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- W987776557 abstract "This concluding chapter brings together the methods and processes that were discussed in this book, augmented with some of the operational details needed in building a successful data quality practice. Data quality is not an unclear concept but something that can be quantified, measured, and improved, all with a strict focus on return on investment. The first step in building a successful data quality practice is problem recognition. The other steps involved in this process include management support and the data ownership policy, data quality awareness, mapping the information chain, data quality scorecard, current state assessment, requirements assessment, project selection (choosing a data quality problem), team selection, defining the metadata model, defining the data quality rules, archaeology/data mining, supplier management, executing the solution and measure improvement. The success of any small project only contributes to the greater success of the entire program. Therefore, each small success should be used as leverage with the senior-level sponsors to gain access to bigger and better problems. The goal of becoming an organization that leverages its data resource into a source of enterprise knowledge can be achieved but not without a firm base of high-quality data." @default.
- W987776557 created "2016-06-24" @default.
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- W987776557 date "2001-01-01" @default.
- W987776557 modified "2023-09-25" @default.
- W987776557 title "Building the data quality practice" @default.
- W987776557 doi "https://doi.org/10.1016/b978-012455840-3.50018-2" @default.
- W987776557 hasPublicationYear "2001" @default.
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