Traditional Data Science approaches face multiple challenges
- Modeling approaches are too complex to build or maintain
- The relationship between rigorous models and data driven models is unclear
- Data scientists are specialists in the latest scientific techniques but have little understanding of the domain
How did we perform and why?
- Capture and store data
- Perform root cause analysis
- Derive statistics and analytical models
How can we maximize performance now?
Where is my attention needed?
- Automated anomaly detection
- Orchestration of business processes
- Real-time optimization
How will I perform in the near future? How can I improve?
- Prediction of risk / failures
- Integrated planning
- Condition-based maintenance
How will I perform in the long-term future?
- Assets benchmarking
- Opportunity evaluation
- What-if scenario’s