As part of the LEARNTEC Congress, you will be talking about gamified learning in the context of data. How do you define data culture and data literacy in this framework?

Isabelle Kranabetter: Data literacy and data culture belong together. Without the necessary culture - i.e. the general awareness of the relevance of data and practices for maintaining and using it as a corporate asset - individual data-literate employees cannot use their skills.

Another description of data culture that I like is very clear: An organization has a strong data culture when the opinion of the highest-paid person is not the only deciding factor: convincing data arguments made by an intern can influence a decision.

Data literacy in itself is role dependent. In leadership roles, it should not only contribute to strategic decision making based on habit or experience, but also by using data to support it. In very mature organizations, for example, business cases are commonly not only calculated on a euro basis, but also to incorporate data potential: For example, when data is viewed as a corporate asset, a potential partnership that provides access to strategic data can gain a different significance.

For employees, data literacy also means different things depending on their role. For example, if my task is only to collect data, I should at least have a basic awareness of data quality. A lot of data in companies is incorrect or "cryptic" - departments work in silos and have a language for their data that is only understandable to them. Well-paid data professionals invest up to 80% of their working time on data cleansing before they can create real value with data analysis.

What goals can companies derive or target from this?

Isabelle Kranabetter: If a company wants to invest in data skills and seeks to generate genuine added value for the business, a program also has to focus on cultural development. Learning, practicing, and applying together with colleagues is really crucial. In gamified simulations, participants learn about data concepts or use cases, but they also play a specific case through communally to see how they can better achieve their goals with data: for example, how to attract more new customers through an individualized marketing campaign for different target groups.

And it's about how they can work well together to achieve this. The sales department needs information on how the respective customers found out about the offer. Marketing can then decide which channel can be used to target which client (social media, other advertising media, etc.). If this information is not relevant for sales itself, the employees may not have recorded it either - and marketing cannot set up such a campaign for the time being.

We therefore need to work together across departmental boundaries and reach agreements on who collects what data and in what form we need it. This requires a more global perspective, which a simulation can provide, because we can experience the dynamics of action or the consequences of bad data in the company in a very impactful way.

What types of companies should consider this approach? What are the basic requirements?

Isabelle Kranabetter: In principle, in a data context, simulations or gamified learning are suitable for all companies. Even if advanced data analysis isn’t used, attention has to be paid to data protection and security in order to avoid losing time through the need to clean data for simple reports.

However, it always makes sense for managers to educate themselves first, as they need to answer the question of what is to be achieved with the data? A data strategy is helpful in any company. It is then also clear which stakeholders, skills, data and processes, etc. should be targeted by a training and organizational development programme in order to support the business objectives.

For management, it is often a matter of understanding that a solid data basis needs to be created before the potential of analyses - and AI in particular - can be exploited, and learning how they can support this process of organizational and skills development.

What experience have you already gained with this concept?

Isabelle Kranabetter: Larger companies usually have a data infrastructure and a strategy in place, but not automatically a data culture. Depending on the type of company and its history, there are also different challenges when it comes to data culture.

The simulation also facilitates fact finding in that it makes the abstract topic tangible and discussable for the participants, enabling them to recognize their own "pain points". Depending on what the challenges are, certain new practices and competences are needed. One customer also reported back to me that after our workshop, employees have begun to use the simulation game as a kind of neutral reference in their day-to-day work: together they have experienced what good data collaboration should look like and agreed on it. When faced with challenges, they can now refer to their shared experience in the simulation workshop without "finger pointing".