describe the data analysis procedure used in the research

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Data Analysis in Research: Types & Methods

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Why analyze data in research?

Types of data in research, finding patterns in the qualitative data, methods used for data analysis in qualitative research, preparing data for analysis, methods used for data analysis in quantitative research, considerations in research data analysis, what is data analysis in research.

Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers to reduce data to a story and interpret it to derive insights. The data analysis process helps reduce a large chunk of data into smaller fragments, which makes sense. 

Three essential things occur during the data analysis process — the first is data organization . Summarization and categorization together contribute to becoming the second known method used for data reduction. It helps find patterns and themes in the data for easy identification and linking. The third and last way is data analysis – researchers do it in both top-down and bottom-up fashion.

LEARN ABOUT: Research Process Steps

On the other hand, Marshall and Rossman describe data analysis as a messy, ambiguous, and time-consuming but creative and fascinating process through which a mass of collected data is brought to order, structure and meaning.

We can say that “the data analysis and data interpretation is a process representing the application of deductive and inductive logic to the research and data analysis.”

Researchers rely heavily on data as they have a story to tell or research problems to solve. It starts with a question, and data is nothing but an answer to that question. But, what if there is no question to ask? Well! It is possible to explore data even without a problem – we call it ‘Data Mining’, which often reveals some interesting patterns within the data that are worth exploring.

Irrelevant to the type of data researchers explore, their mission and audiences’ vision guide them to find the patterns to shape the story they want to tell. One of the essential things expected from researchers while analyzing data is to stay open and remain unbiased toward unexpected patterns, expressions, and results. Remember, sometimes, data analysis tells the most unforeseen yet exciting stories that were not expected when initiating data analysis. Therefore, rely on the data you have at hand and enjoy the journey of exploratory research. 

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Every kind of data has a rare quality of describing things after assigning a specific value to it. For analysis, you need to organize these values, processed and presented in a given context, to make it useful. Data can be in different forms; here are the primary data types.

• Qualitative data: When the data presented has words and descriptions, then we call it qualitative data . Although you can observe this data, it is subjective and harder to analyze data in research, especially for comparison. Example: Quality data represents everything describing taste, experience, texture, or an opinion that is considered quality data. This type of data is usually collected through focus groups, personal qualitative interviews , qualitative observation or using open-ended questions in surveys.
• Quantitative data: Any data expressed in numbers of numerical figures are called quantitative data . This type of data can be distinguished into categories, grouped, measured, calculated, or ranked. Example: questions such as age, rank, cost, length, weight, scores, etc. everything comes under this type of data. You can present such data in graphical format, charts, or apply statistical analysis methods to this data. The (Outcomes Measurement Systems) OMS questionnaires in surveys are a significant source of collecting numeric data.
• Categorical data: It is data presented in groups. However, an item included in the categorical data cannot belong to more than one group. Example: A person responding to a survey by telling his living style, marital status, smoking habit, or drinking habit comes under the categorical data. A chi-square test is a standard method used to analyze this data.

Learn More : Examples of Qualitative Data in Education

Data analysis in qualitative research

Data analysis and qualitative data research work a little differently from the numerical data as the quality data is made up of words, descriptions, images, objects, and sometimes symbols. Getting insight from such complicated information is a complicated process. Hence it is typically used for exploratory research and data analysis .

Although there are several ways to find patterns in the textual information, a word-based method is the most relied and widely used global technique for research and data analysis. Notably, the data analysis process in qualitative research is manual. Here the researchers usually read the available data and find repetitive or commonly used words. 

For example, while studying data collected from African countries to understand the most pressing issues people face, researchers might find  “food”  and  “hunger” are the most commonly used words and will highlight them for further analysis.

LEARN ABOUT: Level of Analysis

The keyword context is another widely used word-based technique. In this method, the researcher tries to understand the concept by analyzing the context in which the participants use a particular keyword.  

For example , researchers conducting research and data analysis for studying the concept of ‘diabetes’ amongst respondents might analyze the context of when and how the respondent has used or referred to the word ‘diabetes.’

The scrutiny-based technique is also one of the highly recommended  text analysis  methods used to identify a quality data pattern. Compare and contrast is the widely used method under this technique to differentiate how a specific text is similar or different from each other. 

For example: To find out the “importance of resident doctor in a company,” the collected data is divided into people who think it is necessary to hire a resident doctor and those who think it is unnecessary. Compare and contrast is the best method that can be used to analyze the polls having single-answer questions types .

Metaphors can be used to reduce the data pile and find patterns in it so that it becomes easier to connect data with theory.

Variable Partitioning is another technique used to split variables so that researchers can find more coherent descriptions and explanations from the enormous data.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

There are several techniques to analyze the data in qualitative research, but here are some commonly used methods,

• Content Analysis:  It is widely accepted and the most frequently employed technique for data analysis in research methodology. It can be used to analyze the documented information from text, images, and sometimes from the physical items. It depends on the research questions to predict when and where to use this method.
• Narrative Analysis: This method is used to analyze content gathered from various sources such as personal interviews, field observation, and  surveys . The majority of times, stories, or opinions shared by people are focused on finding answers to the research questions.
• Discourse Analysis:  Similar to narrative analysis, discourse analysis is used to analyze the interactions with people. Nevertheless, this particular method considers the social context under which or within which the communication between the researcher and respondent takes place. In addition to that, discourse analysis also focuses on the lifestyle and day-to-day environment while deriving any conclusion.
• Grounded Theory:  When you want to explain why a particular phenomenon happened, then using grounded theory for analyzing quality data is the best resort. Grounded theory is applied to study data about the host of similar cases occurring in different settings. When researchers are using this method, they might alter explanations or produce new ones until they arrive at some conclusion.

LEARN ABOUT: 12 Best Tools for Researchers

Data analysis in quantitative research

The first stage in research and data analysis is to make it for the analysis so that the nominal data can be converted into something meaningful. Data preparation consists of the below phases.

Phase I: Data Validation

Data validation is done to understand if the collected data sample is per the pre-set standards, or it is a biased data sample again divided into four different stages

• Fraud: To ensure an actual human being records each response to the survey or the questionnaire
• Screening: To make sure each participant or respondent is selected or chosen in compliance with the research criteria
• Procedure: To ensure ethical standards were maintained while collecting the data sample
• Completeness: To ensure that the respondent has answered all the questions in an online survey. Else, the interviewer had asked all the questions devised in the questionnaire.

Phase II: Data Editing

More often, an extensive research data sample comes loaded with errors. Respondents sometimes fill in some fields incorrectly or sometimes skip them accidentally. Data editing is a process wherein the researchers have to confirm that the provided data is free of such errors. They need to conduct necessary checks and outlier checks to edit the raw edit and make it ready for analysis.

Phase III: Data Coding

Out of all three, this is the most critical phase of data preparation associated with grouping and assigning values to the survey responses . If a survey is completed with a 1000 sample size, the researcher will create an age bracket to distinguish the respondents based on their age. Thus, it becomes easier to analyze small data buckets rather than deal with the massive data pile.

LEARN ABOUT: Steps in Qualitative Research

After the data is prepared for analysis, researchers are open to using different research and data analysis methods to derive meaningful insights. For sure, statistical analysis plans are the most favored to analyze numerical data. In statistical analysis, distinguishing between categorical data and numerical data is essential, as categorical data involves distinct categories or labels, while numerical data consists of measurable quantities. The method is again classified into two groups. First, ‘Descriptive Statistics’ used to describe data. Second, ‘Inferential statistics’ that helps in comparing the data .

Descriptive statistics

This method is used to describe the basic features of versatile types of data in research. It presents the data in such a meaningful way that pattern in the data starts making sense. Nevertheless, the descriptive analysis does not go beyond making conclusions. The conclusions are again based on the hypothesis researchers have formulated so far. Here are a few major types of descriptive analysis methods.

Measures of Frequency

• Count, Percent, Frequency
• It is used to denote home often a particular event occurs.
• Researchers use it when they want to showcase how often a response is given.

Measures of Central Tendency

• Mean, Median, Mode
• The method is widely used to demonstrate distribution by various points.
• Researchers use this method when they want to showcase the most commonly or averagely indicated response.

Measures of Dispersion or Variation

• Range, Variance, Standard deviation
• Here the field equals high/low points.
• Variance standard deviation = difference between the observed score and mean
• It is used to identify the spread of scores by stating intervals.
• Researchers use this method to showcase data spread out. It helps them identify the depth until which the data is spread out that it directly affects the mean.

Measures of Position

• Percentile ranks, Quartile ranks
• It relies on standardized scores helping researchers to identify the relationship between different scores.
• It is often used when researchers want to compare scores with the average count.

For quantitative research use of descriptive analysis often give absolute numbers, but the in-depth analysis is never sufficient to demonstrate the rationale behind those numbers. Nevertheless, it is necessary to think of the best method for research and data analysis suiting your survey questionnaire and what story researchers want to tell. For example, the mean is the best way to demonstrate the students’ average scores in schools. It is better to rely on the descriptive statistics when the researchers intend to keep the research or outcome limited to the provided  sample  without generalizing it. For example, when you want to compare average voting done in two different cities, differential statistics are enough.

Descriptive analysis is also called a ‘univariate analysis’ since it is commonly used to analyze a single variable.

Inferential statistics

Inferential statistics are used to make predictions about a larger population after research and data analysis of the representing population’s collected sample. For example, you can ask some odd 100 audiences at a movie theater if they like the movie they are watching. Researchers then use inferential statistics on the collected  sample  to reason that about 80-90% of people like the movie. 

Here are two significant areas of inferential statistics.

• Estimating parameters: It takes statistics from the sample research data and demonstrates something about the population parameter.
• Hypothesis test: I t’s about sampling research data to answer the survey research questions. For example, researchers might be interested to understand if the new shade of lipstick recently launched is good or not, or if the multivitamin capsules help children to perform better at games.

These are sophisticated analysis methods used to showcase the relationship between different variables instead of describing a single variable. It is often used when researchers want something beyond absolute numbers to understand the relationship between variables.

Here are some of the commonly used methods for data analysis in research.

• Correlation: When researchers are not conducting experimental research or quasi-experimental research wherein the researchers are interested to understand the relationship between two or more variables, they opt for correlational research methods.
• Cross-tabulation: Also called contingency tables,  cross-tabulation  is used to analyze the relationship between multiple variables.  Suppose provided data has age and gender categories presented in rows and columns. A two-dimensional cross-tabulation helps for seamless data analysis and research by showing the number of males and females in each age category.
• Regression analysis: For understanding the strong relationship between two variables, researchers do not look beyond the primary and commonly used regression analysis method, which is also a type of predictive analysis used. In this method, you have an essential factor called the dependent variable. You also have multiple independent variables in regression analysis. You undertake efforts to find out the impact of independent variables on the dependent variable. The values of both independent and dependent variables are assumed as being ascertained in an error-free random manner.
• Frequency tables: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Analysis of variance: The statistical procedure is used for testing the degree to which two or more vary or differ in an experiment. A considerable degree of variation means research findings were significant. In many contexts, ANOVA testing and variance analysis are similar.
• Researchers must have the necessary research skills to analyze and manipulation the data , Getting trained to demonstrate a high standard of research practice. Ideally, researchers must possess more than a basic understanding of the rationale of selecting one statistical method over the other to obtain better data insights.
• Usually, research and data analytics projects differ by scientific discipline; therefore, getting statistical advice at the beginning of analysis helps design a survey questionnaire, select data collection methods , and choose samples.

LEARN ABOUT: Best Data Collection Tools

• The primary aim of data research and analysis is to derive ultimate insights that are unbiased. Any mistake in or keeping a biased mind to collect data, selecting an analysis method, or choosing  audience  sample il to draw a biased inference.
• Irrelevant to the sophistication used in research data and analysis is enough to rectify the poorly defined objective outcome measurements. It does not matter if the design is at fault or intentions are not clear, but lack of clarity might mislead readers, so avoid the practice.
• The motive behind data analysis in research is to present accurate and reliable data. As far as possible, avoid statistical errors, and find a way to deal with everyday challenges like outliers, missing data, data altering, data mining , or developing graphical representation.

LEARN MORE: Descriptive Research vs Correlational Research The sheer amount of data generated daily is frightening. Especially when data analysis has taken center stage. in 2018. In last year, the total data supply amounted to 2.8 trillion gigabytes. Hence, it is clear that the enterprises willing to survive in the hypercompetitive world must possess an excellent capability to analyze complex research data, derive actionable insights, and adapt to the new market needs.

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Home » Data Analysis – Process, Methods and Types

Data Analysis – Process, Methods and Types

Table of Contents

Data Analysis

Definition:

Data analysis refers to the process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, drawing conclusions, and supporting decision-making. It involves applying various statistical and computational techniques to interpret and derive insights from large datasets. The ultimate aim of data analysis is to convert raw data into actionable insights that can inform business decisions, scientific research, and other endeavors.

Data Analysis Process

The following are step-by-step guides to the data analysis process:

Define the Problem

The first step in data analysis is to clearly define the problem or question that needs to be answered. This involves identifying the purpose of the analysis, the data required, and the intended outcome.

Collect the Data

The next step is to collect the relevant data from various sources. This may involve collecting data from surveys, databases, or other sources. It is important to ensure that the data collected is accurate, complete, and relevant to the problem being analyzed.

Clean and Organize the Data

Once the data has been collected, it needs to be cleaned and organized. This involves removing any errors or inconsistencies in the data, filling in missing values, and ensuring that the data is in a format that can be easily analyzed.

Analyze the Data

The next step is to analyze the data using various statistical and analytical techniques. This may involve identifying patterns in the data, conducting statistical tests, or using machine learning algorithms to identify trends and insights.

Interpret the Results

After analyzing the data, the next step is to interpret the results. This involves drawing conclusions based on the analysis and identifying any significant findings or trends.

Communicate the Findings

Once the results have been interpreted, they need to be communicated to stakeholders. This may involve creating reports, visualizations, or presentations to effectively communicate the findings and recommendations.

Take Action

The final step in the data analysis process is to take action based on the findings. This may involve implementing new policies or procedures, making strategic decisions, or taking other actions based on the insights gained from the analysis.

Types of Data Analysis

Types of Data Analysis are as follows:

Descriptive Analysis

This type of analysis involves summarizing and describing the main characteristics of a dataset, such as the mean, median, mode, standard deviation, and range.

Inferential Analysis

This type of analysis involves making inferences about a population based on a sample. Inferential analysis can help determine whether a certain relationship or pattern observed in a sample is likely to be present in the entire population.

Diagnostic Analysis

This type of analysis involves identifying and diagnosing problems or issues within a dataset. Diagnostic analysis can help identify outliers, errors, missing data, or other anomalies in the dataset.

Predictive Analysis

This type of analysis involves using statistical models and algorithms to predict future outcomes or trends based on historical data. Predictive analysis can help businesses and organizations make informed decisions about the future.

Prescriptive Analysis

This type of analysis involves recommending a course of action based on the results of previous analyses. Prescriptive analysis can help organizations make data-driven decisions about how to optimize their operations, products, or services.

Exploratory Analysis

This type of analysis involves exploring the relationships and patterns within a dataset to identify new insights and trends. Exploratory analysis is often used in the early stages of research or data analysis to generate hypotheses and identify areas for further investigation.

Data Analysis Methods

Data Analysis Methods are as follows:

Statistical Analysis

This method involves the use of mathematical models and statistical tools to analyze and interpret data. It includes measures of central tendency, correlation analysis, regression analysis, hypothesis testing, and more.

Machine Learning

This method involves the use of algorithms to identify patterns and relationships in data. It includes supervised and unsupervised learning, classification, clustering, and predictive modeling.

Data Mining

This method involves using statistical and machine learning techniques to extract information and insights from large and complex datasets.

Text Analysis

This method involves using natural language processing (NLP) techniques to analyze and interpret text data. It includes sentiment analysis, topic modeling, and entity recognition.

Network Analysis

This method involves analyzing the relationships and connections between entities in a network, such as social networks or computer networks. It includes social network analysis and graph theory.

Time Series Analysis

This method involves analyzing data collected over time to identify patterns and trends. It includes forecasting, decomposition, and smoothing techniques.

Spatial Analysis

This method involves analyzing geographic data to identify spatial patterns and relationships. It includes spatial statistics, spatial regression, and geospatial data visualization.

Data Visualization

This method involves using graphs, charts, and other visual representations to help communicate the findings of the analysis. It includes scatter plots, bar charts, heat maps, and interactive dashboards.

Qualitative Analysis

This method involves analyzing non-numeric data such as interviews, observations, and open-ended survey responses. It includes thematic analysis, content analysis, and grounded theory.

Multi-criteria Decision Analysis

This method involves analyzing multiple criteria and objectives to support decision-making. It includes techniques such as the analytical hierarchy process, TOPSIS, and ELECTRE.

Data Analysis Tools

There are various data analysis tools available that can help with different aspects of data analysis. Below is a list of some commonly used data analysis tools:

• Microsoft Excel: A widely used spreadsheet program that allows for data organization, analysis, and visualization.
• SQL : A programming language used to manage and manipulate relational databases.
• R : An open-source programming language and software environment for statistical computing and graphics.
• Python : A general-purpose programming language that is widely used in data analysis and machine learning.
• Tableau : A data visualization software that allows for interactive and dynamic visualizations of data.
• SAS : A statistical analysis software used for data management, analysis, and reporting.
• SPSS : A statistical analysis software used for data analysis, reporting, and modeling.
• Matlab : A numerical computing software that is widely used in scientific research and engineering.
• RapidMiner : A data science platform that offers a wide range of data analysis and machine learning tools.

Applications of Data Analysis

Data analysis has numerous applications across various fields. Below are some examples of how data analysis is used in different fields:

• Business : Data analysis is used to gain insights into customer behavior, market trends, and financial performance. This includes customer segmentation, sales forecasting, and market research.
• Healthcare : Data analysis is used to identify patterns and trends in patient data, improve patient outcomes, and optimize healthcare operations. This includes clinical decision support, disease surveillance, and healthcare cost analysis.
• Education : Data analysis is used to measure student performance, evaluate teaching effectiveness, and improve educational programs. This includes assessment analytics, learning analytics, and program evaluation.
• Finance : Data analysis is used to monitor and evaluate financial performance, identify risks, and make investment decisions. This includes risk management, portfolio optimization, and fraud detection.
• Government : Data analysis is used to inform policy-making, improve public services, and enhance public safety. This includes crime analysis, disaster response planning, and social welfare program evaluation.
• Sports : Data analysis is used to gain insights into athlete performance, improve team strategy, and enhance fan engagement. This includes player evaluation, scouting analysis, and game strategy optimization.
• Marketing : Data analysis is used to measure the effectiveness of marketing campaigns, understand customer behavior, and develop targeted marketing strategies. This includes customer segmentation, marketing attribution analysis, and social media analytics.
• Environmental science : Data analysis is used to monitor and evaluate environmental conditions, assess the impact of human activities on the environment, and develop environmental policies. This includes climate modeling, ecological forecasting, and pollution monitoring.

When to Use Data Analysis

Data analysis is useful when you need to extract meaningful insights and information from large and complex datasets. It is a crucial step in the decision-making process, as it helps you understand the underlying patterns and relationships within the data, and identify potential areas for improvement or opportunities for growth.

Here are some specific scenarios where data analysis can be particularly helpful:

• Problem-solving : When you encounter a problem or challenge, data analysis can help you identify the root cause and develop effective solutions.
• Optimization : Data analysis can help you optimize processes, products, or services to increase efficiency, reduce costs, and improve overall performance.
• Prediction: Data analysis can help you make predictions about future trends or outcomes, which can inform strategic planning and decision-making.
• Performance evaluation : Data analysis can help you evaluate the performance of a process, product, or service to identify areas for improvement and potential opportunities for growth.
• Risk assessment : Data analysis can help you assess and mitigate risks, whether it is financial, operational, or related to safety.
• Market research : Data analysis can help you understand customer behavior and preferences, identify market trends, and develop effective marketing strategies.
• Quality control: Data analysis can help you ensure product quality and customer satisfaction by identifying and addressing quality issues.

Purpose of Data Analysis

The primary purposes of data analysis can be summarized as follows:

• To gain insights: Data analysis allows you to identify patterns and trends in data, which can provide valuable insights into the underlying factors that influence a particular phenomenon or process.
• To inform decision-making: Data analysis can help you make informed decisions based on the information that is available. By analyzing data, you can identify potential risks, opportunities, and solutions to problems.
• To improve performance: Data analysis can help you optimize processes, products, or services by identifying areas for improvement and potential opportunities for growth.
• To measure progress: Data analysis can help you measure progress towards a specific goal or objective, allowing you to track performance over time and adjust your strategies accordingly.
• To identify new opportunities: Data analysis can help you identify new opportunities for growth and innovation by identifying patterns and trends that may not have been visible before.

Examples of Data Analysis

Some Examples of Data Analysis are as follows:

• Social Media Monitoring: Companies use data analysis to monitor social media activity in real-time to understand their brand reputation, identify potential customer issues, and track competitors. By analyzing social media data, businesses can make informed decisions on product development, marketing strategies, and customer service.
• Financial Trading: Financial traders use data analysis to make real-time decisions about buying and selling stocks, bonds, and other financial instruments. By analyzing real-time market data, traders can identify trends and patterns that help them make informed investment decisions.
• Traffic Monitoring : Cities use data analysis to monitor traffic patterns and make real-time decisions about traffic management. By analyzing data from traffic cameras, sensors, and other sources, cities can identify congestion hotspots and make changes to improve traffic flow.
• Healthcare Monitoring: Healthcare providers use data analysis to monitor patient health in real-time. By analyzing data from wearable devices, electronic health records, and other sources, healthcare providers can identify potential health issues and provide timely interventions.
• Online Advertising: Online advertisers use data analysis to make real-time decisions about advertising campaigns. By analyzing data on user behavior and ad performance, advertisers can make adjustments to their campaigns to improve their effectiveness.
• Sports Analysis : Sports teams use data analysis to make real-time decisions about strategy and player performance. By analyzing data on player movement, ball position, and other variables, coaches can make informed decisions about substitutions, game strategy, and training regimens.
• Energy Management : Energy companies use data analysis to monitor energy consumption in real-time. By analyzing data on energy usage patterns, companies can identify opportunities to reduce energy consumption and improve efficiency.

Characteristics of Data Analysis

Characteristics of Data Analysis are as follows:

• Objective : Data analysis should be objective and based on empirical evidence, rather than subjective assumptions or opinions.
• Systematic : Data analysis should follow a systematic approach, using established methods and procedures for collecting, cleaning, and analyzing data.
• Accurate : Data analysis should produce accurate results, free from errors and bias. Data should be validated and verified to ensure its quality.
• Relevant : Data analysis should be relevant to the research question or problem being addressed. It should focus on the data that is most useful for answering the research question or solving the problem.
• Comprehensive : Data analysis should be comprehensive and consider all relevant factors that may affect the research question or problem.
• Timely : Data analysis should be conducted in a timely manner, so that the results are available when they are needed.
• Reproducible : Data analysis should be reproducible, meaning that other researchers should be able to replicate the analysis using the same data and methods.
• Communicable : Data analysis should be communicated clearly and effectively to stakeholders and other interested parties. The results should be presented in a way that is understandable and useful for decision-making.

Advantages of Data Analysis

Advantages of Data Analysis are as follows:

• Better decision-making: Data analysis helps in making informed decisions based on facts and evidence, rather than intuition or guesswork.
• Improved efficiency: Data analysis can identify inefficiencies and bottlenecks in business processes, allowing organizations to optimize their operations and reduce costs.
• Increased accuracy: Data analysis helps to reduce errors and bias, providing more accurate and reliable information.
• Better customer service: Data analysis can help organizations understand their customers better, allowing them to provide better customer service and improve customer satisfaction.
• Competitive advantage: Data analysis can provide organizations with insights into their competitors, allowing them to identify areas where they can gain a competitive advantage.
• Identification of trends and patterns : Data analysis can identify trends and patterns in data that may not be immediately apparent, helping organizations to make predictions and plan for the future.
• Improved risk management : Data analysis can help organizations identify potential risks and take proactive steps to mitigate them.
• Innovation: Data analysis can inspire innovation and new ideas by revealing new opportunities or previously unknown correlations in data.

Limitations of Data Analysis

• Data quality: The quality of data can impact the accuracy and reliability of analysis results. If data is incomplete, inconsistent, or outdated, the analysis may not provide meaningful insights.
• Limited scope: Data analysis is limited by the scope of the data available. If data is incomplete or does not capture all relevant factors, the analysis may not provide a complete picture.
• Human error : Data analysis is often conducted by humans, and errors can occur in data collection, cleaning, and analysis.
• Cost : Data analysis can be expensive, requiring specialized tools, software, and expertise.
• Time-consuming : Data analysis can be time-consuming, especially when working with large datasets or conducting complex analyses.
• Overreliance on data: Data analysis should be complemented with human intuition and expertise. Overreliance on data can lead to a lack of creativity and innovation.
• Privacy concerns: Data analysis can raise privacy concerns if personal or sensitive information is used without proper consent or security measures.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Data analysis techniques in research are essential because they allow researchers to derive meaningful insights from data sets to support their hypotheses or research objectives.

Data Analysis Techniques in Research : While various groups, institutions, and professionals may have diverse approaches to data analysis, a universal definition captures its essence. Data analysis involves refining, transforming, and interpreting raw data to derive actionable insights that guide informed decision-making for businesses.

A straightforward illustration of data analysis emerges when we make everyday decisions, basing our choices on past experiences or predictions of potential outcomes.

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Table of Contents

What is Data Analysis?

Data analysis is the systematic process of inspecting, cleaning, transforming, and interpreting data with the objective of discovering valuable insights and drawing meaningful conclusions. This process involves several steps:

• Inspecting : Initial examination of data to understand its structure, quality, and completeness.
• Cleaning : Removing errors, inconsistencies, or irrelevant information to ensure accurate analysis.
• Transforming : Converting data into a format suitable for analysis, such as normalization or aggregation.
• Interpreting : Analyzing the transformed data to identify patterns, trends, and relationships.

Types of Data Analysis Techniques in Research

Data analysis techniques in research are categorized into qualitative and quantitative methods, each with its specific approaches and tools. These techniques are instrumental in extracting meaningful insights, patterns, and relationships from data to support informed decision-making, validate hypotheses, and derive actionable recommendations. Below is an in-depth exploration of the various types of data analysis techniques commonly employed in research:

1) Qualitative Analysis:

Definition: Qualitative analysis focuses on understanding non-numerical data, such as opinions, concepts, or experiences, to derive insights into human behavior, attitudes, and perceptions.

• Content Analysis: Examines textual data, such as interview transcripts, articles, or open-ended survey responses, to identify themes, patterns, or trends.
• Narrative Analysis: Analyzes personal stories or narratives to understand individuals’ experiences, emotions, or perspectives.
• Ethnographic Studies: Involves observing and analyzing cultural practices, behaviors, and norms within specific communities or settings.

2) Quantitative Analysis:

Quantitative analysis emphasizes numerical data and employs statistical methods to explore relationships, patterns, and trends. It encompasses several approaches:

Descriptive Analysis:

• Frequency Distribution: Represents the number of occurrences of distinct values within a dataset.
• Central Tendency: Measures such as mean, median, and mode provide insights into the central values of a dataset.
• Dispersion: Techniques like variance and standard deviation indicate the spread or variability of data.

Diagnostic Analysis:

• Regression Analysis: Assesses the relationship between dependent and independent variables, enabling prediction or understanding causality.
• ANOVA (Analysis of Variance): Examines differences between groups to identify significant variations or effects.

Predictive Analysis:

• Time Series Forecasting: Uses historical data points to predict future trends or outcomes.
• Machine Learning Algorithms: Techniques like decision trees, random forests, and neural networks predict outcomes based on patterns in data.

Prescriptive Analysis:

• Optimization Models: Utilizes linear programming, integer programming, or other optimization techniques to identify the best solutions or strategies.
• Simulation: Mimics real-world scenarios to evaluate various strategies or decisions and determine optimal outcomes.

Specific Techniques:

• Monte Carlo Simulation: Models probabilistic outcomes to assess risk and uncertainty.
• Factor Analysis: Reduces the dimensionality of data by identifying underlying factors or components.
• Cohort Analysis: Studies specific groups or cohorts over time to understand trends, behaviors, or patterns within these groups.
• Cluster Analysis: Classifies objects or individuals into homogeneous groups or clusters based on similarities or attributes.
• Sentiment Analysis: Uses natural language processing and machine learning techniques to determine sentiment, emotions, or opinions from textual data.

Also Read: AI and Predictive Analytics: Examples, Tools, Uses, Ai Vs Predictive Analytics

Data Analysis Techniques in Research Examples

To provide a clearer understanding of how data analysis techniques are applied in research, let’s consider a hypothetical research study focused on evaluating the impact of online learning platforms on students’ academic performance.

Research Objective:

Determine if students using online learning platforms achieve higher academic performance compared to those relying solely on traditional classroom instruction.

Data Collection:

• Quantitative Data: Academic scores (grades) of students using online platforms and those using traditional classroom methods.
• Qualitative Data: Feedback from students regarding their learning experiences, challenges faced, and preferences.

Data Analysis Techniques Applied:

1) Descriptive Analysis:

• Calculate the mean, median, and mode of academic scores for both groups.
• Create frequency distributions to represent the distribution of grades in each group.

2) Diagnostic Analysis:

• Conduct an Analysis of Variance (ANOVA) to determine if there’s a statistically significant difference in academic scores between the two groups.
• Perform Regression Analysis to assess the relationship between the time spent on online platforms and academic performance.

3) Predictive Analysis:

• Utilize Time Series Forecasting to predict future academic performance trends based on historical data.
• Implement Machine Learning algorithms to develop a predictive model that identifies factors contributing to academic success on online platforms.

4) Prescriptive Analysis:

• Apply Optimization Models to identify the optimal combination of online learning resources (e.g., video lectures, interactive quizzes) that maximize academic performance.
• Use Simulation Techniques to evaluate different scenarios, such as varying student engagement levels with online resources, to determine the most effective strategies for improving learning outcomes.

5) Specific Techniques:

• Conduct Factor Analysis on qualitative feedback to identify common themes or factors influencing students’ perceptions and experiences with online learning.
• Perform Cluster Analysis to segment students based on their engagement levels, preferences, or academic outcomes, enabling targeted interventions or personalized learning strategies.
• Apply Sentiment Analysis on textual feedback to categorize students’ sentiments as positive, negative, or neutral regarding online learning experiences.

By applying a combination of qualitative and quantitative data analysis techniques, this research example aims to provide comprehensive insights into the effectiveness of online learning platforms.

Also Read: Learning Path to Become a Data Analyst in 2024

Data Analysis Techniques in Quantitative Research

Quantitative research involves collecting numerical data to examine relationships, test hypotheses, and make predictions. Various data analysis techniques are employed to interpret and draw conclusions from quantitative data. Here are some key data analysis techniques commonly used in quantitative research:

1) Descriptive Statistics:

• Description: Descriptive statistics are used to summarize and describe the main aspects of a dataset, such as central tendency (mean, median, mode), variability (range, variance, standard deviation), and distribution (skewness, kurtosis).
• Applications: Summarizing data, identifying patterns, and providing initial insights into the dataset.

2) Inferential Statistics:

• Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. This technique includes hypothesis testing, confidence intervals, t-tests, chi-square tests, analysis of variance (ANOVA), regression analysis, and correlation analysis.
• Applications: Testing hypotheses, making predictions, and generalizing findings from a sample to a larger population.

3) Regression Analysis:

• Description: Regression analysis is a statistical technique used to model and examine the relationship between a dependent variable and one or more independent variables. Linear regression, multiple regression, logistic regression, and nonlinear regression are common types of regression analysis .
• Applications: Predicting outcomes, identifying relationships between variables, and understanding the impact of independent variables on the dependent variable.

4) Correlation Analysis:

• Description: Correlation analysis is used to measure and assess the strength and direction of the relationship between two or more variables. The Pearson correlation coefficient, Spearman rank correlation coefficient, and Kendall’s tau are commonly used measures of correlation.
• Applications: Identifying associations between variables and assessing the degree and nature of the relationship.

5) Factor Analysis:

• Description: Factor analysis is a multivariate statistical technique used to identify and analyze underlying relationships or factors among a set of observed variables. It helps in reducing the dimensionality of data and identifying latent variables or constructs.
• Applications: Identifying underlying factors or constructs, simplifying data structures, and understanding the underlying relationships among variables.

6) Time Series Analysis:

• Description: Time series analysis involves analyzing data collected or recorded over a specific period at regular intervals to identify patterns, trends, and seasonality. Techniques such as moving averages, exponential smoothing, autoregressive integrated moving average (ARIMA), and Fourier analysis are used.
• Applications: Forecasting future trends, analyzing seasonal patterns, and understanding time-dependent relationships in data.

7) ANOVA (Analysis of Variance):

• Description: Analysis of variance (ANOVA) is a statistical technique used to analyze and compare the means of two or more groups or treatments to determine if they are statistically different from each other. One-way ANOVA, two-way ANOVA, and MANOVA (Multivariate Analysis of Variance) are common types of ANOVA.
• Applications: Comparing group means, testing hypotheses, and determining the effects of categorical independent variables on a continuous dependent variable.

8) Chi-Square Tests:

• Description: Chi-square tests are non-parametric statistical tests used to assess the association between categorical variables in a contingency table. The Chi-square test of independence, goodness-of-fit test, and test of homogeneity are common chi-square tests.
• Applications: Testing relationships between categorical variables, assessing goodness-of-fit, and evaluating independence.

These quantitative data analysis techniques provide researchers with valuable tools and methods to analyze, interpret, and derive meaningful insights from numerical data. The selection of a specific technique often depends on the research objectives, the nature of the data, and the underlying assumptions of the statistical methods being used.

Also Read: Analysis vs. Analytics: How Are They Different?

Data Analysis Methods

Data analysis methods refer to the techniques and procedures used to analyze, interpret, and draw conclusions from data. These methods are essential for transforming raw data into meaningful insights, facilitating decision-making processes, and driving strategies across various fields. Here are some common data analysis methods:

• Description: Descriptive statistics summarize and organize data to provide a clear and concise overview of the dataset. Measures such as mean, median, mode, range, variance, and standard deviation are commonly used.
• Description: Inferential statistics involve making predictions or inferences about a population based on a sample of data. Techniques such as hypothesis testing, confidence intervals, and regression analysis are used.

3) Exploratory Data Analysis (EDA):

• Description: EDA techniques involve visually exploring and analyzing data to discover patterns, relationships, anomalies, and insights. Methods such as scatter plots, histograms, box plots, and correlation matrices are utilized.
• Applications: Identifying trends, patterns, outliers, and relationships within the dataset.

4) Predictive Analytics:

• Description: Predictive analytics use statistical algorithms and machine learning techniques to analyze historical data and make predictions about future events or outcomes. Techniques such as regression analysis, time series forecasting, and machine learning algorithms (e.g., decision trees, random forests, neural networks) are employed.
• Applications: Forecasting future trends, predicting outcomes, and identifying potential risks or opportunities.

5) Prescriptive Analytics:

• Description: Prescriptive analytics involve analyzing data to recommend actions or strategies that optimize specific objectives or outcomes. Optimization techniques, simulation models, and decision-making algorithms are utilized.
• Applications: Recommending optimal strategies, decision-making support, and resource allocation.

6) Qualitative Data Analysis:

• Description: Qualitative data analysis involves analyzing non-numerical data, such as text, images, videos, or audio, to identify themes, patterns, and insights. Methods such as content analysis, thematic analysis, and narrative analysis are used.
• Applications: Understanding human behavior, attitudes, perceptions, and experiences.

7) Big Data Analytics:

• Description: Big data analytics methods are designed to analyze large volumes of structured and unstructured data to extract valuable insights. Technologies such as Hadoop, Spark, and NoSQL databases are used to process and analyze big data.
• Applications: Analyzing large datasets, identifying trends, patterns, and insights from big data sources.

8) Text Analytics:

• Description: Text analytics methods involve analyzing textual data, such as customer reviews, social media posts, emails, and documents, to extract meaningful information and insights. Techniques such as sentiment analysis, text mining, and natural language processing (NLP) are used.
• Applications: Analyzing customer feedback, monitoring brand reputation, and extracting insights from textual data sources.

These data analysis methods are instrumental in transforming data into actionable insights, informing decision-making processes, and driving organizational success across various sectors, including business, healthcare, finance, marketing, and research. The selection of a specific method often depends on the nature of the data, the research objectives, and the analytical requirements of the project or organization.

Also Read: Quantitative Data Analysis: Types, Analysis & Examples

Data Analysis Tools

Data analysis tools are essential instruments that facilitate the process of examining, cleaning, transforming, and modeling data to uncover useful information, make informed decisions, and drive strategies. Here are some prominent data analysis tools widely used across various industries:

1) Microsoft Excel:

• Description: A spreadsheet software that offers basic to advanced data analysis features, including pivot tables, data visualization tools, and statistical functions.
• Applications: Data cleaning, basic statistical analysis, visualization, and reporting.

2) R Programming Language:

• Description: An open-source programming language specifically designed for statistical computing and data visualization.
• Applications: Advanced statistical analysis, data manipulation, visualization, and machine learning.

3) Python (with Libraries like Pandas, NumPy, Matplotlib, and Seaborn):

• Description: A versatile programming language with libraries that support data manipulation, analysis, and visualization.
• Applications: Data cleaning, statistical analysis, machine learning, and data visualization.

4) SPSS (Statistical Package for the Social Sciences):

• Description: A comprehensive statistical software suite used for data analysis, data mining, and predictive analytics.
• Applications: Descriptive statistics, hypothesis testing, regression analysis, and advanced analytics.

5) SAS (Statistical Analysis System):

• Description: A software suite used for advanced analytics, multivariate analysis, and predictive modeling.
• Applications: Data management, statistical analysis, predictive modeling, and business intelligence.

6) Tableau:

• Description: A data visualization tool that allows users to create interactive and shareable dashboards and reports.
• Applications: Data visualization , business intelligence , and interactive dashboard creation.

7) Power BI:

• Description: A business analytics tool developed by Microsoft that provides interactive visualizations and business intelligence capabilities.
• Applications: Data visualization, business intelligence, reporting, and dashboard creation.

8) SQL (Structured Query Language) Databases (e.g., MySQL, PostgreSQL, Microsoft SQL Server):

• Description: Database management systems that support data storage, retrieval, and manipulation using SQL queries.
• Applications: Data retrieval, data cleaning, data transformation, and database management.

9) Apache Spark:

• Description: A fast and general-purpose distributed computing system designed for big data processing and analytics.
• Applications: Big data processing, machine learning, data streaming, and real-time analytics.

10) IBM SPSS Modeler:

• Description: A data mining software application used for building predictive models and conducting advanced analytics.
• Applications: Predictive modeling, data mining, statistical analysis, and decision optimization.

These tools serve various purposes and cater to different data analysis needs, from basic statistical analysis and data visualization to advanced analytics, machine learning, and big data processing. The choice of a specific tool often depends on the nature of the data, the complexity of the analysis, and the specific requirements of the project or organization.

Also Read: How to Analyze Survey Data: Methods & Examples

Importance of Data Analysis in Research

The importance of data analysis in research cannot be overstated; it serves as the backbone of any scientific investigation or study. Here are several key reasons why data analysis is crucial in the research process:

• Data analysis helps ensure that the results obtained are valid and reliable. By systematically examining the data, researchers can identify any inconsistencies or anomalies that may affect the credibility of the findings.
• Effective data analysis provides researchers with the necessary information to make informed decisions. By interpreting the collected data, researchers can draw conclusions, make predictions, or formulate recommendations based on evidence rather than intuition or guesswork.
• Data analysis allows researchers to identify patterns, trends, and relationships within the data. This can lead to a deeper understanding of the research topic, enabling researchers to uncover insights that may not be immediately apparent.
• In empirical research, data analysis plays a critical role in testing hypotheses. Researchers collect data to either support or refute their hypotheses, and data analysis provides the tools and techniques to evaluate these hypotheses rigorously.
• Transparent and well-executed data analysis enhances the credibility of research findings. By clearly documenting the data analysis methods and procedures, researchers allow others to replicate the study, thereby contributing to the reproducibility of research findings.
• In fields such as business or healthcare, data analysis helps organizations allocate resources more efficiently. By analyzing data on consumer behavior, market trends, or patient outcomes, organizations can make strategic decisions about resource allocation, budgeting, and planning.
• In public policy and social sciences, data analysis is instrumental in developing and evaluating policies and interventions. By analyzing data on social, economic, or environmental factors, policymakers can assess the effectiveness of existing policies and inform the development of new ones.
• Data analysis allows for continuous improvement in research methods and practices. By analyzing past research projects, identifying areas for improvement, and implementing changes based on data-driven insights, researchers can refine their approaches and enhance the quality of future research endeavors.

However, it is important to remember that mastering these techniques requires practice and continuous learning. That’s why we highly recommend the Data Analytics Course by Physics Wallah . Not only does it cover all the fundamentals of data analysis, but it also provides hands-on experience with various tools such as Excel, Python, and Tableau. Plus, if you use the “ READER ” coupon code at checkout, you can get a special discount on the course.

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Data Analysis Techniques in Research FAQs

What are the 5 techniques for data analysis.

The five techniques for data analysis include: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis Qualitative Analysis

What are techniques of data analysis in research?

Techniques of data analysis in research encompass both qualitative and quantitative methods. These techniques involve processes like summarizing raw data, investigating causes of events, forecasting future outcomes, offering recommendations based on predictions, and examining non-numerical data to understand concepts or experiences.

What are the 3 methods of data analysis?

The three primary methods of data analysis are: Qualitative Analysis Quantitative Analysis Mixed-Methods Analysis

What are the four types of data analysis techniques?

The four types of data analysis techniques are: Descriptive Analysis Diagnostic Analysis Predictive Analysis Prescriptive Analysis

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Data Analysis 6 Steps: A Complete Guide Into Data Analysis Methodology

We explore the 6 key steps in carrying out a data analysis process through examples and a comprehensive guide.

Despite being a science very much linked to technology, data analysis is still a science. Like any science, a data analysis process involves a rigorous and sequential procedure based on a series of steps that cannot be ignored. Discover the essential steps of a data analysis process through examples and a comprehensive guide.

Often, when we talk about data analysis, we focus on the tools and technological knowledge associated with this scientific field which, although fundamental, are subordinate to the methodology of the data analysis process.

In this article we focus on the 6 essential steps of a data analysis process with examples and addressing the core points of the process' methodology : how to establish the objectives of the analysis , how to collect the data and how to perform the analysis . Each of the steps listed in this publication requires different expertise and knowledge. However, understanding the entire process is crucial to drawing meaningful conclusions.

Don't miss: The Role of Data Analytics in Business

On the other hand, it is important to note that an enterprise data analytics process depends on the maturity of the company's data strategy . Companies with a more developed data-driven culture will be able to conduct deeper, more complex and more efficient data analysis.

If you are interested in improving your corporate data strategy or in discovering how to design an efficient data strategy , we encourage you to download the e-book: "How to create a data strategy to leverage the business value of data" .

The 6 steps of a data analysis process in business

Step 1 of the data analysis process: define a specific objective.

The initial phase of any data analysis process is to define the specific objective of the analysis . That is, to establish what we want to achieve with the analysis. In the case of a business data analysis, our specific objective will be linked to a business goal and, as a consequence, to a performance indicator or KPI .

To define your objective effectively, you can formulate a hypothesis and define an evaluation strategy to test it. However, this step should always start from a crucial question:

What business objective do I want to achieve?

What business challenge am I trying to address?

While this process may seem simple, it is often more complicated than it first appears. For a data analytics process to be efficient, it is essential that the data analyst has a thorough understanding of the company's operations and business objectives .

Once the objective or problem we want to solve has been defined, the next step is to identify the data and data sources we need to achieve it. Again, this is where the business vision of the data analyst comes into play. Identifying the data sources that will provide the information to answer the question posed involves extensive knowledge of the business and its activity.

Bismart Tip: How to set the right objective?

Setting the objective of an analysis depends, in part, on our creative problem-solving skills and our level of knowledge about the field under study. However, in the case of a business data analysis, it is most effective to pay attention to established performance indicators and business metrics about the field of study we want to solve . Exploring the company's activity reports and dashboards will provide valuable information about the organisation's areas of interest.

Step 2 of the data analysis process: Data collection

Once the objective has been defined, it is time to design a plan to obtain and consolidate the necessary data . At this point it is essential to identify the specific types of data you need, which can be quantitative (numerical data such as sales figures) or qualitative (descriptive data such as customer feedback).

On the other hand, you should also consider the typology of data in terms of the data source , which can be classified as: first-party data, second-party data and third-party data.

First-party data:

First-party data is the information that you or your organisation collects directly . It typically includes transactional tracking data or information obtained from your company's customer relationship management system, whether it is a CRM or a Customer Data Platform (CDP) .

Regardless of its source, first-party data is usually presented in a structured and well-organised way. Other sources of first-party data may include customer satisfaction surveys, feedback from focus groups, interviews or observational data.

Second-party data:

Second-party data is information that other organisations have directly collected . It can be understood as first-party data that has been collected for a different purpose than your analysis.

The main advantage of second-party data is that it is usually organised in a structured way. That is, it often is structured data that will make your work easier. It also tends to have a high degree of reliability. Examples of second-hand data include website, apps or social media activity, as well as online purchase or shipping data.

Third-party data:

Third-party data is information collected and consolidated from various sources by an external entity . Third-party data often comprises a wide range of unstructured data points. Many organisations collect data from third parties to generate industry reports or conduct a market research.

A specific example of third-party data collection is provided by the consultancy Gartner, which collects and distributes data of high business value to other companies.

Step 3 of the data analysis process: Data cleaning

Once we have collected the data we need, we need to prepare it for analysis. This involves a process known as data cleaning or consolidation, which is essential to ensure that the data we are working with is of quality .

The most common tasks in this part of the process are:

Eliminating significant errors, duplicated data and inconsistencies, which are inherent issues when aggregating data from different sources.

Getting rid of irrelevant data , i.e. extracting observations that are not relevant to the intended analysis.

Organising and structuring the data : performing general "cleaning" tasks, such as rectifying typographical errors or layout discrepancies, to facilitate data mapping and manipulation.

Fixing important gaps in the data : during the cleaning process, important missing data may be identified and should be remedied as soon as possible.

It is important to understand that this is the most time-consuming part of the process. In fact, it is estimated that a data analyst typically spends around 70-90% of their time cleaning data . If you are interested in learning more about the specific steps involved in this part of the process, you can read our post on data processing .

Bismart Tip: Resources to speed up data cleansing

Manually cleaning datasets can be a very time consuming task. Fortunately, there are several tools available to simplify this process. Open source tools such as OpenRefine are excellent options for basic data cleansing and even offer advanced scanning functions. However, free tools can have limitations when dealing with very large datasets. For more robust data cleaning, Python libraries such as Pandas and certain R packages are more suitable. Fluency in these programming languages is essential for their effective use.

Step 4 of the data analysis process: Data analysis

Once the data has been cleaned and prepared, it is time to dive into the most exciting phase of the process, data analysis .

At this point, we should bear in mind that there are different types of data analysis and that the type of data analysis we choose will depend , to a large extent, on the objective of our analysis . On the other hand, there are also multiple techniques to carry out data analysis. Some of the best known are univariate or bivariate analysis, time series analysis and regression analysis.

In a broader context, all forms of data analysis fall into one of the following four categories.

Types of data analysis

Descriptive analysis.

Descriptive analysis is a type of analysis that explores past events . It is the first step that companies usually take before going into more in-depth investigations. 

Diagnostic analysis

Diagnostic analysis revolves around unravelling the "why" of something. In other words, the objective of this type of analysis is to discover the causes or reasons for an event of interest to the company.

Predictive analytics

The focus of predictive analytics is to forecast future trends based on historical data . In business, predictive analytics is becoming increasingly relevant.

Unlike the other types of analysis, predictive analytics is linked to artificial intelligence and, typically, to machine learning and deep learning . Recent advances in machine learning have significantly improved the accuracy of predictive analytics and it is now one of the most valued types of analysis by companies.

Predictive analytics enables a company's senior management to take high-value actions such as solving problems before they happen, anticipating future market trends or taking strategic actions ahead of the competition.

Prescriptive analysis

Prescriptive analysis is an evolution of the three types of analysis mentioned so far. It is a methodology that combines descriptive, diagnostic and predictive analytics to formulate recommendations for the future . In other words, it goes one step further than predictive analytics. Rather than simply explaining what will happen in the future, it offers the most appropriate courses of action based on what will happen. In business, prescriptive analytics can be very useful in determining new product projects or investment areas by aggregating information from other types of analytics.

An example of prescriptive analytics is the algorithms that guide Google's self-driving cars. These algorithms make a multitude of real-time decisions based on historical and current data, ensuring a safe and smooth journey. 

Step 5 of the data analysis process: Transforming results into reports or dashboards

Once the analysis is complete and conclusions have been drawn, the final stage of the data analysis process is to share these findings with a wider audience . In the case of a business data analysis, to the organisation's stakeholders.

This step requires interpreting the results and presenting them in an easily understandable way so that senior management can make data-driven decisions . It is therefore essential to convey clear, concise and unambiguous ideas. Data visualisation plays a key role in achieving this and data analysts frequently use reporting tools such as Power BI to transform data into interactive reports and dashboards to support their conclusions.

The interpretation and presentation of results significantly influences the trajectory of a company. In this regard, it is essential to provide a complete, clear and concise overview that demonstrates a scientific and fact-based methodology for the conclusions drawn. On the other hand, it is also critical to be honest and transparent and to share with stakeholders any doubts or unclear conclusions you may have about the analysis and its results.

The best data visualisation and reporting tools

If you want to delve deeper into this part of the data analysis process, don't miss our post on the best business intelligence tools .

However, we anticipate that Power BI has been proclaimed the leading BI and analytics platform in the market in 2023 by Gartner .

At Bismart, as a Microsoft Power BI partner , we have a large team of Power BI experts and, in addition, we also have our set of specific solutions to improve the productivity and performance of Power BI .

Recently, we have created an e-book in which we explore the keys for a company to develop an efficient self-service BI strategy with Power BI . Don't miss it!

Step 6 of the data analysis process: Transforming insights into actions and business opportunities

The final stage of a data analysis process involves turning the intelligence obtained into actions and business opportunities .

On the other hand, it is essential to be aware that a data analysis process is not a linear process, but rather a complex process full of ramifications . For example, during the data cleansing phase, you may identify patterns that raise new questions, leading you back to the first step of redefining your objectives. Similarly, an exploratory analysis may uncover a set of data that you had not previously considered. You may also discover that the results of your central analysis seem misleading or incorrect, perhaps due to inaccuracies in the data or human error earlier in the process.

Although these obstacles may seem like setbacks, it is essential not to become discouraged. Data analysis is intricate and setbacks are a natural part of the process.

In this article, we have delved i nto the key stages of a data analysis process , which, in brief, are as follows:

Defining the objective : Define the business challenge we intend to address. Formulating it as a question provides a structured approach to finding a clear solution.

Collect the data : Developing a strategy for gathering the data needed to answer our question and identifying the data sources most likely to have the information we need.

Clean the data : Drill down into the data, cleaning, organising and structuring it as necessary.

Analyse the data using one of four main types of data analysis : descriptive, diagnostic, predictive and prescriptive.

Disseminate findings : Choose the most effective means to disseminate our insights in a way that is clear, concise and encourages intelligent decision-making.

Learning from setbacks : Recognising and learning from mistakes is part of the journey. Challenges that arise during the process are learning opportunities that can also transform our analysis process into a more effective strategy.

Before you go...

Companies with a well-defined and efficient data strategy are much more likely to obtain truly useful business intelligence.

We encourage you to explore in more depth the steps to take to consolidate an enterprise data strategy through our e-book "How to create a data strategy" :

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What is Data Analysis? An Expert Guide With Examples

What is data analysis.

Data analysis is a comprehensive method of inspecting, cleansing, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making. It is a multifaceted process involving various techniques and methodologies to interpret data from various sources in different formats, both structured and unstructured.

Data analysis is not just a mere process; it's a tool that empowers organizations to make informed decisions, predict trends, and improve operational efficiency. It's the backbone of strategic planning in businesses, governments, and other organizations.

Consider the example of a leading e-commerce company. Through data analysis, they can understand their customers' buying behavior, preferences, and patterns. They can then use this information to personalize customer experiences, forecast sales, and optimize marketing strategies, ultimately driving business growth and customer satisfaction.

Learn more about how to become a data analyst in our separate article, which covers everything you need to know about launching your career in this field and the skills you’ll need to master.

AI Upskilling for Beginners

The importance of data analysis in today's digital world.

In the era of digital transformation, data analysis has become more critical than ever. The explosion of data generated by digital technologies has led to the advent of what we now call 'big data.' This vast amount of data, if analyzed correctly, can provide invaluable insights that can revolutionize businesses.

Data analysis is the key to unlocking the potential of big data. It helps organizations to make sense of this data, turning it into actionable insights. These insights can be used to improve products and services, enhance experiences, streamline operations, and increase profitability.

A good example is the healthcare industry . Through data analysis, healthcare providers can predict disease outbreaks, improve patient care, and make informed decisions about treatment strategies. Similarly, in the finance sector, data analysis can help in risk assessment, fraud detection, and investment decision-making.

The Data Analysis Process: A Step-by-Step Guide

The process of data analysis is a systematic approach that involves several stages, each crucial to ensuring the accuracy and usefulness of the results. Here, we'll walk you through each step, from defining objectives to data storytelling. You can learn more about how businesses analyze data in a separate guide.

The data analysis process in a nutshell

Step 1: Defining objectives and questions

The first step in the data analysis process is to define the objectives and formulate clear, specific questions that your analysis aims to answer. This step is crucial as it sets the direction for the entire process. It involves understanding the problem or situation at hand, identifying the data needed to address it, and defining the metrics or indicators to measure the outcomes.

Step 2: Data collection

Once the objectives and questions are defined, the next step is to collect the relevant data. This can be done through various methods such as surveys, interviews, observations, or extracting from existing databases. The data collected can be quantitative (numerical) or qualitative (non-numerical), depending on the nature of the problem and the questions being asked.

Step 3: Data cleaning

Data cleaning, also known as data cleansing, is a critical step in the data analysis process. It involves checking the data for errors and inconsistencies, and correcting or removing them. This step ensures the quality and reliability of the data, which is crucial for obtaining accurate and meaningful results from the analysis.

Step 4: Data analysis

Once the data is cleaned, it's time for the actual analysis. This involves applying statistical or mathematical techniques to the data to discover patterns, relationships, or trends. There are various tools and software available for this purpose, such as Python, R, Excel, and specialized software like SPSS and SAS.

Step 5: Data interpretation and visualization

After the data is analyzed, the next step is to interpret the results and visualize them in a way that is easy to understand. This could involve creating charts, graphs, or other visual representations of the data. Data visualization helps to make complex data more understandable and provides a clear picture of the findings.

Step 6: Data storytelling

The final step in the data analysis process is data storytelling. This involves presenting the findings of the analysis in a narrative form that is engaging and easy to understand. Data storytelling is crucial for communicating the results to non-technical audiences and for making data-driven decisions.

The Types of Data Analysis

Data analysis can be categorized into four main types, each serving a unique purpose and providing different insights. These are descriptive, diagnostic, predictive, and prescriptive analyses.

The four types of analytics

Descriptive analysis

Descriptive analysis , as the name suggests, describes or summarizes raw data and makes it interpretable. It involves analyzing historical data to understand what has happened in the past.

This type of analysis is used to identify patterns and trends over time.

For example, a business might use descriptive analysis to understand the average monthly sales for the past year.

Diagnostic analysis

Diagnostic analysis goes a step further than descriptive analysis by determining why something happened. It involves more detailed data exploration and comparing different data sets to understand the cause of a particular outcome.

For instance, if a company's sales dropped in a particular month, diagnostic analysis could be used to find out why.

Predictive analysis

Predictive analysis uses statistical models and forecasting techniques to understand the future. It involves using data from the past to predict what could happen in the future. This type of analysis is often used in risk assessment, marketing, and sales forecasting.

For example, a company might use predictive analysis to forecast the next quarter's sales based on historical data.

Prescriptive analysis

Prescriptive analysis is the most advanced type of data analysis. It not only predicts future outcomes but also suggests actions to benefit from these predictions. It uses sophisticated tools and technologies like machine learning and artificial intelligence to recommend decisions.

For example, a prescriptive analysis might suggest the best marketing strategies to increase future sales.

Data Analysis Techniques

There are numerous techniques used in data analysis, each with its unique purpose and application. Here, we will discuss some of the most commonly used techniques, including exploratory analysis, regression analysis, Monte Carlo simulation, factor analysis, cohort analysis, cluster analysis, time series analysis, and sentiment analysis.

Exploratory analysis

Exploratory analysis is used to understand the main characteristics of a data set. It is often used at the beginning of a data analysis process to summarize the main aspects of the data, check for missing data, and test assumptions. This technique involves visual methods such as scatter plots, histograms, and box plots.

You can learn more about exploratory data analysis with our course, covering how to explore, visualize, and extract insights from data using Python.

Regression analysis

Regression analysis is a statistical method used to understand the relationship between a dependent variable and one or more independent variables. It is commonly used for forecasting, time series modeling, and finding the causal effect relationships between variables.

We have a tutorial exploring the essentials of linear regression , which is one of the most widely used regression algorithms in areas like machine learning.

Linear and logistic regression

Factor analysis

Factor analysis is a technique used to reduce a large number of variables into fewer factors. The factors are constructed in such a way that they capture the maximum possible information from the original variables. This technique is often used in market research, customer segmentation, and image recognition.

Learn more about factor analysis in R with our course, which explores latent variables, such as personality, using exploratory and confirmatory factor analyses.

Monte Carlo simulation

Monte Carlo simulation is a technique that uses probability distributions and random sampling to estimate numerical results. It is often used in risk analysis and decision-making where there is significant uncertainty.

We have a tutorial that explores Monte Carlo methods in R , as well as a course on Monte Carlo simulations in Python , which can estimate a range of outcomes for uncertain events.

Example of a Monte Carlo simulation

Cluster analysis

Cluster analysis is a technique used to group a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups. It is often used in market segmentation, image segmentation, and recommendation systems.

You can explore a range of clustering techniques, including hierarchical clustering and k-means clustering, in our Cluster Analysis in R course.

Cohort analysis

Cohort analysis is a subset of behavioral analytics that takes data from a given dataset and groups it into related groups for analysis. These related groups, or cohorts, usually share common characteristics within a defined time span. This technique is often used in marketing, user engagement, and customer lifecycle analysis.

Our course, Customer Segmentation in Python , explores a range of techniques for segmenting and analyzing customer data, including cohort analysis.

Graph showing an example of cohort analysis

Time series analysis

Time series analysis is a statistical technique that deals with time series data, or trend analysis. It is used to analyze the sequence of data points to extract meaningful statistics and other characteristics of the data. This technique is often used in sales forecasting, economic forecasting, and weather forecasting.

Our Time Series with Python skill track takes you through how to manipulate and analyze time series data, working with a variety of Python libraries.

Sentiment analysis

Sentiment analysis, also known as opinion mining, uses natural language processing, text analysis, and computational linguistics to identify and extract subjective information from source materials. It is often used in social media monitoring, brand monitoring, and understanding customer feedback.

To get familiar with sentiment analysis in Python , you can take our online course, which will teach you how to perform an end-to-end sentiment analysis.

Data Analysis Tools

In the realm of data analysis, various tools are available that cater to different needs, complexities, and levels of expertise. These tools range from programming languages like Python and R to visualization software like Power BI and Tableau. Let's delve into some of these tools.

Python is a high-level, general-purpose programming language that has become a favorite among data analysts and data scientists. Its simplicity and readability, coupled with a wide range of libraries like pandas , NumPy , and Matplotlib , make it an excellent tool for data analysis and data visualization.

" dir="ltr">Resources to get you started

• You can start learning Python today with our Python Fundamentals skill track, which covers all the foundational skills you need to understand the language.
• You can also take out Data Analyst with Python career track to start your journey to becoming a data analyst.
• Check out our Python for beginners cheat sheet as a handy reference guide.

R is a programming language and free software environment specifically designed for statistical computing and graphics. It is widely used among statisticians and data miners for developing statistical software and data analysis. R provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, and more.

• Our R Programming skill track will introduce you to R and help you develop the skills you’ll need to start coding in R.
• With the Data Analyst with R career track, you’ll gain the skills you need to start your journey to becoming a data analyst.
• Our Getting Started with R cheat sheet helps give an overview of how to start learning R Programming.

SQL (Structured Query Language) is a standard language for managing and manipulating databases. It is used to retrieve and manipulate data stored in relational databases. SQL is essential for tasks that involve data management or manipulation within databases.

• To get familiar with SQL, consider taking our SQL Fundamentals skill track, where you’ll learn how to interact with and query your data.
• SQL for Business Analysts will boost your business SQL skills.
• Our SQL Basics cheat sheet covers a list of functions for querying data, filtering data, aggregation, and more.

Power BI is a business analytics tool developed by Microsoft. It provides interactive visualizations with self-service business intelligence capabilities. Power BI is used to transform raw data into meaningful insights through easy-to-understand dashboards and reports.

• Explore the power of Power BI with our Power BI Fundamentals skill track, where you’ll learn to get the most from the business intelligence tool.
• With Exploratory Data Analysis in Power BI you’ll learn how to enhance your reports with EDA.
• We have a Power BI cheat sheet which covers many of the basics you’ll need to get started.

Tableau is a powerful data visualization tool used in the Business Intelligence industry. It allows you to create interactive and shareable dashboards, which depict trends, variations, and density of the data in the form of charts and graphs.

• The Tableau Fundamentals skill track will introduce you to the business intelligence tool and how you can use it to clear, analyze, and visualize data.
• Analyzing Data in Tableau will give you some of the advanced skills needed to improve your analytics and visualizations.
• Check out our Tableau cheat sheet , which runs you through the essentials of how to get started using the tool.

Microsoft Excel is one of the most widely used tools for data analysis. It offers a range of features for data manipulation, statistical analysis, and visualization. Excel's simplicity and versatility make it a great tool for both simple and complex data analysis tasks.

• Check out our Data Analysis in Excel course to build functional skills in Excel.
• For spreadsheet skills in general, check out Marketing Analytics in Spreadsheets .
• The Excel Basics cheat sheet covers many of the basic formulas and operations you’ll need to make a start.

Understanding the Impact of Data Analysis

Data analysis, whether on a small or large scale, can have a profound impact on business performance. It can drive significant changes, leading to improved efficiency, increased profitability, and a deeper understanding of market trends and customer behavior.

Informed decision-making

Data analysis allows businesses to make informed decisions based on facts, figures, and trends, rather than relying on guesswork or intuition. It provides a solid foundation for strategic planning and policy-making, ensuring that resources are allocated effectively and that efforts are directed towards areas that will yield the most benefit.

Impact on small businesses

For small businesses, even simple data analysis can lead to significant improvements. For example, analyzing sales data can help identify which products are performing well and which are not. This information can then be used to adjust marketing strategies, pricing, and inventory management, leading to increased sales and profitability.

Impact on large businesses

For larger businesses, the impact of data analysis can be even more profound. Big data analysis can uncover complex patterns and trends that would be impossible to detect otherwise. This can lead to breakthrough insights, driving innovation and giving the business a competitive edge.

For example, a large retailer might use data analysis to optimize its supply chain, reducing costs and improving efficiency. Or a tech company might use data analysis to understand user behavior, leading to improved product design and better user engagement.

The critical role of data analysis

In today's data-driven world, the ability to analyze and interpret data is a critical skill. Businesses that can harness the power of data analysis are better positioned to adapt to changing market conditions, meet customer needs, and drive growth and profitability.

Get started with DataCamp for Business

Build a data-driven workforce with DataCamp for business

Top Careers in Data Analysis in 2023

In the era of Big Data, careers in data analysis are flourishing. With the increasing demand for data-driven insights, these professions offer promising prospects. Here, we will discuss some of the top careers in data analysis in 2023, referring to our full guide on the top ten analytics careers .

1. Data scientist

Data scientists are the detectives of the data world, uncovering patterns, insights, and trends from vast amounts of information. They use a combination of programming, statistical skills, and machine learning to make sense of complex data sets. Data scientists not only analyze data but also use their insights to influence strategic decisions within their organization.

We’ve got a complete guide on how to become a data scientist , which outlines everything you need to know about starting your career in the industry.

Key skills :

• Proficiency in programming languages like Python or R
• Strong knowledge of statistics and probability
• Familiarity with machine learning algorithms
• Data wrangling and data cleaning skills
• Ability to communicate complex data insights in a clear and understandable manner

Essential tools :

• Jupyter Notebook
• Machine learning libraries like Scikit-learn, TensorFlow
• Data visualization libraries like Matplotlib, Seaborn

2. Business intelligence analyst

Business intelligence analysts are responsible for providing a clear picture of a business's performance by analyzing data related to market trends, business processes, and industry competition. They use tools and software to convert complex data into digestible reports and dashboards, helping decision-makers to understand the business's position and make informed decisions.

• Strong analytical skills
• Proficiency in SQL and other database technologies
• Understanding of data warehousing and ETL processes
• Ability to create clear visualizations and reports
• Business acumen
• Power BI, Tableau

3. Data engineer

Data engineers are the builders and maintainers of the data pipeline. They design, construct, install, test, and maintain highly scalable data management systems. They also ensure that data is clean, reliable, and preprocessed for data scientists to perform analysis.

Read more about what a data engineer does and how you can become a data engineer in our separate guide.

• Proficiency in SQL and NoSQL databases
• Knowledge of distributed systems and data architecture
• Familiarity with ETL tools and processes
• Programming skills, particularly in Python and Java
• Understanding of machine learning algorithms
• Hadoop, Spark
• Python, Java

4. Business analyst

Business analysts are the bridge between IT and business stakeholders. They use data to assess processes, determine requirements, and deliver data-driven recommendations and reports to executives and stakeholders. They are involved in strategic planning, business model analysis, process design, and system analysis.

• Understanding of business processes and strategies
• Proficiency in SQL
• Ability to communicate effectively with both IT and business stakeholders
• Project management skills

A table outlining different data analysis careers

How to Get Started with Data Analysis

Embarking on your journey into data analysis might seem daunting at first, but with the right resources and guidance, you can develop the necessary skills and knowledge. Here are some steps to help you get started, focusing on the resources available at DataCamp.

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Understand the basics

Before diving into data analysis, it's important to understand the basics. This includes familiarizing yourself with statistical concepts, data types, and data structures. DataCamp's Introduction to Data Science in Python or Introduction to Data Science in R courses are great starting points.

Learn a programming language

Data analysis requires proficiency in at least one programming language. Python and R are among the most popular choices due to their versatility and the vast array of libraries they offer for data analysis. We offer comprehensive learning paths for both Python and R .

Master data manipulation and visualization

Data manipulation and visualization are key components of data analysis. They allow you to clean, transform, and visualize your data, making it easier to understand and analyze. Courses like Data Manipulation with pandas or Data Visualization with ggplot2 can help you develop these skills.

Dive into Specific Data Analysis Techniques

Once you've mastered the basics, you can delve into specific data analysis techniques like regression analysis , time series analysis , or machine learning . We offer a wide range of courses across many topics, allowing you to specialize based on your interests and career goals.

Practice, Practice, Practice

The key to mastering data analysis is practice. DataCamp's practice mode and projects provide hands-on experience with real-world data, helping you consolidate your learning and apply your skills. You can find a list of 20 data analytics projects for all levels to give you some inspiration.

Remember, learning data analysis is a journey. It's okay to start small and gradually build up your skills over time. With patience, persistence, and the right resources, you'll be well on your way to becoming a proficient data analyst.

Become a ML Scientist

Final thoughts.

In the era of digital transformation, data analysis has emerged as a crucial skill, regardless of your field or industry. The ability to make sense of data, to extract insights, and to use those insights to make informed decisions can give you a significant advantage in today's data-driven world.

Whether you're a marketer looking to understand customer behavior, a healthcare professional aiming to improve patient outcomes, or a business leader seeking to drive growth and profitability, data analysis can provide the insights you need to succeed.

Remember, data analysis is not just about numbers and statistics. It's about asking the right questions, being curious about patterns and trends, and having the courage to make data-driven decisions. It's about telling a story with data, a story that can influence strategies, change perspectives, and drive innovation.

So, we encourage you to apply your understanding of data analysis in your respective fields. Harness the power of data to uncover insights, make informed decisions, and drive success. The world of data is at your fingertips, waiting to be explored.

Data Analyst with Python

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Data analysis is a comprehensive method that involves inspecting, cleansing, transforming, and modeling data to discover useful information, make conclusions, and support decision-making. It's a process that empowers organizations to make informed decisions, predict trends, and improve operational efficiency.

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The data analysis process involves several steps, including defining objectives and questions, data collection, data cleaning, data analysis, data interpretation and visualization, and data storytelling. Each step is crucial to ensuring the accuracy and usefulness of the results.

What are the different types of data analysis?

Data analysis can be categorized into four types: descriptive, diagnostic, predictive, and prescriptive analysis. Descriptive analysis summarizes raw data, diagnostic analysis determines why something happened, predictive analysis uses past data to predict the future, and prescriptive analysis suggests actions based on predictions.

What are some commonly used data analysis techniques?

There are various data analysis techniques, including exploratory analysis, regression analysis, Monte Carlo simulation, factor analysis, cohort analysis, cluster analysis, time series analysis, and sentiment analysis. Each has its unique purpose and application in interpreting data.

What are some of the tools used in data analysis?

Data analysis typically utilizes tools such as Python, R, SQL for programming, and Power BI, Tableau, and Excel for visualization and data management.

How can I start learning data analysis?

You can start learning data analysis by understanding the basics of statistical concepts, data types, and structures. Then learn a programming language like Python or R, master data manipulation and visualization, and delve into specific data analysis techniques.

How can I become a data analyst?

Becoming a Data Analyst requires a strong understanding of statistical techniques and data analysis tools. Mastery of software such as Python, R, Excel, and specialized software like SPSS and SAS is typically necessary. Read our full guide on how to become a Data Analyst and consider our Data Analyst Certification to get noticed by recruiters.

A writer and content editor in the edtech space. Committed to exploring data trends and enthusiastic about learning data science.

Adel is a Data Science educator, speaker, and Evangelist at DataCamp where he has released various courses and live training on data analysis, machine learning, and data engineering. He is passionate about spreading data skills and data literacy throughout organizations and the intersection of technology and society. He has an MSc in Data Science and Business Analytics. In his free time, you can find him hanging out with his cat Louis.

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A Step-by-Step Guide to the Data Analysis Process

Like any scientific discipline, data analysis follows a rigorous step-by-step process. Each stage requires different skills and know-how. To get meaningful insights, though, it’s important to understand the process as a whole. An underlying framework is invaluable for producing results that stand up to scrutiny.

In this post, we’ll explore the main steps in the data analysis process. This will cover how to define your goal, collect data, and carry out an analysis. Where applicable, we’ll also use examples and highlight a few tools to make the journey easier. When you’re done, you’ll have a much better understanding of the basics. This will help you tweak the process to fit your own needs.

Here are the steps we’ll take you through:

• Defining the question
• Collecting the data
• Cleaning the data
• Analyzing the data
• Sharing your results
• Embracing failure

On popular request, we’ve also developed a video based on this article. Scroll further along this article to watch that.

Ready? Let’s get started with step one.

1. Step one: Defining the question

The first step in any data analysis process is to define your objective. In data analytics jargon, this is sometimes called the ‘problem statement’.

Defining your objective means coming up with a hypothesis and figuring how to test it. Start by asking: What business problem am I trying to solve? While this might sound straightforward, it can be trickier than it seems. For instance, your organization’s senior management might pose an issue, such as: “Why are we losing customers?” It’s possible, though, that this doesn’t get to the core of the problem. A data analyst’s job is to understand the business and its goals in enough depth that they can frame the problem the right way.

Let’s say you work for a fictional company called TopNotch Learning. TopNotch creates custom training software for its clients. While it is excellent at securing new clients, it has much lower repeat business. As such, your question might not be, “Why are we losing customers?” but, “Which factors are negatively impacting the customer experience?” or better yet: “How can we boost customer retention while minimizing costs?”

Now you’ve defined a problem, you need to determine which sources of data will best help you solve it. This is where your business acumen comes in again. For instance, perhaps you’ve noticed that the sales process for new clients is very slick, but that the production team is inefficient. Knowing this, you could hypothesize that the sales process wins lots of new clients, but the subsequent customer experience is lacking. Could this be why customers don’t come back? Which sources of data will help you answer this question?

Tools to help define your objective

Defining your objective is mostly about soft skills, business knowledge, and lateral thinking. But you’ll also need to keep track of business metrics and key performance indicators (KPIs). Monthly reports can allow you to track problem points in the business. Some KPI dashboards come with a fee, like Databox and DashThis . However, you’ll also find open-source software like Grafana , Freeboard , and Dashbuilder . These are great for producing simple dashboards, both at the beginning and the end of the data analysis process.

2. Step two: Collecting the data

Once you’ve established your objective, you’ll need to create a strategy for collecting and aggregating the appropriate data. A key part of this is determining which data you need. This might be quantitative (numeric) data, e.g. sales figures, or qualitative (descriptive) data, such as customer reviews. All data fit into one of three categories: first-party, second-party, and third-party data. Let’s explore each one.

What is first-party data?

First-party data are data that you, or your company, have directly collected from customers. It might come in the form of transactional tracking data or information from your company’s customer relationship management (CRM) system. Whatever its source, first-party data is usually structured and organized in a clear, defined way. Other sources of first-party data might include customer satisfaction surveys, focus groups, interviews, or direct observation.

What is second-party data?

To enrich your analysis, you might want to secure a secondary data source. Second-party data is the first-party data of other organizations. This might be available directly from the company or through a private marketplace. The main benefit of second-party data is that they are usually structured, and although they will be less relevant than first-party data, they also tend to be quite reliable. Examples of second-party data include website, app or social media activity, like online purchase histories, or shipping data.

What is third-party data?

Third-party data is data that has been collected and aggregated from numerous sources by a third-party organization. Often (though not always) third-party data contains a vast amount of unstructured data points (big data). Many organizations collect big data to create industry reports or to conduct market research. The research and advisory firm Gartner is a good real-world example of an organization that collects big data and sells it on to other companies. Open data repositories and government portals are also sources of third-party data .

Tools to help you collect data

Once you’ve devised a data strategy (i.e. you’ve identified which data you need, and how best to go about collecting them) there are many tools you can use to help you. One thing you’ll need, regardless of industry or area of expertise, is a data management platform (DMP). A DMP is a piece of software that allows you to identify and aggregate data from numerous sources, before manipulating them, segmenting them, and so on. There are many DMPs available. Some well-known enterprise DMPs include Salesforce DMP , SAS , and the data integration platform, Xplenty . If you want to play around, you can also try some open-source platforms like Pimcore or D:Swarm .

Want to learn more about what data analytics is and the process a data analyst follows? We cover this topic (and more) in our free introductory short course for beginners. Check out tutorial one: An introduction to data analytics .

3. Step three: Cleaning the data

Once you’ve collected your data, the next step is to get it ready for analysis. This means cleaning, or ‘scrubbing’ it, and is crucial in making sure that you’re working with high-quality data . Key data cleaning tasks include:

• Removing major errors, duplicates, and outliers —all of which are inevitable problems when aggregating data from numerous sources.
• Removing unwanted data points —extracting irrelevant observations that have no bearing on your intended analysis.
• Bringing structure to your data —general ‘housekeeping’, i.e. fixing typos or layout issues, which will help you map and manipulate your data more easily.
• Filling in major gaps —as you’re tidying up, you might notice that important data are missing. Once you’ve identified gaps, you can go about filling them.

A good data analyst will spend around 70-90% of their time cleaning their data. This might sound excessive. But focusing on the wrong data points (or analyzing erroneous data) will severely impact your results. It might even send you back to square one…so don’t rush it! You’ll find a step-by-step guide to data cleaning here . You may be interested in this introductory tutorial to data cleaning, hosted by Dr. Humera Noor Minhas.

Carrying out an exploratory analysis

Another thing many data analysts do (alongside cleaning data) is to carry out an exploratory analysis. This helps identify initial trends and characteristics, and can even refine your hypothesis. Let’s use our fictional learning company as an example again. Carrying out an exploratory analysis, perhaps you notice a correlation between how much TopNotch Learning’s clients pay and how quickly they move on to new suppliers. This might suggest that a low-quality customer experience (the assumption in your initial hypothesis) is actually less of an issue than cost. You might, therefore, take this into account.

Tools to help you clean your data

Cleaning datasets manually—especially large ones—can be daunting. Luckily, there are many tools available to streamline the process. Open-source tools, such as OpenRefine , are excellent for basic data cleaning, as well as high-level exploration. However, free tools offer limited functionality for very large datasets. Python libraries (e.g. Pandas) and some R packages are better suited for heavy data scrubbing. You will, of course, need to be familiar with the languages. Alternatively, enterprise tools are also available. For example, Data Ladder , which is one of the highest-rated data-matching tools in the industry. There are many more. Why not see which free data cleaning tools you can find to play around with?

4. Step four: Analyzing the data

Finally, you’ve cleaned your data. Now comes the fun bit—analyzing it! The type of data analysis you carry out largely depends on what your goal is. But there are many techniques available. Univariate or bivariate analysis, time-series analysis, and regression analysis are just a few you might have heard of. More important than the different types, though, is how you apply them. This depends on what insights you’re hoping to gain. Broadly speaking, all types of data analysis fit into one of the following four categories.

Descriptive analysis

Descriptive analysis identifies what has already happened . It is a common first step that companies carry out before proceeding with deeper explorations. As an example, let’s refer back to our fictional learning provider once more. TopNotch Learning might use descriptive analytics to analyze course completion rates for their customers. Or they might identify how many users access their products during a particular period. Perhaps they’ll use it to measure sales figures over the last five years. While the company might not draw firm conclusions from any of these insights, summarizing and describing the data will help them to determine how to proceed.

Learn more: What is descriptive analytics?

Diagnostic analysis

Diagnostic analytics focuses on understanding why something has happened . It is literally the diagnosis of a problem, just as a doctor uses a patient’s symptoms to diagnose a disease. Remember TopNotch Learning’s business problem? ‘Which factors are negatively impacting the customer experience?’ A diagnostic analysis would help answer this. For instance, it could help the company draw correlations between the issue (struggling to gain repeat business) and factors that might be causing it (e.g. project costs, speed of delivery, customer sector, etc.) Let’s imagine that, using diagnostic analytics, TopNotch realizes its clients in the retail sector are departing at a faster rate than other clients. This might suggest that they’re losing customers because they lack expertise in this sector. And that’s a useful insight!

Predictive analysis

Predictive analysis allows you to identify future trends based on historical data . In business, predictive analysis is commonly used to forecast future growth, for example. But it doesn’t stop there. Predictive analysis has grown increasingly sophisticated in recent years. The speedy evolution of machine learning allows organizations to make surprisingly accurate forecasts. Take the insurance industry. Insurance providers commonly use past data to predict which customer groups are more likely to get into accidents. As a result, they’ll hike up customer insurance premiums for those groups. Likewise, the retail industry often uses transaction data to predict where future trends lie, or to determine seasonal buying habits to inform their strategies. These are just a few simple examples, but the untapped potential of predictive analysis is pretty compelling.

Prescriptive analysis

Prescriptive analysis allows you to make recommendations for the future. This is the final step in the analytics part of the process. It’s also the most complex. This is because it incorporates aspects of all the other analyses we’ve described. A great example of prescriptive analytics is the algorithms that guide Google’s self-driving cars. Every second, these algorithms make countless decisions based on past and present data, ensuring a smooth, safe ride. Prescriptive analytics also helps companies decide on new products or areas of business to invest in.

Learn more:  What are the different types of data analysis?

5. Step five: Sharing your results

You’ve finished carrying out your analyses. You have your insights. The final step of the data analytics process is to share these insights with the wider world (or at least with your organization’s stakeholders!) This is more complex than simply sharing the raw results of your work—it involves interpreting the outcomes, and presenting them in a manner that’s digestible for all types of audiences. Since you’ll often present information to decision-makers, it’s very important that the insights you present are 100% clear and unambiguous. For this reason, data analysts commonly use reports, dashboards, and interactive visualizations to support their findings.

How you interpret and present results will often influence the direction of a business. Depending on what you share, your organization might decide to restructure, to launch a high-risk product, or even to close an entire division. That’s why it’s very important to provide all the evidence that you’ve gathered, and not to cherry-pick data. Ensuring that you cover everything in a clear, concise way will prove that your conclusions are scientifically sound and based on the facts. On the flip side, it’s important to highlight any gaps in the data or to flag any insights that might be open to interpretation. Honest communication is the most important part of the process. It will help the business, while also helping you to excel at your job!

Tools for interpreting and sharing your findings

There are tons of data visualization tools available, suited to different experience levels. Popular tools requiring little or no coding skills include Google Charts , Tableau , Datawrapper , and Infogram . If you’re familiar with Python and R, there are also many data visualization libraries and packages available. For instance, check out the Python libraries Plotly , Seaborn , and Matplotlib . Whichever data visualization tools you use, make sure you polish up your presentation skills, too. Remember: Visualization is great, but communication is key!

You can learn more about storytelling with data in this free, hands-on tutorial .  We show you how to craft a compelling narrative for a real dataset, resulting in a presentation to share with key stakeholders. This is an excellent insight into what it’s really like to work as a data analyst!

6. Step six: Embrace your failures

The last ‘step’ in the data analytics process is to embrace your failures. The path we’ve described above is more of an iterative process than a one-way street. Data analytics is inherently messy, and the process you follow will be different for every project. For instance, while cleaning data, you might spot patterns that spark a whole new set of questions. This could send you back to step one (to redefine your objective). Equally, an exploratory analysis might highlight a set of data points you’d never considered using before. Or maybe you find that the results of your core analyses are misleading or erroneous. This might be caused by mistakes in the data, or human error earlier in the process.

While these pitfalls can feel like failures, don’t be disheartened if they happen. Data analysis is inherently chaotic, and mistakes occur. What’s important is to hone your ability to spot and rectify errors. If data analytics was straightforward, it might be easier, but it certainly wouldn’t be as interesting. Use the steps we’ve outlined as a framework, stay open-minded, and be creative. If you lose your way, you can refer back to the process to keep yourself on track.

In this post, we’ve covered the main steps of the data analytics process. These core steps can be amended, re-ordered and re-used as you deem fit, but they underpin every data analyst’s work:

• Define the question —What business problem are you trying to solve? Frame it as a question to help you focus on finding a clear answer.
• Collect data —Create a strategy for collecting data. Which data sources are most likely to help you solve your business problem?
• Clean the data —Explore, scrub, tidy, de-dupe, and structure your data as needed. Do whatever you have to! But don’t rush…take your time!
• Analyze the data —Carry out various analyses to obtain insights. Focus on the four types of data analysis: descriptive, diagnostic, predictive, and prescriptive.
• Share your results —How best can you share your insights and recommendations? A combination of visualization tools and communication is key.
• Embrace your mistakes —Mistakes happen. Learn from them. This is what transforms a good data analyst into a great one.

What next? From here, we strongly encourage you to explore the topic on your own. Get creative with the steps in the data analysis process, and see what tools you can find. As long as you stick to the core principles we’ve described, you can create a tailored technique that works for you.

To learn more, check out our free, 5-day data analytics short course . You might also be interested in the following:

• These are the top 9 data analytics tools
• 10 great places to find free datasets for your next project
• How to build a data analytics portfolio

Data Analysis

• Introduction to Data Analysis
• Quantitative Analysis Tools
• Qualitative Analysis Tools
• Mixed Methods Analysis
• Geospatial Analysis
• Further Reading

What is Data Analysis?

According to the federal government, data analysis is "the process of systematically applying statistical and/or logical techniques to describe and illustrate, condense and recap, and evaluate data" ( Responsible Conduct in Data Management ). Important components of data analysis include searching for patterns, remaining unbiased in drawing inference from data, practicing responsible  data management , and maintaining "honest and accurate analysis" ( Responsible Conduct in Data Management ). 

In order to understand data analysis further, it can be helpful to take a step back and understand the question "What is data?". Many of us associate data with spreadsheets of numbers and values, however, data can encompass much more than that. According to the federal government, data is "The recorded factual material commonly accepted in the scientific community as necessary to validate research findings" ( OMB Circular 110 ). This broad definition can include information in many formats. 

Some examples of types of data are as follows:

• Photographs 
• Hand-written notes from field observation
• Machine learning training data sets
• Ethnographic interview transcripts
• Sheet music
• Scripts for plays and musicals 
• Observations from laboratory experiments ( CMU Data 101 )

Thus, data analysis includes the processing and manipulation of these data sources in order to gain additional insight from data, answer a research question, or confirm a research hypothesis. 

Data analysis falls within the larger research data lifecycle, as seen below. 

( University of Virginia )

Why Analyze Data?

Through data analysis, a researcher can gain additional insight from data and draw conclusions to address the research question or hypothesis. Use of data analysis tools helps researchers understand and interpret data. 

What are the Types of Data Analysis?

Data analysis can be quantitative, qualitative, or mixed methods. 

Quantitative research typically involves numbers and "close-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). Quantitative research tests variables against objective theories, usually measured and collected on instruments and analyzed using statistical procedures ( Creswell & Creswell, 2018 , p. 4). Quantitative analysis usually uses deductive reasoning. 

Qualitative  research typically involves words and "open-ended questions and responses" ( Creswell & Creswell, 2018 , p. 3). According to Creswell & Creswell, "qualitative research is an approach for exploring and understanding the meaning individuals or groups ascribe to a social or human problem" ( 2018 , p. 4). Thus, qualitative analysis usually invokes inductive reasoning. 

Mixed methods  research uses methods from both quantitative and qualitative research approaches. Mixed methods research works under the "core assumption... that the integration of qualitative and quantitative data yields additional insight beyond the information provided by either the quantitative or qualitative data alone" ( Creswell & Creswell, 2018 , p. 4). 

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• Last Updated: Aug 28, 2024 1:41 PM
• URL: https://guides.library.georgetown.edu/data-analysis

Research Process Guide

• Step 1 - Identifying and Developing a Topic
• Step 2 - Narrowing Your Topic
• Step 3 - Developing Research Questions
• Step 4 - Conducting a Literature Review
• Step 5 - Choosing a Conceptual or Theoretical Framework
• Step 6 - Determining Research Methodology
• Step 6a - Determining Research Methodology - Quantitative Research Methods
• Step 6b - Determining Research Methodology - Qualitative Design
• Step 7 - Considering Ethical Issues in Research with Human Subjects - Institutional Review Board (IRB)
• Step 8 - Collecting Data
• Step 9 - Analyzing Data
• Step 10 - Interpreting Results
• Step 11 - Writing Up Results

Step 9: Analyzing Data

Once you collect the data, you need to analyze the data. Depending on your methodology and your research questions, you will determine how you will analyze the data.

What is Data Analysis?

For most researchers, data analysis involves a continuous review of the data. Analysis for both quantitative and qualitative (numerical and non-numerical) data requires the researcher to repeatedly revisit the data while examining (Kumar, 2015):

• The relationship between data and abstract concepts.
• The relationship between description and interpretation.
• The data through inductive and deductive reasoning.

Regardless of your methodology, these are the 4 steps in the data analysis process:

• Describe the data clearly.
• Identify what is typical and atypical among the data.
• Uncover relationships and other patterns within the data.
• Answer research questions or test hypotheses.

Quantitative data analysis

The first thing that you want to do is discuss a step-by-step procedure for the analysis process. For example, Gall et al. (2006) outlined steps used to review the pretest and posttest design with matching participants in the experimental and control groups:

• Administer measures of dependent variables to research participants.
• Assign participants to matched pairs on the basis of their scores based on step 1.
• Randomly assign one member of each group to the experimental groups and the other to the control group.
• Administer the experimental “treatment” to group.
• Administer the measures of dependent variable to the experimental and control groups.
• Compare the performance of experimental and control groups on posttest using tests of statistical significance.

Then you want to tell the reader about the kinds of statistical tests that will be implemented on the dataset (Creswell & Creswell, 2018):

• Report descriptive statistics including frequencies (i.e., how many male, female, non-binary participants?), means (i.e., what is the mean age?), and standard deviation values for the primary outcome measures. Standard deviation is formally designed as the average distance to scores away from the mean.
• Indicate inferential statistics test used to examine the hypothesis of your study. For experimental design with categorical variables you might use t-tests or univariate analysis of variance (ANOVA), analysis of covariance (ANCOVA), or multivariate analysis of variance (MANOVA). There are several tests mentioned below categorized based on your measurement.

Kinds of statistical analysis:

Using software like SPSS, you can conduct statistical tests to examine your hypothesis and research questions (Bryman & Cramer, 2009; Ong & Puteh, 2017; Kumar, 2015):

• Frequency distribution
• Proportions/percentage values
• Percentile rank
• Spearman rank order
• Correlation
• Mann-Whitney test
• Standard deviation
• Pearson's Product-moment
• Inferential procedures (T-tests, ANOVA)
• Geometric mean
• Percentage variance
• Inferential procedures (T-test, ANOVA)

Qualitative Data Analysis

Qualitative data analysis, unlike quantitative, does not require hypothesis testing but rather deals with non-numerical data, in the form of words. Qualitative data is inductive, therefore at its root, it is about creating a theory or understanding through analysis and interpretation (Miles et al., 2018; Bogdan & Biklen, 2007). Traditional qualitative researchers have historically completed their data analysis by hand. There is something about getting your hands dirty with the data. That said, there are other options for qualitative data transcription and analysis. Also, there are many methods of coding. Below is a review of the general process of coding techniques.

All qualitative data analysis involves the same four essential steps:

• Raw data management - "data cleaning"
• Data coding cycle I - "chunking," "coding"
• Data coding cycle II - "coding," "clustering"
• Data interpretation - "telling the story," "making sense of the data for others"

Transcription - Raw data management

Qualitative data usually involves transcribing interviews or focus group data that has been previously recorded with participant consent.

Data Coding - Cycle I

There are several steps in the first cycle of coding. The first thing you need to do is to immerse yourself in the transcript data. Read the data. Read it again. Then, read the data again. Do this several times, and as you do so, you will start to get a sense of the data as a whole. Start annotating in the margins, “chunking” data into categories that make sense to you. This step is your very first preliminary pass at coding. As you do the “chunking,” read over the chunks and see if you start to identify patterns or contradictions. Some really excellent guides and resources for you as you begin your coding process are:

The Coding Manual for Qualitative Researchers, 4th edition - Johnny Saldana (2021)

            Qualitative Data Analysis: A Methods Sourcebook, 4th edition - Matthew Miles, Michael Huberman, & Johnny Saldana (2020)  Both of these books are held by Kean University's Library.  

Data coding - Cycle II

During your second cycle (and third, if need be) of coding, you start clustering chunks of data that have similarities. As you are doing this, you are reading over the chunks of data, refining your code book, and narrowing down the scope of each code.  You will go through 2 or 3 cycles of narrowing down codes, grouping them together, and winnowing down the data. You will most likely move from 25-30 codes to grouping them together in clusters to develop themes. These themes are the core of your data analysis. You will end up with 5-7 central themes that tell the story across the data (Saldaña, 2021). Kinds of Coding

As you work your way through the data analysis, you will be going through three different kinds of coding as you progress (Miles et al., 2018; Bogdan & Biklen, 2007; Creswell & Creswell, 2018):

• Open Coding - assigning a word or phrase that accurately describes the data chunk. You do open coding line by line in the transcriptions of all interview data. This is the first coding step.
• Axial Coding - is the process of looking for categories across data sets. Takes place after open coding. More in the second or third cycle of coding. Remember: you cannot categorize something as a theme unless it cuts across data sets.
• Cluster Coding - taking chunks of data that share similarities and review and code in several cycles. Reduce codes by removing redundancies. Here you are refining your code book to develop themes across data.

Bogdan, R., & Biklen, S. K. (2007). Qualitative research for education. (5th ed.). Allyn & Bacon.

Bryman, A., & Cramer, D. (2009). Quantitative data analysis with SPSS 14, 15 & 16: A guide for social scientists. Routledge/Taylor & Francis Group.

Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches. Sage.

Gall, M. D., Borg, W. R., & Gall, J. P. (2006). The methods of quantitative and qualitative research in education sciences and psychology.  (A. R. Nasr, M. Abolghasemi, K. H. Bagheri, M. J. Pakseresht, Z. Khosravi, M. Shahani Yeilagh, Trans.). (2nd ed.). Samt Publications.

Kumar, S. (2015). IRS introduction to research in special and inclusive education. [PowerPoint slides 4, 5, 37, 38, 39,43]. Informační systém Masarykovy univerzity. https://is.muni.cz/el/1441/podzim2015/SP_IRS/

Miles, M. B., Huberman, A. M., & Saldaña, J. (2018). Qualitative data analysis: A methods sourcebook. Sage.

Ong, M. H. A., & Puteh, F. (2017). Quantitative data analysis: Choosing between SPSS, PLS, and AMOS in social science research. International Interdisciplinary Journal of Scientific Research, 3 (1), 14-25.

Saldaña, J. (2021). The coding manual for qualitative researchers (4th ed.). Sage.

• Last Updated: Jun 29, 2023 1:35 PM
• URL: https://libguides.kean.edu/ResearchProcessGuide

• University Libraries
• Research Guides
• Topic Guides
• Research Methods Guide
• Data Analysis

Research Methods Guide: Data Analysis

• Introduction
• Research Design & Method
• Survey Research
• Interview Research
• Resources & Consultation

Tools for Analyzing Survey Data

• R (open source)
• Stata 
• DataCracker (free up to 100 responses per survey)
• SurveyMonkey (free up to 100 responses per survey)

Tools for Analyzing Interview Data

• AQUAD (open source)
• NVivo 

Data Analysis and Presentation Techniques that Apply to both Survey and Interview Research

• Create a documentation of the data and the process of data collection.
• Analyze the data rather than just describing it - use it to tell a story that focuses on answering the research question.
• Use charts or tables to help the reader understand the data and then highlight the most interesting findings.
• Don’t get bogged down in the detail - tell the reader about the main themes as they relate to the research question, rather than reporting everything that survey respondents or interviewees said.
• State that ‘most people said …’ or ‘few people felt …’ rather than giving the number of people who said a particular thing.
• Use brief quotes where these illustrate a particular point really well.
• Respect confidentiality - you could attribute a quote to 'a faculty member', ‘a student’, or 'a customer' rather than ‘Dr. Nicholls.'

Survey Data Analysis

• If you used an online survey, the software will automatically collate the data – you will just need to download the data, for example as a spreadsheet.
• If you used a paper questionnaire, you will need to manually transfer the responses from the questionnaires into a spreadsheet.  Put each question number as a column heading, and use one row for each person’s answers.  Then assign each possible answer a number or ‘code’.
• When all the data is present and correct, calculate how many people selected each response.
• Once you have calculated how many people selected each response, you can set up tables and/or graph to display the data.  This could take the form of a table or chart.
• In addition to descriptive statistics that characterize findings from your survey, you can use statistical and analytical reporting techniques if needed.

Interview Data Analysis

• Data Reduction and Organization: Try not to feel overwhelmed by quantity of information that has been collected from interviews- a one-hour interview can generate 20 to 25 pages of single-spaced text.   Once you start organizing your fieldwork notes around themes, you can easily identify which part of your data to be used for further analysis.
• What were the main issues or themes that struck you in this contact / interviewee?"
• Was there anything else that struck you as salient, interesting, illuminating or important in this contact / interviewee? 
• What information did you get (or failed to get) on each of the target questions you had for this contact / interviewee?
• Connection of the data: You can connect data around themes and concepts - then you can show how one concept may influence another.
• Examination of Relationships: Examining relationships is the centerpiece of the analytic process, because it allows you to move from simple description of the people and settings to explanations of why things happened as they did with those people in that setting.
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• Last Updated: Aug 21, 2023 10:42 AM

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What Is the Data Analysis Process? (A Complete Guide)

Written by: Akansha Rukhaiyar

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The term “data analysis” can be a bit misleading, as it can seemingly imply that data analysis is a single step that’s only conducted once. In actuality, data analysis is an iterative process. And while this is obvious to any experienced data analyst, it’s important for aspiring data analysts, and those who are interested in a career in data analysis, to understand this too. 

Want to learn more about the data analysis process and how it’s used? Then you’re in the right place. Below, we’ll tell you all about the data analysis process, the different steps of the process, how data analysis is used, and how to do it the right way. 

Ready? Then let’s get started! 

What Is Data Analysis?

Data analysis starts with identifying a problem that can be solved with data. Once you’ve identified this problem, you can collect, clean, process, and analyze data. The purpose of analyzing this data is to identify trends, patterns, and meaningful insights, with the ultimate goal of solving the original problem. 

Is There a Specific Process for Data Analysis?

There is indeed a specific process for data analysis. Suppose you are looking to create the best recipe for pizza dough. You could frame your problem as a lack of knowledge—not having a sufficient pizza dough recipe. 

What data could help you solve this problem? One way would be to comb through the plethora of online recipes available. You could then sort this data, filtering out recipes with low reviews or comments noting flaws in the recipe. Then, once you’ve collated the best recipes, you can begin to analyze them. What are the commonalities that emerge? Maybe you find that the best recipe depends on the style of pizza you want to make and that it’s best to group certain recipes together. The data analysis process won’t create the perfect pizza dough recipe for you, but it can get you headed in the right direction. 

The Data Analysis Process

Let’s take a more in-depth look into the data science process:

Establish the Purpose of the Process

This is arguably the most critical step, as it can set you up for success. The purpose is often defined as a business question or problem statement related to your organization’s goals. Examples include:

• Would customers respond positively to the launch of X product?
• What are some ways to reduce employee attrition?
• Will incorporating AI tools reduce production costs?

Data Collection

Once you’ve defined the problem, then you can start collecting data. Broadly speaking, there are three different categories of data, and the ones you use will depend on the nature of your problem. Most data analysis problems require a combination of the three. 

First-party data is data that your own organization generates. Oftentimes, this is data about previous customer interactions that can be used to make accurate predictions about your customers’ behavior in the future. 

You could also use second-party data—data that’s generated by external sources, but is about your company specifically. This can include what customers are saying on social media platforms or review websites.

Third-party data comes from groups like think tanks and government sources and is more concerned with the nature of your customer base, rather than a specific interaction that a customer has had with your company. 

Data Cleaning

Not all the data you collect will be useful or accurate, and you’ll need to discard the data points that are irrelevant, duplicated, inconsistent, or outdated.

This is called data cleaning . When combining multiple sources of data, you’ll likely wind up with duplicates and outliers. And when you’re dealing with millions of data points, as is often the case with data analysis, you can’t comb through each piece of data on your own to find the duplicates or outliers. Data analysts estimate that the time spent cleaning data consumes about 70-90% of the data analysis process. 

At this stage, you can also do an exploratory analysis, which is an initial and cursory data analysis. Exploratory data analysis will also assist with identifying other data points you may need.

Data Processing

Once you have all the relevant data, you can begin to process it. This entails organizing the data, sorting the data into relevant categories, and labeling them for easy organization. Now the data is prepped for analysis.

Data Analysis

Data analysis can be done in numerous ways. One way is to use algorithms and mathematical models to manipulate data variables, which helps extract relevant information and valuable insights that tie into the problem defined in the first step. 

Types of Data Analysis

Let’s look at the various data analysis techniques , which can be used in combination, depending on your problem.

Descriptive Analysis

As the name suggests, descriptive analysis describes or summarizes the data and its characteristics. It doesn’t go beyond explaining what has happened. You use this type of data analysis to deliver a narrative of what has occurred. Descriptive statistics and analysis present scattered data into digestible pointers. You can also do a part of this at the stage of exploratory data analysis.

Diagnostic Analysis

With diagnostic analysis, you begin to focus on the “why,” and diagnose why something is occurring. At this stage, you are not looking for solutions or predictions. The goal is to understand the factors that are contributing to the problem. You use this technique when you want to go into issue identification mode.

Predictive Analysis

Here’s where you start generating forecasts based on your data. Data analysts perform predictive analysis when they want to establish a situation in the future. This prediction helps stakeholders gauge business performance.

Prescriptive Analysis

This kind of analysis brings together all of these data analysis techniques to offer recommendations. These form the basis of data-driven decisions.

Inferential Analysis

With this technique, you derive conclusions based on the data you have collected and analyzed, such as, “lack of employee training is a cause of employee attrition” or “employee attrition affects customer satisfaction.”

Data Visualization and Presentation

Data visualization is a vital skill, especially when presenting your findings to non-technical stakeholders. Using data visualization tools you can share your insights with stakeholders and other target audiences. The statistical analysis needs to be easy to understand and easier to apply while making data-driven decisions. Interactive dashboards and visual representations of your findings will help.

Biases and Pitfalls To Avoid in the Data Analysis Process

Be mindful of these biases throughout the data analysis process:

Selection Bias

Selection bias happens when you’re collecting data and cleaning it. There are several types of data analysis, including:

• Attrition bias. When participants who leave the research study have similar characteristics, leaving the participant pool skewed in terms of diversity.
• Self-selection bias. When the study gives the sample a choice to participate in the study. Those who are not inclined to respond to the survey or questionnaire because they are just not interested will likely be from similar groups. This will affect the inclusivity of the study.
• Survivorship bias. When the study or survey results focus only on the results that are favorable to their purpose.
• Undercoverage bias. When the study excludes entire target groups.
• Non-response bias. When a significant category of people gets excluded from the study because they haven’t responded due to poorly constructed questionnaires, forgetfulness, or plain refusal.

Confirmation Bias

Confirmation bias is when you use data to support a pre-determined conclusion, rather than seeing what conclusions the data offers. You can avoid confirmation bias by covering all angles of the argument or problem. Give each perspective equal importance.

Outlier Bias

When organizations ignore anomalies in data to show a more streamlined picture, they engage in outlier bias. The most common example of outlier bias is revenue projections based on an average of factors, with well-performing variables hiding failures. 

Other Pitfalls

The biases we spoke about can be a result of shoddy data analysis or a consequence of other unavoidable pitfalls. These include:

• Not using quality data
• Not properly cleaning data
• Not siloing data appropriately

You can avoid these pitfalls by having a clear strategy based on robust statistical analysis and data collection. Knowing the level of data readiness within your organization is also an excellent way to prevent unwanted surprises. Most of all, your analysis should always be tied to a core business question.

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Tools for Data Analysis

Here are the top tools for data analysis. They will help you collect, clean and mine data for efficient analysis:

Microsoft Excel

An advanced understanding of Excel will help you clean and visualize your data. It allows you to use charts and conditional formatting to identify trends and patterns. You can perform the following activities with Excel :

• Regression analysis
• Statistical analysis
• Inferential statistics
• Descriptive statistics
• Exploratory data analysis

As the name suggests, this tool is primarily used for data mining. But you can also use it for various statistical techniques, such as inferential statistics and descriptive statistics, to generate summaries and conclusions.

Tableau is a data visualization platform that allows you to share insights, collaborate over data analysis tasks, and share reports with stakeholders. Tableau has robust analytical features, such as limitless what-if analysis, and enables you to perform calculations with as many types of variables as you need.

Apache Spark

Apache Spark helps with large-scale data engineering, regression analysis, and exploratory analysis, allowing you to analyze massive datasets.

FAQs About the Data Analysis Process

We’ve got the answers to your most frequently asked questions:

What Is Data Analysis Used For?

Data analysis is used in many ways, but its most common applications include tracking customer behavior based on their purchase decisions, buying habits, and other consumer data points. Businesses then use this data to offer recommendations, improve customer experiences, inform marketing campaigns, and guide new product launches.

Why Is Data Cleaning Important for Data Analysis?

Garbage in, garbage out. Data cleaning is important for data analysis because data sources can be inconsistent, unreliable, and inaccurate. And no matter the size of your datasets, you’ll need to remove duplicate entries and outliers.

Is Data Analysis Easy To Learn?

Data analysis is easy to learn if you have a plan. And that plan needn’t include a college degree . Today,  data analysis bootcamps, like Springboard’s Data Analysis Career Track , can get you job-ready much quicker than a traditional university. Springboard also offers a money-back guarantee, so if you don’t land a job soon after graduation, then you’ll receive a full refund!

Since you’re here… Interested in a career in data analytics? You will be after scanning this data analytics salary guide . When you’re serious about getting a job, look into our 40-hour Intro to Data Analytics Course for total beginners, or our mentor-led Data Analytics Bootcamp .  

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