The Costs of Data Quality Failure


The first response that I hear when I start to talk about “Big Data” and information sharing is that we don’t have enough people, time and resources to slow down to look through all of that information and make sense out of it.  It’s just too daunting of a task.

As I start to look more and more into “BigData” and see the role of understanding what is going on within billions of records, I have really begun to understand that we don’t have time NOT to engage in this discussion.  This point was driven home extremely well in a recent blog post entitled “The Costs of Data Quality Failure.”  In this post, the following statement should be a wakeup to everyone who thinks that this isn’t important.

“A recent report from Artemis Ventures indicated that poor data quality costs the United States economy roughly $3.1 trillion per year. To provide some perspective on this unimaginably large figure, that’s twice the size of the US Federal deficit. An estimate from the US Insurance Data Management Association puts the cost of poor quality data at 15% to 20% of corporations’ operating revenue.”

Can you live with your organization, agency or company losing 15-20% every year? Put another way, how effective are you really when faced with 15-20% cuts each and every year. (You might want to look at a post from November entitled “Surviving the Coming 50 Percent Budget Cuts“)

You can choose to ignore the issue but it is like treating any other financial or systemic problems… the longer it goes unaddressed the more challenging the fix is later on.

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2 comments on “The Costs of Data Quality Failure

  1. At Match2Lists we recommend continuous data quality development – data is changing every second, so a fast and accurate data quality solution is vital along with an adaptable DQ strategy. The only way to reduce the losses mentioned is to improve the quality of the data reaching the end user. Thanks for mentioning our blog post!

    • I couldn’t agree more. Some people don’t want feedback because of the headaches they can bring. But negative feedback and areas for improvement drive your products and data to improve. Better data and improved understanding of how pieces fit together leads to more effective business processes. Thanks again for your post. It helped to reference existing work to show context. Thanks again Sophie!

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