Data Analysis is the process that involves the collection, transformation, and cleaning. It also deals with the modeling of data with the objective of discovering and identifying the required information. The results obtained henceforth are then communicated with suggestions for conclusions to support decision-making. At certain times, the visualization of data is used for the purpose of portraying the necessary data and information, which thereby eases the discovery of useful patterns within the data that has been obtained. Data Analysis and Data Modelling are exactly identical terms used in the field of big data.


The term ‘analysis’ in the term ‘data analysis’ refers to the procedure of breaking something into its separate individual components for the purpose of individual scrutiny and examination. Data analysis is actually the process of obtaining the raw data and thereby converting it into useful and important information, which is used for decision making by the users. The data, which has been collected, is analyzed to answer questions, disprove theories, or test hypotheses. 

There are multiple and numerous approaches and facets in data analysis, which encompasses diverse methodologies under various names that are used in different types of scientific, business, and socially scientific domains. In the modern era, data analysis plays an extremely important and essential role in making any form of decision that is much more scientific than without its implementation. For this reason, it helps the respective business enterprises to conduct their proceedings and operate much more effectively than ever before.


  • Confirming the fact that the main total is the sum of the subtotals
  • Checking the relationship between the numbers
  • Checking the raw data files for abnormalities prior to the performance of the user’s analysis
  • Re-performing important calculations, such as the process of verifying the columns containing the respective data which are formula driven
  • Normalizing the numbers for the purpose of making the process of comparison easier. It includes analyzing the amount per person, anything related to GDP, or as the index value which is relative to the base year
  • Breaking the problems into separate individual components by analyzing the factors like the DuPoint analysis of the return on equity. 
  • Identifying the areas for the purpose of increasing the efficiency and the automation of processes.
  • Setting up and maintaining automated data processes.
  • Identifying, evaluating as well as implementing external tools and services for the purpose of supporting cleansing and data validation.
  • Creating graphs, dashboards, and visualizations.
  • Providing competitor and sector benchmarking
  • Analyzing and mining large sets of data, drawing valid influences, and presenting them successfully to the management with the use of a reporting tool.
  • Designing and carrying out surveys as well as analyzing the survey data
  • Manipulating, analyzing, and interpreting complex sets of data that are related to the business of the employer.
  • Preparing reports for the external and internal audience through the use of business analytics reporting tools.

Inference: Data analysis and data analytics play an extremely vital role in the invention, manufacture, and production of technologies and products of the next generation. It is due to this reason that data analytics has gained so much importance in the private sector and various Multinational Companies (MNCs).    

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People can pursue a career in data analytics with any kind of degree or subject if they possess the relevant skills and requirements for filling the respective posts. Postgraduate degrees in the field of data science is getting more popular day by day.

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