Big Data and Predictive Analytics WhatÕs All the Rage About?

In 2010, forward-thinking organizations were using data mining to detect fraud, minimize risk, anticipate resource demands, increase response rates for marketing campaigns and curb customer attrition. This type of predictive analytics proved to be an important source of competitive advantage.  What is different just two years later?    

Advances in analytic technologies and business intelligence are allowing companies to go big, go fast, go deep, go cheap and go mobile with business data.  For years business folks kept asking for the data.  “Just give me the data, and I’ll figure out what to do with it” was the call; however, IT kept saying, “You’ll bring down the house with runaway queries” or  some other excuse as to why this was not possible. However, the primary reason that it was not possible was because technology was not ready.

Today, technology is READY. Databases such as Greenplum and analytics tools like SAS are blended together, providing speed against scores of data so large and queries so complex that traditional relational database management systems would be incapable of handling in a timely manner.   These tools are proving to be capable of reducing analytics processing from hours to minutes – but this isn’t the big deal within itself. Let’s understand WHY this is such a big deal.

Data is growing at an exponential rate.  The world is collecting data from digital sensors, all feeding data into storage somewhere. Traffic on city roads, water flow in rivers, sugar levels in grapes as they grow, shipments of pencils (and everything else you can think of), movement of everything from freight on train cars to whales in the ocean.  The data deluge keeps gaining speed.

Speed is the operative word – speed not only in the sense of how much and how fast data is being captured,  but also about how fast the data is changing.  I heard it said that “you don’t go to the corner of a busy intersection, take a picture and then go back to analyze when you want to cross the street.” That may sound ridiculous, but when historical data is used for analytics, that is basically what you get. Now the power of predictive analytics enables you to know “just in time” what you expect to happen so that you can respond before or as it happens.  

Given this concept, “the right data to the right person at the right time,”  an age-old saying, becomes relevant once again.  Today, it is not the gathering of digital data that is the challenge, but rather quickly making sense of it.  In an environment where the velocity of change is faster than at any other time in history, organizations who can use predictive processes to anticipate change and gain a competitive advantage will shape the future.

For further reading, I suggest  The Two-Second Advantage  by Vivek Ranadive and Kevin Maney

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