Saturday, December 04, 2010

Predictive Analytics

Predictive analytics is  hot.  Advances in hardware,statistics and business intelligence software  have made it usable and performant. Predictive analytics , as the name suggests helps gain business intelligence from data using various data mining, pattern recognition and probablistic algorithms. Consider the following example - Given the history of orders for a product , how likely is a customer belonging to a certain age group to buy a certain product ? The answer can be computed in several ways.

1.Find out clusters of customers who bought the product. Find out the distance of the cluster from this customer to indicate the likelihood of buying.
2.Use regression analysis.   Y = a1*X1 + a2*X2 .....
where Y  represents  revenue for a product and X1,X2,X3  could represent causal factors such as age,geography etc.a1,a2,a3 are the coefficients. If age is statistically signigicant, the coefficient will have a significant value. Once age is confirmed to be statistically significant, we could have multiple causal variables
for each age bracket and then find out which among those is the most significant.

Oracle offers predictive analytics at several layers. PL/SQL/JAVA  comes with an API for predictive analytics called  DBMS_PREDICTIVE_ANALYTICS . Oracle also has a product called RTD - Real time decision making that is bundled with OBIEE - Oracle business intelligence enterprise edition.
Other tools like crystall ball, excel add on for predictive analytics  and  Oracle data mining are some other tools in Oracle's arsenal . Oracle Demantra  provide predictive analytics related to forecasting and demand management. With IBM having acquired SPSS  , the industry's  landscape has become interesting.

Academic publications like "Competing on Analytics" by Harvard Press  and the recent survey published in
MIT's Sloan Management Review on BI trends have contributed to the hieghtened   interest and investment in this upcoming discipline. Companies like Netflix have grown to a billion dollar by predicting  consumer buying patterns based on  "clicks".

1 comment:

karthiga saravanan said...

just linked this article on my Facebook account. it’s a very interesting article for all.



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