1. Data Warehouse Overview September 28, 2012 presented by Terry Bilskie

2. Presentation Objectives: Data Warehouse Overview –Definition –Benefits & Considerations –Terminology –Architecture Information Access Maturity Roadmap to a more Data Driven Institution

3. Data Warehouse, is it clear to you ?

4. Data Warehouse Definition A data warehouse is -subject-oriented, -integrated, -time-variant, -nonvolatile collection of data in support of management’s decision making process.

5. Data Warehouse is not: A single physical piece of hardware or a software product. A single physical piece of hardware or a software product. A single project with an end A single project with an end A single solution or product A single solution or product

6. Data Warehouse is: A necessary component in order to achieve higher end reporting and analysis capability with respect to historical data, current trends, and future projections. A necessary component in order to achieve higher end reporting and analysis capability with respect to historical data, current trends, and future projections. A data source A data source A combination of software and hardware A combination of software and hardware

7. Subject-oriented Data warehouse is organized around subjects such as admissions, particular degree conferred and students. It focuses on modeling and analysis of data for decision makers. Excludes data not useful in decision support process.

8. Integration Data Warehouse is constructed by integrating multiple heterogeneous sources. Data Preprocessing are applied to ensure consistency. RDBMS Legacy System Data Warehouse Flat File Data Processing Data Transformation

9. Time-variant Provides information from historical perspective e.g. past 5-10 years Every key structure contains either implicitly or explicitly an element of time

10. Nonvolatile Data once recorded cannot be updated. Data warehouse requires two operations in data accessing –Initial loading of data –Access of data load access

11. Data Warehouse Benefits Speed up reporting Reduce reporting load on transactional systems Make institutional data more user-friendly and accessible Integrate data from different source systems Enable ‘point-in-time’ analysis and trending over time To help identify and resolve data integrity issues, either in the warehouse itself or in the source systems that collect the data

12. Data Warehouse Benefits Has a subject area orientation Integrates data from multiple, diverse sources Allows for analysis of data over time Adds ad hoc reporting and enquiry Provides analysis capabilities to decision makers Relieves the development burden on IT

13. Data Warehouse Benefits Provides improved performance for complex analytical queries Relieves processing burden on transaction oriented databases Allows for a continuous planning process Converts corporate data into strategic information

14. Data Warehouse Considerations High-level support Identification of reporting needs by subject area and organizational role Bridging the gap between reporting needs and technical specifications Partnerships with central and campus administrative areas Customer support and training

15. Data Warehouse Terminology Data Warehouse –A copy of transaction data specifically structured for querying and reporting Data Mart –A logical subset of the complete data warehouse OLAP (On-Line Analytic Processing) –The activity of querying and presenting text and number data, usually with underlying multidimensional ‘cubes’ of data Dimensional Modeling –A specific discipline for modeling data that is an alternative to entity-relationship (E/R) modeling; usually employed in data warehouses and OLAP systems.

16. Data Warehouse Architecture What makes up a Data Warehouse ? ConceptsCharacteristics Logical & Physical Components

17. Design Mapping Design Mapping Source OLTP Systems Raw Detail No/Minimal History Integrated Scrubbed History Summaries Targeted Specialized (OLAP) Data Characteristics System Monitoring Meta Data Extract Scrub Transform Extract Scrub Transform Central Repository Load Index Aggregation Load Index Aggregation Data Warehouse Architected Data Mart Replication Data Set Distribution Replication Data Set Distribution Access & Analysis Resource Scheduling & Distribution Access & Analysis Resource Scheduling & Distribution End User Workstations A Data Warehouse Is A Component

18. 18 Tiered Architecture Extract Transform Load Refresh Data Sources Operational Databases External Sources Data Warehouse Data Marts Data Storage Tier1: Data Warehouse Server Serve OLAP Engine OLAP Server Tier2: OLAP Server Analysis Query/Reports Data mining Front-End Tools Tier3: Client s

19. Data Warehouse Architecture Data Warehouse server –almost always a relational DBMS,rarely flat files OLAP servers –to support and operate on multi- dimensional data structures Clients –Query and reporting tools –Analysis tools –Data mining tools

20. Data Warehouse from a logical perspective

21. Another look from a logical perspective

22. How it fits into Business Intelligence Viewpoint

23. Data Warehouse from a conceptual perspective A data warehouse is based on a multidimensional data model which views data in the form of a data cube

24. 24 Conceptual Model Student Profile Data View Type of Student Campus At Rsik sum First Time Transfer Returning 1 2 3 4 Vincennes Jasper Indianapolis Out of State ALL

25. Data to Knowledge Process

26. How a Data Warehouse fits within our overall Data Governance

27. Current Strategy / Approach

28. Current Data Access Delivery Mechanisms & Tools Ad-hoc Reporting Access Scheduled and On-Demand Report Generation Using tools such as e~print, discoverer, ms access and excel, jobsub, population selection, argos, etc.

29. Data Driven Framework Pillars of Success

30. Questions and Answers Data Warehouse Concepts