1. A presentation by W H Inmon KIMBALL vs INMON

2. the essence of the difference between Inmon and Kimball Inmon – there needs to be a single version of the truth data warehouse data mart data mart data mart data mart integrated historical granular sales finance marketing mgmt HR single version of the truth

3. the essence of the difference between Inmon and Kimball data warehouse data mart data mart data mart data mart integrated historical granular sales finance marketing mgmt HR single version of the truth the question being answered – what is the single version of the truth? what is corporate data?

4. the essence of the difference between Inmon and Kimball Kimball – a data warehouse is the union of all of the data marts data mart data mart sales finance HR data mart a data mart is based on business function – Ralph Kimball

5. the essence of the difference between Inmon and Kimball data mart data mart sales finance HR data mart the question being answered – how quickly can I build reports? how quickly can I do analysis?

6. over time the architectures have evolved 1990 2000 2010 Inmon – Single version of the truth Kimball – a union of data marts Inmon – an architecture corporate information factory Kimball – conformed dimension Inmon – DW 2.0, unstructured data Kimball – a need for integration

7. 1990 2000 2010 Inmon – Single version of the truth Kimball – a union of data marts Inmon – an architecture corporate information factory Kimball – conformed dimension Inmon – DW 2.0, unstructured data Kimball – a need for integration Kimball is today where Inmon was in 1990 What has Kimball said to all of those people who followed his teachings in 1990?

8. the essence of the difference between Inmon and Kimball 1990 2000 2010 2020 Inmon – Single version of the truth Kimball – a union of data marts Inmon – an architecture corporate information factory Kimball – conformed dimension Inmon – DW 2.0, unstructured data Kimball – a need for integration Kimball – unstructured data belongs in a data warehouse prediction – in 2020 the Kimballites will “discover” that textual data belongs in a data warehouse

9. appl mktg sales finance mgmt HR Engineering Production data marts appl mktg sales finance mgmt HR Engineering Production data marts Kimball Inmon from an implementation perspective

10. appl mktg sales finance mgmt HR Engineering Production data marts appl mktg sales finance mgmt HR Engineering Production data marts daily refreshment of data each of these lines must be crossed at least once a day

11. appl mktg sales finance mgmt HR Engineering Production data marts appl mktg sales finance mgmt HR Engineering Production data marts daily refreshment of data m n m n m x n m + n

12. appl mktg sales finance mgmt HR Engineering Production data marts appl mktg sales finance mgmt HR Engineering Production data marts daily refreshment of data m n m n m x n m + n how many programs have to be written? have to be maintained?

13. appl mktg sales finance mgmt HR Engineering Production data marts appl mktg sales finance mgmt HR Engineering Production data marts daily refreshment of data m n m n m x n m + n which overnight batch processing window do you want?

14. appl mktg sales finance mgmt HR Engineering Production data marts appl mktg sales finance mgmt HR Engineering Production data marts $1000 $32000 $1,009,087 $1000 $32000 $1,009,087 reconciliation in which environment would you rather do reconciliation?

15. appl mktg sales finance mgmt HR Engineering Production data marts appl mktg sales finance mgmt HR Engineering Production data marts in which environment would you rather add a new data mart?

16. star schema (Kimball) relational based data warehouse (Inmon) from an architectural perspective

17. star schema (Kimball) relational based data warehouse (Inmon) good for fast reports not a short term proposition good for a system of record

18. as an end user I am confused… there are 17 data marts that have information and I don’t know which one to go to. And they all have different information

19. every time there is a new requirement I have to start from scratch. And these darn data marts are hard to maintain. I have to build a new one every time there is a change in requirements

20. we have had data marts for five years now. We have 250 of them and only 10 of them are actually being used today……

21. I’ve got these auditors coming in and I don’t have any data that I trust that I can show them……

22. with Kimball, the star schema is the architecture with Inmon, the relational foundation is only the start of the architecture

23. the Inmon approach is a FULL architecture leading to DW 2.0. And DW 2.0 is a true full scale architecture

24. DW 2.0 supports some really important architectural features – - the life cycle of data within the data warehouse - the accommodation for very large amounts of data - the recognition that cost is the ultimate limiting factor for a data warehouse - unstructured data as an essential component - metadata as an essential component ask Kimball how he supports unstructured data? ask Kimball how he supports metadata? ask Kimball how he supports really large amounts of data? ask Kimball how he supports archival data?

25. corporate data structured data unstructured data the vast majority of corporate data is not structured

26. structured data unstructured data Kimball structured data unstructured data Inmon the Inmon architecture is complete; the Kimball architecture is not

27. Florida South America NYC Chicago Hawaii Sao Paolo Mexico Canada Bermuda Denver Calgary Los Angeles Gold Coast Florida Miami San Francisco Seattle Kimball Inmon Kimball only addresses one small part of architecture. Inmon addresses a much more comprehensive picture

28. data warehouse data mart data mart data mart data mart data mart integrated historical granular sales finance marketing mgmt HR how Inmon/Kimball fit together