I find that the terminology associated with specialized fields like data mining very interesting to track. My first boss, Roger Barron (better described as a mentor and later truly a friend--I owe much of who I am as a professional to him), used to talk of the transitions of terminology in technology: bionics, cybernetics, artificial intelligence, neural networks, etc.
I find that data mining and predictive analytics fall into the same category--they are the same basic technology but described from different perspectives. Sometimes colleagues have tried to point out distinctions, and I think one of the better ones was posted by Eric King here, where my definition of "better" means simple and clear.
Predictive analytics is a term I see more in the CRM and database worlds (TDWI conferences come to mind). Perhaps some of this is due to the encroachment of BI into the data mining world, where queries and OLAP are sometimes called data mining (after all, you are "drilling" down into the data!). This would necessitate creating further distinctions in terminology.
However, I don't see data mining losing hold on the style of predictive modeling that is largely empirical and data driven. So I include predictive analytics in the title of this blog as an alternative to data mining in name only, not in purpose.
Thursday, October 19, 2006
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2 comments:
Dean and readers may be interested in the perspective described in the post, What is Analytics, over on HP Analytics - Bangalore India.
-Will
It seems to me that the main differentiator between ‘data mining’ and ‘predictive analytics’ is the integration, delivery and audience.
I work in telecommunications as a data miner, creating monthly churn datasets, customer segmentations, cross-sell recommendations etc. My work results in two things;
a) a dataset created monthly
- usually provided to the campaign delivery team prior to communication to our customers.
b) a powerpoint presentation
- presented to business sponsors and executives (who have long-term goals to drive business processes and profit).
I consider my data mining work ‘complete’ once the business case is proved and the project is taken as a requirement for business operations to succeed. I don’t have much direct impact on how my data mining is deployed within the business, or *how* business operations use my segmentation, churn predictions etc
Conversely it seems ‘predictive analytics’ it often used in context of *how* the results of data mining projects impact the business operations. For example, Call Centre agents are armed (in real-time) with a score indicating churn likelihood of the customer they are talking to. Sales agents know what type of products are best suited to a customer based upon their prior usage or a few simple questions. This type of business empowerment is more in line with the context of ‘predictive analytics’, in that it seems to compass integration of data mining within business operations.
anyway, that’s my six pence worth…
Tim Manns
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