Here is one of my key mantras: just because you have access to great software, does not automatically make you an expert. I was reminded of this again just this past week, when I saw one of my colleagues attempting to use Photoshop. While he was trying to do something relatively simple, his final result fell short. Just because he had the best-in-class tool, did not make him a graphic designer and knowledgeable in color theory and other design methodologies which would have helped him elevate his output.
What does this have to do with decision makers? Today, the volume of data available to decision makers is simply massive. However, in spite of all this great data, and access to the best analytics tools, poor decisions continue to be made. Why? Here’s the plain truth: investments in analytics are worthless if employees cannot incorporate that information accurately into their decision making process. In fact, they could actually be harmful. There are too many stories of companies that made poor business decisions based off incorrect interpretations of the data, or because of incomplete data sets. Just like my colleague who was using Photoshop but did not have knowledge in color theory, these decision makers may know how to use a tool, but the tool is the means, not the end.
So, what’s the solution? Companies and individuals who want to make better use of their data and information available to them should increase their data literacy skills. Training should not just focus on the tools that are used, but how the tool can be used to make decisions. This training should not just be targeted at the authors or the builders of the visualizations or applications, but also at the consumers. That’s because these consumers are the ones making the decisions.
Think of it this way: in order to either properly set up or use a visualization to ask and answer your analytical questions, you need to be able to describe the visualization, data and the potential trends it shows and make interpretations. This requires learning a wide range of skills like analytic techniques, basic statistical literacy, and other data literacy topics.
For example, decision makers should understand the difference between correlation and causation and how to determine which is which. Many times we hear decision makers say that two factors are correlated, so “let’s proceed”. However, that does not mean there is causation. Misinterpreting correlation as causation can be extremely dangerous and costly for companies.
Other decision makers may view something and think it is statistically significant, but it really is not. This can also lead to poor decisions.
These are just two of many possible examples where it is important decision makers have some data literacy and ability to correctly interpret their data and visualizations.
To help decision makers everywhere make smarter and more informed decisions, our learning platform Qlik Continuous Classroom now includes a free learning pathway for Business Users. This pathway, along with the Business Analyst and Data Architect pathways, includes not only learning modules on using Qlik products, but also on analytic concepts. Take a look at our first sets of modules using the link above, and check back often for our frequent addition of new modules.