Retired from Procter & Gamble after 27 years. Now consulting extensively. Background in mathematics, working on a wide variety of modeling, supply chain, analysis, expertise, business intelligence and social media applications.
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In KDNuggets, a good overview of quality issues, some related to the 'Big' part, but generally applicable to any kind of non-trivial data. I have found that data quality errors are mostly dealt with afterwards in practice. If discovered at all. Worth a closer look.
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