What is Data Analysis? Research, Types & Example

By : Evelyn Clarke Evelyn Clarke
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⚡ Smart Summary

Data analysis is the process of cleaning, transforming and modeling data so that an organization can extract useful information and act on it. Five techniques and a six-phase workflow structure the work.

  • 🔘 Definition: Cleaning, transforming and modeling raw records into information that supports a business decision.
  • ☑️ Five techniques: Text, statistical, diagnostic, predictive and prescriptive analysis answer progressively harder questions.
  • ✅ Question ladder: Each technique moves from what happened, to why, to what is likely, to what to do.
  • 🧪 Six phases: Requirement gathering, collection, cleaning, analysis, interpretation and visualization run in order.
  • 🛠️ Tooling: Spreadsheets, SQL, Python, R and business intelligence platforms cover most analysis workloads.
  • ⚠️ Data quality: Duplicate records, blank fields and stray whitespace distort every result produced downstream.
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What is Data Analysis

What is Data Analysis?

Data analysis is defined as a process of cleaning, transforming and modeling data to discover useful information for business decision-making. The purpose of data analysis is to extract useful information from data and take a decision based on that analysis.

A simple example of data analysis happens whenever we make a decision in day-to-day life by thinking about what happened last time, or what will happen if we choose a particular option. That is nothing but analyzing our past or our future and deciding on the basis of it, gathering memories of the past or expectations of the future. An analyst does the same thing for business purposes, and that is called data analysis.

Data analysis sits inside the wider field of Data Science, which also covers data engineering, machine learning and deployment.

Why is Data Analysis Important?

To grow your business, and even to grow in your life, sometimes all you need to do is analysis.

If your business is not growing, you have to look back, acknowledge your mistakes and make a plan again without repeating them. And even if your business is growing, you have to look forward to making it grow more. All you need to do is analyze your business data and your business processes.

Data Analysis Tools

Every phase described below is carried out with software, and the screenshot underneath shows the kind of interface an analysis tool presents.

Data analysis tool interface showing a dataset ready for exploration
Data Analysis Tools

Data analysis tools make it easier for users to process and manipulate data, analyze the relationships and correlations between data sets, and identify patterns and trends for interpretation. Spreadsheets cover small datasets, SQL queries the warehouse, Python and R handle statistics and modeling, and business intelligence platforms publish the result to a dashboard. Here is a complete list of tools used in research.

Types of Data Analysis: Techniques and Methods

There are several types of data analysis techniques that exist based on business and technology. However, the major data analysis methods are:

  • Text analysis
  • Statistical analysis
  • Diagnostic analysis
  • Predictive analysis
  • Prescriptive analysis

The five build on one another. The table below shows the question each one answers before the sections that follow explain them individually.

Technique Question it answers Typical output
Text analysis What patterns hide in unstructured text? Themes, keywords, mined rules
Statistical analysis What happened? Dashboards, summary statistics
Diagnostic analysis Why did it happen? Root causes, behavior patterns
Predictive analysis What is likely to happen? Forecasts, risk scores
Prescriptive analysis What should we do about it? Recommended actions

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Text Analysis

Text analysis is also referred to as data mining. It is one of the methods of data analysis used to discover a pattern in large data sets using databases or data mining tools. It transforms raw data into business information. Business intelligence tools available on the market are used to take strategic business decisions. Overall, it offers a way to extract and examine data, derive patterns and finally interpret the data.

Statistical Analysis

Statistical analysis shows “What happened?” by using past data in the form of dashboards. It includes the collection, analysis, interpretation, presentation and modeling of data, and it works on a full data set or on a sample. There are two categories of this type of analysis — descriptive analysis and inferential analysis.

Descriptive Analysis

Descriptive analysis summarizes complete data or a sample of numerical data. It reports the mean and standard deviation for continuous data, and percentages and frequencies for categorical data.

Inferential Analysis

Inferential analysis works on a sample drawn from the complete data. In this type of analysis you can reach different conclusions from the same population by selecting different samples.

Diagnostic Analysis

Diagnostic analysis shows “Why did it happen?” by finding the cause behind the insight found in statistical analysis. This analysis is useful for identifying behavior patterns in data. If a new problem appears in your business process, you can look into this analysis to find similar patterns, and there is a good chance a similar prescription will apply to the new problem.

Predictive Analysis

Predictive analysis shows “What is likely to happen?” by using previous data. A simple data analysis example: if last year I bought two dresses based on my savings, and this year my salary doubles, then I can buy four dresses. Of course it is not that easy, because you have to think about other circumstances — clothes prices may have risen this year, or instead of dresses you may want a new bike, or you may need to buy a house.

So this analysis makes predictions about future outcomes based on current or past data. A forecast is only an estimate; its accuracy depends on how much detailed information you have and how deeply you dig into it.

Prescriptive Analysis

Prescriptive analysis combines the insight from all the previous analyses to determine which action to take on a current problem or decision. Most data-driven companies use prescriptive analysis because predictive and descriptive analysis alone are not enough to improve data performance. Based on current situations and problems, they analyze the data and make decisions.

Data Analysis Process

Choosing a technique answers what you want to know; the process below answers how the work is run. The data analysis process is gathering information using a proper application or tool that allows you to explore the data and find a pattern in it. Based on that information, you can make decisions or reach conclusions.

Data analysis consists of the following phases:

  • Data requirement gathering
  • Data collection
  • Data cleaning
  • Data analysis
  • Data interpretation
  • Data visualization

Data Requirement Gathering

First of all, think about why you want to do this data analysis. You need to find the purpose or aim of analyzing the data, and decide which type of data analysis you want to run. In this phase you decide what to analyze and how to measure it, and you clarify why you are investigating and which measures the analysis will use.

Data Collection

After requirement gathering, you will have a clear idea of what you have to measure and what your findings should be. Now it is time to collect data based on those requirements. Once collected, the data must be processed or organized for analysis. Because the data comes from various sources, keep a log with the collection date and the source of every data set.

Data Cleaning

Whatever data is collected may not be useful, or may be irrelevant to your aim, so it has to be cleaned. Collected data may contain duplicate records, stray white space or errors. The data should be clean and error free. This phase must be done before analysis, because the quality of the cleaning decides how close the output is to your expected outcome.

Data Analysis

Once the data is collected, cleaned and processed, it is ready for analysis. As you manipulate the data, you may find you have exactly the information you need, or you may need to collect more. During this phase you use data analysis software to understand, interpret and derive conclusions based on the requirements.

Data Interpretation

After analyzing your data, it is finally time to interpret the results. You can choose how to express or communicate the analysis — plainly in words, or as a table or a chart. Then use the results of your data analysis process to decide your best course of action.

Data Visualization

Data visualization is very common in day-to-day life, and it often appears as charts and graphs. In other words, the data is shown graphically so that the human brain finds it easier to understand and process. Data visualization is often used to discover unknown facts and trends. By observing relationships and comparing data sets, you can surface meaningful information.

FAQs

Data analysis examines an existing data set to answer a defined question. Data analytics is the broader discipline around it — collecting, storing, modeling and operationalising data. In everyday use the two terms are often treated as interchangeable.

Quantitative analysis works with numbers and answers how many, how often and how much through statistical tests. Qualitative analysis works with words, images and observations from interviews or open answers, and explains meaning and context rather than magnitude.

Exploratory data analysis is the first open-ended look at a data set: distributions, missing values, outliers and pairwise relationships, mostly through quick plots. It shapes the hypotheses you later test formally, so it is done before any model is fitted.

Real data arrives with duplicates, inconsistent codes, mixed date formats and missing fields, and each source adds its own quirks. Every one of those defects propagates into the result, so analysts routinely spend the largest share of a project preparing data.

SQL for pulling data, a spreadsheet for quick checks, Python or R for statistics, and one visualization tool. Beyond the tooling, an analyst needs domain knowledge and the ability to explain a finding to people who never see the code.

Reading correlation as causation, sampling a group that does not represent the population, dropping inconvenient outliers without a reason, and testing many hypotheses until one looks significant. Recording every decision in advance protects against most of them.

AI features now sit inside most analysis tools: they profile a data set, propose cleaning rules, write queries from a plain-language question and draft the summary. They accelerate machine learning work, but the analyst still owns the interpretation.

Yes. GitHub Copilot completes pandas, dplyr and SQL statements from a comment describing the goal, which removes most boilerplate. Posit warns that suggestions are non-deterministic, so review every generated line before trusting a number.

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