SAP DI - A quick review
November 30, 2020

SAP DI - A quick review

Anonymous | TrustRadius Reviewer
Score 8 out of 10
Vetted Review
Verified User

Overall Satisfaction with SAP Data Intelligence

We are implementing SAP DI for large customers to manage their ML ecosystem. The DI system will be used for hosting homegrown models and also building newer ones from scratch. We are connecting SAP and Non SAP systems to DI. Inferences from DI would be used to support key business decisions.
  • Data orchestration from diverse sources.
  • Good inbuilt support to develop models (Jupyter Notebook).
  • Data pipelines, container architecture for deployment.
  • Tools for data cleansing.
  • Ability to port existing ML models into DI.
  • Product support documentation and blogs can be better.
  • Debugging can take a bit of time to understand and pinpoint issues.
  • Can be pricey.
  • Positive impact of reducing cycle time for supply.
  • Predict customer churn.
Pretty much covers most features available for managing an ML ecosystem. Product is fairly easy to use and can be quickly provisioned on the cloud or on premise. Setting up new pipelines to ingest data is straightforward, so is cleansing the data. Jupyter notebooks help loading various libraries to develop models that can be easily deployed.
Quick turnaround from the SAP DI support team for ticket resolutions. DI related documentation (help, blogs) is being improved continuously.

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Customers who have SAP S/4HANA/Business suite should definitely look to try DI for widespread adoption of ML in their business processes. The product is fairly stocked with features and typical of SAP, features are upgraded very frequently making it more stable and functionality rich. Support for connecting non SAP products and other systems like IOT is also very useful to blend in diverse sources of data.