1. DATAWAREHOUSE What? Why? How?

2. Contents  What is Datawarehouse ?  What is Datamart ?  DSS Environments (Big Picture)  Steps, Problems, Cautions  Does it worth to pay (or struggle) that much?  TURKCELL (now)  TURKCELL (future)  How to succeed ?

3. What is Datawarehouse ?  Company Data  Company Knowledge  Accurate  Consistent  Integrated  Time varianted  Clear  Sharable  Easy and Standardized Access  Historical  Derived, Aggregated, Summarized  Company based, not departmental

4. What is Datamart ?  Business focused data pools  Departmental  Can be dependent or independent to/from DWs  Has a clear mission  Summarized data  Generally has less granularity

7. Does it worth that much?  Data is not understood  Users disagree on data definitions  Reports are inconsistent  Users don't trust the reports  Data is "dirty"  Data is not shared or shared reluctantly  Data is not integrated  Historical data is not available  No View of Company’s Big Picture  Hard or incorrect executive view and reporting

8. How to succeed ?  Ensure political support  Make the right DW data modeling  Ensure user acceptance and productivity  Accelerate your project's return on investment  Be careful on Testing Strategy  Translate the data model into a list of written rules and obtain business approval  Size the database on the basis of proven rules and criteria  Establish naming standards and enforce them  Define source/target mapping at attribute level during logical/physical data modeling  Train key users in data modeling. Invest time in getting users (IT users as well as end users) to understand and to own the data model.  Do data modeling with real users not developers

9. Thanks Hakan YÜKSEL TO-Performance&Quality-CN-MSIP