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
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.
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
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 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. $$ Raw Data Transfor- mation Process Information Products Analysis & Decision -making Business Decisions Implementation “ Manufacturing” Process Transformed/Summarized Data
The Information Product Simplified Example - Maintenance Management Data $$ Analysis & Decision -Making Business Decisions Implementation
Regulatory and other monitoring data
Defects & counter measures
Corrective action plans
Root cause failure analysis
Bad actors reviews
Mean time between failure analysis
New equipment installation
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!
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”
“ 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 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