Introduction

What is the value of big data if the ability to analyze it and extract business insights is largely confined to data scientists? The answer is “very limited,” as data scientists are as scarce as they are expensive. But that is the reality facing most enterprises today. They are struggling to enable business users and analysts without coding skills to analyze big data, for many reasons.

IT leaders are rapidly arriving at the conclusion that they need a new, distributed approach to big data analytics. Leading industry analysts agree that data volume is growing at a phenomenal rate with “unstructured data” leading the way. It is this data where companies believe they will be able to differentiate from their rivals and gain a competitive edge. Legacy data repositories are ill-suited for this challenging task, which easily explains why big data has been embraced so strongly.

Other terms related to “unstructured data” include “semi-structured data” and “multi-structured” data. These terms have nuanced distinctions, but for the purposes of this paper, the term “unstructured data” is used as the umbrella term for all three types, as the key point is that big data largely deals with data that is not structured per the relational model.

Due to the escalating growth in unstructured data creation, many enterprises are realizing traditional approaches to data management are not enough. As a result, these organizations are exploring options such as looking to data scientists to complement the tasks traditionally assigned to business analysts.

This book will look closely at the emerging trends in big data and the pressing need for analytics solutions that emphasize more user-friendly approaches, such as more sophisticated visualization techniques. There are significant changes brewing that can potentially and irreversibly disrupt the traditional analytics landscape, delivering heretofore unprecedented business insights.

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