Future Skills: a Framework for Data Literacy - Competence Framework and Research Report

What knowledge, skills, and attitudes are needed in society, in the working world and in science, where data is considered a valuable, sometimes the most valuable resource and where decisions are increasingly taken based on data? Digitisation and datafication will undoubtedly change life and work in the 21st century. Artificial intelligence, networked production, communicating machines and self-driving cars are controlled by data and themselves produce data on the conveyor belt. Data is the starting point for knowledge and value creation as the basis for better decisions.
The process of knowledge creation comprises several steps: (A) Establish a data culture – (B) Provide data – (C) Evaluate data – (D) Interpret results – (E) Interpret data – (F) Derive actions. In order to syste¬matically gain knowledge or value from data, the ability to deal with data in a planned manner and to be able to use and scrutinise it in the respective context will be of crucial importance in all sectors and disciplines. This is known as data literacy and includes the skills to collect, manage, evaluate, and apply data in a critical manner. Data literacy is far more than wide and extensive knowledge of constantly changing methods and technologies. Rather, the dimension of data ethics, motivation and value plays a central role in handling data successfully and confidently in the future.
Data literacy is a key competence of the 21st century. Therefore, it must be taught from the very be¬ginning and across the disciplines at universities. This requires a competence framework, i.e. a model for the structured description of effective behaviour in each task context. It includes compe¬tencies, their de¬finitions and behaviour indicators derived from them. Such a competence framework should map all stages of the knowledge or value creation process from data; it should cover all dimensions of competence: (a) knowledge, (b) aptitudes, (c) skills, (d) motivation and attitude. It should allow the competences to be translated into concrete and testable learning or competence goals; and it should reflect the interdisciplinarity of the task, i.e. reflect that, in addition to data experts, technical experts, data protection experts and data ethicists are also required.
Data literacy covers both the creation of data products by the methodically experienced specialist as well as the competent handling of data by the end user, i.e. the critical and adequate interpretation and application of the data. The Data Literacy Framework is designed primarily for universities and other educational institutions, but also for managers and human resources departments in private and public sector organisations and for political decision-makers. In addition, it can be used to formulate data lite¬racy for the responsible citizen, whereby in this case rather low competence levels should be aimed for.
It is important to translate the results into proposals for curricula. For this, pilot universities and pilot cour¬ses must be selected, in which specific learning objectives for the respective disciplines are esta¬blished in accordance with the competence framework. Another aspect to be discussed is the aspect of lifelong learning of key competencies: How can or should data literacy be taught at school and later in the working world and adult education? In any case, one needs didactic approaches regar¬ding the interdisciplinarity of the task. Because complex data projects are executed in teams, they require multi-professional work that includes the ability to manage projects as well as in-depth knowledge of organisational, legal, and ethical framework conditions. Last, but not least, the question of how teachers can be qualified for this challenge needs to be answered.