Data Analysis

19Sep, 2019

What is data Analysis?

  • The role, benefits and application of data analysis cannot be overstated, especially in the research domain.
  • Data analysis is the process of systematically applying statistical and logical techniques to describe and illustrate as well as a recap and condense data.

Inductive inference

  • Data analysis draws inductive inferences from the provided data to distinguish the phenomenon of interest from statistical fluctuations present in the provided data.

Qualitative vs. Quantitative Research

  • Data analysis in quantitative research involves analysis of quantitative data.
  • On the other hand, qualitative research includes statistical procedures and inferential. In this case, analysis becomes a continuous process where data is continuously collected and analyzed simultaneously.

Why analyze Data?

  •  Data is analyzed to determine patterns throughout the data collection phase.
  • But, it is ideal to understand that data integrity is at the core of data analysis. For that reason, an improper statistical analysis distorts scientific findings and could be misleading.

Summary of data Analysis Procedure

  •  In summation, data analysis is the process of examining, cleaning, transforming, and modeling data to come up with useful and applicable information to arrive at a conclusion.

Types of Data Analysis

  • Statistical data analysis is divided into exploratory data analysis, descriptive data analysis, and confirmatory data analysis.
  • The adoption of any of the above categories of analysis depends on the nature and objective of the research.

Application of data Analysis in Business

  • In the context of business environment, data analysis plays a huge role and is vital for the success of businesses.
  • Application of research and data analysis in business is a common phenomenon and organizations use business in areas such as customer segmentation, risk assessment, churn prevention and sales forecasting.
  • Analyzed data can be used in deciding how to divide customer base into groups through segmentation process.
  • Through data analysis, businesses can determine how to accurately target tailored marketing messages.
  •  Data analysis is also applicable in risk assessment in that it allows stakeholders to examine possible problems associated with a given organization.
  •  The role of data in this case is to help build a decision support system which can accurately predict profitable operations for the company or the organization.
  •  Churn prevention is another important area of data analysis application in business.
  • The aim of churn prevention is to accurately predict the relationship between the customers and the company.
  • The phenomenon is relatively expensive compared to other areas of application but is ideal in gaining competitive advantage within a particular industry or market segment.
  • Churn prevention harnesses the power of customer data set to instigation measures before it is too late.

Benefits of Data Analysis

  • There is a wide range of benefits of data analysis, but the most important ones include restructuring of findings from varied sources to come up with a conclusion.
  • Data analysis is ideal in breaking a macro problem into micro parts to come up with solutions to an identified problem or issue.
  • Data analysis is like a filter in acquiring important insights out of a vast data-set. In this context, the researcher has to sort out a massive pile of data collected in order to reach a conclusion.
  • Data is of no use and essence if not analyzed, and so data analysis is a value addition process which helps make meaning out of collected data or information and provides an important base for critical decision making.
  •  Decisions made based on research outcomes and statistical findings and conclusions are not only ideal but are also essential in finding a solution to a particular problem and the same mirrors the role and importance of data analysis as well as its application.

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