This document discusses the importance of data governance and quality for business intelligence systems. It notes that 70-80% of BI projects fail due to issues like poor quality data. The life cycle of data is outlined as creation, operational use, analytical use, and destruction. Both business and IT have responsibilities for data as a corporate resource. The document advocates establishing data governance practices like data stewardship, a data dictionary, and master data management to standardize, cleanse, and manage data across the organization. Strong management promotion and treating governance as a cultural change rather than just a program are also recommended.

1. BI: How Can Your High-Performance BI
System Meet Expectations When You
Feed It 85 Octane Data
Making Data Quality Part of the Data Life Cycle
Ray McGlew raymcglew@gmail.com

2. Brought to You By:

3. BI Failure Reasons
Gartner: 70%-80% BI Projects Fail
• Lack of Business Support and Ownership
• Poor Quality Data
• Lack of Requirements
• Scope Creep
• Funding
• Big-Bang Approach

4. Life Cycle of Data
• Creation (usually transactions)
• Operational Use
• Analytical Use
• Destruction

5. Who “Owns” The Data?
• IT responsible for conserving it
– Restrict use according to rules
– Providing access
– Keeping it safe
• Business responsible for managing it
– Create rules for IT to use
– Providing IT with requirements for access
• Bottom line… it is a Corporate Resource

6. Data Concerns
• Privacy
– Credit Cards
– Health Records
• Security
• Accuracy
• Usability
• Availability

7. Regulatory Compliance
• Privacy regulations
• Legal limits on how long you can keep certain
data
• Providing lineage on data used for reporting
– Sarbanes Oxley
– SEC filings

8. Quality Data
• Easiest to clean at the source
• Some methods to “clean” data
– Standardize
– Validate
• Data Cleansing Tools

9. Data Cleansing

10. Data Governance
“Data governance (DG) refers to the overall
management of the
availability, usability, integrity, and security of
the data employed in an enterprise. “

11. Data Governance Facets
• Data Stewardship
• Data Dictionary/Glossary
• Master Data Management
• Strong Management Promotion

12. Data Stewardship
• Splits responsibility for ensuring great data
• Business
– Defines what important data elements are
– Defines the rules for acquiring data
– Looks for cross-organizational uses
• IT
– Responsible for technical methods
– Acquires and maintains tools

13. Data Dictionary
• Platform for spreading the knowledge
• Is used in conjunction with reporting tools
• The more data knowledge is used, the better
it gets
• Can be started using in-house tools
• Starting point for Master Data Management

14. Master Data Management
• Data Governance should drive MDM
• Technology
– Facilitates
– NOT the driver

15. Strong Management Promotion
• Cross-functional at the highest level of the
organization
• Will require funding
• Must break through “It will cost my
department to improve the data quality so
their department can save time “

16. Data Governance and Lifecycle
• Data Creation
– Standard values
– Validation at the source
• Operational use
– Required for some customers and vendors
• Analytical use
– Easier to integrate across systems and groups
• Destruction

17. Data Governance is NOT a Program!
• Culture Change
• Integrated with other activities
– Business Intelligence
– Business Process Re-engineering
– ERP Implementation
– Mergers

18. Data Governance Tips
• Prioritize
– Based on business value
– Based on Pain
– Low Hanging Fruit
• Don’t try to boil the ocean!

19. Resources
DAMA International (www.dama.org)
Enterprise Data World
DAMA Philadelphia (www.dama-phila.org)
Data Governance (www.datagovernance.com)
Data Governance Professionals Org (www.dgpo.org)
Love your data, and stay the course, for it will be
with you long after flashy apps are gone.