What Is Big Data Analytics and How Useful Is It to Your Business?

What is Big Data Analytics?

The three Vs of Big Data

The nine stages of Big Data Analytics

Types of Big Data Analytics

a. Descriptive Analytics: Looking at past events and making data interpretable by humans.

  • Processing, interpreting, and summarizing historical and raw data to draw useful and understandable comparisons and conclusions.
  • Examining historical data in data mining and data aggregation to answer the question: “What has happened?”
  • Being demonstrated through graphic patterns like bar charts, pie charts, line graphs, etc to lay a solid foundation for further analytics.

b. Diagnostic Analytics: Identifying the root causes of an event in the past.

  • Drilling down and discovering historical data, finding out correlations amongst events from this data to help answer the question: “Why did something happen in the past?
  • Finding out the true meaning of data and the underlying cause of past success or failure to improve the future and better decision making.

c. Predictive Analytics: Making predictions about the future.

  • Using algorithms to obtain the missing data with reasonable guesses based on the existing data as well as the correlation amongst various data sets. It’s used to calculate probabilities of potential risks, opportunities, or trends and answer the question: “What might happen in the future?”.
  • Forecasting the likelihood of future from structured data, which is easier than unstructured data.
  • Analyzing historical data to make predictions about future outcomes. This aids businesses and organizations in any decision or action they intend to take.

d. Prescriptive Analytics: Putting forward recommendations and pieces of advice.

  • Using descriptive and predictive analytics to suggest options and help answer the question: “What should be done?”
  • Showing why something is likely to happen to recommend what decision should be made to take advantage of opportunities and avoid risks.

Why Big Data Analytics?

Raising customers’ level of satisfaction

Enhancing work efficiency

Reducing costs

Big Data use cases



Trends in using Big Data Analytics



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