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FIN923 : Why Choose a Standard DW Data Model?

Discover why choosing a standard data warehouse (DW) data model is crucial for organizations like Alm. Brand. This presentation, led by IT Data Warehouse Manager Preben Gudbergsen, delves into the company's current IT and business situations, the selection process for a DW model, and insights on project implementation phases. Learn how a standardized approach facilitates effective data management, enhances usability, and aligns with strategic enterprise goals, all while managing risks associated with data warehousing projects.

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FIN923 : Why Choose a Standard DW Data Model?

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  1. FIN923 : Why Choose a Standard DW Data Model? Preben GudbergsenIT Data Warehouse Managerpreben.gudbergsen@almbrand.dk / +45 35477478 6th August 2003

  2. Agenda • The Company – Alm. Brand • Business situation • IT situation • Selection process • Project phase 1 • Where are we today ? • Questions

  3. The Company – Alm. Brand • Alm. Brand A/S • Insurance - Property & Casualty • Insurance – Life & Pension • Bank - Investments & savings • Property & Casualty Insurance • Founded 1792 • Products covering – private, business and agriculture • 4th largest in the private segment • Life • Bought in the 1990´s • Market share of 2% • Bank • Bought in the 1990´s • 9th largest bank • Moving into the retail banking market

  4. After internal merger Background Information Business Before

  5. Background Information Business • Common culture values • Enterprise orientation • Enterprise strategic goals • 20% “Helkunde” in 2006

  6. Independend data warehouses Enterprise data warehouse Background Information IT

  7. Background Information IT • New Insurance operational system • Existing insurance data warehouse needs reengineering • Bank data warehouse • Did not match business needs • Enterprise focus not matched • Enterprise view of the customer • IT strategy • Focused on standard systems

  8. Selection Process The stages Project start Gap analysis Vendor selection Arguments to management Business pre-analysis Technical workshops

  9. Selection Process • Market screening • One day technical workshop • Examination of model • Handling of metadata • Ease of use • Knowledge transfer Project start Gap analysis Vendor selection Arguments to management Business pre-analysis Technical workshops Mar / Apr 2002

  10. Selection Process • Exploration of business needs • Based on strategic goals • Identify gaps • Identify data warehouse projects • Prioritize data warehouse projects • Conclusion Project start Gap analysis Vendor selection Arguments to management Business pre-analysis Technical workshops Mar / Apr 2002 May / Jun 2002

  11. Selection Process • Talk data model with business management !!!! • Enterprise focus supported • Speed of implementation • Lower risk • Known and proven method • Data modelling ressources • Performance Project start Gap analysis Vendor selection Arguments to management Business pre-analysis Technical workshops Mar / Apr 2002 May / Jun 2002 Jun / Oct 2002

  12. Selection Process • Selection criteria • Model • Metadata handling • Knowledge transfer • Ease of use • Price Project start Gap analysis Vendor selection Arguments to management Business pre-analysis Technical workshops Mar / Apr 2002 May / Jun 2002 Jun / Oct 2002 Oct / Dec 2002

  13. Selection Process • Fit assesment • One week workshop • Selected definitions and measurements • Important reports • Covering all business areas • Evaluation criteria • Result Project start Gap analysis Vendor selection Arguments to management Business pre-analysis Technical workshops Mar / Apr 2002 May / Jun 2002 Jun / Oct 2002 Oct / Dec 2002 Oct 2002

  14. Selection Process • Three weeks business exploration • Master plan • Phase 1 • Enterprise customer & retail bank • Phase 2 & 3 – Bank • Phase 4 - Insurance Project start Gap analysis Vendor selection Arguments to management Business pre-analysis Technical workshops Mar / Apr 2002 May / Jun 2002 Jun / Oct 2002 Oct / Dec 2002 Oct 2002 Jan 2003

  15. Where are we today ? . • Phase 1 delivered • Enterprise customer • Retail bank to staging area • Challenges • Too ambitious scope • ETL complexity • Estimation skills • Conclusion

  16. Questions ?

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