The sales pitches for data quality solutions often suffer from Chicken Little Syndrome, when vendors and consultants, instead of trying to sell the business benefits of data quality, focus too much on the negative aspects of not investing in data quality, and try scaring people into prioritizing data quality initiatives by exclaiming “your company is failing because your data quality is bad!”
The Chicken Littles of Data Quality use sound bites like “data quality problems cost businesses more than $600 billion a year!” or “poor data quality costs organizations 35% of their revenue!” However, the most common characteristic of these fear mongering estimates about the costs of poor data quality is that, upon closer examination, most of them either rely on anecdotal evidence, or hide behind the curtain of an allegedly proprietary case study, the details of which conveniently can’t be publicly disclosed.
Lacking a tangible estimate for the cost of poor data quality often complicates building the business case for data quality. Even though a data quality initiative has the long-term potential of reducing the costs, and mitigating the risks, associated with poor data quality, its initial 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 to support installation, configuration, application development, testing, and production implementation. When considering these short-term costs, and especially when lacking a tangible estimate for the cost of poor data quality, many organizations understandably conclude that it’s less risky to gamble on not investing in a data quality initiative and hope things are just not as bad as Chicken Little claims.
“The sky isn’t falling on us.”
Furthermore, the reason that citing specific examples of poor data quality (e.g., IQTrainwrecks.com) also doesn’t work very well is not just because of the lack of a verifiable estimate for the associated business costs. Another significant contributing factor is that people naturally dismiss the possibility that something bad that happened to someone else could also happen to them.
So, when Chicken Little undertakes a journey to tell the CEO that the organization is coming to an end due to poor data quality, exclaiming that “the sky is falling!” while citing one of those data quality disaster stories that befell another organization, should we really be surprised when the CEO looks up, scratches their head, and declares that “the sky isn’t falling on us.”
Sometimes, denying the existence of data quality issues is a natural self-defense mechanism for the people responsible for the business processes and technology surrounding data since nobody wants to be blamed for causing, or failing to fix, data quality issues. Other times, people suffer from the illusion-of-quality effect caused by the dark side of data cleansing. In other words, they don’t believe that data quality issues occur very often because the data made available to end users in dashboards and reports often passes through many processes that cleanse or otherwise sanitize the data before it reaches them.
Can We Stop Playing Chicken with Data Quality?
Most of the time, advocating for data quality feels like we are playing chicken with executive sponsors and business stakeholders, as if we were driving toward them at full speed on a collision course, armed with fear mongering and disaster stories, hoping that they swerve in the direction of approving a data quality initiative. But there has to be a better way to advocate for data quality other than constantly exclaiming that “the sky is falling!” (Don’t cry fowl — I realize that I just mixed my chicken metaphors.)
I welcome your suggestions (chicken-metaphor-based or otherwise) by inviting you to post a comment below.
This post originally appeared at OCDQ Blog.
Unlike a product, data has no intrinsic quality metrics. Data quality cannot be measured using physical measurements such as weight, size and volume. Most data quality problems can be traced back to three primary causes: people, policies and processes.
When a data error occurs, the sky has already fallen. Data quality is nothing more than the result of flawed or purposeful policies, processes (also algorithms - Just ask Jamie Dimon) and people. Improve the three P's and you will improve the data quality.
Excellent point about fear mongering poor data quality estimates being themselves examples of poor data quality.
And I agree with you about improving data quality by improving The Three P's (People, Policies, Processes).
Best Regards,
Jim