Reviews (1-2 of 2)
October 31, 2019
Score 10 out of 10
SAP Predictive Analytics is used by many teams from the SAP organization which perfectly fits the "SAP runs SAP" mantra. The use cases range from Sales & Marketing to IT operations and events sometimes in the R&D departments. There are plenty of uses cases covered by SAP Predictive Analytics from typical "classification" problems like "predicting which lead is going to convert into an opportunity" or some more advanced like "content recommendation" on the websites.
- It doesn't require you to have a Ph.D. to build models!
- You can use it to address a very large and wide dataset without worrying about sampling.
- Automation is in the product DNA. You can prepare your data, ingest it into the "Kernel", then get insights about what was found, decide to publish it and schedule scoring tasks or model refresh in the same product.
- The "User Experience" is sometimes lacking some clear basic things. Maybe a migration to a cloud-based environment will help bridge that gap.
- API is probably the next item on my list. The existing one is not easy to access or use which limits the integration capabilities.
Read Abdelhalim DADOUCHE's full review
When you don't have much data science skills or when you need to build something quick, you can get started really fast with SAP Predictive Analytics. There are scenarios, like the ones covered by a neural network, where SAP Predictive Analytics is not well suited, but the big question is usually: "do you really need a neural network to address this use case?"
Score 6 out of 10
We have purchased PA but are in the process of proving its worth. It was brought in with the hope of being able to forecast work that is currently done manually, to give an 80% complete starting state that can be altered vs. starting completely from scratch. Additional use cases that will be explored include using historical data to predict trends going forward, which can be used as thresholds to then limit the scope of the investigation. The plan is to use it across our finance organization and even beyond depending on the outcome of our POCs.
- Ability to use built-in algorithms or expand using R. This means that (with training) casual users can take advantage but also data analysts can do their thing!
- Integration and consistency with Lumira. Even Lumira on its own has a quick 'predict' functionality (although limited/black box)
- Ability to do the analysis and then present visually using the Lumira visualization capabilities
- It's extremely hard to get started. We even have a data scientist who, when we put Predictive Analytics in front of them, they could not intuitively create a data model and start analyzing. Even with deep knowledge of data analysis, the interface isn't intuitive and it's hard to get to the point of having imported a data set and start analyzing/predicting
- Our platform team had confusion installing Predictive Analytics, particularly on the BI Platform. Additional complexity came when it came to using APL Libraries (AFL Wrapper or Stored Procedures)
- We are still working on merging datasets. In theory, we get how to do it but we come across all sorts of issues which don't occur in Spotfire (missing records etc.)
Read Josh Anderson's full review
It's a great tool to merge actual data analysis (which Lumira doesn't do that well) with visualization (which Lumira does well) - so it can be seen as Lumira for data analysts. However, a lot of the 'predictive' side is hidden/black box which can be frustrating for those analysts, so you could argue it is too complex for casual users, but too 'black box' for analysts.
SAP Predictive Analytics Scorecard Summary
About SAP Predictive Analytics
SAP Predictive Analytics Competitors
SAP Predictive Analytics Technical Details