Analysis of Data: Meaning, Purpose and Types

30/01/2021 1 By indiafreenotes

Data analysis is the systematic approach of refining, converting, and shaping data to uncover valuable insights that facilitate informed business decision-making. The primary aim of data analysis is to extract pertinent information from the data and utilize it as a basis for making well-informed decisions.

Data analysis is a process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today’s business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.

Whether your business is experiencing stagnation or growth, it is essential to reflect on past decisions and learn from any mistakes made. By acknowledging these missteps, you can create a new, improved plan that avoids repeating those errors.

Even if your business is currently growing, it is crucial to maintain a forward-looking perspective to drive further expansion. Regularly analyzing your business data and processes can provide valuable insights for future development.

In both scenarios, the key lies in understanding your business’s strengths and weaknesses, identifying opportunities for improvement, and implementing strategic changes. Continuous analysis and adaptation are fundamental to sustaining growth and ensuring long-term success in today’s dynamic business landscape.

Techniques and Methods

Data analysis techniques and methods play a crucial role in understanding business trends and making informed decisions. Below are the different types of data analysis techniques and their applications:

Text Analysis (Data Mining):

This technique involves discovering patterns in large data sets using databases or data mining tools. It transforms raw data into valuable business information, enabling strategic decision-making using Business Intelligence tools.

Statistical Analysis:

This analysis answers the question “What happened?” by using past data in the form of dashboards. It includes data collection, analysis, interpretation, presentation, and modeling. Statistical Analysis can be categorized into Descriptive Analysis and Inferential Analysis.

  • Descriptive Analysis: Examines complete data or summarized numerical data to show mean, deviation for continuous data, and percentage, frequency for categorical data.

  • Inferential Analysis: Analyzes samples from complete data, drawing different conclusions based on different samples.

Diagnostic Analysis:

This analysis aims to identify the causes behind the insights found in Statistical Analysis. It helps in understanding data behavior patterns and can be useful in solving new problems with similar patterns.

Predictive Analysis:

Predictive Analysis answers the question “What is likely to happen?” by using past data to make predictions about future outcomes. It involves forecasting and relies on detailed information and analysis to improve accuracy.

Prescriptive Analysis:

This type of analysis combines insights from previous analyses to determine the best course of action for current problems or decisions. It goes beyond predictive and descriptive analysis to improve overall data performance and decision-making.

By employing these various data analysis techniques, businesses can gain valuable insights from their data and use them to make informed decisions, optimize processes, and drive growth. Each technique serves a specific purpose and complements others in providing a comprehensive understanding of the data and its implications.

Data analysis is a big subject and can include some of these steps:

  • Defining Objectives: Start by outlining some clearly defined objectives. To get the best results out of the data, the objectives should be crystal clear.
  • Posing Questions: Figure out the questions you would like answered by the data. For example, do red sports cars get into accidents more often than others? Figure out which data analysis tools will get the best result for your question.
  • Data Collection: Collect data that is useful to answer the questions. In this example, data might be collected from a variety of sources like DMV or police accident reports, insurance claims and hospitalization details.
  • Data Scrubbing: Raw data may be collected in several different formats, with lots of junk values and clutter. The data is cleaned and converted so that data analysis tools can import it. It’s not a glamorous step but it’s very important.
  • Data Analysis: Import this new clean data into the data analysis tools. These tools allow you to explore the data, find patterns, and answer what-if questions. This is the payoff; this is where you find results!
  • Drawing Conclusions and Making Predictions: Draw conclusions from your data. These conclusions may be summarized in a report, visual, or both to get the right results.