Data quality is everyone’s business — Managing information quality — Part 2 — Tom Breur, Journal of Direct, Data and Digital Marketing Practice Vol. 11 No. 2

The first of these two papers, discussed how to design data quality into your data warehouse (DWH) as you are building it. This second paper deals with maintaining a high level of quality after the DWH has gone live. Once a business intelligence solution has been put in place, ongoing data quality needs to be ensured. Data quality maintenance is supported by an appropriate governance structure: the allocation of decision rights and procedures. For errors that have arisen in batches, a data quality project is appropriate. For process causes of data non-quality, a data quality program is more appropriate. Since both kinds of errors often occur side by side, and also to ensure both short-term improvement and sustainable success, in practice a combination of these two approaches is often called for. As organizations progress through subsequent stages on their data quality journey, different measures and actions are required. To make ongoing data quality certain, you progress through information about sources of non-quality and associated organizational costs, training and awareness throughout the organization in conjunction with supporting tools and technology, and alignment and accountabilities that make producing quality the default. (10 pages)

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01 Oct 2009
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