In his recent Harvard Business Review blog post Break the Bad Data Habit, Tom Redman cautioned against correcting data quality issues without providing feedback to where the data originated. “At a minimum,” Redman explained, “others using the erred data may not spot the error. There is no telling where it might turn up or who might be victimized.” And correcting bad data without providing feedback to its source also denies the organization an opportunity to get to the bottom of the problem.
“And failure to provide feedback,” Redman continued, “is but the proximate cause. The deeper root issue is misplaced accountability — or failure to recognize that accountability for data is needed at all. People and departments must continue to seek out and correct errors. They must also provide feedback and communicate requirements to their data sources.”
In his blog post The Secret to an Effective Data Quality Feedback Loop, Dylan Jones responded to Redman’s blog post with some excellent insights regarding data quality feedback loops and how they can help improve your data quality initiatives.
I definitely agree with Redman and Jones about the need for feedback loops, but I have found, more often than not, that no feedback at all is provided on data quality issues because of the assumption that data quality is someone else’s responsibility.
This general lack of accountability for data quality issues is similar to what is known in psychology as the Bystander Effect, which refers to people often not offering assistance to the victim in an emergency situation when other people are present. Apparently, the mere presence of other bystanders greatly decreases intervention, and the greater the number of bystanders, the less likely it is that any one of them will help. Psychologists believe that the reason this happens is that as the number of bystanders increases, any given bystander is less likely to interpret the incident as a problem, and less likely to assume responsibility for taking action.
In my experience, the most common reason that data quality issues are often neither reported nor corrected is that most people throughout the enterprise act like data quality bystanders, making them less likely to interpret bad data as a problem or, at the very least, not their responsibility. But the enterprise’s data quality is perhaps most negatively affected by this bystander effect, which may make it the worst bad data habit that the enterprise needs to break.
Related OCDQ Radio Episodes
Clicking on the link will take you to the episode’s blog post:
- Data Driven — Guest Tom Redman (aka the “Data Doc”) discusses concepts from one of my favorite data quality books, which is his most recent book: Data Driven: Profiting from Your Most Important Business Asset.
- Organizing for Data Quality — Guest Tom Redman (aka the “Data Doc”) discusses how your organization should approach data quality, including his call to action for your role in the data revolution.
- The Johari Window of Data Quality — Guest Martin Doyle discusses helping people better understand their data and assess its business impacts, not just the negative impacts of bad data quality, but also the positive impacts of good data quality.
- Redefining Data Quality — Guest Peter Perera discusses his proposed redefinition of data quality, as well as his perspective on the relationship of data quality to master data management and data governance.
- The Blue Box of Information Quality — Guest Daragh O Brien on why Information Quality is bigger on the inside, using stories as an analytical tool and change management technique, and why we must never forget that “people are cool.”
- Studying Data Quality — Guest Gordon Hamilton discusses the key concepts from recommended data quality books, including those which he has implemented in his career as a data quality practitioner.