Data and information are essential components of an open society. In order for everyone to participate, it is necessary to promote data literacy at all levels of society. The expansion of data literacy not only creates more security and transparency in handling data but also enables the potential of data to be recognized and utilized more effectively.
The German government has also anchored the promotion of data literacy as a central field of action in its data strategy. A whole range of measures is proposed there and is already being implemented, such as the "data labs" in the federal ministries and in the Federal Chancellery. But is this enough to sustainably anchor a future skill like data literacy in all areas of our society - economy, science, government, and civil society?
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We exchanged views on this with almost 30 experts from federal ministries, federal agencies, and the scientific community in an initial discussion a week ago. The overwhelming response to the invitation, which Dr. Georg Thiel, President of the Federal Statistical Office (Destatis), and I, as CEO of STAT-UP Statistical Consulting & Data Science and board member of the German Statistical Society (DStatG), issued following the Data Literacy Charter, was remarkable. In particular, the numerous inquiries from representatives of the National Research Data Infrastructure (NFDI) from various research fields demonstrate the urgent need for an interdisciplinary approach to the topic of data literacy.
The following guiding questions served as an impulse for our first discussion:
What measures can the government take to promote data literacy, and which target groups should be addressed?
Which actors can be involved in what roles to promote data literacy effectively?
What could a concrete implementation plan ("roadmap") look like?
Data Literacy Charta
The Data Literacy Charter, initiated by the Stifterverband and supported by numerous professional societies, formulates a common understanding of data competencies and their importance for educational processes.
These questions will accompany a series of four workshops over the next twelve months, aiming to provide answers from the perspectives of various stakeholders and users. Already in the first discussion, we found a broad consensus on the essential aspects.
According to the conviction of all participants, data literacy encompasses not only statistical competence or a basic understanding of mathematics but also other skills such as digital literacy and media literacy, as well as the ability to assess and contextualize information (think: fake news/disinformation). Data literacy is about empowering people to transform data into value, i.e., about a value creation process.
The central task of data literacy measures is to increase the data literacy of the population and establish a data culture.
This is also a stated goal of the data strategy of the German government. It is particularly important to reach an audience that is not inclined towards numbers and does not (yet) see a need to increase its data literacy. Especially concerning statistics, it is crucial to provide low-threshold and attractive offers to spark interest and dispel prejudices.
Source: Destatis/STAT-UP
It is certainly worth questioning why anglicisms like "Data Literacy" are needed at all and whether they might even deter some people from engaging with relevant offerings. As one participant noted: "I had no knowledge of this theoretical concept before. Therefore, I had to research what Data Literacy actually is. In contrast, with the German term 'Datenkompetenz,' which seems to be established, I immediately had an understanding. Wouldn't it be more effective to use understandable terminologies when conveying data competencies?"
Indeed, similar concerns were also expressed by the experts who were interviewed for the Data Literacy Study conducted by the Hochschulforum Digitalisierung. A medical professor sees a significant problem in the lack of effort to find precise German terms for this competency. The term "Data Literacy" does not seem to be clearly defined even in English. Therefore, it is all the more important to use or generate appropriate German terms: "I also see it in my field of medicine that many researchers in Germany don't even bother to find German terms, even if it would be quite simple. And then they assume that the English term will be automatically understood by readers. In many cases, this is certainly a misjudgment."
Language shapes thinking. And how can one think when the terms are missing?"
We have repeatedly discussed the question of terminology in the DStatG because Walter Krämer, chairman of the AG "Statistical Literacy" and also chairman of the "Verein Deutsche Sprache," really doesn't have much use for anglicisms. But the term "literacy" makes it clear that it's about a basic or key competency, just like reading and writing skills. To make this clear, one would be most likely to speak of "statistical literacy" or "data literacy." The initiators of the Swiss appeal data-literacy.ch make this reference clear in their response to the question "Why are doctors and statisticians jointly launching an appeal to the population and politicians?"
Home | Data Literacy - Switzerland
Let's work together to improve data literacy!
"Historically, doctors in the 19th and 20th centuries were politically very active in various regions in the introduction of schools for the literacy of the population."
"A well-founded societal data literacy is nowadays as indispensable as reading and writing for the preservation of the fundamental values of our democratic society such as freedom, equality, the right to participate, and inclusion."
"With the launch of this appeal, doctors and statisticians are fulfilling their social responsibility in this regard."
However, "data competencies" are still primarily perceived as technical skills and abilities, without addressing aspects such as data culture, data ethics, etc. "Data literacy" was coined as a term in academia to emphasize a more comprehensive view. This was also adopted by the German government in its key paper on data strategy: "To this end, we will in particular: (...) examine and initiate measures and instruments to increase data competency in terms of comprehensive 'data literacy' in all formal and non-formal educational areas." The German government now defines the term "data competency" in its data strategy as follows:
„Data competency or data literacy describes, among other things, the ability to competently and value-oriented handle data from technical, economic, ethical, and legal perspectives. Data competency is foundational and an essential skill in dealing with digitalization."
Nevertheless, it is observed that many commercial providers now also advertise "data literacy" in their training and consulting offerings, even though these central aspects of literacy are lacking. A major challenge in the future will therefore be to create awareness among the target groups of such offerings about where genuine future skills are conveyed and where primarily skill-oriented training is provided, characterized mainly by the extensive use of catchphrases.
The target groups for data literacy initiatives are diverse: students from all disciplines, researchers and doctoral candidates, educators, students, employees, and all citizens who use data in their daily lives. Participants agreed that all stakeholders in the field of data literacy should better network with each other to leverage synergies. The goal is to disseminate data literacy initiatives, offerings, and products across society and to make the topic of data literacy visible in politics and the media. Already in the first exchange, it became evident that there is already a wide range of offerings - they are just not synchronized and far too little known.
The Statistical Advisory Council, the advisory body of the Federal Statistical Office, will establish a working group on "Data Literacy." This anchoring aims to ensure the Federal Statistical Office's sustained involvement in this field. And nothing is currently more urgently needed than reliable, institutionally anchored expertise in an important future skill, which is not limited to the strategic level but also includes decades of experience in implementation.
This was demonstrated not least by the "Experimental Data" provided by Destatis during the pandemic, for example, in the form of innovative economic indicators, which showed that urgently needed information can be provided both quickly and reliably when the right experts are involved.
Absolutely, it requires experts who not only know how to do it but can also put it into practice.
Indeed, strengthening collaboration between federal ministries and agencies in implementing the data strategy, with official statisticians playing a significant role, is crucial for all of us.