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Predictive analysis techniques pull real-time results out of big data

By Cameron McKenzie

30 Nov 2012 | TheServerSide.com

How does predictive analysis impact big data? For one thing, it makes the Brobdingnagian amounts of information and data residing in those petabyte databases finally worth having. Regulator guidelines are no longer the only reason to hold onto archived data now that data can be mined in ways that were never previously thought possible.

When your insurance company approves that dental bridge you've needed for a while, you have the claims software and the software developers to thank.

Modern big data strategies mean analytics and mining don't happen after the fact, and it's no longer just a few nuggets of information that get uncovered to help your company understand past performance issues. Mining historic data might help you plot a new course over the long term, but by the time you examine old data using traditional methods, it's already staler than last week's coffee. Consistently operating behind the curve means you're always running behind your competitors in the marketplace.

Get big data on instant replay

Big data business intelligence (BI) requires embedded analytics to harvest real-time data for predictive analysis. The combination of recent data and right now data helps enterprise-class customers make better decisions on the spot. It's no longer good enough to supply the data at a strategy meeting that happens six months down the road.

Paul Jones, a business systems architect at dental insurance provider United Concordia, described how the process works within his organization. "We look at predictive analytics as using the data we have currently and regression model analysis on data that's arriving in real time for claims submission. Then we decide the best thing to do with each claim based on the information we have historically and the information we have obtained within the last few minutes." So, when your insurance company approves that dental bridge you've needed for a while, you'll have the claims software and the software developers to thank.

Trying to keep the customer satisfied

Jones said other industries also use this type of approach to enhance their minute-by-minute business decision making process at the level of the individual employee. In customer service, a queue might be managed based on real-time and historical data for phone numbers stored in the system. A representative might be prompted on what action to take for a call based on analysis that indicates the best way to resolve an issue. Organizations can also use predictive analysis techniques to figure out how to market the company more effectively by matching customers and products/services on the fly to increase the success rate for cross-selling or up-selling.

Big data moving forward

Is big data changing how IT architects solutions? Jones said that healthcare reform is having a huge impact on how data is managed within the insurance industry. Organizations like United Concordia are beginning to focus on integration of third-party information. This expansion of access poses challenges with both interoperability and compliance concerns. However, the effort is well worth it. According to Jones, "We get a comprehensive, 360-degree view of the customer. This gives us a more thorough understanding of the customer so we can serve them better."

It's not just health care organizations that are changing. Enterprises in many industries are joining forces and sharing information on an unprecedented scale. This will make it even more imperative for organizations to incorporate predictive analysis into their BI framework. There's simply no other way to look at things now that big data just got a little bit bigger.