Be proactive to maintain stability
December 14, 2020

Be proactive to maintain stability

Prabhu Sundararaj | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Review Source

Overall Satisfaction with SAP Data Intelligence

Our clients belong to the energy and utility sector. They are providing services in three countries. Businesses have shown interest in resolving customer-facing issues by proactively capturing real-time data from Smart Energy Meters which were installed in residences and with corporate consumers. Using Kafka integration in SAP Data Intelligence, we are able to process our data with ML library to forecast system vulnerability.
  • Numerous packages to build business specific models
  • It supports telemetry data to process and organize for analysis
  • Well-performing batch processing pipelines and useful data cleansing tool
  • Need more learning resources to dive deeper into the application to provide more business problem resolutions
  • Application cost is high
  • When it comes to bug fixes, it takes a long time to locate the issue
  • Improved consumer relationship
  • Improving fault tolerant system
  • Proactive detection of system vulnerabilities
I worked with Salesforce Audience Studio for a few months. I feel SAP Data Intelligence has more built-in ML libraries and customization to resolve our business needs.

Do you think SAP Data Intelligence delivers good value for the price?

Yes

Are you happy with SAP Data Intelligence's feature set?

Yes

Did SAP Data Intelligence live up to sales and marketing promises?

I wasn't involved with the selection/purchase process

Did implementation of SAP Data Intelligence go as expected?

Yes

Would you buy SAP Data Intelligence again?

Yes

As our business is providing critical services to consumers, our vision needs to be directed toward future forecasts to prevent any natural or manmade disasters and to provide stable solutions to users. SAP Data Intelligence helps us to engage with ML models to improvise our business to fix issues before it causes any larger impact. Data-trained built-in ML models help to detect which hub or segment in the network caused an issue; we are also able to predict which hub going to cause future impact.