Application areas for SAS HPA
When I talked with SAS about its forthcoming in-memory parallel SAS HPA offering, we talked briefly about application areas. The three SAS cited were:
- Consumer financial services. The idea here is to combine information about customers’ use of all kinds of services — banking, credit cards, loans, etc. SAS believes this is both for marketing and risk analysis purposes.
- Insurance. We didn’t go into detail.
- Mobile communications. SAS’ customers aren’t giving it details, but they’re excited about geocoding/geospatial data.
Meanwhile, in another interview I heard about, SAS emphasized retailers. Indeed, that’s what spawned my recent post about logistic regression.
The mobile communications one is a bit scary. Your cell phone — and hence your cellular company — know where you are, pretty much from moment to moment. Even without advanced analytic technology applied to it, that’s a pretty direct privacy threat. Throw in some analytics, and your cell company might know, for example, who you hang out with (in person), where you shop, and how those things predict your future behavior. And so the government — or just your employer — might know those things too.
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The mobile applications shouldn’t be too scary, because much of this already exists and has been around for some time. Regardless of your choice of phone (iPhone or not).
Look inside any (good) CDR DW and there will be a summary layer with fields for work cell (where a subscriber is between 8am and 5pm), home cell (where they are at night), commute (the cells they touch between the two above) and so on. These can go into minute detail – where they go on holiday, what sports/entertainment events they attend etc. etc. Basic fraud and more advanced SNA models also provide all of the detail around who that subscriber associates with – in person or by phone (voice/SMS) – and the strength of each relationship. Every subscriber is profiled in infinite detail, your wireless provider and the various arms of government already see all this, and the granularity of data simply increases exponentially with every new network generation so that they know more and more.
However, while we know you like to go to the Starbucks on Massachusetts Avenue at certain times of the week, the SAS HPA will be useful in predicting whether you will be there tomorrow, whether Jim will be able to join you, and whether you’ll have the Caramel Macchiato or the Cinnamon Dolce Latte…