C. lwanga Yonke


Published on

  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide
  • DQ Asia Pacific 2008 Conference Sydney, Australia 29 April – 1 May 2008
  • Adapted from the popular story about the blind men asked to describe an elephant.
  • DQ Asia Pacific 2008 Conference Sydney, Australia 29 April – 1 May 2008 TQdM® is not a program; it is a value system , mind set , and habit of continuous improvement of: 1. Application and data development processes 2. Business processes By integrating quality management beliefs , principles and methods into the culture
  • DQ Asia Pacific 2008 Conference Sydney, Australia 29 April – 1 May 2008
  • C. lwanga Yonke

    1. 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. 2. Bio <ul><li>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. </li></ul>
    3. 3. Session Abstract <ul><li>Short presentation from Lwanga Yonke, followed by interactive discussion of topics below and more </li></ul><ul><li>What it means to manage information quality as a process </li></ul><ul><li>Defining information quality management </li></ul><ul><li>Various models for information/data quality process management </li></ul><ul><li>The case for a process approach </li></ul><ul><li>Assigning accountabilities for information quality </li></ul><ul><li>Data cleansing: when is a good time? </li></ul>
    4. 4. Manage Information as a Product <ul><li>Product, not by-product </li></ul><ul><li>Traditional product manufacturing is a useful analog to frame information quality issues </li></ul><ul><li>The needs of analysis and decision-making must dictate the quality of the data we capture </li></ul><ul><li>Data quality is best assured at the source, by first controlling the business processes and activities that create data. </li></ul>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. $$ Raw Data Transfor- mation Process Information Products Analysis & Decision -making Business Decisions Implementation “ Manufacturing” Process Transformed/Summarized Data <ul><li>Business Processes </li></ul><ul><li>Activities, events </li></ul><ul><li>Transactions </li></ul><ul><li>Measurements </li></ul>
    5. 5. The Information Product Simplified Example - Maintenance Management Data $$ Analysis & Decision -Making Business Decisions Implementation <ul><li>Business Processes </li></ul><ul><li>Activities, events </li></ul><ul><li>Transactions </li></ul><ul><li>Measurements </li></ul><ul><li>Equipment histories </li></ul><ul><li>Equipment hierarchies </li></ul><ul><li>Equipment classes </li></ul><ul><li>Equipment specifications </li></ul><ul><li>Regulatory and other monitoring data </li></ul><ul><li>Defects & counter measures </li></ul><ul><li>Corrective action plans </li></ul><ul><li>Vibration data </li></ul><ul><li>etc. </li></ul><ul><li>Root cause failure analysis </li></ul><ul><li>Reliability reviews </li></ul><ul><li>Bad actors reviews </li></ul><ul><li>Mean time between failure analysis </li></ul><ul><li>Kaizen events </li></ul><ul><li>etc. </li></ul><ul><li>Equipment repair </li></ul><ul><li>New equipment installation </li></ul><ul><li>Autonomous maintenance </li></ul><ul><li>Condition-based maintenance </li></ul><ul><li>Predictive maintenance </li></ul><ul><li>Vibration monitoring </li></ul><ul><li>Equipment Improvement </li></ul><ul><li>Measurement processes </li></ul><ul><li>etc. </li></ul>
    6. 6. What is Information Quality Management? It’s MDM! It’s data correction! It’s data profiling! It’s SOA! It’s EIM! It’s data governance!
    7. 7. What is Information Quality Management? My Answer <ul><li>“ The total effort to improve the quality of the information an organization receives, generates, uses and/or provides to others” </li></ul><ul><li>C. Lwanga Yonke </li></ul>
    8. 8. Data Council Set targets for Improvement Quality Planning Defines accountabilities via Deployed to Supports Deployed to Must advance Supports Underlies everything Responsible for meeting Responsible for meeting Monitor conformance using Leads to To better meet Identify “ gaps” using A platform for Data Policy Control Customer Needs Supplier Management Information Chain Management Data Culture Second-Generation Data Quality Systems Tom Redman © 2001 Thomas C. Redman. All rights reserved Quality Planning Improvement Measurement
    9. 9. Total Information Quality Management (TIQM) Larry English P6 Establish the Information Quality Environment P5 Correct Data in Source and Control Redundancy P3 Measure Nonquality Information Costs P2 Assess Information Quality P1 Assess Data Definition & Information Architecture Quality Source: English © 2009 INFORMATION IMPACT International, Inc. All rights reserved. P4 Improve Information Process Quality
    10. 10. The Ten Steps™ Process Danette McGilvray 8 Correct Current Data Errors 7 Prevent Future Data Errors 1 Define Business Need and Approach 2 Analyze Information Environment 3 Assess Data Quality 4 Assess Business Impact 5 Identify Root Causes 6 Develop Improvement Plans 9 Implement Controls 10 Communicate Actions and Results © 2008 Danette McGilvray, Granite Falls Consulting, Inc. All rights reserved.
    11. 11. Total Data Quality Management (TDQM) Richard Wang <ul><li>Define the information product (IP) </li></ul><ul><li>Measure IP </li></ul><ul><li>Analyze IP </li></ul><ul><li>Improve IP </li></ul>Source: Fisher et al , 2006. © 2006 MIT Information Quality Program . All rights reserved.
    12. 12. Managing Information as a Product Wang’s Four Principles <ul><li>Understand information consumers’ needs </li></ul><ul><li>Manage information as the product of a well-defined information production process </li></ul><ul><li>Manage the life cycle of information products </li></ul><ul><ul><li>Creation, growth, maturity, decline </li></ul></ul><ul><li>Appoint an information product manager to manage information processes and products </li></ul>Source: Fisher et al , 2006. © 2006 MIT Information Quality Program . All rights reserved.
    13. 13. Information Quality Certified Professional (IQCP) Framework IAIDQ <ul><li>Information Quality Strategy and Governance </li></ul><ul><li>Information Quality Environment and Culture </li></ul><ul><li>Information Quality Value and Business Impact </li></ul><ul><li>Information Architecture Quality </li></ul><ul><li>Information Quality Measurement and Improvement </li></ul><ul><li>Sustaining Information Quality </li></ul>Source: Yonke et al , 2011. © 2011 IAIDQ . All rights reserved.
    14. 14. <ul><li>“ The journey of a thousand miles begins with one step” Lao Tzu </li></ul>Just Like Safety, Information Quality Requires Constant Vigilance
    15. 15. 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 References