What is Data Analysis?

What is Data Analysis?

Types, methods and techniques

Data analysis is the process of collecting, examining, cleaning, transforming and modeling data to find useful information and achieve useful results in business decision-making. The process of presenting data visually is known as Data Visualization.

The main purpose of analyzing data is to get useful information from raw data and then make a decision based on the data obtained from the analyzed data.

Why Data Analysis?

Data-driven businesses and organizations constantly make data and factual decisions. In this way, they can be more confident in performing the actions necessary to ensure success, as there is data available to support them. Because many people, organizations, and businesses need useful data and facts, the data analyst needs to work to help businesses and organizations work more effectively to avoid mistakes, and to conduct data analysis through research to make more strategic decisions.

Data Analysis Process

The Data Analysis process consists of the following stages;

Data Requirements

The data required for analysis is based on a question or essay. Depending on the requirements of those who manage the analysis, the necessary data are defined as input to the analysis (e.g., human population). Specific variables (e.g. age and income) for a population can be specified and obtained. Data can be numeric or categorical.

Data Collection

Data Collection is the process of collecting information about targeted variables defined as data requirements. The important thing is to ensure that the data is collected correctly and honestly. Data Collection ensures that the collected data is accurate in such a way that the relevant decisions apply. Data Collection provides both a baseline for measurement and an improvement goal.

 Data Processing

The collected data must be processed or edited for analysis. This includes configuring the data as required for the relevant Analysis Tools. For example, data may need to be placed in rows and columns in a table in a Table or Statistical Application. A Data Model may need to be created.

Data Cleaning

Data processed and organized may be missing, contain duplicates, or contain errors. Data cleaning is the process of preventing and correcting these errors. There are several types of data cleaning depending on the data type. For example, when clearing financial data, certain totals can be compared with reliable published figures or defined values. Likewise, quantitative data methods can be used for outlier value detection, which will be excluded later in the analysis.

Data Analysis

Data processed, organized, and cleaned up is ready for analysis. Various data analysis techniques are available to understand, interpret and obtain results based on requirements. Data Visualization can also be used to examine data in graphical format to get additional information about messages within the data.

Statistical data models such as correlation, regression analysis, can be used to define relationships between data variables. These models that identify data help simplify analysis and communicate results.


The results of data analysis should be reported in a format that should support users’ decisions and further action. Feedback from users can result in additional analysis.

Data analysts can choose data visualization techniques, such as tables, that help deliver the message to users clearly and efficiently. Analysis tools provide the ability to highlight the necessary information with color codes and formatting in tables and charts.

What are the Differences Between Data Analytics, Business Analytics, and Web Analytics and Big Data and Business Intelligence?

 Types of Analysis

Data analytics, diagnostic analysis, explanatory analysis, predictive analysis, web analytics and business analytics are just a few examples. We will briefly address the definitions of all these types of analysis and some of the main differences between them.

Web Analytics

Web or website data analysis is defined as the collection, measurement, reporting and analysis of web data used to understand and optimize web pages. It includes everything from web traffic metrics to market research to assessing the effectiveness of the site. Web analytics software helps users collect data, convert them into regular information, develop KPIs, create an online strategy, and work can be done to test user interface changes.

Business Intelligence

Business intelligence (BI) is the comprehensive name of the group of technologies and methods used by companies for the analysis of work-related data. This data provides a historical, current, and predictive overview of a business’s key performance indicators (KPIs). Some typical functions include reporting, dashboards, analytical processing, data mining, event handling, process mining, business performance management, text mining, comparison, as well as predictive and presciive analytics.

Web Analytics and Business Intelligence: Business intelligence is the main category of all types of data analytics used in a business environment; web analytics focuses on website data.

Big Data

Big data in terms of volume, complexity or structure… Past data analysts have had to handle big data in small pieces at once, but with detailing software, it can easily and efficiently configure and analyze such data. Business Intelligence and Business Analytics systems are often equipped to process big data, but this is not always possible; therefore, it is important to determine your data needs before choosing a solution.

Web Analytics and Big Data: Web analytics is a tool that can process big data that defines the data type.

Data Analytics

Similar to business analytics, data analytics is the process of analyzing data to get results. Business analytics can be understood as data analytics that are specifically applied to help businesses make data-driven decisions.

Web Analytics and Data Analytics: Web analytics is data analysis that is limited to your website.

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