How Big Data Brings BI and Predictive Analytics Together

Big data is breathing new life into business intelligence by putting the power of prediction into the hands of everyday decision-makers.


For as long as anyone can remember, the world of predictive analytics has been the exclusive realm of ivory-tower statisticians and data scientists who sit far away from the everyday line of business decision maker. Big data is about to change that.
As more data streams come online and are integrated into existing BI, CRM, ERP and other mission-critical business systems, the ever-elusive (and oh so profitable) single view of the customer may finally come into focus. While most customer service and field sales representatives have yet to feel the impact, companies such as IBM and MicroStrategy are working to see that they do soon.

Big Data Moves Analytics Beyond Pencil-Pushers
Imagine a world in which a CSR sitting at her console can make an independent decision on whether a problem customer is worth keeping or upgrading. Imagine, too, that a field salesman can change a retailer's wine rack on the fly based on the preferences that partiers attending the jazz festival next weekend have contributed on Facebook and Twitter.
Big data is pushing a tool more commonly used for cohort and regression analysis into the hands of line-level managers, who can then use non-transactional data to make strategic, long-term business decisions about, for example, what to put on store shelves and when to put it there.
However, big data is not about to supplant traditional BI tools, says Rita Sallam, Gartner's BI analyst. If anything, big data will make BI more valuable and useful to the business. "We're always going to need to look at the past…and when you have big data, you are going to need to do that even more. BI doesn't go away. It gets enhanced by big data."
How else you will know if what you are seeing in the initial phases of discovery will indeed bear out over time. For example, do red purses really sell better than blue ones in the Midwest? An initial pass through the data may suggest so—more red purses sold last quarter than ever before, therefore, red purses sell better.
But this is a correlation, not a cause. If you look more closely, using historical transaction data gleaned from your BI tools, you may find, say, that it is actually your latest merchandise-positioning-campaign that's paying dividends because the retailers are now putting red purses at eye level.
That's why IBM's Director of Emerging Technologies, David Barnes, is actually more inclined to refer to the resulting output from big data technologies such as Hadoop, map/reduce and R as "insights." You wouldn't want to make mission-critical business decisions based on sentiment analysis of a Twitter stream, for example.

Big Data, the amazing thing

Big data is a term applied to data sets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Big data sizes are a constantly moving target currently ranging from a few dozen terabytes to many petabytes of data in a single data set.

In a 2001 research report^[15] and related conference presentations, then META Group (now Gartner) analyst, Doug Laney, defined data growth challenges (and opportunities) as being three-dimensional, i.e. increasing volume (amount of data), velocity (speed of data in/out), and variety (range of data types, sources). Gartner continues to use this model for describing big data.






Whether through blogs, twitter, or technical articles, you’ve probably heard about Big Data, and a recognition that organizations need to look beyond the traditional databases to achieve the most cost effective storage and processing of extremely large data sets, unstructured data, and/or data that comes in too fast. As the prevalence and importance of such data increases, many organizations are looking at how to leverage technologies such as those in the Apache Hadoop ecosystem. Recognizing one size doesn’t fit all, we began detailing our approach to Big Data at the PASS Summit last October. Microsoft’s goal for Big Data is to provide insights to all users from structured or unstructured data of any size. While very scalable, accommodating, and powerful, most Big Data solutions based on Hadoop require highly trained staff to deploy and manage. In addition, the benefits are limited to few highly technical users who are as comfortable programming their requirements as they are using advanced statistical techniques to extract value. For those of us who have been around the BI industry for a few years, this may sound similar to the early 90s where the benefits of our field were limited to a few within the corporation through the Executive Information Systems.

Analysis on Hadoop for Everyone

Microsoft entered the Business Intelligence industry to enable orders of magnitude more users to make better decisions from applications they use every day. This was the motivation behind being the first DBMS vendor to include an OLAP engine with the release of SQL Server 7.0 OLAP Services that enabled Excel users to ask business questions at the speed of thought. It remained the motivation behind PowerPivot in SQL Server 2008 R2, a self-service BI offering that allowed end users to build their own solutions without dependence on IT, as well as provided IT insights on how data was being consumed within the organization. And, with the release of Power View in SQL Server 2012, that goal will bring the power of rich interactive exploration directly in the hands of every user within an organization.
Enabling end users to merge data stored in a Hadoop deployment with data from other systems or with their own personal data is a natural next step. In fact, we also introduced Hive ODBC driver, currently in Community Technology Preview, at the PASS Summit in October. This driver allows connectivity to Apache Hive, which in turn facilitates querying and managing large datasets residing in distributed storage by exposing them as a data warehouse.