IDQ Conference 2007

The Data Quality Cycle 2.0

Ken Karacsony Evan Terry Ken Karacsony
Enterprise Data Architect
Toyota Motor Sales, Inc.

Evan Terry
Lead Consultant
Clegg Company, Inc.


Thursday 9:45am - 10:45am

Level: All Levels

The success of any data quality effort depends on having a well-defined, repeatable data quality process that produces consistent, value-added deliverables. The Data Quality Cycle 2.0 is a straight-forward methodology for implementing, practicing and promoting data quality. In this presentation, we will describe the four phases of the Data Quality Cycle and the process steps associated with each phase. The phases include:

  • Discover – The first step in the process is to discover which data quality problems to tackle, since different data quality errors are not always equally important. This part of the presentation focuses the data quality efforts of the organization, and provides key practices and deliverables to help discover data quality problems and determine which ones should be addressed first.
  • Define and Measure – Regardless of whether you are verifying product quality or data quality, the definition and measurement of quality is essential. This data quality phase answers the fundamental question: “What are we going to measure?” This part of the presentation will include the process steps required to choose between and conduct measurements of data quality, and the steps used to set effective data quality standards.
  • Remediate – Once discovery and measurement have determined the data quality problems to address, the remediation phase guides the immediate correction/scrubbing activities and the establishment of a foundation for prevention of data quality problems.
  • Prevent – In order to establish a proactive data quality program, prevention activities must be undertaken. This phase focuses on the steps necessary to prevent data quality problems from occurring (or reoccurring), rather than reacting to problems once they have occurred, and how these steps are made more effective when coupled with effective organizational governance.
Each of these phases will be illustrated through the use of real-world examples, and will include a discussion of the process outputs that will communicate the results to others within the each presentation attendee’s organization. By the end of the presentation, attendees will have a solid foundation for understanding how to implement an intelligent, end-to-end data quality process.



Speaker Bios

Ken Karacsony is a Data Architect working at Toyota Motor Sales, USA, Inc. He is a noted author with cover stories featured in DM Review and ComputerWorld magazines dealing with data quality, architecture, and IT management and governance. He presented at the international DAMA conference on data quality and meta data practices and was a presenter at the 2006 IAIDQ conference.

Evan Terry is a lead consultant working at the Clegg Company, specializing in data management. His past and current clients include the State of Idaho, Albertsons, American Honda Motors and Toyota Motor Sales, USA, Inc. He is the co-author of Beginning Relational Data Modeling and was a presenter at the 2006 IAIDQ conference.

Close Window