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Author's profile photo vijaykumar ijeri

Learnings from SAP TECHED Bangalore – Predictive analysis

Gone are those days when we used to have a list of items decided before we go to any grocery merchant and ask him to give the items. These days we go to a super market or any provisional multi-brand stores.  We have list of our own items to buy and the storekeeper has his own list of items which he has to sell us. At the end, if we see what we have bought would be more than actually we had planned. Because we are in the world, where all our activities are tracked. Whatever we buy the history is maintained by the stores. Based on this history of purchase the store keeper or the multi-brand companies do their analysis and predict our interest. They go to an extent and even predict what would be our next purchase; this is called as sentimental analysis. This sentimental analysis is more evident in the online market. From the moment we surf for something over the internet and look for something of our interest, all the page visits of ours will be tracked and would be studied. We would soon start receiving promotional email notifications based on our interest; or rather the search would be refined based on our last login and research over the internet for any product. This is all possible with predictive analytics technology.

                                SAP also has its own predictive analytics tool called SAP PA(Predictive analytics). SAP has acquired KXEN Company who were the leaders in predictive analytics. With acquisition of KXEN, SAP has strengthened its name in the predictive analytics space. SAP PA would be front ending with KXEN at the backend, which has capabilities of acquiring algorithms from different languages and tools.

                                For any predictive analytics tools there is requirement to constantly train your algorithm with the data. There are chances of your predictive algorithm giving  result which is 98% to actual data/happening, at the same time there are also chances where in your algorithm might give a result which is close to 50%. This is because the data on which the algorithm is run is totally new. In such cases the predictive results would be very bad, that’s the reason there is constant need of training and tuning the algorithm.

                                With SAP PA, one can integrate the data from SAP HANA and study the predictions. You can also integrate the algorithms written in R and other statistical languages and  club it into SAP PA. Integrate SAP HANA PAL etc.



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