1. Enterprise Data Warehousing— Planning for the Long Haul Vicky Shaffer and Marti Graham April 18, 2005

3. Virginia Tech is…  Located in Blacksburg, VA  The largest in the commonwealth -- 8 colleges and graduate school -- 60 bachelor’s degree programs -- 110 master’s and doctoral degree programs -- 25,000+ students  Among the Top 50 research institutions in the US

4. Existing Data Marts  Finance– General Ledger, Operating Ledger, Foundation, Accounts Receivable, Proposals  Human Resources– Employee, Job, Position, Job Funding, Payroll, Position Allocation  Alumni– Giving  Student– Undergrad Admissions, Graduate Admissions, Undergrad Recruiting  Parking Services

5. IWA Team consists of…  1 Project Leader  4 Data Warehouse Architects  1 Oracle Data Base Administrator  1 Brio Administrator  1 Applications Analyst/Instructor  1 Training and Metadata Coordinator

6. Presentation Road Map  Managing Growth and Performance  On-going User Support & Education  Strategies and Tools for Maintenance & New Development  Evolution of Data Marts  Questions

7. Managing Growth and Performance  Move to the Storage Area Network (SAN) (high-speed sub-network of storage devices– moving storage to the network) Provides faster backup and recovery Enables us to quickly switch to two-day old backup system Improves query performance and data movement Simplifies storage management (i.e., table spaces, indexes, data

8.  Implement Oracle Partitioning Faster Extraction/Transformation/Loading (ETL) Faster end-user query response Managing Growth and Performance  Parallelization of ETL processes  Change ETL processing from “total refresh” to “incremental changes”  Remove redundancy from ETL processes  Monitor reports and query performance during peak usage

9. On-going User Support & Education  Tool Training combined with Data Training Trainer from Data Warehouse team partners with data expert from Business Area  Monthly Meetings Data Warehouse and Business Area project teams review service requests, future projects, problems Meta Data Business Definitions for tables, views, data elements; Dates recording when data was loaded or last updated

10.  User surveys, training evaluations  Listserve for communicating Data Warehouse status to users  Webjob and Webdist Runs SQR and SAS reports. Runs against the Data Warehouse or ERP Administrative Information System. Has scheduler and “News” feature that alerts users of changes On-going User Support & Education

11.  Ascential DataStage ETL Tool  ETL Process Control Checks Service Request Form Problem Log Strategies and Tools for Maintenance & New Development

12. Strategies and Tools for Maintenance & New Development  ETL Metadata Log ETL Process times Store ETL process references to file names, table names  Include extract date and system date on Data Warehouse records  Store ‘smart keys’ on dimension and fact tables– aids in debugging and can be hidden from end-users in views  Include modification documentation within scripts and code

13. Evolution of Data Marts  Summary tables and snapshots, current views, subsets  Develop Data Marts, plan for Data Warehouse Conformity of dimension data; Unique names for unique data elements; Store facts at detail level for flexibility.

14. ? Questions Vicky Shaffer– shafferv@vt.edu Marti Graham– grahamfa@vt.edu