KENNESAW, Ga. | Dec 8, 2020
Less than two years ago, data literacy was not something I heard many people in the business world talking about. Recently, it is something that comes up in more conversations than not. In this post, I’ll address a few misconceptions about data literacy and then make the case that while it is a challenge, data literacy is actually a great problem to have.
I will start by making clear that the term “data literacy” is being used in the context of a much broader span than just data. Data is certainly part of the equation, but I argue that “data” is not really the key issue organizations are struggling with when they raise the concept of data literacy. For the most part, “data literacy” is used as a proxy for what is really “analytical literacy”, by which I mean the ability to interpret and act upon analytics effectively.
Sure, data is a key component of any analytics generated. But people are not typically having trouble understanding the fundamental underlying data as much as they are having trouble understanding how to make decisions by interpreting and using the various metrics and analytics generated from that data. Note that this means data literacy is not as simple as providing definitions. It requires teaching a set of thought processes and skills that help contextualize analytical results within a real-world setting. It is about enabling people to be competent and comfortable in using data and analytics as part of the course of their jobs.
We often hear data literacy discussed from the consumer side, meaning those who are receiving the analytics and require literacy to make use of those analytics. However, literacy is a two-way street. The definition of literacy includes “the ability to read and write”. We do not just need people to know how to read / interpret analytics. We also need people who know how to write / deliver analytics effectively so that people can read them.
I took part in some terrific discussions led by Valerie Logan earlier this fall. One of her points was that literacy requires a common language and vocabulary that businesspeople and analytics professionals can use to communicate. If someone writes an amazing book in a language I cannot read, then I will get no value from that book. Similarly, without ensuring that the business and analytics teams have a shared language, they risk totally talking past one another. Valerie called establishing this common language an “information as a second language” program. Even as focus is placed on helping businesspeople understand the analytics they are being served, there must also be focus placed on helping those serving the analytics to make them understandable.
You might be wondering how it is that the pain your organization is experiencing with the lack of data literacy a good thing. It is simple! Data literacy is a 2nd order problem that only manifests itself as a problem as you begin to get past some first order problems. In this case, the first order problems involve the availability and cleanliness of data and the ability to effectively analyze that data. In other words, data literacy comes to the forefront when someone has access to data and analytics that they are unable to effectively read and interpret. Until businesspeople have access to a flow of analytical output, a lack of literacy will not be apparent or problematic.
That is not to say that data literacy is not a real problem and that it will not be hard to solve. The point is that as you struggle with this major issue, recognize that it is only an issue because you have solved the first order issues enough that subpar literacy became a problem. It is far better to struggle with instilling the literacy required to make effective use of your analytics and data than it is to struggle with getting the data and analytics to exist in the first place.
It is through success that you get to a point where data literacy becomes an issue. Make sure your organization understands this. To solve the literacy challenge, it
will take a concerted effort to create a second language within your organization
and to get everyone literate in it. It will certainly be hard and frustrating, but
your organization should be proud that it has had enough success to face this next
By Bill Franks | December 8, 2020
Originally published by the International Institute for Analytics