In psychology, there’s something known as the Q Test, which asks you to use one of your fingers to trace an upper case letter Q on your forehead. Before reading this blog post any further, please stop and perform the Q Test on your forehead right now.
Essentially, there’s only two ways you can complete the Q Test, which are differentiated by how you trace the tail of the Q. Most people start by tracing a letter O, and then complete the Q by tracing its tail either toward their right eye or toward their left eye.
If you trace the tail of the Q toward your right eye, you’re imagining what a letter Q would look like from your perspective. But if you trace the tail of the Q toward your left eye, you’re imagining what it would look like from the perspective of another person.
Basically, the point of the Q Test is to determine whether or not you have a natural tendency to consider the perspective of others.
Although considering the perspective of others is a positive under different circumstances, if you traced the letter Q with its tail toward your left eye, psychologists say that you failed the Q Test since it reveals a negative — you’re a good liar. The reason why is that you have to be good at considering the perspective of others in order to be good at deceiving them with a believable lie.
So, as I now consider your perspective, dear reader, I bet you’re wondering: What does the Q Test have to do with data quality?
Like truth, beauty, and art, data quality can be said to be in the eyes of the beholder, or when data quality is defined, as it most often is, as fitness for the purpose of use — the eyes of the user. But since most data has both multiple uses and users, data fit for the purpose of one use or user may not be fit for the purpose of other uses and users. However, these multiple perspectives are considered irrelevant from the perspective of an individual user, who just needs quality data fit for the purpose of their own use.
The good news is that when it comes to data quality, most of us pass the Q Test, which means we’re not good liars. The bad news is that since most of us pass the Q Test, we’re often only concerned about our own perspective about data quality, which is why so many organizations struggle to define data quality standards.
At the next discussion about your organization’s data quality standards, try inviting the participants to perform the Q Test.
Related OCDQ Radio Episodes
Clicking on the link will take you to the episode’s blog post:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.”