In the first post of this series, I defined the differences between business intelligence, big data, and predictive analytics. In the second entry, I looked at the benefits of predictive analytics. In this third and final entry to the series, I’m summarizing why predictive analytics is important to the future of business. In the words of Newton’s Third Law, “For every action there is an equal and opposite reaction.”

Predictive analytics is powered by the world’s most potent, booming unnatural resource: data. Accumulated in large part as the by-product of routine tasks, filling out sales funnel information, sending emails to customers, calling customers, social media conversations, etc. Data is being accumulated at staggering rates worldwide, and every day, the data mass is growing. As organizations keep churning away,  unsalted, even flavourless deposits on masse, predictive analytics are looking at the big data heap as a gold mine bonanza.

Big data embodies an extraordinary wealth of experience from which to learn. As our world starts to make this quantum shift we will soon have predictive sensors everywhere and in everything.

Predictive analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behaviour of customers, individuals, and companies. Perfect prediction is not possible, but putting odds on the future — lifting a bit of the fog off our increasingly complex world that we live in. The world has simply become far too complex for us as humans to decipher.

Predictive analytics is becoming big news. The release of the movie ‘Her’, is about a man falling for his virtual assistant modeled after Apple’s Siri ,and you know we have begun the journey of geek-dom going mainstream. The buzz words include: cognitive computing, deep learning, artificial intelligence.

On a more practical side, these tools can give us visual queues such as traffic congestion patterns by recording all the traffic dynamics and advising in real time impacts on travel destinations, to sensing what we may want to do by watching our facial expressions in our home, like the Siri app on my iPhone saying “you look sad and tired Cindy, I will start running your bath for you, would you like that, or did you have something else in mind?”

Diving into the realities of big business, there is nothing more important than accurately predicting revenue forecasts, and making financial market expectations. As predictive analytics embed more deeply into main stream core applications in both sales and marketing processes, we will start to see improvements, which McKinsey is touting could indeed offer 10 CAGR growth rates to companies.

In other words, if you are not hard wiring your company to be smarter in using smarter predictive analytics and have a strategy at the board level, taking this out of the leadership of the CIO as companies will need chief data scientists that are also chief innovation officer, reporting to the CEO will start coming out of the wood work. We are moving into a period where it is also possible the CEOs of the future will come from mathematics backgrounds as everything we know can be distilled down into math, and in the future genetic approaches will overlay all business practices and processes.

I will cover genetic programming and predictive analytics implications in my next blog post, a fascinating area to be alert to.

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