Depending on one’s perspective, business intelligence (BI) can be a discipline unto itself, a superset and even a subset of other business information systems. Add in data mining (DM), data and/or business analytics and predictive analytics (PA), and (nevermind BPM, PM and OLAP) and one gets a murky picture of what solution solves what problem and how solutions differ from each other. A quick perusal of WhatIs.com brings in some clarity:
- Business intelligence is “a broad category of applications and technologies for gathering, storing, analyzing, and providing access to data to help enterprise users make better business decisions.”
- Data mining is “sorting through data to identify patterns and establish relationships.”
- Predictive analytics is the “branch of data mining concerned with the prediction of future probabilities and trends.”
- Data analytics is “the science of examining raw data with the purpose of drawing conclusions about that information.”
Reading those definitions side-by-side, they sound remarkably similar: these solutions all help end users manage and understand data to facilitate decision-making.
One industry consultant took a deeper look into the minute differentiation between these terms and concepts. Dean Abbott of Abbott Analytics published a blog piece on “Overlap in the Business Intelligence/Predictive Analytics Space.”
As you’ll read in Abbott’s analysis, business intelligence and data mining/analytics include many of the same components. Business intelligence (in Abbott’s interpretation of Gartner’s definition of business intelligence) includes OLAP (online analytical processing), visualization, scorecards and data mining. Data mining and analytics deliver similar components: scorecards, dashboards, reports and decision-support systems.
Abbott’s approach to finding clarity in this space is to look at the vendors listed on both the Gartner Magic Quadrant for Business Intelligence, 2009, and the top 10 vendors in the Rexer Analytics 2008 software tool survey. Abbott compares these listed vendors with those he views as “top data mining vendors,” and finds only a few vendors emerge as leaders in both business intelligence and data mining.
Finding a takeaway in these overlapping categories and top vendor lists can be challenging. In the end, buyers of business intelligence, analytics and other related solutions need to determine what they really want to find in the data, how they want to find it, and how they want to work with that data. The answers to those simple questions can be a good starting point to sorting out the ABCs of business intelligence.
Kelley Kassa
Spotfire Blogging Team
Image Credit: Microsoft Office Clip Art
