1. Data Warehouse Overview September 28, 2012 presented by Terry Bilskie
Set Up:
Computer with PowerPoint or recent version of IE and network.
Projector
Objective: Clear, informative, Consistent message across the institution.
This update is particular to SIS to Banner Student migration.

2. Presentation Objectives:
Data Warehouse Overview
Definition
Benefits & Considerations
Terminology
Architecture
Information Access Maturity
Roadmap to a more Data Driven Institution
In essence, provide an overview of what a data warehouse is, where it fits in the overall information access maturity process, status of where we (VU) is and state what our current strategy has been to date. Once this committee better qualifies what our immediate assessment data needs are and with respect to assessment and future needs are, we’ll need to assess whether our existing approach of “build it as requested” is the appropriate strategy or whether we need to “buy” solution/s to mature us sooner rather than later.

3. Data Warehouse, isn’t 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.
What is it ?, as you can see it is merely a collection of data,,,,,,nothing more.

5. Data Warehouse is not:
• A single physical piece of hardware or a software product.
• A single project with an end
• 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 data source
• A combination of software and hardware

7. Subject-oriented
Data warehouse is organized around subjects such as sales,product,customer.
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
Data
Warehouse
Legacy
System
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
Relieves the development burden on IT
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
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 ?
Concepts
Characteristics
Logical & Physical
Components

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

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

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 to Knowledge Process

24. How a Data Warehouse fits within our overall Data Goverenance

25. Current Strategy / Approach

26. Data Access Delivery Mechanisms
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.

27. 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

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

29. Roadmap to Data Driven Institution

30. Data Driven Framework
Pillars of Success

31. Questions and Answers Data Warehouse Concepts
Summary: Data 2 Information Process is a journey not a destination, thus is incremental.
Next Step: Identification of our immediate needs and “best guess” what our future needs will be.
Qualify Student Profile Data needed ?
Qualify what will it take to get to a more data driven insitution ? Build vs. Buy, go shopping Demos