Essentially, I write this book that will serve as a guide and answer the key questions, “What,” “How,” and “Why” of “Big Data.” I envisioned this book to comprise tutorials that will help navigate through the information wonderland and lead to the end goal of extracting useful business insight from “Big Data.”
In addition to my experience in business intelligence, data architecture, and IT infrastructure, I had to draw upon technical white papers, innumerable blogs, and operating manuals to share the state-of-the-art tutorials with my readers. These tutorials relate to data migration, data ingestion, data management, data visualization, and data virtualization processes.
Gartner, IBM, Accenture, and many others have asserted that 80% or more of the world’s information is un-structured – and inherently hard to analyze. What does that mean? And what is required to extract insight from un-structured data?
First, we need to understand un-structured data. It is infinitely variable in quality and format, because it is produced by humans who can be unpredictable and ill-informed, but always unique in their own way, that is, not standard in any way. Recent advances in natural language processing provide the notion that un-structured content can be included in modern day data analytics. There is a realm of R programming that can be used for this purpose.