1. Business Analytics, Part I Introduction Presented by Scott Koegler Editor, ec-bp.org

2. Scott Koegler Editor of ec-bp.orgec-bp.org scott@ec-bp.org Speaker

3. Business Analytics What is it? Where did it come from? What is it supposed to do?

4. BI – A Starting Point Business Intelligence (BI) Discovering what happened Look at past events Typical of ERP reports

5. BI & BA What differentiates BA from BI? Looking forward Trend moving to predictions Predictive analysis

6. BA & Data Data is the key to BA Lots of data Real-time or near-time Widest collection of data

7. Challenges Data? Access? Reporting? Outcomes?

8. Why Is BA a Hot Topic? Optimization is the new growth Expansion was the best way to grow Now too expensive Difficult to open new markets

9. Not About the Tools Tools do exist Know the desired outcomes

10. Outcomes Outcomes define the project Stakeholders must drive the quest Business in / technology out

11. How Far to Reach Not far-reaching Best to start with smaller goals Tactical goals first

12. Possibly Too Limited Analytics are not in a box Think of analytics as part of the holistic environment Tactical goals are part of the overall plan

13. Leakage Organizational process leakage The key findings may be lost along the way

14. Focus on the Delta Difference between: Current situation What is possible

15. Close the Gap The Gap is the difference between what is and what is possible Don’t worry about closing the gap completely Incremental improvements do count

16. 80/20 Rule Applies Determine the most important changes Monitor progress Evaluate the results

17. Good Enough Good enough is good enough

18. It’s a Process BA is not “buy and push the button” Every implementation is different Tools for custom outcomes

19. Processes Create numerical results Implement in meaningful ways Integrate outcome to technology Integrate Monitor and fine-tune

20. Refine & Evaluate Continuous loop Measure the Gap Fix what doesn’t work Measure the Gap …

21. Categories of Analytics Descriptive Analytics Prepares and analyzes historical data Identifies patterns from samples for reporting of trends

22. Categories of Analytics Predictive Analytics Predicts future probabilities and trends Finds relationships in data not readily apparent with traditional analysis

23. Categories of Analytics Prescriptive Analytics Evaluates and determines new ways to operate Targets business objectives and balances all constraints

24. Limits to Predictions Long-term projections are difficult 5- to10-year projections Changes are difficult to predict

25. Barriers to Achievement Massive amounts of data Need for real-time access Traditional data in transactional systems Requires optimized computing platforms Disk drives can’t keep up

26. Combination of Changes De-normalized databases Removes multiple tables Flat data file Optimized data structures Optimized computing

27. What About ROI? ROI is not always immediately obvious Results of analytics may be available only after years of following the prescription Requires long-term efforts

28. Returns Defined Viable Business Analytics Results based on the business Define the desired results Agree on definition of success

29. Recommendations BA initiatives are different Commonality is in the approach Treat BA as any project Generally longer term Iterative process Constant updates

30. Recommendations Monitor progress Focus on outcomes Review validity Revise data collections

31. Analytics Everywhere Increasingly used Volume of data collected driving use Optimization of business = growth Look for opportunities Data collection Future outcomes Uncertainty