C. lwanga Yonke

Copyright 2007 C. Lwanga Yonke

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 © 2011 C. Lwanga Yonke. All rights reserved. 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.


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?


Manage Information as a Product

$$Raw Data

Transfor-mation

Process Information Products Analysis &

Decision-making

Business Decisions

Implementation“Manufacturing”

Process

Transformed/Summarized Data

Business Processes• Activities, events• Transactions• Measurements

•Product, not by-product

•Traditional product manufacturing is a useful analog to frame information quality issues

•The needs of analysis and decision-making must dictate the 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.

Information Product PrincipleData 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.

Data

$$Analysis &

Decision-Making

Business Decisions

Implementation

Business Processes• Activities, events• Transactions• Measurements

•Equipment histories

•Equipment hierarchies

•Equipment classes

•Equipment specifications

•Regulatory and other monitoring data

•Defects & counter measures

•Corrective action plans

•Vibration data

•etc.

•Root cause failure analysis

•Reliability reviews

•Bad actors reviews

•Mean time between failure analysis

•Kaizen events

•etc.

•Equipment repair

•New equipment installation

•Autonomous maintenance

•Condition-based maintenance

•Predictive maintenance

•Vibration monitoring

•Equipment Improvement

•Measurement processes

•etc.

The Information Product Simplified Example - Maintenance Management


What is Information Quality Management?

6

It’s MDM!

It’s data correctio

n!

It’s data profiling!

It’s SOA!It’s EIM!

It’s data governanc

e!


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

7


Data Council

Set targets for

Improvement QualityPlanning

Definesaccountabilities via

Deployed to

Supports

Deployed to

Mustadvance

Supports

Underlieseverything

Responsible for meetingResponsible for meeting

Monitorconformance using

Leads to

Tobetter meet

Identify “gaps” using

Aplatform for

Data Policy

Control

CustomerNeeds

SupplierManagement

InformationChain

Management

Data Culture

Second-Generation Data Quality SystemsTom Redman

© 2001 Thomas C. Redman. All rights reserved

Quality PlanningImprovementMeasurement


Total Information Quality Management (TIQM)Larry English

P6

Establish the Information Quality Environment

P4

ImproveInformation

Process Quality

P5

Correct Data in Source

and Control

Redundancy

P3

MeasureNonqualityInformation

Costs

P2

AssessInformation

Quality

P1Assess DataDefinition & InformationArchitecture

Quality

Source: English © 2009 INFORMATION IMPACT International, Inc. All rights reserved.


The Ten Steps™ Process Danette McGilvray

8Correct Current

Data Errors

7Prevent

Future Data Errors1

Define Business Need and Approach

2 Analyze

Information Environment

3Assess Data

Quality

4Assess

Business Impact

5Identify

Root Causes

6Develop

Improvement Plans

9 Implement Controls

10Communicate Actions and Results

© 2008 Danette McGilvray, Granite Falls Consulting, Inc. All rights reserved.


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.


Managing Information as a ProductWang’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.


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.


“The journey of a thousand miles begins with one step” Lao Tzu

Just Like Safety, Information Quality Requires Constant Vigilance


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 sitesInternational Association for Information and Data Quality (IAIDQ)

www.iaidq.org www.iaidq.org/main/fundamentals-process-mgt-imp.shtml

LinkedInwww.apac.iaidq.org www.linkedin.iaidq.org

References

Documents

C. lwanga Yonke