The Secret Weapon of Predictive Analytics Revealed

January 8, 2016

I like it when secrets are revealed. I learned how to unlock the treasure chest containing predictive analytics secret weapon. You can too. Navigate to “Contextual Integration Is the Secret Weapon of Predictive Analytics.”

The write up reports:

Predictive analytics has been around for years, but only now have data teams begun to refine the process to develop more accurate predictions and actionable business insights. The availability of tremendous amounts of data, cheap computation, and advancements in artificial intelligence has presented a massive opportunity for businesses to go beyond their legacy methodologies when it comes to customer data.

And what is the secret?

Contextual transformation.

Here’s the explanation:

A major part of this transformation is the realization that data needs to be looked at from as many angles as possible in an effort to create a multi-dimensional profile of the customer. As a consequence, we view recommendations through the lens of ensembles in which each modeled dimension may be weighted differently based on real-time contextual information. This means that, rather than looking at just transactional information, layering in other types of information, such as behavioral data, gives context and allows organizations to make more accurate predictions.

Is this easy?

Nope. The article reminds the reader:

A sound approach follows the scientific method, starting with understanding the business domain and the underlying data that is available. Then data scientists can prepare to test a particular hypothesis, build a model, evaluate results, and refine the model to draw general conclusions.

I would point out that folks at Palantir, Recorded Future, and other outfits have been working for years to deal with integration, math, and sense making.

I wonder if the wonks at these firms have realized that contextual integration is the secret? I assume one could ask IBM Watson or just understand the difference between interpreting marketing inputs from a closed user base and dealing with slightly more slippery data has more than one secret.

Stephen E Arnold, January 8, 2016

Written by Stephen E. Arnold · Filed Under Analytics, News

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Stephen E. Arnold monitors search, content processing, text mining and related topics from his high-tech nerve center in rural Kentucky. He tries to winnow the goose feathers from the giblets. He works with colleagues worldwide to make this Web log useful to those who want to go "beyond search". Contact him at sa [at] arnoldit.com. His Web site with additional information about search is arnoldit.com.