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#BigData 's Dangerous New Era of Discrimination | #segmentation

#BigData 's Dangerous New Era of Discrimination | #segmentation | e-Xploration | Scoop.it
Where does value-added segmentation end and harmful discrimination begin?
luiy's insight:

Big Data creates Big Dilemmas. Greater knowledge of customers creates new potential and power to discriminate. Big Data — and its associated analytics — dramatically increase both the dimensionality and degrees of freedom for detailed discrimination. So where, in your corporate culture and strategy, does value-added personalization and segmentation end and harmful discrimination begin?

Let’s say, for example, that your segmentation data tells you the following:


Your most profitable customers by far are single women between the ages of 34 and 55 closely followed by “happily married” women with at least one child. Divorced women are slightly more profitable than “never marrieds.” Gay males — single and in relationships — are also disproportionately profitable. The “sweet spot” is urban and 28 to 50. These segments collectively account for roughly two-thirds of your profitability.  (Unexpected factoid: Your most profitable customers are overwhelmingly Amazon Prime subscriber. What might that mean?)

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Rescooped by luiy from Big Data Technology, Semantics and Analytics

Predictive Analysis: 7 Reasons You Need It Today

Predictive Analysis: 7 Reasons You Need It Today | e-Xploration | Scoop.it
With today’s enterprise software you no longer have to take a shot in the dark at decision making. Regardless of your organization’s size, industry, or the

Via Tony Agresta
Tony Agresta's curator insight, December 18, 2012 8:47 AM

I would expect predictive analytics technology to surge in growth, especially with the deluge of data arriving every day.   We are well beyond the early days when direct marketing pioneers applied predictive models to forecast response or performance (although that still happens more often than you think!).   Today, models can take advantage of more independent attributes than ever before – including structured and unstructured data    In turn, the predictive precision of the models increases.  There are more than a few ways to apply the results.   The obvious one is applying the scoring algorithms to new sets of data.   But don't lose sight of the fact that model scores can also be used as filters in queries to segment and report on your big data.   They can also be used as attributes in link analysis graphs designed to pinpoint fraud, cyber breaches, and networks of frequent buyers.  Imagine a network graph where links between people are scaled based on their predicted spend or the number of products they will buy during the holiday season.  It would easy to identify clusters of loyal customers which you could then study in more detail.  When coupled with other characteristics and contact info, targeting becomes precise and the meaning behind your big data becomes obvious.