I get many questions on this subject and it often turns into almost a religious debate. Let's throw some structure into it. Here's a decision-to-raw-data stack.

  1. Decisions
  2. Strategy
  3. Policies
  4. Objectives (e.g., clear understanding of what is driving revenue performance)
  5. Goals (e.g., achieve x% income growth)
  6. Calculated metrics (any combination, variation of the standard metrics or KPIs)
  7. KPIs (e.g., profitability, liquidity, shareholders value)
  8. KPMs (e.g., enterprise value, trailing/forward price/earnings)
  9. Metrics (e.g., fee income growth %, non fee income growth %)
  10. Dimensions (e.g., customers, customer segments, products, time, region)
  11. Pre-calculated attributes (standard, cross enterprise metrics, KPIs and KPMs)
  12. Pre-built aggregates (used to speed up reports and queries)
  13. Analytical data (DW, DM)
  14. Operational data (ERP, CRM, financials, HR)

Obviously, it's never a clear cut, binary decision, but in my humble opinion

  • 1-6 should emphasize business ownership
  • 10-14 should emphasize IT ownership
  • 7-9 is where it gets murky, and ownership depends on whether metric/KPI/KPM is 1) standard and fixed, 2) fluid and changes frequently, 3) different by product, line of business, region

What did I miss? Thoughts?
Boris also blogs at http://blogs.forrester.com/boris_evelson/.

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