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Data quality has a significant impact on corporate results. In addition to the fact that poor quality data is more expensive to manage, a more significant impact comes from the fact that misunderstanding the key subsurface features increases the risk of unwelcome outcomes.
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A color PDF file containing the figures from this chapter can be downloaded from here for private study |
Since the book was written we have identified a number of new items that help illuminate the material in Chapter 15:
 New Item 15.8 | Doing a good job with data quality requires a wide range of concepts |
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New Item 15.7 | When documenting quality rules both the intent and the implementation should be available |
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 New Item 15.6 | ETL article on Data Quality in Oil and Gas | http://www.etlsolutions.com/data-quality-in-the-oil-gas-industry/ |
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New Item 15.5 | Gartner study on measuring the impact of data quality | https://www.data.com/export/sites/data/common/assets/pdf/DS_Gartner.pdf |
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New Item 15.3 | Once data is corrupted it can only be fixed if the system has redundancy |
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New Item 15.2 | A review of data quality dimensions as defined by various authors | http://www.information-management.com/news/dimensions-of-data-quality-under-the-microscope-10024529-1.html |
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New Item 15.1 | A short article about data quality dimensions, and how they relate to fish and ska music |
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In the book Chapter 15 contains references to the following related material:
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Book Ref 41 | Turner, N. Harvey, N. & Husband, Z. (2012) "Successfully selling the case for data quality" presented at the UK DAMA meeting | http://www.damauk.org/RWFilePub.php?&cat=403 |
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Book Ref 42 | Curtis, T. (2009) "Business Rules & Data Quality" presented at PNEC | http://dl.ppdm.org/dl/748 |
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