Data quality refers to the tools and processes that result in the creation of correct, complete and valid data that is required to support sound decision-making.


CommentaryThe current premise is that the more data an organization can access and analyze, the greater the insights that can be acted upon – so focus on data, data and more data. This approach loses sight of “NO” Data ...
Industry EventsData Modeling Zone 2013 contains many sessions from fundamental through advanced levels, so you can better capture and communicate data requirements, leading to more successful and sustainable solutions.
BlogCritical topics for data quality practitioners, emphasizing the need to take action
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BlogMaybe the data governance message is similar to a sound only dogs can hear
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FeatureMaintaining the quality of your enterprise’s data starts with preventing bad data in the first place and it requires tools, business processes and people
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ReportA new study analyzes errors related to “default values” which are standardized medication order sets in electronic health records and computerized physician order entry systems
BlogIn theory, a metadata dictionary is a trusted authority for all your data questions, but can it deliver on that promise?
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blogData quality is a journey, not a destination
BlogIs big data another random brick on an already crowded foundataion?
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BlogBig data gurus say that data quality isn't important, but business stakeholders still complain about poor data quality
featureIn the capstone article of this series, we'll look at how the varying definitions of data quality dimensions converge
TrendsThe vast majority of financial institutions do not believe their data technology is very effective, according to a global risk survey from Deloitte

Are you actively evaluating master data management technologies and their ability to scale and support emerging trends around big data, social and mobile?

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Not Applicable 6%

 

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