sensitive data: Apply data classification techniques to mark duplicate or out-of-date data for destruction, thus improving the relatively simplified protection of data.  

Adoption of such best practices would manage data successfully in these organizations, ensure compliance, and maintain the organizations within better data security.

Ready to master your data? Drive out the best—accuracy and efficiency—in classifying sensitive information with Fidelis Elevate ® . Mitigate risk, ensure compliance, and drive data-led decisions. Elevate your data protection strategy today.

Frequently Asked Questions

How can a data classification standard help with asset classification.

A data classification standard provides an organization with a structured approach to classifying data based on its sensitivity, value, and criticality. This will help in asset classification by:  

• Identification of critical assets: With data classification standards, an organization will be in a better position to identify and give priority to the most sensitive and valuable data assets.  
• Improved security: Assets will be classified in accordance with data sensitivity, improving proper security controls for better protection.  
• Simplification of compliance: Proper data categorization with the aim that data assets will comply with regulatory and industry standards such as GDPR and PCI DSS.

How to deal with imbalanced data in classification?

There are several strategies related to imbalanced data in classification.  

• Resampling techniques , either by oversampling using SMOTE or under sampling to rebalance class distribution.  
• Algorithm selection: Choose algorithms that work fine on imbalanced datasets; examples are decision trees or ensemble methods.   
• Performance metrics: Metrics like precision, recall, and the F1 score over accuracy in class imbalance problems.  
• Data augmentation: Generation of synthetic data to improve the minority class.

What is a data classification policy?

A data classification policy is a formal document outlining the structure for classifying data in an organization. It generally provides for the following:  

• Classification levels: This defines categories which would be something like public, internal, confidential, and restricted.  
• Responsibilities: Specify who is responsible for classifying data—this may be the data creators, the subject matter experts, or the data stewards.  
• Procedures: Details how often and how the data should be classified and what type of data needs classification; methods of classification.   
• Compliance requirements: It confirms that data classification abides by all regulatory and industry standards, such as GDPR and PCI DSS.   
• Security measures: Ensure that for each level of classification, a proper sensitive information security protocol is in place.  
• About Author

Srestha is a cybersecurity expert and passionate writer with a keen eye for detail and a knack for simplifying intricate concepts. She crafts engaging content and her ability to bridge the gap between technical expertise and accessible language makes her a valuable asset in the cybersecurity community. Srestha's dedication to staying informed about the latest trends and innovations ensures that her writing is always current and relevant.

Share this post

• classification of data. data classification standards , data classification , data classification methods , data classification process , sensitive data classification , what is data classification

Related Readings

Comprehensive data security: protecting data at rest, in motion, and in use.

Data is the foundation of any organization’s operations. Security is paramount for all financial records and intellectual property, as well

Comprehensive Guide to Healthcare Data Security: Essential Safety and Compliance Tips

Healthcare data security is one of the top responsibilities in this digital age. Since patients’ sensitive information can be stored

How to Prevent Data Loss? Tips & Strategies

Data loss prevention (DLP) is no longer a luxury; it is a necessary security approach for businesses of all sizes.

One Platform for All Adversaries

See Fidelis in action. Learn how our fast and scalable platforms provide full visibility, deep insights, and rapid response to help security teams across the World protect, detect, respond, and neutralize advanced cyber adversaries.

• Privacy Policy Privacy Policy
• Terms of Service Terms of Service
• Trust Center Trust Center
• Security Security

Home  >  Learning Center  >  Data Classification  

Article's content

Data classification, what is data classification.

Data classification tags data according to its type, sensitivity, and value to the organization if altered, stolen, or destroyed. It helps an organization understand the value of its data, determine whether the data is at risk, and implement controls to mitigate risks. Data classification also helps an organization comply with relevant industry-specific regulatory mandates such as SOX , HIPAA , PCI DSS , and GDPR .

Blog: Top Challenges to Implementing Data Privacy: Nailing Down Discovery and Classification First is Key.

Data Sensitivity Levels

Data is classified according to its sensitivity level—high, medium, or low.

• High sensitivity data —if compromised or destroyed in an unauthorized transaction, would have a catastrophic impact on the organization or individuals. For example, financial records, intellectual property, authentication data.
• Medium sensitivity data —intended for internal use only, but if compromised or destroyed, would not have a catastrophic impact on the organization or individuals. For example, emails and documents with no confidential data.
• Low sensitivity data —intended for public use. For example, public website content.

Data Sensitivity Best Practices

Since the high, medium, and low labels are somewhat generic, a best practice is to use labels for each sensitivity level that make sense for your organization. Two widely-used models are shown below.

If a database, file, or other data resource includes data that can be classified at two different levels, it’s best to classify all the data at the higher level.

Solution Spotlight: Enable Data Discovery and Classification.

Types of Data Classification

Data classification can be performed based on content, context, or user selections:

• Content-based classification —involves reviewing files and documents, and classifying them
• Context-based classification —involves classifying files based on meta data like the application that created the file (for example, accounting software), the person who created the document (for example, finance staff), or the location in which files were authored or modified (for example, finance or legal department buildings).
• User-based classification —involves classifying files according to a manual judgement of a knowledgeable user. Individuals who work with documents can specify how sensitive they are—they can do so when they create the document, after a significant edit or review, or before the document is released.

Data States and Data Format

Two additional dimensions of data classifications are:

• Data states —data exists in one of three states—at rest, in process, or in transit. Regardless of state, data classified as confidential must remain confidential.
• Data format —data can be either structured or unstructured. Structured data are usually human readable and can be indexed. Examples of structured data are database objects and spreadsheets. Unstructured data are usually not human readable or indexable. Examples of unstructured data are source code, documents, and binaries. Classifying structured data is less complex and time-consuming than classifying unstructured data.

Blog: How Organizations Manage to Understand Millions of Unstructured Data Files at Scale.

Data Discovery

Classifying data requires knowing the location, volume, and context of data. Most modern businesses store large volumes of data, which may be spread across multiple repositories:

• Databases deployed on-premises or in the cloud
• Big data platforms
• Collaboration systems such as Microsoft SharePoint
• Cloud storage services such as Dropbox and Google Docs
• Files such as spreadsheets, PDFs, or emails

Before you can perform data classification, you must perform accurate and comprehensive data discovery. Automated tools can help discover sensitive data at large scale. See our article on Data Discovery for more information.

The Relation Between Data Classification and Compliance

Data classification must comply with relevant regulatory and industry-specific mandates, which may require classification of different data attributes. For example, the Cloud Security Alliance (CSA) requires that data and data objects must include data type, jurisdiction of origin and domicile, context, legal constraints, sensitivity, etc. PCI DSS does not require origin or domicile tags.

Creating Your Data Classification Policy

A data classification policy defines who is responsible for data classification—typically by defining Program Area Designees (PAD) who are responsible for classifying data for different programs or organizational units.

The data classification policy should consider the following questions:

• Which person, organization or program created and/or owns the information?
• Which organizational unit has the most information about the content and context of the information?
• Who is responsible for the integrity and accuracy of the data?
• Where is the information stored?
• Is the information subject to any regulations or compliance standards, and what are the penalties associated with non-compliance?

Data classification can be the responsibility of the information creators, subject matter experts, or those responsible for the correctness of the data.

The policy also determines the data classification process: how often data classification should take place, for which data, which type of data classification is suitable for different types of data, and what technical means should be used to classify data. The data classification policy is part of the overall information security policy, which specifies how to protect sensitive data.

Data Classification Examples

Following are common examples of data that may be classified into each sensitivity level.

See how Imperva Data Security Solutions can help you with data classification.

Imperva Data Protection Solutions

Imperva provides automated data discovery and classification, which reveals the location, volume, and context of data on premises and in the cloud.

In addition to data classification, Imperva protects your data wherever it lives—on premises, in the cloud and in hybrid environments. It also provides security and IT teams with full visibility into how the data is being accessed, used, and moved around the organization.

Our comprehensive approach relies on multiple layers of protection, including:

• Database firewall —blocks SQL injection and other threats, while evaluating for known vulnerabilities.
• User rights management —monitors data access and activities of privileged users to identify excessive, inappropriate, and unused privileges.
• Data masking and encryption —obfuscates sensitive data so it would be useless to the bad actor, even if somehow extracted.
• Data loss prevention (DLP) —inspects data in motion, at rest on servers, in cloud storage, or on endpoint devices.
• User behavior analytics —establishes baselines of data access behavior, uses machine learning to detect and alert on abnormal and potentially risky activity.
• Data discovery and classification —reveals the location, volume, and context of data on premises and in the cloud.
• Database activity monitoring —monitors relational databases, data warehouses, big data and mainframes to generate real-time alerts on policy violations.
• Alert prioritization —Imperva uses AI and machine learning technology to look across the stream of security events and prioritize the ones that matter most.

Latest Blogs

Lynne Murray

Apr 25, 2024 4 min read

Brian Robertson

Apr 19, 2024 3 min read

• Industry Perspective

Apr 2, 2024 3 min read

Mar 11, 2024 4 min read

Feb 28, 2024 5 min read

, Paul Steen

Feb 26, 2024 5 min read

Latest Articles

• Data Security

184.6k Views

155.6k Views

118.1k Views

90.6k Views

90.3k Views

87.1k Views

77.2k Views

2024 Bad Bot Report

Bad bots now represent almost one-third of all internet traffic

The State of API Security in 2024

Learn about the current API threat landscape and the key security insights for 2024

Protect Against Business Logic Abuse

Identify key capabilities to prevent attacks targeting your business logic

The State of Security Within eCommerce in 2022

Learn how automated threats and API attacks on retailers are increasing

Prevoty is now part of the Imperva Runtime Protection

Protection against zero-day attacks

No tuning, highly-accurate out-of-the-box

Effective against OWASP top 10 vulnerabilities

An Imperva security specialist will contact you shortly.

Top 3 US Retailer

Solutions By Use Case

Solutions by industry, proofpoint vs. the competition.

• Support Log-in
• Proofpoint Cybersecurity Academy
• Digital Risk Portal
• Email Fraud Defense
• ET Intelligence
• Proofpoint Essentials
• Sendmail Support Log-in
• English (Americas)
• English (Europe, Middle East, Africa)
• English (Asia-Pacific)

What Is Data Classification?

Table of contents, reasons to perform data classification, types of data classification, methods of data classification, data classification levels, aligning on an asset list, data classification process, streamlining the data classification process, data classification examples, using artificial intelligence (ai) for data classification, importance of data classification, data classification best practices.

Data classification is a method for defining and categorizing files and other critical business information. It’s mainly used in large organizations to build security systems that follow strict compliance guidelines but can also be used in small environments. The most important use of data classification is to understand the sensitivity of stored information to build the right cybersecurity tools, access controls, and monitoring around it.

Data classification is the process of categorizing data assets based on their information sensitivity. By classifying data, organizations can determine two key things:

• Who should be authorized to access it.
• What protection policies to apply when storing and transferring it.

Classification can also help determine applicable regulatory standards to protect the data. Overall, data classification helps organizations better manage their data for privacy, compliance, and cybersecurity.

Cybersecurity Education and Training Begins Here

Here’s how your free trial works:.

• Meet with our cybersecurity experts to assess your environment and identify your threat risk exposure
• Within 24 hours and minimal configuration, we’ll deploy our solutions for 30 days
• Experience our technology in action!
• Receive report outlining your security vulnerabilities to help you take immediate action against cybersecurity attacks

Fill out this form to request a meeting with our cybersecurity experts.

Thank you for your submission.

Every organization should classify the data it creates, manages, and stores. But it’s even more critical for large enterprise environments. That’s because large enterprises have data assets spread across many locations, including the cloud.

Administrators must track and audit this information to ensure it has the proper authentication and access controls. Data classification enables administrators to identify the locations that store sensitive data and determine how it should be accessed and shared.

Classification is an essential first step to meeting almost any data compliance mandate. HIPAA, GDPR, FERPA , and other regulatory governing bodies require data to be labeled so that security and authentication controls can limit access. Labeling data helps organize and secure it. The exercise also reduces needlessly duplicated data, cuts storage costs, increases performance, and keeps it trackable as it’s shared.

Data classification is the foundation for effective data protection policies and data loss prevention (DLP) rules. For effective DLP rules, you first must classify your data to ensure that you know the data stored in every file.

Any stored data can be classified into categories. To classify your data, you must ask several questions as you discover and review it. Use the following sample questions as you review each section of your data:

• What information do you store for customers, employees, and vendors?
• What types of data does the organization create when generating a new record?
• How sensitive is the data using a numeric scale (e.g., 1-10, with 1 being the most sensitive)?
• Who must access this data to continue productive operations?

Using these questions, you can loosely define categories for your data, including:

• High sensitivity: This data must be secured and monitored to protect it from threat actors. It often falls under compliance regulations as information that requires strict access controls that also minimize the number of users who can access the data.
• Medium sensitivity: Files and data that cannot be disclosed to the public, but a data breach would not pose a significant risk could be considered medium risk. It requires access controls like high-sensitivity data, but a wider range of users can access it.
• Low sensitivity: This data is typically public information that doesn’t require much security to protect it from a data breach.

Data classification works closely with other technology to better protect and govern data. Should the organization suffer a data breach, data classification helps administrators identify lost data and potentially help track down the cyber-criminal.

Here are technologies that rely on data classification:

• Identity access management (IAM): IAM tools enable administrators to determine who and what can access data. Users with similar permissions can be grouped. Groups are given authorization levels and managed as a single unit. When one user leaves, the user can be removed from the group, which eliminates all permissions for that user. This type of grouping and organization streamlines permission management across the network.
• Data encryption: Certain data assets must be encrypted at rest and in motion. “At-rest” data is data being stored—typically on a hard drive—on any storage device. Data “in motion” refers to data as it’s transferred across a network. Encrypting data makes it unreadable when attackers intercept it.
• Automation: Automation works with monitoring tools to find, classify, and label data for administrative review. Some tools integrate artificial intelligence (AI) and machine learning (ML) to detect, label, and classify data automatically. The technologies can also help identify threats that could be used to steal it. With labeled data, administrators can use IAM to apply permissions and stop specific threats from gaining access to stored data.
• Data forensics: Forensics is the process of identifying what went wrong and who breached the network. After a data breach, data forensics collects and preserves evidence for further investigation. Data forensics is usually a two-part process. Automation tools first collect data, then a human analyst identifies anomalies and investigates.

Intelligent Compliance

As you consider these levels, you can better classify your data. Data classification typically is broken down into four categories:

Public Data

This data is available to the public either locally or over the internet. Public data requires little security because its disclosure would not violate compliance.

Internal-Only Data

Memos, intellectual property, and email messages are a few examples of data that should be restricted to internal employees.

Confidential Data

The difference between internal-only data and confidential data is that confidential data requires clearance to access it. You can assign clearance to specific employees or authorized third-party vendors.

Restricted Data

Restricted data usually refers to government information that only authorized individuals can access. Disclosure of restricted data may result in irrefutable damage to corporate revenue and reputation.

Before you begin a data classification review, Proofpoint and your organization must be on the same page. At the start of the review, Proofpoint and your organization create an asset list to define your business categories. For example, you may have files that store technology, financial, and customer data. Defining categories aligns your security requirements with your data.

This step also involves applying data classification levels defined in the previous section. For each category, you will likely have different classification levels for each group of files. This beginning step builds a foundation for the entire data classification process.

When you decide it’s time to classify data to meet compliance standards, the first step is implementing procedures to assist with data location, classification, and determining the proper cybersecurity. Executing each procedure depends on your organization’s compliance standards and the infrastructure that best secures data. The general data classification steps are:

• Perform a risk assessment: A risk assessment determines the sensitivity of data and identifies how an attacker could breach network defenses.
• Develop classification policies and standards: If you generate additional data in the future, a classification policy enables streamlining of a repeatable process, making it easier for staff members while minimizing mistakes in the process.
• Categorize data: With a risk assessment and policies in place, categorize your data based on its sensitivity, who should be able to access it, and any compliance penalties should it be disclosed publicly.
• Find the storage location of your data: Before deploying the right cybersecurity defenses, you need to know where data is stored. Identifying data storage locations points to the type of cybersecurity necessary to protect data.
• Identify and classify your data: With data identified, you can now classify it. Third-party software helps you with this step to make it easier to classify data and track it.
• Deploy controls: The controls you employ should require authentication and authorization access requests from every user and resource needing data access. That access should be on a “need to know” basis, meaning users only receive access if they need to see data to perform a job function.
• Monitor access and data: Monitoring data is a requirement for compliance and the privacy of your data. Without monitoring, an attacker could have months to exfiltrate data from the network. The proper monitoring controls detect anomalies and reduce the time necessary to detect, mitigate, and eradicate a threat from the network.

While you can streamline the data classification process and even automate some of it, the process still requires elements of human review and manual procedures.

Automated systems suggest labeling and classification, but a human review determines whether these labels are correct. Objectives and standards must be outlined and defined, which requires human reviewers and IT staff.

Automated tools flag digital assets for human review. The list displays the objects (such as data around a given customer) and the rules (such as HIPAA or PCI-DSS) that apply to each. Some automation tools can index objects. (Indexing is a process of sorting and organizing data to enable quick and efficient searching on the network.)

Other policies also apply during the process of data classification. General Data Protection Regulation (GDPR) is an EU regulation that gives consumers the right to have their data deleted. Organizations must comply when they store consumer data in the EU. Some data classification tools index objects so that they can be quickly removed when customers ask.

Data Discover

One of the most challenging steps in classifying data is understanding the risks. While compliance standards oversee most private sensitive data, organizations must adhere to compliance regulations applicable to different data stored in files and databases. Data classification helps secure data and ensure compliance. It’s essential for following GDPR requirements. (Organizations must index EU consumer data so it can be deleted on request, for instance.)

GDPR also mandates protecting secondary personal information such as customers’ ethnic origin, political opinions, race, and religious beliefs. To do so, organizations must classify this data and set the proper permissions across digital assets. Classification determines who can access this data so that it’s not misused. Only then can they avoid disclosing private consumer information and costly data breaches.

Three steps for classifying GDPR include:

• Locate and audit data. Before classification, administrators must identify where data is stored and the rules that affect it.
• Create a classification policy. To stay compliant, create data classification standards and procedures to define how your organization stores and transfers sensitive data.
• Organize and prioritize data. With prioritization, your organization can determine data classification and the permissions to access it.

Here are some examples of data sensitivity that could be categorized as high, medium, and low.

• High sensitivity: Suppose your company collects credit card numbers as a payment method from customers buying products. This data should have strict authorization controls, auditing to detect access requests, and encryption applied to stored and transmitted data. A data breach would likely cause harm to both the customer and the organization, so it should be classified as highly sensitive with strict cybersecurity controls.
• Medium sensitivity: For every third-party vendor, you have a contract with signatures executing an agreement. This data would not harm customers, but it still is sensitive information describing business details. These files could be considered medium sensitive.
• Low sensitivity: Data for public consumption could be considered low sensitivity. For example, marketing material published on your site would not need strict controls since it’s publicly available and created for a general audience.

Data classification requires human interaction, but much of the process can be automated. To add automation with decision-making capabilities, Proofpoint created a data classification engine that offers 99% accuracy in its predictions. AI automation ensures that organizations can identify, classify, and protect their documents on an ongoing basis, meaning the engine continually scans and reviews new documents as they are added to the environment.

Proofpoint balances human reviews with AI-based classification. The Active Learning module ingests about 20 documents per category to start the process and improve accuracy. The data classification engine uses machine-learning models to recognize patterns. Every group of files should be diverse so that the machine learning algorithms will have better accuracy.

Machine learning models predict labels for documents and determine the accuracy of their predictions. A “confidence level” is shown to a reviewer to reassess model data for another round of information classification. If the model says accuracy is low, human reviewers can update models to have more diverse sets of files to improve accuracy. The engine will retrain itself by leveraging the new information to yield new, optimal results. Proofpoint built its engine to be an access-based assignment of documents, so it assigns users access permissions only on files required to perform their job functions.

Proofpoint’s AI-powered data classification software reduces much of the overhead for a process that could take months. It automatically scans all your files, identifies file content, assigns the correct category and classification levels, and then lets you determine the right safeguarding security.

The data “sensitivity level” dictates how you process and protect it. Even if you know data is important, you must assess its risks. The data classification process helps you discover potential threats and deploy cybersecurity solutions most beneficial for your business.

By assigning sensitivity levels and categorizing data, you understand the access rules surrounding critical data. You can monitor data better for potential data breaches and, most importantly, remain compliant. Compliance guidelines help you determine the proper cybersecurity controls, but you must perform a risk assessment and classify data first. Organizations often require a third party to help with data classification so that cybersecurity deployment can be more efficiently executed.

Accuracy of data classification is essential for future DLP strategies; therefore, many organizations, small and large, have turned to AI-driven automation. Artificial intelligence leverages machine-learning models to determine the proper classification level and category.

Following data classification best practices makes policy creation and its entire process much more efficient. Best practices define the steps to fully index and label digital assets so that none are overlooked or mismanaged.

Organizations should follow these best practices:

• Carefully identify where all sensitive data, including intellectual property, is located across all storage locations.
• Define data categories so sensitive data can be labeled and set with the right permissions. Categories should be granular—so that permissions can also be granular. Categories should also allow administrators to categorize data within groups.
• Identify the most critical and sensitive data. Automation tools can then tag it with the correct classification and regulatory mandates.
• Educate employees so that they understand how to handle sensitive data. Give them the tools they need to protect sensitive data and follow cybersecurity practices.
• Review all regulatory standards so that rules are followed and penalties avoided.
• Build policies that allow users to identify misclassified or unclassified data and fix the issue.
• Use AI where you can improve accuracy and speed up the data classification process.

Related Resources

Analyst report, best practices for e-discovery and regulatory compliance in office 365, does data loss prevention success hinge on data classification yes and no, subscribe to the proofpoint blog, ready to give proofpoint a try.

Start with a free Proofpoint trial.

• Privacy Policy

Home » Research Data – Types Methods and Examples

Research Data – Types Methods and Examples

Table of Contents

Research Data

Research data refers to any information or evidence gathered through systematic investigation or experimentation to support or refute a hypothesis or answer a research question.

It includes both primary and secondary data, and can be in various formats such as numerical, textual, audiovisual, or visual. Research data plays a critical role in scientific inquiry and is often subject to rigorous analysis, interpretation, and dissemination to advance knowledge and inform decision-making.

Types of Research Data

There are generally four types of research data:

Quantitative Data

This type of data involves the collection and analysis of numerical data. It is often gathered through surveys, experiments, or other types of structured data collection methods. Quantitative data can be analyzed using statistical techniques to identify patterns or relationships in the data.

Qualitative Data

This type of data is non-numerical and often involves the collection and analysis of words, images, or sounds. It is often gathered through methods such as interviews, focus groups, or observation. Qualitative data can be analyzed using techniques such as content analysis, thematic analysis, or discourse analysis.

Primary Data

This type of data is collected by the researcher directly from the source. It can include data gathered through surveys, experiments, interviews, or observation. Primary data is often used to answer specific research questions or to test hypotheses.

Secondary Data

This type of data is collected by someone other than the researcher. It can include data from sources such as government reports, academic journals, or industry publications. Secondary data is often used to supplement or support primary data or to provide context for a research project.

Research Data Formates

There are several formats in which research data can be collected and stored. Some common formats include:

• Text : This format includes any type of written data, such as interview transcripts, survey responses, or open-ended questionnaire answers.
• Numeric : This format includes any data that can be expressed as numerical values, such as measurements or counts.
• Audio : This format includes any recorded data in an audio form, such as interviews or focus group discussions.
• Video : This format includes any recorded data in a video form, such as observations of behavior or experimental procedures.
• Images : This format includes any visual data, such as photographs, drawings, or scans of documents.
• Mixed media: This format includes any combination of the above formats, such as a survey response that includes both text and numeric data, or an observation study that includes both video and audio recordings.
• Sensor Data: This format includes data collected from various sensors or devices, such as GPS, accelerometers, or heart rate monitors.
• Social Media Data: This format includes data collected from social media platforms, such as tweets, posts, or comments.
• Geographic Information System (GIS) Data: This format includes data with a spatial component, such as maps or satellite imagery.
• Machine-Readable Data : This format includes data that can be read and processed by machines, such as data in XML or JSON format.
• Metadata: This format includes data that describes other data, such as information about the source, format, or content of a dataset.

Data Collection Methods

Some common research data collection methods include:

• Surveys : Surveys involve asking participants to answer a series of questions about a particular topic. Surveys can be conducted online, over the phone, or in person.
• Interviews : Interviews involve asking participants a series of open-ended questions in order to gather detailed information about their experiences or perspectives. Interviews can be conducted in person, over the phone, or via video conferencing.
• Focus groups: Focus groups involve bringing together a small group of participants to discuss a particular topic or issue in depth. The group is typically led by a moderator who asks questions and encourages discussion among the participants.
• Observations : Observations involve watching and recording behaviors or events as they naturally occur. Observations can be conducted in person or through the use of video or audio recordings.
• Experiments : Experiments involve manipulating one or more variables in order to measure the effect on an outcome of interest. Experiments can be conducted in a laboratory or in the field.
• Case studies: Case studies involve conducting an in-depth analysis of a particular individual, group, or organization. Case studies typically involve gathering data from multiple sources, including interviews, observations, and document analysis.
• Secondary data analysis: Secondary data analysis involves analyzing existing data that was collected for another purpose. Examples of secondary data sources include government records, academic research studies, and market research reports.

Analysis Methods

Some common research data analysis methods include:

• Descriptive statistics: Descriptive statistics involve summarizing and describing the main features of a dataset, such as the mean, median, and standard deviation. Descriptive statistics are often used to provide an initial overview of the data.
• Inferential statistics: Inferential statistics involve using statistical techniques to draw conclusions about a population based on a sample of data. Inferential statistics are often used to test hypotheses and determine the statistical significance of relationships between variables.
• Content analysis : Content analysis involves analyzing the content of text, audio, or video data to identify patterns, themes, or other meaningful features. Content analysis is often used in qualitative research to analyze open-ended survey responses, interviews, or other types of text data.
• Discourse analysis: Discourse analysis involves analyzing the language used in text, audio, or video data to understand how meaning is constructed and communicated. Discourse analysis is often used in qualitative research to analyze interviews, focus group discussions, or other types of text data.
• Grounded theory : Grounded theory involves developing a theory or model based on an analysis of qualitative data. Grounded theory is often used in exploratory research to generate new insights and hypotheses.
• Network analysis: Network analysis involves analyzing the relationships between entities, such as individuals or organizations, in a network. Network analysis is often used in social network analysis to understand the structure and dynamics of social networks.
• Structural equation modeling: Structural equation modeling involves using statistical techniques to test complex models that include multiple variables and relationships. Structural equation modeling is often used in social science research to test theories about the relationships between variables.

Purpose of Research Data

Research data serves several important purposes, including:

• Supporting scientific discoveries : Research data provides the basis for scientific discoveries and innovations. Researchers use data to test hypotheses, develop new theories, and advance scientific knowledge in their field.
• Validating research findings: Research data provides the evidence necessary to validate research findings. By analyzing and interpreting data, researchers can determine the statistical significance of relationships between variables and draw conclusions about the research question.
• Informing policy decisions: Research data can be used to inform policy decisions by providing evidence about the effectiveness of different policies or interventions. Policymakers can use data to make informed decisions about how to allocate resources and address social or economic challenges.
• Promoting transparency and accountability: Research data promotes transparency and accountability by allowing other researchers to verify and replicate research findings. Data sharing also promotes transparency by allowing others to examine the methods used to collect and analyze data.
• Supporting education and training: Research data can be used to support education and training by providing examples of research methods, data analysis techniques, and research findings. Students and researchers can use data to learn new research skills and to develop their own research projects.

Applications of Research Data

Research data has numerous applications across various fields, including social sciences, natural sciences, engineering, and health sciences. The applications of research data can be broadly classified into the following categories:

• Academic research: Research data is widely used in academic research to test hypotheses, develop new theories, and advance scientific knowledge. Researchers use data to explore complex relationships between variables, identify patterns, and make predictions.
• Business and industry: Research data is used in business and industry to make informed decisions about product development, marketing, and customer engagement. Data analysis techniques such as market research, customer analytics, and financial analysis are widely used to gain insights and inform strategic decision-making.
• Healthcare: Research data is used in healthcare to improve patient outcomes, develop new treatments, and identify health risks. Researchers use data to analyze health trends, track disease outbreaks, and develop evidence-based treatment protocols.
• Education : Research data is used in education to improve teaching and learning outcomes. Data analysis techniques such as assessments, surveys, and evaluations are used to measure student progress, evaluate program effectiveness, and inform policy decisions.
• Government and public policy: Research data is used in government and public policy to inform decision-making and policy development. Data analysis techniques such as demographic analysis, cost-benefit analysis, and impact evaluation are widely used to evaluate policy effectiveness, identify social or economic challenges, and develop evidence-based policy solutions.
• Environmental management: Research data is used in environmental management to monitor environmental conditions, track changes, and identify emerging threats. Data analysis techniques such as spatial analysis, remote sensing, and modeling are used to map environmental features, monitor ecosystem health, and inform policy decisions.

Advantages of Research Data

Research data has numerous advantages, including:

• Empirical evidence: Research data provides empirical evidence that can be used to support or refute theories, test hypotheses, and inform decision-making. This evidence-based approach helps to ensure that decisions are based on objective, measurable data rather than subjective opinions or assumptions.
• Accuracy and reliability : Research data is typically collected using rigorous scientific methods and protocols, which helps to ensure its accuracy and reliability. Data can be validated and verified using statistical methods, which further enhances its credibility.
• Replicability: Research data can be replicated and validated by other researchers, which helps to promote transparency and accountability in research. By making data available for others to analyze and interpret, researchers can ensure that their findings are robust and reliable.
• Insights and discoveries : Research data can provide insights into complex relationships between variables, identify patterns and trends, and reveal new discoveries. These insights can lead to the development of new theories, treatments, and interventions that can improve outcomes in various fields.
• Informed decision-making: Research data can inform decision-making in a range of fields, including healthcare, business, education, and public policy. Data analysis techniques can be used to identify trends, evaluate the effectiveness of interventions, and inform policy decisions.
• Efficiency and cost-effectiveness: Research data can help to improve efficiency and cost-effectiveness by identifying areas where resources can be directed most effectively. By using data to identify the most promising approaches or interventions, researchers can optimize the use of resources and improve outcomes.

Limitations of Research Data

Research data has several limitations that researchers should be aware of, including:

• Bias and subjectivity: Research data can be influenced by biases and subjectivity, which can affect the accuracy and reliability of the data. Researchers must take steps to minimize bias and subjectivity in data collection and analysis.
• Incomplete data : Research data can be incomplete or missing, which can affect the validity of the findings. Researchers must ensure that data is complete and representative to ensure that their findings are reliable.
• Limited scope: Research data may be limited in scope, which can limit the generalizability of the findings. Researchers must carefully consider the scope of their research and ensure that their findings are applicable to the broader population.
• Data quality: Research data can be affected by issues such as measurement error, data entry errors, and missing data, which can affect the quality of the data. Researchers must ensure that data is collected and analyzed using rigorous methods to minimize these issues.
• Ethical concerns: Research data can raise ethical concerns, particularly when it involves human subjects. Researchers must ensure that their research complies with ethical standards and protects the rights and privacy of human subjects.
• Data security: Research data must be protected to prevent unauthorized access or use. Researchers must ensure that data is stored and transmitted securely to protect the confidentiality and integrity of the data.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Primary Data – Types, Methods and Examples

Quantitative Data – Types, Methods and Examples

Information in Research – Types and Examples

Qualitative Data – Types, Methods and Examples

Secondary Data – Types, Methods and Examples

Classifying your research data

UNSW has a  Data Classification Standard  for assessing data sensitivity, measured by the adverse impact a breach of the data would have on researchers and on UNSW.

The following guide provides an aid for classifying your research data. It is not intended to be definitive, for a detailed view you can consult the  Data Classification Standard .If you want to discuss how to classify your research data please contact  RDM@UNSW   and we can assist you.

UNSW Sydney NSW 2052 Australia Telephone +61 2 93851000 Authorised by Deputy Vice-Chancellor (Research) UNSW CRICOS Provider Code: 00098G ABN: 57 195 873 179

Join the conversation

• Member Group of Eight
• Member Universitas 21
• Member Global Alliance of Technological Universities
• Member Association of Pacific Rim Universities
• Member PLuS Alliance
• UNSW Futures
• Interdisciplinary Research
• World Class Research Infrastructure
• Grand Challenges
• Thought Leadership & Public Engagement
• Knowledge Exchange
• ARTU - Aggregate Ranking of Top Universities
• Deputy Vice-Chancellor (Research & Enterprise)
• Pro Vice-Chancellor (Research)
• Applying for a Scholarship
• External Scholarships
• Higher Degree Research Programs
• Submitting an Application
• Finding a Supervisor
• Fees and Costs
• Thesis Preparation
• Examination
• Arc UNSW – Postgraduate Council
• Arc UNSW – Student Life
• Financial Support
• New to UNSW Research
• Events & Workshops
• Study with Us
• Extend Your Career
• Research Career Opportunities
• Biomedical Sciences
• Contemporary Humanities and Creative Arts
• Defence and Security
• Fundamental and Enabling Sciences
• ICT, Robotics and Devices
• Law, Business and Economics
• Next Generation Materials and Technologies
• Social Policy, Government and Health Policy
• Water, Climate, Environment and Sustainability
• Fellows of Learned Academies
• Research News & Stories
• Research Ethics & Compliance Support
• Research Integrity for Staff
• Supervisors and Research Integrity
• Gene Technology
• Animal Research Ethics
• Human Research Ethics
• Radiation Safety
• Research Export Controls
• About the RSO
• Partner with us
• Partner With Us
• Services for Researchers
• Current Funding Opportunities
• Managing your Funding
• Resources and Quicklinks
• Analytical Centre (MWAC)
• Contacts & About Us
• Data Management, Storage and Tools
• Network Labs
• Other Infrastructure Funding Schemes
• Shared Research Equipment
• UNSW Research Infrastructure Scheme
• Pricing Tool
• Researcher Hub (UNSW login)
• Research News
• UNSW Centres and Institutes
• Find a Researcher
• FAQ: Researcher Profiles - Tags & Codes

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Data Collection | Definition, Methods & Examples

Data Collection | Definition, Methods & Examples

Published on June 5, 2020 by Pritha Bhandari . Revised on June 21, 2023.

Data collection is a systematic process of gathering observations or measurements. Whether you are performing research for business, governmental or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem .

While methods and aims may differ between fields, the overall process of data collection remains largely the same. Before you begin collecting data, you need to consider:

• The  aim of the research
• The type of data that you will collect
• The methods and procedures you will use to collect, store, and process the data

To collect high-quality data that is relevant to your purposes, follow these four steps.

Table of contents

Step 1: define the aim of your research, step 2: choose your data collection method, step 3: plan your data collection procedures, step 4: collect the data, other interesting articles, frequently asked questions about data collection.

Before you start the process of data collection, you need to identify exactly what you want to achieve. You can start by writing a problem statement : what is the practical or scientific issue that you want to address and why does it matter?

Next, formulate one or more research questions that precisely define what you want to find out. Depending on your research questions, you might need to collect quantitative or qualitative data :

• Quantitative data is expressed in numbers and graphs and is analyzed through statistical methods .
• Qualitative data is expressed in words and analyzed through interpretations and categorizations.

If your aim is to test a hypothesis , measure something precisely, or gain large-scale statistical insights, collect quantitative data. If your aim is to explore ideas, understand experiences, or gain detailed insights into a specific context, collect qualitative data. If you have several aims, you can use a mixed methods approach that collects both types of data.

• Your first aim is to assess whether there are significant differences in perceptions of managers across different departments and office locations.
• Your second aim is to gather meaningful feedback from employees to explore new ideas for how managers can improve.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

Based on the data you want to collect, decide which method is best suited for your research.

• Experimental research is primarily a quantitative method.
• Interviews , focus groups , and ethnographies are qualitative methods.
• Surveys , observations, archival research and secondary data collection can be quantitative or qualitative methods.

Carefully consider what method you will use to gather data that helps you directly answer your research questions.

When you know which method(s) you are using, you need to plan exactly how you will implement them. What procedures will you follow to make accurate observations or measurements of the variables you are interested in?

For instance, if you’re conducting surveys or interviews, decide what form the questions will take; if you’re conducting an experiment, make decisions about your experimental design (e.g., determine inclusion and exclusion criteria ).

Operationalization

Sometimes your variables can be measured directly: for example, you can collect data on the average age of employees simply by asking for dates of birth. However, often you’ll be interested in collecting data on more abstract concepts or variables that can’t be directly observed.

Operationalization means turning abstract conceptual ideas into measurable observations. When planning how you will collect data, you need to translate the conceptual definition of what you want to study into the operational definition of what you will actually measure.

• You ask managers to rate their own leadership skills on 5-point scales assessing the ability to delegate, decisiveness and dependability.
• You ask their direct employees to provide anonymous feedback on the managers regarding the same topics.

You may need to develop a sampling plan to obtain data systematically. This involves defining a population , the group you want to draw conclusions about, and a sample, the group you will actually collect data from.

Your sampling method will determine how you recruit participants or obtain measurements for your study. To decide on a sampling method you will need to consider factors like the required sample size, accessibility of the sample, and timeframe of the data collection.

Standardizing procedures

If multiple researchers are involved, write a detailed manual to standardize data collection procedures in your study.

This means laying out specific step-by-step instructions so that everyone in your research team collects data in a consistent way – for example, by conducting experiments under the same conditions and using objective criteria to record and categorize observations. This helps you avoid common research biases like omitted variable bias or information bias .

This helps ensure the reliability of your data, and you can also use it to replicate the study in the future.

Creating a data management plan

Before beginning data collection, you should also decide how you will organize and store your data.

• If you are collecting data from people, you will likely need to anonymize and safeguard the data to prevent leaks of sensitive information (e.g. names or identity numbers).
• If you are collecting data via interviews or pencil-and-paper formats, you will need to perform transcriptions or data entry in systematic ways to minimize distortion.
• You can prevent loss of data by having an organization system that is routinely backed up.

Finally, you can implement your chosen methods to measure or observe the variables you are interested in.

The closed-ended questions ask participants to rate their manager’s leadership skills on scales from 1–5. The data produced is numerical and can be statistically analyzed for averages and patterns.

To ensure that high quality data is recorded in a systematic way, here are some best practices:

• Record all relevant information as and when you obtain data. For example, note down whether or how lab equipment is recalibrated during an experimental study.
• Double-check manual data entry for errors.
• If you collect quantitative data, you can assess the reliability and validity to get an indication of your data quality.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Likert scale

Research bias

• Implicit bias
• Framing effect
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

When conducting research, collecting original data has significant advantages:

• You can tailor data collection to your specific research aims (e.g. understanding the needs of your consumers or user testing your website)
• You can control and standardize the process for high reliability and validity (e.g. choosing appropriate measurements and sampling methods )

However, there are also some drawbacks: data collection can be time-consuming, labor-intensive and expensive. In some cases, it’s more efficient to use secondary data that has already been collected by someone else, but the data might be less reliable.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Bhandari, P. (2023, June 21). Data Collection | Definition, Methods & Examples. Scribbr. Retrieved August 6, 2024, from https://www.scribbr.com/methodology/data-collection/

Is this article helpful?

Pritha Bhandari

Other students also liked, qualitative vs. quantitative research | differences, examples & methods, sampling methods | types, techniques & examples, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

• Accountancy
• Business Studies
• Organisational Behaviour
• Human Resource Management
• Entrepreneurship

Classification of Data in Statistics | Meaning and Basis of Classification of Data

Classification of data refers to the systematic organization of raw data into groups or categories based on shared characteristics or attributes. This process transforms unstructured data into a structured format, making it easier to analyze and draw meaningful conclusions. Data can be classified based on location, time, descriptive characteristics, and measurable characteristics.

What is Classification of Data?

For performing statistical analysis, various kinds of data are gathered by the investigator or analyst. The information gathered is usually in raw form which is difficult to analyze. To make the analysis meaningful and easy, the raw data is converted or classified into different categories based on their characteristics. This grouping of data into different categories or classes with similar or homogeneous characteristics is known as the Classification of Data . Each division or class of the gathered data is known as a Class. The different basis of classification of statistical information are Geographical, Chronological, Qualitative (Simple and Manifold), and Quantitative or Numerical.

For example, if an investigator wants to determine the poverty level of a state, he/she can do so by gathering the information of people of that state and then classifying them on the basis of their income, education, etc.

According to Conner , “Classification is the process of arranging things (either actually or notionally) in groups or classes according to their resemblances and affinities, and gives expression to the unity of attributes that may exist amongst a diversity of individuals.”

Table of Content

Basis of Classification of Data

1. geographical classification, 2. chronological classification, 3. qualitative classification, simple classification, manifold classification, 4. quantitative classification, basis of classification of data – faqs.

The main objectives of Classification of Data are as follows:

• Explain similarities and differences of data
• Simplify and condense data’s mass
• Facilitate comparisons
• Study the relationship
• Prepare data for tabular presentation
• Present a mental picture of the data

The classification of statistical data is done after considering the scope, nature, and purpose of an investigation and is generally done on four bases; viz., geographical location, chronology, qualitative characteristics, and quantitative characteristics. 

The classification of data on the basis of geographical location or region is known as Geographical or Spatial Classification. For example, presenting the population of different states of a country is done on the basis of geographical location or region. 

The classification of data with respect to different time periods is known as Chronological or Temporal Classification. For example, the number of students in a school in different years can be presented on the basis of a time period. 

The classification of data on the basis of descriptive or qualitative characteristics like region, caste, sex, gender, education, etc., is known as Qualitative Classification. A qualitative classification can not be quantified and can be of two types; viz., Simple Classification and Manifold Classification. 

When based on only one attribute, the given data is classified into two classes, which is known as Simple Classification . For example, when the population is divided into literate and illiterate, it is a simple classification. 

When based on more than one attribute, the given data is classified into different classes, and then sub-divided into more sub-classes, which is known as Manifold Classification. For example, when the population is divided into literate and illiterate, then sub-divided into male and female, and further sub-divided into married and unmarried, it is a manifold classification.

The classification of data on the basis of the characteristics, such as age, height, weight, income, etc., that can be measured in quantity is known as Quantitative Classification. For example, the weight of students in a class can be classified as quantitative classification.

Also Read: Organization of Data Objectives and Characteristics of Classification of Data

What is classification of data?

Classification of data is the process of organizing raw data into meaningful categories based on shared characteristics or attributes. This process helps in simplifying complex data sets, making them easier to analyze and interpret.

What are the benefits of classifying data?

The benefits of classifying data include: Simplification: Reduces the complexity of large data sets. Organization: Provides a systematic arrangement of data. Analysis: Facilitates easier and more efficient data analysis. Comparison: Allows for comparison across different categories or groups. Trend Identification: Helps in identifying trends and patterns over time or across different regions.

What is the difference between qualitative and quantitative classification?

Qualitative Classification involves categorizing data based on non-numeric attributes or qualities, such as colors, types, or names. Quantitative classification involves categorizing data based on numeric values or measurements, such as income, height, or age.

How does geographical classification help in economic analysis?

Geographical Classification helps in economic analysis by organizing data based on location. This allows economists to study regional economic activities, compare economic performance across different areas, and identify location-specific trends and issues.

What is chronological classification and how is it useful?

Chronological classification organizes data based on time periods, such as years, quarters, or months. It is useful for analyzing trends over time, understanding seasonal variations, and forecasting future economic activities.

Can data belong to more than one classification type?

Yes, data can belong to more than one classification type. For example, sales data can be classified both geographically (by region) and chronologically (by month). This multidimensional classification provides a more comprehensive analysis of the data.

Please Login to comment...

Similar reads.

• Statistics for Economics
• Commerce - 11th

Improve your Coding Skills with Practice

What kind of Experience do you want to share?

• Utility Menu

9fabbac9824aa84e9e6a1fe930ae5e05

Data classification - research examples.

See also: Guides

Classification Levels

Dsl1 - publicly available and unrestricted data, dsl1 examples.

• Published research data
• Data that is publicly available
• Research will NOT involve merging any of the data sets in such a way that individuals might be identified
• Researcher will NOT enhance the public data set with identifiable, or potentially identifiable data

DSL2 - Unpublished non-sensitive research data, whether identifiable or not. Active research data at Harvard is at least DSL2 until published.

Dsl2 examples.

• Self-collected de-identified data, anonymized survey data or aggregate statistics 
• Self-collected, de-identified biospecimens or genomic data  
• Other research data that is identifiable but is not considered sensitive 
• Self-collected non-sensitive survey data, qualitative data such as interviews, or intervention outcome data 
• Usability data 
• Non-sensitive audio or video recordings   
• Non-sensitive observational notes

DSL3 - Sensitive Data:  Some regulated data, or data that could be damaging to the subject’s financial standing, career or economic prospects, personal relationships, insurability, reputation, or be stigmatizing

Dsl3 examples.

• Education records covered by FERPA
• Employment records, employee performance  data 
• Sensitive self-reported health history 
• Constellation of variables, when merged, becomes sensitive 
• Personal or family financial circumstances (record via surveys or interviews) 
• Data collection about controversial, stigmatized, embarrassing behaviors (e.g., infidelity, divorce, racist attitudes) 
• U.S. prisoner administrative data that would not cause criminal or civil liability 
• Information about U.S. criminal conduct that, if disclosed, could damage the subject’s reputation, relationships, or economic prospects 1
• Other information about U.S. criminal conduct that, if disclosed, would not place the subject at risk of significant criminal punishment (see DSL4)
• Non-US criminal data: PI should consult with Research Compliance or OGC for guidance
• Data sets shared with Harvard under contractual obligation (e.g. corporate NDA, DUA, other contracts at OVPR) at DSL3 controls or with general expectation of confidentiality or data ownership 
• GDPR data not reaching level of “extra sensitive” – this includes racial or ethnic origin, political opinions, religious, or philosophical beliefs, trade union membership, sex life or sexual orientation

1 This could include past crimes for which the subject has served time but that are not matters of public record or are not known to the subject’s family, employer, or local community.

Dsl4 - sensitive data that could place the subject at risk of significant criminal or civil liability or data that require stronger security measures per regulation, dsl4 examples.

• Government issued identifiers (e.g. Social Security Number, Passport number, driver’s license, travel visa, known traveler number)
• Individually identifiable financial account information (e.g. bank account, credit or debit card numbers)
• HIPAA-regulated PHI (including 18 identifiers)/ HIPAA-regulated Limited Data Set (even if Not Human Subject Research) 2
• Information that, if disclosed, could place the subject at risk of significant criminal punishment (e.g., violent crimes, theft and robbery, homicide, sexual assault, drug trafficking, fraud and financial crimes) 3
• Information that, if disclosed, could put the subject at risk of violent reprisals from the government or other social or political groups
• Identifiable U.S. prisoner data that could lead to additional criminal or civil liability
• Individually identifiable genetic information that is not DSL5
• Data sets shared with Harvard under contractual obligation at DSL4 controls (whether corporate NDA, DUA, other contracts at OVPR)
• GDPR “extra sensitive” data – biometric, genetic, or health information.

2 Harvard is a hybrid entity, meaning that only certain divisions (HUHS, HSDM Clinic) are HIPAA-covered entities. Each Harvard Investigator is required to comply with all applicable privacy and security policies of the HIPAA-covered entity in which that Investigator, as part of a research protocol, is handling PHI or from which the Investigator is drawing PHI. However, data that leaves the covered entity and is transferred to a non-HIPAA covered entity of Harvard is not considered to be HIPAA regulated data.

3 investigators should consider the criminal laws applicable to the subject. for example, a subject’s abortion history could be level 4 data if she resides in a jurisdiction that criminalizes abortion; and a subject’s political activism may expose the subject to prosecution in certain nations. investigators should also take into account the likelihood of prosecution, considering, among other factors, how much time has passed, the severity of the conduct in question, and the nature and extent of punishment ordinarily imposed in the jurisdiction. information about conduct that is punishable by civil or even criminal fines, but not imprisonment, often may not merit level 4 classification., dsl5 - sensitive data that could place the subject at severe risk of harm or data with contractual requirements for exceptional security measures, dsl5 examples.

• Data with implications for national security
• Certain individually identifiable medical records and genetic information categorized as extremely sensitive.
• Data that would put subject’s life at risk, if disclosed

Using the Levels

Know the policy.

Data management plans for all research data that contain elements from DSL 3, 4 or 5 are required to be submitted in the Data Safety Application for review with your School Security Officer. The full policy and additional resources are at the Harvard Research Data Security Policy website .

Seek assistance

If you have questions or concerns about the policy, or if you know of data plans or protocols that are out of compliance with policy, please contact your IRB Coordinator, Faculty Advisor or a Research Compliance Officer .

Use good judgment

The lists above are only examples, not definitive classifications.

Print the current handout for reference

• Data Governance
• Data Quality
• Data Stewardship
• Metadata Management
• Business Intelligence
• Master Data Management
• Media Collab
• Lights On Data Show
• Fun Data Merchandise
• Become a Contributor

What is the difference between data classification and data categorization?

by  George Firican

Data classification is often used as a synonym with data categorization. Are they the same? Not quite, so let's clarify this confusion as these terms are often used interchangeably when in fact they shouldn't. 

Is there a difference between data classification and data categorization in the information and data environment? Some say this: "There's no difference, it's the same thing. Data classification, data categorization. Potato, potahto."

Other think otherwise. What makes it equally confusing is that these terms are sometimes used interchangeably and sometimes they reference each other. So are they different? According to this research paper, " Classification and Categorization: A Difference that Makes a Difference ", they are. 

What is data classification?

Classification as a process involves the orderly and systematic assignment of each entity to one and only one class within a system of mutually exclusive and nonoverlapping classes.

You can watch the following video on data classification and its importance if you'd like to find out more, but the key takeaway is that assignment of each entity to one and only one class that is mutually exclusive from other classes.

In data management, in particular within data privacy and security, data classification is used to tag structured and unstructured data most often according to its sensitivity level into mutually exclusive categories such as:

• High sensitivity data
• Medium sensitivity data
• Low sensitivity data 

What is data categorization?

Categorization is the process of dividing the world into groups of entities whose members are in some way similar to each other.

So data could then be categorized as high sensitivity data, medium sensitivity data and low sensitivity data. The difference is that these groups referred in the data categorization don't need to be mutually exclusive, but in data classification they have to.

Examples of data classification and data categorization

1. Manufacturing example

Let's say that we need to organize a list of products that a company manufactures. Let's say that they produce products such as: bunk beds, adjustable beds, cradles, waterbeds, murphy beds, couches, canape, Klippan, futons, etc. Some of these can be categorized as beds, some as couches, and some could go under either. Such as the futon: 

As data classification, it would basically have to go either under the Bed area or under the Couch area.

2. Social Insurance Number example

A Social Insurance Number can be categorized under "Employee" data as well as under "Customer" data, depending who it belongs to. Then you can also have sub-categories such as: 

• Social Insurance Number
• Passport Number
• Permanent Resident Card Number
• Military Identification Number
• Employee Unique Identifier
• Driver License Number
• Customer Unique Identifier

So it can belong in both of these categories. If we are classifying the Social Insurance Number, it can only go under one of the following:

The Social Insurance Number would be considered high sensitivity data.

If you'd like more examples, check out this video here:

Relationship between data classification and data categorization

Usually, a human or even a software would usually first categorize the data. Think of it as many different ways of slicing and dicing your data.

Once that's done, a different process kicks in and assigns the applicable sensitivity level based on some pre-determined rules.

For example, you could say that:

• Regardless of the type of file, if it is categorized as a health record then that will be classified as high sensitivity
• All job postings will be classified as low sensitivity data, but all job postings that include data that was categorized under passport number that will go up to high sensitivity and so on

And this process could be manual and done by a human or automatic and done by a script or a program.

I hope that this clarifies the relationship between data classification and data categorization and their differences. In the end you'll encounter them as synonyms or as being different terms representing different processes. That's why I recommend finding out their definition from the person you're talking to, from the article you're reading, from the vendor pitching in their solution.

How do you define data categorization and data classification? Do you differentiate between them?

About the author 

George Firican

George Firican is the Director of Data Governance and Business Intelligence at the University of British Columbia, which is ranked among the top 20 public universities in the world. His passion for data led him towards award-winning program implementations in the data governance, data quality, and business intelligence fields. Due to his desire for continuous improvement and knowledge sharing, he founded LightsOnData, a website which offers free templates, definitions, best practices, articles and other useful resources to help with data governance and data management questions and challenges. He also has over twelve years of project management and business/technical analysis experience in the higher education, fundraising, software and web development, and e-commerce industries.

You may also like:

How to become a data science freelancer, data governance in 2024, 5 steps to achieve proactive data observability – explained over beers.

Session expired

Please log in again. The login page will open in a new tab. After logging in you can close it and return to this page.

Privacy Overview

Research Square Company condemns Russia's invasion of Ukraine. Read our statement →

Share early., improve your manuscript., make an impact..

X-Ray crystallography . Kaspar Kallip [ CC BY-SA ]

Featured Preprints

Recent videos, learn how research square videos can boost the impact of your research, featured subjects, from draft to impact, we offer a full range of services no matter where you are in your research., share early.

Post your manuscript as a preprint directly to Research Square or while under consideration at a participating journal through In Review . Posting early lets you showcase your work to funders and potential collaborators and get more citations.

• Preprint with Research Square

Improve your manuscript

Improve your manuscript with AJE’s English language editing, formatting, and figure preparation services. Research Square supports community commenting and inline annotation, allowing you to gather feedback prior to peer review.

• Editing services

Make an impact

Communicating your research clearly and accurately has never been more important. Our Research Promotion products are custom created by expert illustrators and scientific script writers to provide a snapshot of the key findings from your latest study.

• Research Promotion

Trusted, proven, and ready to help you succeed

Research Square is a leading author, editorial, and video services provider. We are a trusted partner to many of the leading academic publishers, institutions, and societies worldwide.

How can we support your research?

• List of Commerce Articles
• Meaning And Objectives Of Classification Of Data

Meaning and Objectives of Classification of Data

Meaning of classification of data.

•     It is the process of arranging data into homogeneous (similar) groups according to their common characteristics.
•     Raw data cannot be easily understood, and it is not fit for further analysis and interpretation. Arrangement of data helps users in comparison and analysis.
• For example, the population of a town can be grouped according to sex, age, marital status, etc.

Classification of data

The method of arranging data into homogeneous classes according to the common features present in the data is known as classification.

A planned data analysis system makes the fundamental data easy to find and recover. This can be of particular interest for legal discovery, risk management, and compliance. Written methods and sets of guidelines for data classification should determine what levels and measures the company will use to organise data and define the roles of employees within the business regarding input stewardship.

Once a data -classification scheme has been designed, the security standards that stipulate proper approaching practices for each division and the storage criteria that determines the data’s lifecycle demands should be discussed.

Objectives of Data Classification

The primary objectives of data classification are:

•     To consolidate the volume of data in such a way that similarities and differences can be quickly understood. Figures can consequently be ordered in sections with common traits.
•     To aid comparison.
•     To point out the important characteristics of the data at a flash.
•     To give importance to the prominent data collected while separating the optional elements.
• To allow a statistical method of the materials gathered.

Leave a Comment Cancel reply

Your Mobile number and Email id will not be published. Required fields are marked *

Request OTP on Voice Call

Post My Comment

This is a Very nice app to learn and make notes of any chapter

Register with BYJU'S & Download Free PDFs

Register with byju's & watch live videos.

MBA Knowledge Base

Business • Management • Technology

Home » Research Methodology » Classification and Tabulation of Data in Research

Classification and Tabulation of Data in Research

Classification of data.

Classification is the way of arranging the data in different classes in order to give a definite form and a coherent structure to the data collected, facilitating their use in the most systematic and effective manner. It is the process of grouping the statistical data under various understandable homogeneous groups for the purpose of convenient interpretation. A uniformity of attributes is the basis criterion for classification; and the grouping of data is made according to similarity. Classification becomes necessary when there is diversity in the data collected for meaningful presentation and analysis. However, in respect of homogeneous presentation of data, classification may be unnecessary.

Characteristics of classification of data are;

• Classification performs homogeneous grouping of data.
• It brings out points of similarity and dissimilarities.
• The classification may be either real or imaginary.
• Classification is flexible to accommodate adjustments.
• To group heterogeneous data under the homogeneous group of common characteristics;
• To facility similarity of various group;
• To facilitate effective comparison;
• To present complex, haphazard and scattered dates in a concise, logical, homogeneous, and intelligible form;
• To maintain clarity and simplicity of complex data;
• To identify independent and dependent variables and establish their relationship;
• To establish a cohesive nature for the diverse data for effective and logical analysis;
• To make logical and effective quantification.

A good classification should have the characteristics of clarity, homogeneity, and equality of scale, purposefulness, accuracy, stability, flexibility, and unambiguity. Following are the general guiding principles for good classifications.

• Exhaustive: Classification should be exhaustive. Each and every item in data must belong to one of class. Introduction of residual class (i.e. either, miscellaneous etc.) should be avoided.
• Mutually exclusive: Each item should be placed at only one class
• Suitability: The classification should confirm to object of inquiry.
• Stability: Only one principle must be maintained throughout the classification and analysis.
• Homogeneity: The items included in each class must be homogeneous.
• Flexibility: A good classification should be flexible enough to accommodate new situation or changed situations.

Classification is of two types, viz., quantitative classification, which is on the basis of variables or quantity; and qualitative classification (classification according to attributes). The former is the way of grouping the variables, say quantifying the variables in cohesive groups, while the latter group the data on the basis of attributes or qualities. Again, it may be multiple classification or dichotomous classification. The former is the way of making many (more than two) groups on the basis of some quality or attributes, while the latter is the classification into two groups on the basis of the presence or absence of a certain quality. Grouping the workers of a factory under various income (class intervals) groups comes under multiple classifications; and making two groups into skilled workers and unskilled workers is dichotomous classification. The tabular form of such classification is known as statistical series, which may be inclusive or exclusive.

Tabulation of Data

Differences between classification and tabulation.

• First data are classified and presented in tables; classification is the basis for tabulation.
• Tabulation is a mechanical function of classification because is tabulation classified data are placed in row and columns.
• Classification is a process of statistical analysis while tabulation is a process of presenting data is suitable structure.

Related posts:

• Schedule as a Data Collection Technique in Research
• Interpretation of Research Data
• Data Analysis in Research Methodology
• Secondary Data Sources for Research
• Methods of Data Processing in Research
• Interview Method of Data Collection in Research
• Observation Method of Research Data Collection
• Using Different Types of Surveys for Data Collection in Research
• Pre-Testing Research Data Collection Instruments
• Market Research – Definition, Classification and Process

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Check out our new AI-powered transformation tool!

Extremely robust system with new features coming regularly

Cars.com streamlined data from 160 weekly hours to just 5 and doubled return on ad spend

600+ data sources.

"A universal marketing schema for all of our client data.

Adverity lets our analysts source data and provide client focused reporting faster than ever. No more client specific data processes or reporting. Our analysts can be significantly more efficient and effective."

Related articles

What is data reconciliation a guide for analysts and marketers (2024), data classification tools: a comprehensive guide for marketers (2024), data management vs. data governance: understanding the key differences.

Blog / What is Data Classification? A Beginner’s Guide for Marketers (2024)

What is Data Classification? A Beginner’s Guide for Marketers (2024)

Share this article on social media

In the age of information overload, how marketers manage their data can make or break their campaigns. One essential aspect of this management is data classification, which involves organizing data into categories so it can be cleanly combined for its most effective and efficient use. 

By identifying data types, marketers can start to build the key for their data maps so that disparate data sources can be standardized into a single source of truth. In turn, this creates a strong foundation of data to create more personalized and effective marketing strategies.

In this article, we’ll define data classification, explain its relevance for marketers, and conclude with the best practices for implementation.

What is data classification?

Data classification is the process of sorting and categorizing data into different types, forms, or classes. Think of it as organizing your marketing data into neat, understandable categories, making it easier to find and use the information you need.

For marketers, data classification is a game-changer and one of the 6 building blocks of data governance. It helps you understand your data better, ensuring that you can target your audience more effectively and personalize your marketing efforts.

How does data classification work?

Not all incoming data arrives with accurate classifications. Post-unification, some datasets may lose their original data types. So, once you have all your data sets in one place, it’s important to use data classification to categorize and tag data by type. Common data types include: 

• Boolean (Representing logical values): Represents true or false values, used for binary conditions and logical operations in programming.
• Characters (Encoding text numerically): Individual letters, digits, or symbols, encoded as numbers for computer processing and text representation.
• Dates: Represent specific points in time, including day, month, and year, used for scheduling and timelines.
• Floating point numbers (Numbers with a decimal point): Numbers that include fractions, represented with a decimal point, used for precise calculations.
• Integers (Whole numbers): Whole numbers without fractions, used for counting and discrete values in computations.
• Strings (Alphanumeric characters): Sequences of characters, including letters and numbers, used for text manipulation and storage in databases.

Data classification is an important part of the ETL process which needs to happen before marketers can standardize naming conventions and calculated metrics. If you don’t have a platform to automate this process, it may require manual data scrubbing in Excel to ensure accurate formatting.  

Why should marketers care about data classification?

Proper data classification is a necessary step in bringing disparate data sources together into a single source of truth. Without understanding the types of data arriving from each source, marketers face a chaos of disorganized information and inefficiencies. So, getting the hang of data classification is really important. 

By categorizing and classifying data, marketers can effectively prep their data sources to be transformed into one clean, combined data set. Different platforms have unique terminology, and it’s important to standardize these terms to ensure consistency in your data. This means standardizing terms like 'Spend,' 'Cost,' and 'costinlocalCurrency' into a consistent naming convention, which marketers will often use a data dictionary for.  

Understanding what type of data you’re combining is a crucial step in standardizing it to allow for cross-channel, department, and country reporting. With a strong data foundation, marketers can build a single source of truth and compare performance across channels based on customer behaviors, preferences, and demographics. This segmentation makes it much easier to analyze and extract meaningful insights, enabling marketers to identify trends, measure campaign performance, and deliver personalized content that resonates with individual customers. 

What are the risks of poor data classification?

Data classification is a crucial but often overlooked aspect of data management. While it may not always be in the spotlight, it plays a fundamental role in ensuring that data is used effectively.

When it’s not handled well, the risks can be severe. It’s crucial to understand these risks and tackle them before they become major problems. Here’s a look at what can go wrong with poor data classification and why it’s essential to get it right:

• Operational inefficiency: Poor data organization leads to inefficient marketing operations, increased costs, and wasted resources.
• Inaccurate analytics: Misclassified data results in faulty analytics, leading to misguided marketing strategies and poor decision-making.
• Customer distrust: Mishandling sensitive data erodes customer trust, resulting in loss of business and negative brand perception.
• Competitive disadvantage: Inability to target and personalize marketing efforts effectively results in falling behind competitors who use data classification strategically.

Implementing data classification in your marketing strategy: Best practices

Understanding why data classification matters is the first step. Now, let’s dive into how you can put this knowledge into practice. We’ll cover the steps to implementing data classification and best practices that will help you get your data in order and tackle the usual obstacles along the way. 

This section outlines some key tips and best practices to help marketers implement and maintain an effective data classification strategy.

1. Understand your data

Explanation: Start with a comprehensive data inventory. Knowing exactly what data you have, where it’s stored, and how it’s used forms the foundation of effective data management. This understanding helps ensure that classification efforts are based on accurate and complete information.

Action points:

• Conduct a detailed audit of all your data sources.
• Categorize data based on its source, type, and intended use.
• Document data locations and usage to keep track of all assets.

2. Develop clear classification criteria

Explanation: Establish clear criteria for data classification levels (e.g., public, internal, confidential, restricted). These criteria should align with your business objectives and regulatory requirements, providing a consistent framework for handling data.

• Define classification levels and their corresponding handling requirements.
• Ensure alignment with regulatory standards and business goals.
• Document and communicate these criteria across your team.

3. Automate the process

Explanation: Use data classification tools and software to streamline and scale your classification efforts. Automation reduces manual errors and handles large volumes of data efficiently, making the process more reliable and less time-consuming.

• Implement data classification tools to manage large datasets.
• Explore machine learning and AI solutions to enhance accuracy.
• Regularly update and maintain these tools to keep up with data changes.

4. Ensure strong data governance

Explanation: Establish a data governance framework that includes policies, procedures, and designated roles for data classification. This ensures that classification practices are standardized and consistently applied across the organization.

• Develop a comprehensive data governance policy.
• Assign data stewards to oversee and enforce classification policies.
• Regularly review and update governance practices as needed.

5. Collaborate across departments

Explanation: Effective data classification requires collaboration with IT, legal, and compliance teams to ensure alignment with overall data governance and regulatory requirements. Cross-departmental cooperation helps address potential challenges and maintain consistency.

• Form a cross-functional team with representatives from marketing, IT, and legal departments.
• Develop and document shared goals and procedures for data classification.
• Maintain open communication channels to resolve any issues promptly.

6. Conduct regular training and audits

Explanation: Training ensures that all team members understand and adhere to data classification policies. Regular audits help in assessing the effectiveness of your classification system and identifying areas for improvement.

• Organize regular training sessions on data classification policies.
• Schedule periodic audits to review classification accuracy and compliance.
• Use audit findings to refine and improve your classification approach.

7. Monitor and adapt

Explanation: Continuously monitor your data classification efforts to ensure they remain effective and aligned with evolving data needs and regulatory requirements. Regular updates help maintain the relevance and accuracy of your classification system.

• Implement monitoring tools to track classification performance.
• Regularly review and update classification criteria and practices.
• Stay informed about changes in data regulations and adjust your policies accordingly.

Common challenges and practical solutions

Understanding why data classification is key is one thing, but dealing with the real-world challenges is another. 

Marketers frequently face issues like data volume and resistance to change. Here’s a closer look at these challenges and some no-nonsense solutions to help you manage them.

1. Data volume and complexity

Challenge: Large volumes of data and complex data structures can make classification daunting.

Solution: 

• Implement automated tools that can handle large datasets efficiently.
• Use scalable solutions like cloud-based platforms to manage data growth.
• Break down the classification process into manageable phases, starting with the most critical data.

2. Lack of clear guidelines

Challenge: Inconsistent classification due to unclear or absent guidelines.

• Develop comprehensive classification criteria and guidelines.
• Standardize classification rules and ensure they are well-documented and easily accessible.
• Conduct training sessions to ensure everyone understands and follows the guidelines.

3. Resistance to change

Challenge: Employees may resist new data classification processes and policies.

• Communicate the benefits of data classification clearly to all stakeholders.
• Involve employees in the development of classification policies to gain their buy-in.
• Provide incentives for compliance and recognize employees who adhere to the new processes.

4. Integration with existing systems

Challenge: Difficulty in integrating classification tools with existing data management systems.

• Choose data classification tools that are compatible with your current systems.
• Work with IT to ensure smooth integration and minimal disruption.
• Consider phased implementation to address integration challenges incrementally.

5. Maintaining accuracy

Challenge: Ensuring the accuracy of data classification over time as data and business needs evolve.

• Implement machine learning algorithms that improve classification accuracy over time.
• Regularly review and update classification rules to adapt to changes.
• Perform periodic audits to verify the accuracy and relevance of classified data.

Data classification is a crucial part of building a single source of truth and, therefore, a critical practice for marketers aiming to optimize their marketing strategies. By organizing data into meaningful categories, marketers can support effective data management, make more informed decisions, and ultimately create more successful and targeted marketing campaigns.

As the digital landscape continues to evolve, data classification will remain a fundamental practice for ensuring the responsible and effective use of data in marketing.

Luisind is a Senior Solutions Consultant he helps platform users maximize their data insights.

Make insights-driven decisions faster and easier!

Pardon Our Interruption

As you were browsing something about your browser made us think you were a bot. There are a few reasons this might happen:

• You've disabled JavaScript in your web browser.
• You're a power user moving through this website with super-human speed.
• You've disabled cookies in your web browser.
• A third-party browser plugin, such as Ghostery or NoScript, is preventing JavaScript from running. Additional information is available in this support article .

To regain access, please make sure that cookies and JavaScript are enabled before reloading the page.

• Endocrine System Diseases
• Thyroid Diseases

Machine Learning Techniques for the Classification of Thyroid Disease

• International Journal for Research in Applied Science and Engineering Technology 12(V):375-380
• 12(V):375-380

• Shivaji University, Kolhapur

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations
• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up