What is big data analytics?

by Daniel J. Power
Editor, DSSResources.COM

Big data analytics combines buzz words and is a buzz phrase. The basic idea of this new business jargon is that the huge volume and variety of data being generated must be analyzed. Mashing together sources, big data analytics is defined as the analysis of very large, diverse, varied data sets known as "big data". "Big data" includes different types of data such as structured/unstructured and streaming/batch, and different sizes/volumes from terabytes to exabytes.

The goal of the "big data" analysis process is to use advanced analytic techniques to identify patterns, correlations, market trends, customer preferences and other useful information that can help organizations make more-informed business decisions. In general, the term analytics refers to quantitative and statistical analysis of data. Various sources identify three categories of analytics, including: 1) descriptive/reporting, 2) prescriptive, and 3) predictive. Sometimes diagnostic analytics is added as a fourth category. The goal of analytics is discovering useful information that supports decision making.

Common big data sources, include social media, mobile device files, and sensor analytics. Activity-generated data primarily comes from computer and mobile device log files (cf., Morris, 2012). Mobile devices, wearable technology, connected devices, sensors, social media, loyalty card programs and website browsing are generating large volumes of structured and unstructured data. This customer-generated data is creating new opportunities to analyze data and understand customers. If someone refers to social media analytics and mobile and sensor analytics, the reference is to the data source that is analyzed. For example, social analytics involves using sensor data, video data, social media data to gain actionable insights. Mobile data analytics comprises a set of tools that process, analyze, and visualize data originating from mobile devices.

There are four broad categories or types of big data analytics:

Descriptive/reporting – What is happening? What happened?

Diagnostic – Why did XXX happen?

Predictive – What will happen and when? What will happen next?

Prescriptive – What should happen next? What should I do? What should we do?

These four categories or types of big data analytics are common to the more general terms business analytics or data analytics. Descriptive, diagnostic, predictive, and prescriptive analysis can be performed using many data sources as input, including "big data" sources like social media.

Descriptive or reporting analytics describes or summarizes past results, actions, or activities. Predictive analytics extrapolate data bout the past into the future and potentially help understand the future. Diagnostic analytics emphasizes understanding causes and why something happened. Prescriptive analytics tools help quantify the impact of a decision before it is actually made prescribe an action to take based upon an analysis of outcomes.

Descriptive analytics primarily uses data aggregation and statistical tools like averages and differences. Predictive analytics use more complex statistical models like regression and correlation and forecasting techniques like moving averages. Diagnostic analytics uses tools like drill down, interactive data visualization and data mining. Finally, prescriptive analytics uses tools like optimization, simulation, scenario analysis, and case-based reasoning.

All four types of analytics can be embedded in specific decision support systems (DSS). For example, diagnostic analytics can be a major component of some knowledge-driven DSS. Descriptive analytics are often part of data-driven DSS providing business intelligence. Predictive analytics especially using forecasting models are part of some specialized model-driven DSS. Finally, prescriptive analytics whether using models or knowledge can be incorporated in a DSS when used repeatedly. Decision support is flourishing.

So if you are asked about the types of big data analytics, the question might refer to the types of data used or the types of analytics used or both. If asked what type of big data analytics an organization is using, a specific, meaningful reply might be "the company is using predictive analytics with social media data".

Business jargon can hinder understanding. Often all that is needed is a short and simple explanation of a "new" term. For many managers, big data analytics combines buzz words and is a buzz phrase. Analytics is important to successfully using big data. The only change with big data analytics compared to business analytics or data analytics is the "data" used for description, diagnosis, prediction and prescription. Big data is different, but not inherently better than other data. In general, big data analytics uses the same analytics tools as "small" data analytics.


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Ingram Micro Advisor, "Four Types of Big Data Analytics and Examples of Their Use," See

Lassen, N. B., L. la Cour, and R. Vatrapu, "Predictive Analytics with Social Media Data," at URL

Morris, J., "Top 10 categories for Big Data sources and mining technologies," July 16, 2012, at URL

Power, D. J. (2012), "What is analytics?" DSS News, Vol. 13, No. 18, September 16, 2012.

Power, D. J. (2014) "Using ‘Big Data’ for analytics and decision support," Journal of Decision Systems, 23:2, 222-228, DOI: 10.1080/12460125.2014.888848

Power, D. J. (2016) “'Big Brother' can watch us," Journal of Decision Systems, 25:sup1, 578-588, DOI: 10.1080/12460125.2016.1187420

What is a buzz phrase? (buzz-phrase [plural buzz-phrases]) A phrase drawn from or imitative of technical jargon, and often rendered meaningless and fashionable through abuse by non-technical persons in a seeming show of familiarity with the subject, cf.,

Please cite as:

Power, D., "What is big data analytics?" Decision Support News, Vol. 19, No. 07, April 1, 2018 at URL .

Copyright © 2018 by D. J. Power

Last update: 2018-07-15 05:25
Author: Daniel Power

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