what is data classification in research

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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.

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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

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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

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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 .

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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)

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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.

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The process of data classification can be broadly described as the organization of data into relevant categories, allowing it to be accessed and protected more efficiently. In the simplest terms, the data classification process ranks data based on its security needs and makes it easier to locate and retrieve data. Classification is especially useful to organizations storing significantly large amounts of data.  

Julia Duncan, a director at the University of Toronto,  explained , 

“Data is all around us. Data classification helps us to understand the most appropriate ways of handling and protecting it – who can see or use it, where to store it and for how long, whether it can be shared and what protective measures are most appropriate. Whether it is for a research project, as part of data collection, or a day-to-day data use and its sharing for academic and administrative purposes, data classification is a very important step as we continue to strengthen data security.”

The data classification process also eliminates the duplication of data, which, in turn, improves the accuracy of the data ( data quality and data integrity ). 

Data tagging is applied during the data classification process. It is considered an essential step in data classification. These tags are used to identify the data and can communicate the level of confidentiality/sensitivity – for security purposes – and the level of data quality. The sensitivity of data determines its security rating.

Data Tagging

Data tagging identifies data by including the tag within the metadata. A “tag” is a keyword, number, or term that is assigned to a data file. In a business, an employee ID can provide a unique way of identifying individual employees.  When the employee number is entered, the search engine presents a single employee, rather than multiple employees sharing a common key word. 

Similarly, in a soccer game, a seat number can be used to communicate the assignment of a seat to a specific ticket, establishing temporary ownership. A tagging system within the metadata promotes locating and accessing a data file quickly and easily, and can eliminate any confusion about who “owns” the seat.

Data tagging uses metadata to provide a unique identification process, promoting efficiency.

Tagging data is an essential step in the data classification process. The tags are used to communicate the type of data, its level of sensitivity, and its level of data quality . Sensitivity is normally based on the importance or confidentiality of the data, and aligned with the appropriate security measures needed. 

Common Types of Data

Data classification can provide both improved understanding and accessibility to the organization’s data. This situation promotes the use of data analysis and improved data security. The effective use of data classification can help an organization with massive amount of stored data to function more efficiently. 

To better understand how data classification works, it is important to understand the most common types of data, which are listed below:

• Public data: Provides information that is freely available to the general public to read, research, and store. It typically supports minimal amounts of data security , because it is easily shared and has little risk of damaging individuals, or the general public. Examples of public data include people’s names, news and educational articles, and some government websites.
• Private data: Contains information that should not be shared with the public. Sharing this type of information – passwords, browsing/research history, credit card numbers (without pin numbers and expiration dates) – might present a small risk to an individual or organization, and can usually be corrected quickly.
• Internal data: Normally, this describes the data used specifically within an organization and relates to an organization’s internal functions. Examples of internal data include business plans, employees’ personal information, emails, and memos. Internal data is often spread out over different levels of security.
• Confidential data: Only a limited number of individuals within the organization can access confidential data (sometimes referred to as “sensitive data”). Confidential data access might involve specialized passwords or retinal scans in order to view the content. Examples of confidential data are social security numbers, medical records, credit card numbers with pin numbers and expiration dates.
• Restricted data: This is data that, if compromised, can lead to massive legal fines or criminal charges. It typically has very strict security controls to limit access to the data, and often uses some form of data encryption. If it is accessed by people with malicious intent, an organization’s proprietary information could be copied, or made inaccessible, with demands for a ransom. Restricted data may also have the potential to put the general public’s health at risk. Examples of restricted data include intellectual property, protected health information, and some federal contracts. 

Methods of Data Classification

The process of data classification normally includes tagging to communicate the type of data, its corresponding security level, and its data quality. 

Basically, three types of data classification have been developed: 

• Content-based data classification: This often focuses on sensitive information – financial records, personally identifiable information – and uses software to inspect and interpret files while looking for sensitive information.
• Context-based data classification: Uses software that focuses on context-based information, such as the application, its source location, or the creator, to determine its storage location. 
• User-based data classification: A manual process that requires the person performing the task to have an understanding of data classification. This form of data classification is significantly slower, and much more error-prone, than the content and context-based data classification systems, which use software.

Datamation has published a review of classification  software tools  for 2024.

Compliance Standards and Data Classification

A growing number of countries, and some states in the U.S., have created regulations and compliance standards that require businesses and organizations establish a data classification system. Requirements may vary, depending on the country, the organization, and the types of data it is using. Listed below are some examples of why compliance can be a concern.

• General Data Protection Regulation (GDPR): Europe’s efforts to protect their citizens’ privacy resulted in regulations that require businesses to classify all their collected data.  The GDPR  is concerned with data related to race, health care, political opinions, ethnic origin, and the use of biometrics. (Businesses that are not storing massive amounts of data can use a fairly simple classification system – the goal is to provide the requested data to EU officials in a fast and efficient manner.)
• Payment Card Industry Data Security Standard (PCI DSS): Created by the credit card industry, Requirement 9.6.1 stipulates that businesses and organizations must “classify data so that sensitivity of the data can be determined.” This is not a law,  but a legal agreement .
• Health Insurance Portability and Accountability Act (HIPAA): This is a U.S. federal law.  It considers personal health information (PHI) to be confidential information, and requires medical facilities to protect the medical records of individuals. The HIPAA Privacy Rule restricts the use and disclosure of personal health information, and requires medical facilities and their associates develop a data classification system.
• California Consumer Privacy Act (CCPA): The  CCPA  states that “data classification should identify which data types are sold, shared with third parties, or used for marketing purposes. Any rights requests for specific data types should also be recorded in the data inventory as proof that you’re CCPA compliant.”

It is important for organizations to  research legal concerns , or consult expert advice, when doing business over the internet. 

The Challenges of Classifying Data

The data classification process is very useful for in terms of security and data retrieval. However, there are some problems that may develop. Some of the common challenges are:

• False positives: This takes place when the same data appears in different contexts and different formats, and the software doesn’t recognize it as a duplicate. Classification software that does not examine the data’s context and format has a higher probability of generating false classifications. Because large amounts of data are normally used in classification projects, even an extremely small false positive rates may distort the classification process.
• False negatives: These occur as a result of confusion regarding context. For example, a name would not normally be considered sensitive information. However, when it is part of a medical record, that name becomes sensitive information. Classifying data without an understanding of its context can cause data can be incorrectly classified.
• The cost: The price of implementing and operating data classification tools will depend on the number of controls established and the amount of data being processed. Data classification can become quite expensive and cumbersome. Manual efforts to classify large amounts of data can be extremely expensive, with larger amounts of data costing more.

ChatGPT is being experimented with as a tool for classifying data, but there are concerns about the system’s  lack of security .

Data Classification: Definition, Types, & Best Practices

Hanna Kleinings

Customer Operations Manager

What is Data Classification?

The short answer: Data Classification is the process of organizing data into categories for its most effective and efficient use.

In a time where nearly everything is digitized, from personal records to highly sensitive corporate data, it's about time we take a closer look into classification. Data Classification in data science refers to the process that tags and categorizes any kind of data so that it can be better understood and analyzed. The latter is what we'll be focusing on.

But also, 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 compliance.

Upward of 80% of enterprise data today is unstructured. - Gartner

Unstructured data specifically reveals insights that structured data is unable to deliver. Images analyzed for content moderation can serve as a great example – without a way to understand and classify visual data, there is a risk of not being able to filter out inappropriate content.

Before we proceed to review the various data types and applications, let’s answer a question:

How do we understand data?

Data is a collection of facts and statistics and is essentially anything that can be classified, including text, images, files, and audio. It can be formatted in both structured and unstructured forms . While structured data is easy to search and analyze, unstructured data is generally in its original format and not organized in a predefined manner . This makes it harder to interpret - unless you use AI-powered tools or put in hours of manual labor.

That said, it is also important to mention that data can be categorized in several ways . Firstly, you can approach this process creatively by the end goal, where it comes down to your pain points and bottlenecks:

The Big Question: What information and insight do you actually want to get out of classification?

In such a case, your team determines the labels (or classes) that will result in the highest business value. Another common method is to classify data per how it is being performed and further used – i.e., rule-based or Machine Learning -based.

Types of Data Classification

In the most simple terms, data can be recognized and categorized in three approaches. These are:

• Content-based classification: In this classification type, the contents of each file are the basis for categorization.
• User-based classification : User-based classification relies on the user’s knowledge of creation, editing, reviewing, or dissemination to label sensitive documents. These individuals can specify how sensitive each document is.
• Context-based classification : Context-based classification focuses on the context of the data, such as the location, application, and creator, as well as other variables that affect the data.

How do you create a classifier?

While some might assume that setting up a system to categorize data is difficult - we'll vouch for it - it simply isn’t the case:

• Define the tags for the classifier of your choice making sure the terms aren’t too vague. To be effective, a classification scheme should be simple enough that all employees can execute it properly.

• Tag examples of the data to help teach the classifier.

• Continuously test and adjust the classifier. In the end, it all comes down to using the right software , which can help you categorize data - even without coding skills.

The business value of Data Classification

Data Classification has many benefits, such as helping your company successfully pass audits, knowing who needs access to what information, understanding the value of sensitive data, and empowering end-users. Problems such as what should be labeled as urgent, determining what language a text is written in, or what to tag topics with can be solved with versions of Data Classification.

End-User Empowerment

Empowering your employees to do meaningful work is a value driver for businesses, and Data Classification makes it possible. That being said, let’s take security benefits as an example.

With a solid Data Classification strategy, data leaks can be prevented. For instance, just by classifying documents or emails by permission (such as ‘confidential’ or ‘C-level suite information only’), users could become more security-oriented and recognize the different data sensitivity tiers. Plus you can build a workflow that considers who should have access to what.

Some of the problems it can solve include:

• Urgency detection : A pre-trained model can classify inbound texts and support tickets to determine whether they should be labeled as urgent or not urgent.
• Sentiment detection : NLP, or Natural Language Processing , can be used to detect the sentiment of any given content is - save time by routing the right messages to the right people.
• Topic labeling : Topic labeling consists of tagging topics with a couple of descriptive words or phrases. This is done by using an NLP technique to identify themes and meanings - e.g. classify any incoming email attachment and forward it to the right folder in your storage system.

Classifying data can also be helpful in terms of meeting legal compliance. A lack of Data Classification doesn’t confine you to informational chaos – it can also mean you’re not GDPR or HIPAA-compliant . How so?

For instance, without Data Classification, you might not be able to recognize that a newsletter subscriber requested to be removed from the mailing list. Let’s assume they haven’t clicked the “unsubscribe” button, but have hit reply and asked to be removed via email. If you don’t catch this, you might end up keeping data against GDPR and look at a potential fine if your company is reported.

Time and manual task management go hand in hand. Imagine conducting an NPS or any other customer satisfaction survey, and going through all the free-text answers manually. Build a classifier to categorize responses by sentiment, or topic, uncover underlying trends or test out your assumptions. Combine it with other data visualization tools (e.g. word clustering), and you'll get better insights into what your customers are saying.

Data Classification applications

Alright, so we now understand the value of classification. Let's dig into how we translate all this knowledge to practice.

Text Classification

Text classification is a powerful tool for utilizing these unstructured data we all sit on top of by utilizing NLP. In the words of our users, it feels like wizardry when you create your first classifier and see hundreds of survey responses categorized in seconds.

Document Classification

Document Classification focuses on processes that mainly apply content-specific classification - e.g. classifying incoming email attachments by type. It differs from text classification, as instead of specific phrases or paragraphs being classified, the whole document is taken into consideration.

Take shipping documents as an example - more often than not, a signature is needed on multiple pages. By training a model to classify correctly filled documents versus documents where one or more signatures are missing, the process can be sped up significantly.

And time saved is the value gained.

Learn more about the powers of Document Classification.

Image Classification

Image Classification categorizes any incoming image file by predetermined labels. It is often combined with object detection. These days you can create your own image classifier and teach the model to make subjective decisions based on your logic: whether an incoming ad creative is good or not; whether the image fits into the product portfolio; whether an image you snapped on your holidays is appropriate to show to your grandparents.

Or let's say that you work with an e-commerce platform where image content is user-generated. It's a marketplace where anyone can sell their goods. Even if you can handle manually moderating the content by filtering out low-quality or inappropriate images, there will come a time when the scale of this task is just not efficient.

Identifying business areas for the biggest benefit

Here are some examples of how to apply Data Classification in your business:

Customer service

Customer support is one of the lifelines of any organization. Data Classification can be used for recording and sorting support tickets, incoming emails, and text messages - or even contact management for transaction history, tasks, and reminders.

Let's zoom into support tickets: customer support messages are often subjective in their nature. By leveraging AI-powered tools, the system flags the tone of each of the tickets as either positive, negative, or neutral, allowing for better prioritization.

Another example of Data Classification apps is AIaaS tools which use Data Classification to categorize support tickets or recognize images for content moderation . There are also chatbots, which can organize data and either respond to or tag your query as “product”, “payment,” “refund,” etc., before taking you to a human agent.

Customer care is also significantly improved through systems such as NPS, CSAT, and CES. They all often include long free-form text answers that more often than not are analyzed manually. When you scale, it doesn't sound very efficient, does it?

By training an AI-powered assistant, thousands of these responses can be categorized into clusters that matter to you most. Automatically.

Companies use Data Classification if they need to fix a software bug quickly. For instance, categorizing crashes and bug reports allow them to identify the type of software defect. For companies with a lack of resources such as skilled employees and time, this triage process is essential for software development.

Marketing Ops

Content moderation is a field mainly shifting to a Data Classification moderation system. With humongous amounts of images and articles being created every day, it is nearly impossible for Ops to keep up with moderating the content.

With NLP, it is possible to learn what is the tone of voice surrounding your brand. Classifying data can also be used to help make better strategic decisions. Sentiment Analysis shows whether people generally have a positive, negative, or neutral feeling toward your brand as a percentage breakdown

Data Classification uses both content-based classification and context-based classification to moderate what is being posted online. These classification systems are able to screen both text and video for inappropriate and illegal content that should be removed from the public.

Analyzing text responses lets you categorize your customer feedback based on the sentiment and uncover any underlying patterns. In most cases this is where rule-based automation fails - people don't naturally speak in keywords.

Manufacturing

Data Classification can also be used for quality assurance. The classifier just needs to be programmed to screen for defects in images . The performance level of Data Classification is often higher than manual quality assurance - there is just no room for human error.

Speed is necessary when it comes to inspecting images or file quality. With ML (Machine Learning) type of classification, a visual quality inspection can be performed for 100 images in just one or two seconds.

Get Started

Though Data Classification sounds daunting, it is easier to implement than it sounds. It is simply the process of tagging and labeling any form of data to be presented in a structured manner. By classifying data, businesses can be more efficient, improve their customer service, and implement better data security... You name it!

You can of course always hire a team of engineers to do it for you. But there's plenty of cost-efficient software out there. If you're ready to get started - we'd love to hear from you !

Try it out yourself

Create your own AI for documents, images, or text to take daily, repetitive tasks off your shoulders.

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Levity is a tool that allows you to train AI models on images, documents, and text data. You can rebuild manual workflows and connect everything to your existing systems without writing a single line of code.‍If you liked this blog post, you'll love Levity.

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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.

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.

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Data Classification Explained

Why data classification matters, data classification levels, data classification use cases, how does data classification improve data security, data classification faqs, what is data classification.

• 1. Data Classification Explained
• 2. Why Data Classification Matters
• 3. Data Classification Levels
• 4. Data Classification Use Cases
• 5. How Does Data Classification Improve Data Security?
• 6. Data Classification FAQs

Data classification — or organizing and categorizing data based on its sensitivity, importance, and predefined criteria — is foundational to data security . It enables organizations to efficiently manage, protect, and handle their data assets by assigning classification levels. In doing so, organizations can prioritize resources and apply security measures tailored to each data category's requirements.

Data classification helps identify and protect sensitive information, such as personally identifiable information (PII), protected health information (PHI), and financial data. By categorizing data according to its level of sensitivity, importance, or other criteria, organizations can effectively protect and handle data assets with security measures appropriate to each data type. Compliance with regulatory standards, such as GDPR, HIPAA, or CCPA, rely heavily on data classification.

How Data Classification Works

Performing data classification starts with defining a classification schema, which outlines the categories and criteria for each data type. Common classification levels include public, internal use, restricted, and confidential. Organizations then identify their data assets, both structured and unstructured, and determine the appropriate classification level for each asset.

Automated tools and solutions can assist in the classification process, using advanced algorithms to scan and analyze data, matching it to the defined categories based on content, metadata, or other attributes. Additionally, manual classification involving human intervention may come into play when subject matter expertise is required to evaluate data sensitivity or significance.

Once data is classified, organizations can act on this information by implementing appropriate security controls and policies for each classification level. These measures may include encryption for sensitive data, access controls based on user roles, and data retention policies tailored to each category's requirements.

Integrating data classification into their security practices enables organizations to optimize resource allocation, prioritize protection measures, and make informed decisions about data storage, access controls, data sharing, and retention periods. As in all things cloud security, a proactive and targeted approach mitigates risks and fortifies security posture.

Understanding the significance of data classification is pivotal to safeguarding sensitive information and mitigating risks. Security experts can identify the most critical and sensitive assets within an organization’s data ecosystem by classifying data. This knowledge allows them to allocate appropriate security measures, such as encryption, access controls, and monitoring, to the highest-risk data categories.

‍Using data classification, organizations can target security protocols in the most efficient way to achieve the greatest protection of their valuable and sensitive information. Beyond security, different types of data classification enable organizations to align their security efforts to industry-specific regulations and legal requirements.

What Is PCI?

Organizations across industries grapple with the formidable Payment Card Industry (PCI) standards. These standards, established by major credit card companies, serve as a bulwark safeguarding cardholder data during payment transactions. Enter the Payment Card Industry Data Security Standard (PCI DSS), a framework that imposes guidelines and requirements on businesses handling, processing, or storing payment card information.

Compliance with PCI is non-negotiable for entities involved in accepting, transmitting, or housing cardholder data — think merchants, financial institutions, and service providers. The PCI DSS unleashes a barrage of security measures: fortifying network security, employing encryption, tightening access controls, and conducting regular vulnerability assessments.

What Is PII?

When it comes to sensitive information, another area of concern is data that identifies a person, otherwise known as personally identifiable information (PII). This term broadly covers a wide variety of data, including but not limited to:

• Social security numbers (SSN)
• Phone numbers
• Email addresses
• Financial account details
• Biometric data

PII holds significant value for individuals and organizations, as it is easily exploitable for identity theft, fraud, or other malicious activities. Identifying and safeguarding PII is crucial for privacy protection and regulatory compliance. Organizations must implement robust security measures, such as encryption, access controls, and data anonymization, to ensure the confidentiality and integrity of PII.

What Is PHI?

In the medical field, protected health information (PHI) covers all sensitive data related to an individual’s health, medical conditions, or treatments, often including PII. This valuable information covers a range of data, including:

• medical records
• diagnostic results
• prescriptions
• health insurance details
• any other personally identifiable health-related data

Managing PHI in the U.S. is challenging, as it’s highly regulated under the Health Insurance Portability and Accountability Act (HIPAA) , which ensures the privacy and security standards that care providers must follow. Healthcare workers and organizations must safeguard the confidentiality of PHI to protect patients’ privacy, prevent unauthorized access, and comply with legal requirements. Meeting these requirements involves extreme security measures that include the highest protocols for access controls, encryption, and audit trails.

Challenges of GDPR

For any organizations that store data of citizens or residents of the European Union (EU), they have a more significant data privacy challenge than just identifying specific data types. They must comply with the General Data Protection Regulation (GDPR) , which sets strict requirements for organizations handling personal data, and ensure transparency, accountability, and control over how personal information is collected, processed, and stored. As an incentive to comply, GDPR also imposes significant penalties for non-compliance, with fines reaching up to 4% of a company’s global annual revenue or €20 million, whichever is higher, making it extremely cost prohibitive for companies to ignore the mandate.

On top of this, it grants EU citizens and residents various rights, including the right to access their data, the right to be forgotten, and the right to data portability. Each of these rights must be facilitated by organizations storing their data, requiring them to at all times know where the corresponding data is stored, along with who can access it to maintain GDPR compliance. They must also include processes for deleting this data for an individual upon request, which relies upon knowing where the relevant data resides.

Data classification can be done manually or automatically, using a combination of human judgment and advanced algorithms. The data classification levels can vary, ranging from simple labels such as “public,” “confidential,” and “sensitive” to more detailed categories based on specific regulations and industry standards.

Example of data classification levels:

• Confidential Data: This is the most sensitive category and includes data that must be protected at all costs, such as trade secrets, financial information, personally identifiable information (PII), and confidential business information.
• Internal Use Only: This category includes sensitive data but is not as critical as confidential data, such as employee payroll information, internal memos, and project plans.
• Restricted Data: This category includes sensitive data but is not as critical as confidential data, such as customer information, marketing plans, and pricing information.
• Public Data: This category includes data that is not sensitive and can be freely shared with the public, such as company press releases and marketing materials.
• Archived Data: This category includes data that is no longer actively used but still needs to be retained for legal, regulatory, or historical reasons, such as old financial reports and personnel records.

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.

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By industry, data mapping for effective privacy impact assessments, provincial workers compensation board, quebec law 25 compliance: guide for businesses, business wire press release - denodo & data sentinel join forces, join data sentinel at the iapp canada privacy symposium 2023 event, business wire: denodo & data sentinel partner, solutions engineer, what is data classification, data classification is incredibly important for organizations that deal with high volumes of data. let’s break down what data classification actually means for your unique business..

Perhaps you're the CIO of a massive 100,000-employee enterprise that deals with data. Maybe you're a small tech startup owner that deals with hundreds of files and emails daily. Either way, you likely have a lot of data on your hands but may not have the best classification processes in place.

When you don't know what data you have and where it is, it might be nearly impossible to prioritize risk mitigation or comply with privacy rules. This is where the classification of data comes in.

In this guide, we’ll explore everything you need to know as an organization leader about data classification – from its definition to use cases to types of data to types of classification.

Everything You Need to Know About Data Classification

Let’s start this in-depth guide with a definition of data classification.

What is data classification?

Organizations today generate, store, and manage more data than ever before, including sensitive information like spreadsheets holding Social Security numbers of customers, clients, and employees. Maintaining the privacy, security, and compliance of this vast amount of data necessitates a greater degree of data management and control than ever before. This necessitates the implementation of a variety of tools and techniques. Data categorization is one of the most often used privacy techniques and procedures.

The process of dividing and arranging data into appropriate categories based on their shared features, such as their level of sensitivity and the risks they pose, as well as the compliance requirements that protect them, is known as data classification or categorization.To keep sensitive information safe, it must first be found, then categorized and marked according to its level of sensitivity. Then, for each type of data, businesses must handle it in such a manner that only authorized persons have access to it, both internally and externally, and that it is always handled in complete conformity with all applicable legislations.

Why should I classify my data?

Organizations that don't know their data, including where it lives and how it needs to be secured, risk data security and privacy issues. Knowing where all "sensitive" data is housed across an organization is referred to as "knowing your data." Data privacy experts, such as Data Privacy Officers (DPOs), can't properly secure consumer, employee, and business information if they don't know the following:

• What types of data exist across the organization.
• Where that data is stored.
• The individuals allowed access to that data.
• The government regulations that involve that data.
• The data’s overall value as well as risk to the enterprise.

Data categorization provides this knowledge by establishing a standard procedure for identifying and tagging all sensitive data throughout an organization, including networks, sharing platforms, endpoints, and cloud files. It works by allowing the development of data characteristics that specify how each group should be handled and secured in accordance with business and regulatory standards. Because the data is easily accessible, businesses may implement safeguards that decrease data exposure risks, reduce data footprints, eliminate data protection redundancy, and direct security resources to the most important tasks. Organizations' data privacy and security protection strategies are streamlined and strengthened as a result of categorization.

The advantages of data classification

The advantages of data classification are virtually endless. A majority of business leaders out there don’t know exactly where their most sensitive data is stored, nor do they know how to properly protect that data. This is a major issue, as data breaches and cybersecurity crimes are at an all-time high. With a well-designed data classification program or process in place, business leaders can keep that valuable information safe. In addition to this, there are many more benefits to data classification that go beyond just knowing where one’s data resides.

Better data security

Data classification makes it possible for organizations to protect internal and customer data by identifying a few key things. What data is available? How much of that data is extremely sensitive, such as social security numbers or payment accounts? Who can access that data, and how can a data leak affect the organization as a whole?

By identifying the answers to these questions through data classification, business leaders can do a number of things. They can reduce vulnerable data footprints, reduce overall access to sensitive data, grasp different types of data for the purpose of protecting it, and optimize the overall costs of managing unneeded or obsolete data.

Overall risk reduction

Data categorization may assist companies in successfully securing, storing, and managing their data from the moment it is generated until it is destroyed. Data categorization may help companies get a better understanding of and control over the data they collect and distribute. Such procedures can help organizations get more efficient access to and use of protected data. Data categorization also aids risk management by assisting companies in determining the worth of their data as well as the consequences of it being lost, stolen, abused, or hacked.

Regulatory compliance

Data categorization aids in locating regulated data within the organization, as well as ensuring that adequate security measures are in place and that the data is traceable and searchable, as needed by compliance standards. Data categorization guarantees that sensitive data, such as medical, credit card, and personally identifiable information, is handled effectively for various requirements. It also makes it easier to stay in compliance with all essential rules, regulations, and privacy laws on a daily basis. Data classification can also help satisfy modern compliance regulations by allowing for the speedy retrieval of specified information within a given deadline.

The disadvantages of data classification

While data classification is quite important for the modern business, it does have its downsides.

It can be pricey

Traditional data categorization methods are typically manual, expensive and generally inaccurate. This poses a number of difficulties. Sensitive information has the potential to get lost in data silos, where it will be unknown, unreachable and vulnerable. Mishandling regulated data can result in fines and penalties for businesses. Client data breaches can result in litigation, degrade an organization's brand, and reduce goodwill. The key to a successful data classification program is automation and the ability to scale with near perfect accuracy. This is where organizations like Data Sentinel come in.

Policies aren’t easy to enforce

Many firms have theoretical rather than operational data categorization policies. In other words, the corporate policy is either ignored or left to the discretion of business users and data owners. This problem originates from a variety of oversights, but a common discussion point it that the problem is too complex and large to be undertaken. Ultimately leaving the company's data exposed to undue risk.

Poor execution can cause more problems

A range of data security and privacy issues might occur as a result of poor data categorization execution. Typically, companies tend to start with the easier to manage and identify structured data sources, leaving the truly risky unstructured data to last or not at all. Data and privacy issues are then ultimately pushed to the back burner in favor of more important goals like sales growth and efficiency. Businesses overcomplicate data classification more because of legacy approaches to the problem, resulting in a lack of practical outcomes.

The four levels of sensitive data

The sensitivity levels of data are used to classify it. The regulations established by various countries and states might categorize sensitivity differently, but in general we can simplify by saying that there are four categories of sensitive data: low, moderate, high, and restricted data sensitivity.

Low sensitivity

Low data sensitivity refers to information that provides little to no risk to the company. Because there are little or no constraints on who may access the data in this class, it can be viewed by anybody. In retrospect, this knowledge is public and may be discussed by anyone, anywhere. For an organization, data in this class include any publicly available information on the organization. Information on founders, business niches, and leadership might all be included.

Moderate sensitivity

Data in this category is subject to contractual agreements between the parties interested in the data. Notably, the loss of such data in this category usually results in significant consequences for the company. IT service information, internal staff information, and business processes details are examples of data that fall under this category.

High sensitivity

This categorization contains sensitive information that should be kept private. A breach of this data collection might result in serious consequences for the company, including criminal responsibility and /or consumer litigation. Furthermore, a data breach might jeopardize the company's ability to operate. IT security information, controlled unclassified information, PHI, and PII are examples of this data category.

Restricted sensitivity

Data in this category is regarded as highly confidential and is frequently subject to a nondisclosure agreement (NDA). Industry-specific data, trade secrets, and clients' financial information are examples of limited, sensitive data . A compromise of this sort of sensitive data might result in the organization being shut down completely, as well as legal repercussions and unfathomable financial damages.

Why data classification is so important

Data categorization is a hygiene practice for most firms. It increases data security and enables them to comply with regulatory requirements. It also implies that information can be more readily reviewed and examined, both in terms of correctness and how it is kept.

Customer information that is sensitive must be maintained securely and removed after a set length of time. These regulatory requirements can be accomplished by categorizing data and applying security rules to it. The advantages of data classification mostly stem from this premise, although there are also functional benefits. Instead of having to check each endpoint, businesses may grant central rights to manage who can read, alter, and remove essential intelligence by classifying data fields.

Permissions can be provided to different programs to guarantee that only the most correct records are updated in data fields. As a consequence, a system that can restrict access to sensitive data , track the usage of intellectual property assets, and maintain security has been created.

User vs automated data classification

If done manually, you must establish sensitivity levels, teach your users to recognize each level, and offer a way for them to tag and categorize every new file they produce when you want them to classify their own data.

The majority of classification systems integrate with policy-enforcing solutions, such as data loss prevention (DLP)software, which tracks and secures sensitive data marked by users. The benefit of using user categorization is that people are quite competent at determining whether or not material is sensitive. Classification accuracy may be fairly excellent with the right equipment and simple rules, but it is extremely dependent on your users' vigilance and won't scale to keep up with data generation.

Manually marking data is time-consuming, and many users will forget or ignore it. Furthermore, getting people to go back and retrospectively annotate past data is a massive issue if you have significant volumes of pre-existing data (or machine-generated data).

Automated data classification

To discover and understand data in systems, modern and automated data categorization engines use machine learning and other techniques to read and analyze the data. Automated classification is far more efficient than manual categorization, although accuracy is dependent on the engine's architecture, technology and configuration. The closeness of text, negative keywords, match ranges, and validation methods are all common characteristics in data categorization services or engines that assist validating findings and reducing false positives.

When choosing an automated categorization product, accuracy, efficiency, and scalability are all critical factors to consider. For situations with hundreds of huge data stores, you'll need a distributed, multi-threaded engine that can scan numerous systems at the same time without taking too much of the resources on the stores being scanned.

It can take a long time to conduct an initial categorization scan of a big multi-petabyte environment. Following scans can be sped considerably by using true incremental scanning. Some classification engines necessitate the creation of an index for each object they categorize. Look for an engine that doesn't require an index or only indexes items that meet a certain policy or pattern if storage space is an issue.

The process of data classification

The process of data classification is both complex and somewhat simple in nature. Basically, most data classification processes follow these steps:

Define the dataClassification process's long-term and short-term objectives

• What exactly are you looking for and why?
• What systems are included in the preliminary categorization phase?
• What rules do you have to follow when it comes to compliance?
• Are there any additional business goals you'd want to pursue? Risk mitigation, storage optimization, and analytics are just a few examples.

Classify your data types

• Determine the types of data that the company generates, such as customer lists, financial information, source code, and product plans.
• Distinguish between private and public data.
• Are you looking for GDPR, CCPA, or other regulated information?

Determine the levels of classification

• How many categorization levels are you going to require?
• Each level should be documented and examples should be provided.
• If manual categorization is to be used, people need to be trained to complete the task and provide and and resources.

Define the process of automated classification

• Determine which data to scan first and how to prioritize it.
• Determine the frequency or near real time processes for automated data classification.

Define the categories and criteria for classification

• Define your broad categories and give examples, such as PII, PCI, PHI, and so on.
• Define or allow categorization patterns and labels that are appropriate.
• Define risk categorizations
• Define any automated categorized classification customizations needed.
• Create a procedure for reviewing and validating both user-defined and automatic outcomes.

Define classified data outcomes and use cases

• Steps for risk mitigation and automated policies should be documented. These can include policies that require PHI to be moved or archived after ninety days, or that automatically remove global access groups from sensitive data folders.
• Define a method for analyzing classification results using analytics.
• Determine what you want to happen as a result of the analytic analysis.
• Determine remediation processes.

Observe and maintain

• Create a continuous pipeline for classifying new or updated data.
• Examine the categorization process and make any required modifications as a result of company developments or new legislation.

How to implement sound data classification practices

Implementation of a good data classification process can be difficult, which is why we recommend employing the help of a trusted partner like Data Sentinel to take on the task for your company. However, there are a few best practices for implementing good data classification:

• Conduct a risk assessment of sensitive data . Learn all there is to know about the company's privacy and confidentiality policies, including corporate, regulatory, and contractual needs. Define the goals for data classification with all stakeholders.
• Create a written categorization policy. The classification policy of an organization summarizes the who, what, where, when, why, and how of data categorization across the company so that everyone is aware of its importance. Objectives, processes, data owners, and schema are all topics to include in the policy.
• Sort the data into several categories. Each company will have its own definition of sensitive data. Furthermore, sensitivity is defined differently by state and federal rules. Determine the categories of sensitive information that exist inside the company. Determine if your data originates from customers or partners to fulfill this assignment (or both.) Consider how that information is used and what proprietary data is generated.
• Research and know your data residency regulatory compliance obligations.
• Check to see whether your data makes sense. It doesn't mean the data are right just because you have a perfectly clean, categorized, documented, and organized dataset. Putting two or more pieces of data together can sometimes expose mistakes that would otherwise be difficult to detect, so it's a good idea to do a few simple calculations on each variable to ensure that the data follows reasonable norms. Minimum/maximum/mean, variable counts, and data computations are examples of these calculations.
• Participate in data categorization with your users. When it comes to data classification, the more your consumers are involved from the start, the better. Plan a public awareness campaign to educate your consumers about data categorization. Engage them as early as possible in the process and give them time to learn about this additional layer of protection for your company or organization. Contribute to the development of the best-fit policy.
• Make sure your classification system is simple and quick to utilize. Because most staff and users are unfamiliar with data categorization, ensuring that they implement classification needs must be as simple as feasible. It must be a seamless element of all productivity tools that users use on a daily basis, therefore keep all of your users in mind while dealing with enormous amounts of data. Users will be able to navigate between important productivity programs without having to learn new techniques thanks to a uniform user experience across all of them.

How Data Sentinel can help

If you need a bit of help starting the process of data classification or are interesting in automating the process now and on an ongoing basis, Data Sentinel is here to help.

Data Sentinel’s proprietary deep learning discovery technology illuminates the true nature of an organization’s data across all sources and systems, monitoring, measuring, and remediating the data to ensure compliance with company policies and evolving data management privacy regulations.

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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.

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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.

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• 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.

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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.

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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 .

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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.

Objectives of classification of data;

• 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

The classified data may be arranged in tabular forms (tables) in columns and rows. Tabulation is the simplest way of arranging the data, so that anybody can understand it in the easiest way. It is the most systematic way of presenting numerical data in an easily understandable form. It facilitates a clear and simple presentation of the data, a clear expression of the implication, and an easier and more convenient comparison. There can be simple or complex tables, and general purpose or summary tables. Classification and tabulation are interdependent events in a research .

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

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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.

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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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.

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- Restricted - Fully - FOIA (b)(1) National Security - Secret - FOIA (b)(3) Statute - Restricted Data/Formerly Restricted Data - Restricted - Partially - FOIA (b)(1) National Security - Confidential - Restricted - Partially - FOIA (b)(1) National Security - Unmarked - Restricted - Partially - Presidential Records Act (p)(1) National Security Classified - Unmarked Previous Element Next Element Table of Contents Lifecycle Data Requirements Guide Numbers, Facts and Trends Shaping Your World Read our research on: Full Topic List Regions & Countries Publications Our Methods Short Reads Tools & Resources Read Our Research On: Are you in the American middle class? Find out with our income calculator About half of U.S. adults (52%) lived in middle-income households in 2022, according to a Pew Research Center analysis of the most recent available government data. Roughly three-in-ten (28%) were in lower-income households and 19% were in upper-income households. Our calculator below, updated with 2022 data, lets you find out which group you are in, and compares you with: Other adults in your metropolitan area U.S. adults overall U.S. adults similar to you in education, age, race or ethnicity, and marital status Find more research about the U.S. middle class on our topic page . Our latest analysis shows that the estimated share of adults who live in middle-income households varies widely across the 254 metropolitan areas we examined, from 42% in San Jose-Sunnyvale-Santa Clara, California, to 66% in Olympia-Lacey-Tumwater, Washington. The share of adults who live in lower-income households ranges from 16% in Bismarck, North Dakota, to 46% in Laredo, Texas. The share living in upper-income households is smallest in Muskegon-Norton Shores, Michigan (8%), and greatest in San Jose-Sunnyvale-Santa Clara, California (41%). How the income calculator works The calculator takes your household income and adjusts it for the size of your household. The income is revised upward for households that are below average in size and downward for those of above-average size. This way, each household’s income is made equivalent to the income of a three-person household. (Three is the whole number nearest to the  average size of a U.S. household , which was 2.5 people in 2023.) Pew Research Center does not store or share any of the information you enter. We use your size-adjusted household income and the cost of living in your area to determine your income tier. Middle-income households – those with an income that is two-thirds to double the U.S. median household income – had incomes ranging from about $56,600 to $169,800 in 2022. Lower-income households had incomes less than $56,600, and upper-income households had incomes greater than $169,800. (All figures are computed for three-person households, adjusted for the cost of living in a metropolitan area, and expressed in 2022 dollars.) The following example illustrates how cost-of-living adjustment for a given area was calculated: Jackson, Tennessee, is a relatively inexpensive area, with a  price level in 2022 that was 13.0% less than the national average. The San Francisco-Oakland-Berkeley metropolitan area in California is one of the most expensive, with a price level that was 17.9% higher than the national average. Thus, to step over the national middle-class threshold of $56,600, a household in Jackson needs an income of only about $49,200, or 13.0% less than the national threshold. But a household in the San Francisco area needs an income of about $66,700, or 17.9% more than the U.S. threshold, to be considered middle class. The income calculator encompasses 254 of 387 metropolitan areas in the United States, as defined by the Office of Management and Budget  . If you live outside of one of these 254 areas, the calculator reports the estimates for your state. The second part of our calculator asks about your education, age, race or ethnicity, and marital status. This allows you to see how other adults who are similar to you demographically are distributed across lower-, middle- and upper-income tiers in the U.S. overall. It does not recompute your economic tier. Note: This post and interactive calculator were originally published Dec. 9, 2015, and have been updated to reflect the Center’s new analysis.   Former Senior Researcher Rakesh Kochhar and former Research Analyst Jesse Bennett also contributed to this analysis. The Center recently published an analysis of the distribution of the  American population across income tiers . In that analysis, the estimates of the overall shares in each income tier are slightly different, because it relies on a separate government data source and includes children as well as adults. Pew Research Center designed this calculator as a way for users to find out, based on our analysis, where they appear in the distribution of U.S. adults by income tier, as well as how they compare with others who match their demographic profile. The data underlying the calculator come from the 2022 American Community Survey (ACS). The ACS contains approximately 3 million records, or about 1% of the U.S. population. In our analysis, “middle-income” Americans are adults whose annual household income is two-thirds to double the national median, after incomes have been adjusted for household size. Lower-income households have incomes less than two-thirds of the median, and upper-income households have incomes more than double the median. American adults refers to those ages 18 and older who reside in a household (as opposed to group quarters). In 2022, the  national  middle-income range was about $56,600 to $169,800 annually for a household of three. Lower-income households had incomes less than $56,600, and upper-income households had incomes greater than $169,800. (Incomes are calculated in 2022 dollars.) The median adjusted household income used to derive this middle-income range is based on household heads, regardless of their age. These income ranges vary with the cost of living in metropolitan areas and with household size. A household in a metropolitan area with a higher-than-average cost of living, or one with more than three people, needs more than $56,600 to be included in the middle-income tier. Households in less expensive areas or with fewer than three people need less than $56,600 to be considered middle income. Additional details on the methodology are available in our  earlier analyses . Income & Wages Middle Class Richard Fry is a senior researcher focusing on economics and education at Pew Research Center . Income inequality is greater among Chinese Americans than any other Asian origin group in the U.S. Is college worth it, 7 facts about americans and taxes, methodology: 2023 focus groups of asian americans, 1 in 10: redefining the asian american dream (short film), most popular. 901 E St. NW, Suite 300 Washington, DC 20004 USA (+1) 202-419-4300 | Main (+1) 202-857-8562 | Fax (+1) 202-419-4372 |  Media Inquiries Research Topics Email Newsletters ABOUT PEW RESEARCH CENTER  Pew Research Center is a nonpartisan, nonadvocacy fact tank that informs the public about the issues, attitudes and trends shaping the world. It does not take policy positions. The Center conducts public opinion polling, demographic research, computational social science research and other data-driven research. Pew Research Center is a subsidiary of The Pew Charitable Trusts , its primary funder. © 2024 Pew Research Center 358 COMMENTS 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. Data Classification: The Beginner's Guide 1. Collect the data. The first step of data classification often overlaps with the data aggregation phase of a typical data lifecycle management framework. At this step of the data classification process, users collect raw data based on attributes and parameters that may be useful for classification at a later stage. 2. How to Classify Research Data 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: Step 1. Start by identifying the purpose and nature of the research and the data to be classified. Data Classification Concepts and Considerations for Improving Data 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. What is Data Classification? Data classification is the practice of organizing and categorizing data elements according to pre-defined criteria. Classification makes data easier to locate and retrieve. Classifying data is instrumental in promoting risk management, security, and regulatory compliance. What Is Data Classification? Your Ultimate Guide 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. What is Data Classification? Guidelines and Process Data Classification Definition. Data classification is the process of analyzing structured or unstructured data and organizing it into categories based on file type, contents, and other metadata. Data classification helps organizations answer important questions about their data that inform how they mitigate risk and manage data governance ... Fundamentals of Data Classification Fundamentals of Data Classification. The process of data classification can be broadly described as the organization of data into relevant categories, allowing it to be accessed and protected more efficiently. In the simplest terms, the data classification process ranks data based on its security needs and makes it easier to locate and retrieve ... Data Classification explained Data Classification is the process of identifying the most specific type of information represented by a field or an individual value in a data set, in regard to the real world entities that the data relate to. Well, this doesn't make the definition simpler, but it describes in a more accurate way, what this article is about. ... Data Classification: Definition, Types, & Best Practices The short answer: Data Classification is the process of organizing data into categories for its most effective and efficient use. In a time where nearly everything is digitized, from personal records to highly sensitive corporate data, it's about time we take a closer look into classification. Data Classification in data science refers to the ... What is Data Classification? Definition, Levels & Examples 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 ... An Introduction to Statistics The purpose of research is to gather data, which can then be used to inform decision-making. Data can be of various types and an understanding of this is crucial for its proper analysis and interpretation. ... Classification of Data. At the highest level, data can be broadly classified as qualitative data (also known as categorical data) or ... What Is Data Classification? Data classification — or organizing and categorizing data based on its sensitivity, importance, and predefined criteria — is foundational to data security. It enables organizations to efficiently manage, protect, and handle their data assets by assigning classification levels. In doing so, organizations can prioritize resources and apply ... What is Data Classification? The classification policy of an organization summarizes the who, what, where, when, why, and how of data categorization across the company so that everyone is aware of its importance. Objectives, processes, data owners, and schema are all topics to include in the policy. Sort the data into several categories. Data Classification: What It Is and How to Implement It 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 ... 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 ... PDF Data Classification Concepts and Considerations for Improving Data 224 1. Define the organization's data classification policy, which is the taxonomy of data asset 225 types and the rules for identifying data assets of each type. 226 2. Identify the organization's data assets to be classified. 227 3. Analyze the data assets and determine the appropriate data classifications for each. 5 Types of Data Classification (With Examples) Data classification often involves five common types. Here is an explanation of each, along with specific examples to better help you understand the various levels of classification: 1. Public data. Public data is important information, though often available material that's freely accessible for people to read, research, review and store. Study designs: Part 1 Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem. Research study designs are of many types, each with its advantages and limitations. ... Figure 1 depicts a simple classification of research study designs. Data Collection 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 ... Classification and Tabulation of Data in Research 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 ... 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 ... Classification of Data in Statistics 3. Qualitative Classification. 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. Security Classification Mandatory Repeatable Data Type Authority Level Available A/V Only Public Element No Yes Variable Character Length (40) Security Classification Authority List Series File Unit Item No Yes Definition: The highest level of national security protections or classified nuclear information protections on the archival materials. Purpose: Alerts users to the national security classification of, or ... Are you in the U.S. middle class? Try our income calculator About half of U.S. adults (52%) lived in middle-income households in 2022, according to a Pew Research Center analysis of the most recent available government data. Roughly three-in-ten (28%) were in lower-income households and 19% were in upper-income households. Our calculator below, updated with ... essay homework paper phd project resume review speech thesis