In his recent blog post Questions about the “Cost of Poor Data Quality”, David Loshin examined a common characteristic of estimates about the costs of poor data quality, namely that, upon close examination, most of them rely exclusively on anecdotal evidence.
As David noted, the sources of many of the cited estimates used throughout the data management industry can be traced to survey responses or proprietary case studies, which makes it exceedingly difficult to put a tangible number on the cost of poor data quality.
Lacking a tangible estimate for the cost of poor data quality often complicates building the business case for data quality. Although the data quality initiative has the long-term potential of reducing the costs, and mitigating the risks, associated with poor data quality, its upfront costs are very tangible. For example, the short-term increased costs of a data quality initiative can include the purchase of data quality software, and the professional services needed for training and consulting for installation, configuration, application development, testing, and production implementation.
This leads to, as Dylan Jones has blogged, The Data Quality Trainwreck as a Sales Vehicle. “We are often short of success stories about data quality,” explains Henrik Liliendahl Sørensen, “because no one measures what could have happened if we didn’t prevent poor data quality. Instead we can merely present trainwrecks and hope someone learns from these.” For example, in his 2010 blog post Big Time ROI in Identity Resolution, Henrik shares the news story about the carbon trade scam that cost five billion Euros.
However, as Dylan notes, “the problem with using a data quality trainwreck to emphasize the need for data quality management is that, whilst they make fascinating stories, they fail to connect the prospective stakeholder or decision-maker with the type of issues they typically face internally.” As usual, I agree with Dylan, and I usually refer to this challenge as the Cassandra Effect.
In Search of an Anecdotal Antidote for Poor Data Quality
As David stated, “I am very open to have suggestions about actual case studies or reports that provide researchable numbers (that means the numbers are published and can be reviewed) about evaluating the costs of poor data quality. Having access to these types of articles, reports, etc. will enable people like me to refine our approaches to evaluating the value of data quality improvement and helping to truly come up with a model to define clear return on your data quality investment.”
Since their inception, the data management industry and data quality profession have been trying to find ways to sell the business benefits of data quality with tangible evidence.
In other words, we have long been in search of an Anecdotal Antidote for Poor Data Quality.
Do you have an anecdotal antidote? If so, please share it by posting a comment below.