Abstract
Projects and programs are two fundamental ways of putting data quality into practice. A data quality (DQ) project includes a plan of work with clear beginning and end points and specific deliverables and uses data quality activities, methods, tools, and techniques to address a particular business issue. A data quality program, on the other hand, often spearheaded by an initial project, ensures that data quality continues to be put into practice over the long term. This chapter focuses on the components necessary for successful data quality projects and programs and introduces various frameworks to illustrate these components, including the Ten Steps to Quality Data and Trusted InformationTM methodology (Ten StepsTM). A discussion of two companies—one housing a mature data quality program, the other a more recent “DQ start-up” initiative—shows two examples of how data quality components and frameworks were applied to meet their organizations’ specific needs, environments, and cultures. Readers should come away from the chapter understanding the foundation behind the execution of data quality projects, the development of data quality programs, and generate ideas for incorporating data quality work into their own organization.
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Notes
- 1.
I may use the words business or company when referring to an organization. Realize that everything said here applies to any type of organization—for profits, nonprofits, government, education, and healthcare because all depend on information to succeed.
- 2.
See Fig. 1, RRISCC refers to broad-impact components, which are additional factors that affect information quality (Requirements and Constraints, Responsibility, Improvement and Prevention, Structure and Meaning, Communication, Change). You can lower your risk of poor data quality by ensuring the components have been appropriately addressed. If they are not addressed you increase the risk of having poor quality data.
References
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Acknowledgements
Thanks to those from Company A and Company B—real companies with real people who shared their experiences while choosing to remain anonymous. Thanks to Lisa Jones for her editorial help and Lori Silverman for her content review and suggestions. Special thanks to Shazia Sadiq for her vision for this book and inviting me to contribute.
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© 2013 Springer-Verlag Berlin Heidelberg
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McGilvray, D. (2013). Data Quality Projects and Programs. In: Sadiq, S. (eds) Handbook of Data Quality. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36257-6_3
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DOI: https://doi.org/10.1007/978-3-642-36257-6_3
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