Benjamin Dammertz: In our Webtalk A while ago we talked about the different aspects of digital transformation, ie strategy, technology, data and culture. On the last two topics in particular, there were various inquiries about what that actually means in practice and what empirical values you were able to gather. Let's talk about a topic that is currently being discussed intensively: data literacy, can you please define that?
Markus Pfründer: Data literacy is the ability to read, understand and analyze data and to draw knowledge from it. For companies of all types and sizes, it is of vital importance that employees develop and continuously improve precisely this skill. There is statistical evidence that companies with higher data literacy have higher company value.
Benjamin Dammertz: Got it, data literacy is important. But why is it like that?
Markus Pfründer: This is due to the fact that the volume of data, driven by digitization, among other things, is increasing explosively. In most cases, however, companies can only get real value from data if employees are able to generate insights, be it about customers, staff, suppliers or other reference points
Benjamin Dammertz: Can you be more specific, please? What does that mean in corporate reality?
Markus Pfründer: The more complex the issues that are to be solved with data, the more important it is to anchor literacy in the organization. Senior management often underestimates how big the gap is and how difficult it can be to close it. What is important here is a vision, a plan to achieve your goals and continuous training. Of course, not everyone in an organization needs to become a data analyst. However, very specific measures such as the introduction and measurement of North Star KPIs are important, for example. Many teams don't really know what goal they are working towards. It helps to go through a definition process together. And then it's time to measure and improve. And all over again.
Benjamin Dammertz: Finally, the question: What do you recommend to companies, how they can best improve the data literacy of their employees?
Markus Pfründer: Of course, that always has to be seen in the respective company context. In general, however, it can be said that an intelligent combination of several factors is required. These are more formal aspects such as the definition of measurement parameters or roles, the articulation of an understandable target image and probably most importantly, the example and also the demand for the use of data for decision making.
We thank Markus Pfründer for the interview!