7 Steps to Scalable Data Quality

Data quality does not happen by chance or sheer luck. Rather, data quality requires an ongoing plan that addresses current weak points in your data, delivers the desired results, and can be accomplished with available resources.   Building a robust data quality plan also should be forward-thinking. It should anticipate future issues, be flexible enough to grow with your organization…

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Uncomfortable Questions to Ask Your Data Quality Vendor – Answered

A recent blog post in the Data Quality Pro, "10 (Uncomfortable) Questions for Your Data Quality Vendor" - caught our attention. The author, Dylan Jones, lists questions you should consider asking a potential data quality software/solution vendor.   From a vendor perspective, we thought the questions were right on the money. Questions pertaining to future innovations, long-term product strategies, customer…

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The Cost of Poor Data Quality in a Global Economy

Once a number gets into public circulation, sometimes it never changes no matter what happens. How many people in America don't have health insurance? For about 10 years, the number bandied about has been around 45 million. (Factcheck.org 2009)    The data quality arena has its own 10-year-old, commonly accepted number--the cost of poor data quality on American businesses. The…

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Is the Data Quality Glass Half Empty …or Full?

Jim Harris, a well-known industry thought leader on all-things data quality, brought up a great point in his recent blog entry, "Why Isn't Our Data Quality Worse?" His point - instead of asking ourselves, why isn't our data quality better, we should also ponder, why isn't our data quality worse? It's an interesting point and a different take on determining…

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