About a year back, one fine morning at the office I was called to the room of my business director. Typically, such meetings are spent discussing some high-level strategic issues in the business. Well, this one was no different!
Director — What’s your take on data quality in our business?
Me — Well, there are certainly various gaps in how we collect, maintain, and consume data in our business. Given the size of our business, it’s quite challenging to ensure all the gaps at are sealed in the right way.
Director — What do you mean in the right way? My focus at this moment is to ensure that quality leakages can be prevented at all costs. You see, the business is bleeding due to such data quality issues.
Me — The problem here is not one dimensional. It has multiple dimensions. Something we usually term as “Wicked Problems”.
Director — (After a brief pause) What do you mean by “Wicked Problems”?
What is a Wicked Problem?
For those of you who are new to this term, let me provide a quick overview. It was Horst Rittel, a design theorist who coined this term in 1960s. According to Rittel, wicked problems are such complex and multi-dimensional problems often found in the areas of social and cultural aspects. Classic examples are Global Warming, Terrorism, Poverty, etc.
Now Rittel attributed 10 characteristics that can identify wicked problems. For a better understanding, let me draw a comparison between ordinary and wicked problems.
When can Data Issues become Wicked?
Organizations are quite rigorous about the amount of data they capture. Given “Data is the new oil”, organizations try to harness it’s energy as much as possible. With Great Power Comes Great Responsibility. This is no different for data. While data can provide immense benefits, mismanagement of it can result in chaos which can soon turn into a “Wicked Problem”. Let’s see how.
Now as can be seen the various groups are interdependent and each group has many stakeholders. With the above structure commonly followed in most organizations, let’s analyze if a data issue can be wicked.
Based on experiences with several data projects across mid-sized and large organizations, here’s my assessment on how data quality fares on the “Wicked Problem” parameters. Here a high score indicates that Data Quality problems score high on that particular parameter (i.e. more wicked!)
Obviously, the above assessment would vary for smaller organizations where the volume of data has not reached to that significant level to pose any critical threat to the business.
Data Quality issues can be quite significant and can lead to problems falling under the “wicked” category. As a result, it’s not surprising to see that organizations are spending huge bucks to fix their data to ensure that they can solve such problems in the budding stage rather than waiting for it to turn itself into a “wicked” and big monster.
I would like to hear about your experiences with Data Quality issues. So, feel free to comment below.
