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What is Big Data Analytics and Why It is Important?

Big Data Analytics

Big Data is today, the hottest buzzword around, and with the amount of data being generated every minute by consumers, or and businesses worldwide, there is huge value to be found in Big Data analytics. What enables this is the techniques, tools, and frameworks that are a result of Big Data analytics.


What is Big Data Analytics? Big Data analytics is a process used to extract meaningful insights, such as hidden patterns, unknown correlations, market trends, and customer preferences. Big Data analytics provides various advantages which it can be used for better decision making, preventing fraudulent activities, among other things.

Product Development and Innovations Use Case: Rolls-Royce, one of the largest manufacturers of jet engines for airlines and armed forces across the globe, uses Big Data analytics to analyze how efficient the engine designs are and if there is any need for improvements. Now, let's review the life cycle of Big Data analytics. 

Stage 1 - Business case evaluation - The Big Data analytics life cycle begins with a business case, which defines the reason and goal behind the analysis.

 Stage 2 - Identification of data - Here, a broad variety of data sources are identified.

Stage 3 - Data filtering - All of the identified data from the previous stage is filtered here to remove corrupt data.

 Stage 4 - Data extraction - Data that is not compatible with the tool is extracted and then transformed into a compatible form.

Stage 5 - Data aggregation - In this stage, data with the same fields across different datasets are integrated.

 Stage 6 - Data analysis - Data is evaluated using analytical and statistical tools to discover useful information.

Stage 7 - Visualization of data - With tools like Tableau, Power BI, and others,Big Data analysts can produce graphic visualizations of the analysis.

 Stage 8 - Final analysis result - This is the last step of the Big Data analytics life cycle, where the final results of the analysis are made available to business stakeholders who will take action.

Predictive Analytics This type of analytics looks into the historical and present data to make predictions of the future. Predictive analytics uses data mining, AI, and machine learning to analyze current data and make predictions about the future.


Credit : (November, 2020)


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