SAP Predictive Analytics Tools

[Former Member]

What are the feature differences when we develop the predictive analytics in

1.SAP Business Objects predictive analytics

&

2.SAP Analytics Cloud for Predictive analysis

[achab · Accepted solution]

Hello, this is a good question. SAP Predictive Analytics makes it possible to create and use predictive models for many different types of use. SAP Analytics Cloud for Predictive Analytics primary intention is to help the BI or the Planning user with predictive insights tailored for his specific work context. Obviously this depends on data made available in SAP Analytics Cloud. Kind regards Antoine

[Former Member · Accepted solution]

Thanks for the quick reply.

One more doubt to clarify.

So what is the difference of prediction

1.when being done at HANA DB using PAL

&

2.when its carried at SAP BO predictive analytics or any other tool like SAP Analytics cloud??

We have already a SAS(Software as service) developed in SAP analytics cloud tied with SAP HANA DB.

So to add predictive features to the existing product which is the right option to choose??

1.SAP Analytics cloud (with predictive) ?

2.SAP BO predictive analytics?

3.OR Doing the predictive modelling in HANA DB using PAL and then expose it through any of these kind of tools

what is the right direction to go with??

[Henry_Banks · Accepted solution]

Hi,

my opinion is the following:

1. Analytics Cloud is very much for 'light touch', user-driven insights. it's calling basic predictive services on the sap cloud platform, and surfaces them in a simple to use, consumer-grade interface.

2. Predictive Analytics allows both the creation of automated and/or advanced predictive model-creation - by citizen data scientists - including the embedding of model into business applications. It offers both a desktop client and, optionally, a server deployment to productionize the model.

3. finally, lucky customers who are in the position to benefit from HANA DB and those Predictive Libraries installed in HANA Platform are in the best position. this is where the most advanced data scientist use-cases are implemented and where true 'heavy lifting' and processing can happen.

actually, under the covers, the predictive and ML foundations are moreless the same. it's the architectural stacks which are different. 1) is considered 'bronze' in terms of capabilities 2) is considered 'silver' 3) is definitely 'gold' standard. however, 2) is archaic in terms of technology stacks, so the future is a combination of 1) and 3)

regards, H