1. Data Quality as a Process not Just an End Result
C. Lwanga Yonke
Data Quality 2011 Asia Pacific Congress
28 – 30 March 2011
Sydney, Australia
Copyright 2007 C. Lwanga Yonke

2. Bio
C. Lwanga Yonke is a seasoned information quality practitioner and
leader. He has successfully designed and implemented projects in
multiple areas, including information quality, data governance,
business intelligence, data warehousing and data architecture. His
initial experience is in petroleum engineering and operations..
An ASQ Certified Quality Engineer, Lwanga earned an MBA from
California State University and holds a BS degree in petroleum
engineering from the University of California at Berkeley.
Lwanga is a founding member of IAIDQ and currently serves as an
Advisor to the IAIDQ Board and as a board member for several other
non-profit organizations. He is a member of the Society of Petroleum
Engineers (SPE), a senior member of the American Society for Quality
(ASQ ), and the recipient of the 2008 SPE Western North America
Regional Management and Information Award.
Copyright © 2011 C. Lwanga Yonke. All rights reserved. 2

3. Session Abstract
Short presentation from Lwanga Yonke, followed by
interactive discussion of topics below and more
• What it means to manage information quality as a process
• Defining information quality management
• Various models for information/data quality process management
• The case for a process approach
• Assigning accountabilities for information quality
• Data cleansing: when is a good time?
Copyright © 2011 C. Lwanga Yonke. All rights reserved. 3

4. Manage Information as a Product
• Product, not by-product
• Traditional product manufacturing is a useful analog to frame
Business Processes
information quality issues
• Activities, events
• Transactions • The needs of analysis and decision-making must dictate the
• Measurements quality of the data we capture
• Data quality is best assured at the source, by first controlling
the business processes and activities that create data.
Transformed/
Transfor-
Summarized
mation Business
Data Information
Process Decisions
Products Analysis &
Raw “Manufacturing”
Data Process
Decision Implementation $$
-making
Information Product Principle
Data is an integral product of our business processes. Work is not
complete until data resulting from the work is collected and captured, as
part of the work process and activities that create or modify it.
Copyright © 2011 C. Lwanga Yonke. All rights reserved. 4

5. The Information Product
Simplified Example - Maintenance Management
Business Processes Business
Data Analysis &
• Activities, events Decisions
Decision Implementation
• Transactions
-Making
$$
• Measurements
•Equipment repair •Equipment histories
•Equipment hierarchies •Root cause failure
•New equipment installation
analysis
•Autonomous maintenance •Equipment classes
•Reliability reviews
•Condition-based •Equipment specifications
•Bad actors reviews
maintenance •Regulatory and other
monitoring data •Mean time
•Predictive maintenance
between failure
•Vibration monitoring •Defects & counter measures analysis
•Equipment Improvement •Corrective action plans •Kaizen events
•Measurement processes •Vibration data •etc.
•etc. •etc.
Copyright © 2011 C. Lwanga Yonke. All rights reserved.

6. What is Information Quality Management?
It’s data
profiling!
It’s data
correction!
It’s MDM!
It’s data
governance!
It’s EIM!
It’s SOA!
Copyright © 2011 C. Lwanga Yonke. All rights reserved. 6

7. What is Information Quality Management?
My Answer
“The total effort to improve the quality of the
information an organization receives,
generates, uses and/or provides to others”
C. Lwanga Yonke
Copyright © 2011 C. Lwanga Yonke. All rights reserved. 7

8. Second-Generation Data Quality Systems
Tom Redman
Data Council
Defines Must
accountabilities advance
via
Data Policy Data Culture
Supports Supports
Deployed
Deployed to
to Underlies
Information everything
Supplier
Management Chain
Management
Responsible
for meeting Responsible for meeting
Customer
Monitor
Needs
conformance Identify
using To “gaps”
better using
meet
A
Leads Quality
Measurement Control platform Improvement
Improvement Quality Planning
to for Planning
Set
targets
for
© 2001 Thomas C. Redman. All rights reserved
Copyright © 2011 C. Lwanga Yonke. All rights reserved.

9. Total Information Quality Management (TIQM)
Larry English
P6
Establish the Information Quality Environment
P4
Improve
Information
P1 P2 P3 Process
Assess Data Quality
Definition & Assess Measure
Information Information Nonquality
Architecture Quality Information P5
Quality Costs
Correct Data
in Source
and
Control
Redundancy
Source: English © 2009 INFORMATION IMPACT International, Inc. All rights reserved.
Copyright © 2011 C. Lwanga Yonke. All rights reserved.

10. The Ten Steps™ Process
Danette McGilvray
3 7
Assess Prevent
Data Future Data
1 Quality Errors
2 6
Define 5 9
Analyze Develop
Business Identify Implement
Information Improvement
Need and Root Causes Controls
Environment Plans
Approach
4
8
Assess
Correct
Business
Current Data
Impact
Errors
10
Communicate Actions and Results
© 2008 Danette McGilvray, Granite Falls Consulting, Inc. All rights reserved.
Copyright © 2011 C. Lwanga Yonke. All rights reserved.

11. Total Data Quality Management (TDQM)
Richard Wang
• Define the information product (IP)
• Measure IP
• Analyze IP
• Improve IP
Source: Fisher et al, 2006. © 2006 MIT Information Quality Program. All rights reserved.
Copyright © 2011 C. Lwanga Yonke. All rights reserved.

12. Managing Information as a Product
Wang’s Four Principles
• Understand information consumers’ needs
• Manage information as the product of a well-defined
information production process
• Manage the life cycle of information products
– Creation, growth, maturity, decline
• Appoint an information product manager to manage
information processes and products
Source: Fisher et al, 2006. © 2006 MIT Information Quality Program. All rights reserved.
Copyright © 2011 C. Lwanga Yonke. All rights reserved.

13. Information Quality Certified Professional (IQCP) Framework
IAIDQ
• Information Quality Strategy and Governance
• Information Quality Environment and Culture
• Information Quality Value and Business Impact
• Information Architecture Quality
• Information Quality Measurement and Improvement
• Sustaining Information Quality
Source: Yonke et al, 2011. © 2011 IAIDQ. All rights reserved.
Copyright © 2011 C. Lwanga Yonke. All rights reserved.

14. Just Like Safety, Information Quality Requires
Constant Vigilance
“The journey of a thousand miles begins with one step”
Lao Tzu
Copyright © 2011 C. Lwanga Yonke. All rights reserved.

15. References
English, L., (2009). Information Quality Applied: Best Practices for Improving Business Information,
Processes and Systems, New York: Wiley & Sons.
Fisher, C., Lauría, E., Chengalur-Smith, S., Wang, R., (2008). Introduction to Information Quality, MITIQ
Press, Boston
McGilvray., D., (2008). Executing Data Quality Projects: Ten Steps to Quality Data and Trusted
Information, Morgan Kaufmann
Redman, T. C., (2001). The Field Guide, Digital Press, Inc., New York, NY
Redman, T. C. (2008). Data Driven: Profiting from Your Most Important Business Asset, Harvard
Business School Press
Yonke, C. L., Walenta, C., Talburt, J.R., (2011). The Job of the Information/Data Quality Professional ,
IAIDQ
Web sites
International Association for Information and Data Quality (IAIDQ)
www.iaidq.org
www.iaidq.org/main/fundamentals-process-mgt-imp.shtml
LinkedIn
www.apac.iaidq.org
www.linkedin.iaidq.org
Copyright © 2011 C. Lwanga Yonke. All rights reserved. 15