Big Data Identity Crisis: Business Intelligence or Business Analytics?

by Daniel Mandell | 02/26/2013

The proliferation of “Big Data” technology has shifted the fundamentals of business intelligence to a more prospective view. This change in concept, however, has failed to maintain a common definition throughout businesses across the world. The distinction between Business Intelligence (BI) and Business Analytics (BA) is becoming increasingly blurred, and will worsen with the maturation of Big Data. After surveying several vendor websites, blogs, forums and other materials, we have consolidated our findings to provide some clarity and help readers better identify and relate to these terms.

You would likely be hard-pressed to find the same definitions from any two sources – even between professionals who have worked with business data for decades. Some will argue that the terms are interchangeable, while others will adamantly defend what they see as strict differences. Since being branded by the masses as a standalone discipline for organizations, BI has been identified by some to be a process of which BA is simply a part. Furthermore, some BI tools and services with analytic properties have fallen prey to marketing departments that have had no real understanding as to how they should be named. So, where should we draw a “line in the sand?”

In a nutshell, BI examines mostly internal, structured data from past decisions and actions to produce helpful insights for decision making, whereas BA focuses on data in the present (real-time or near real-time) and helps to model and predict the future. They are complementary concepts grown with, and out of, each other. Traditional data storage systems are not conductive for dynamic analysis, and employ several established, reactive BI applications including alerts, OLAP, ad hoc reports and more. The advent of M2M connectivity and new analytic software solutions, however, are contributing to the rapid growth and importance of forward-thinking BA applications like data/text mining, optimization, forecasting, and predictive modeling.

So where does Big Data fit in?

Big Data is a relative term, defined as a collection of both structured and unstructured data so large and complex that it cannot be efficiently handled by conventional database management systems. Big Data platforms can, and do, use legacy data, but BI initiatives may or may not use Big Data. In an increasingly connected world, enterprise decision-makers must adapt to match data analysis capabilities with the rising volume and velocity of data to remain competitive. A winning business strategy applies both BI and BA to maximize the ability to make predictive decisions while addressing immediate needs of the organization.

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