The disciplines of data analytics are evolving to meet the new challenges of big data.
Written by Thomas M. Stockwell
Most traditional approaches to data analytics aim to align the operational activities of the organization with the specific business strategies of C-level executives. The process accomplishes this by identifying key measurements of business performance, developing the internal (or external) metrics and governance principles, and then implementing a data analytics framework to deliver the information accurately. This enables management to monitor the key performance metrics and instruct managers where improvements are required.
By comparison, big data analytics is at a completely different level of complexity. Instead of having too little data, big data provides an overwhelming number of data points from myriad input devices and services that are constantly refreshing in real time—often providing unstructured information, conflicting signals, and contextless meanings. Most of the time, C-level execs have no idea of how these data points (cell phone data, social media data, sensor data, GPS data, etc.) might benefit their business strategies. Or they may have heard about specific big data projects that offer transformative business benefits. In other words, they recognize big data's potential but don't yet have the tools to develop the insights or make meaningful decisions.
This is the realm of big data analytics that Dr. Arvind Sathi explores in his book Big Data Analytics: Disruptive Technologies for Changing the Game.
The book covers a number of big data use cases, architecture considerations, and the rise of emerging observation spaces (social, geospatial, etc.). It also addresses some of the thorny issues around data privacy. The key driver is the rapidly changing nature of communications: organizations must be able to sense and respond to transactions happening now and must be able to deeply reflect over what has been observed. Using case study anecdotes and examining personal and professional observations, Sathi charts out the challenges and the necessary approaches to transforming the wild world of big data into real analytics information that organizations can begin to use.
Of particular importance are Sathi's chapters on Advance Analytics Platforms and the Implementation of Big Data Analytics. These chapters offer a deep conceptual architectural insight, yet remain wholly accessible to readers who may not have technical backgrounds in the areas of either analytics or big data. By making these topics accessible, Sathi's book is a perfect primer to crack a window into the opportunities of big data so that execs and analysts can creatively discuss and explore the challenges of this emerging discipline.
The book is nearly 100 pages in length, with a separate small but adequate list of definitions of the abbreviations used. It's casual but informative style makes it a perfect read for the executive or manager who is trying to grasp the importance of the big data opportunity.
This book is a good read by an enlightened author; it's a useful addition to the reading material of any enterprise seeking insight into the disruptive technologies of big data.
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