For a long time, people relied primarily on gut feeling and experience to make decisions. However, as modern systems and processes become increasingly complex, it also becomes necessary to use data as the basis for decision-making: Probabilities of failure are predicted by machine data or purchasing behavior is derived from customer data, to name just two examples. In order to handle these sometimes enormous amounts of data, it is not enough for technically skilled data experts to provide solutions and technologies for business and science. It is equally important that users are able to deal with this data and use it for decision support and innovation. Unfortunately, there is still a lot of catching up to do in this area. A recent study that surveyed companies worldwide concluded that there is a large data skills gap: 74% of all people who regularly work with data said they have too little expertise in handling data, though 87% agreed that data is an important cornerstone for value creation and technological progress. Data literacy is therefore clearly a central competence of the 21st century.
Data literacy prevents leaky data pipelines
Data literacy describes the ability to handle and understand data correctly. This includes capturing, managing, analyzing, interpreting, presenting and applying data - a wide range of individual competencies that are closely intertwined. Being able to handle data in a self-determined, responsible and competent manner is necessary for economic and technological development, value enhancement and the acquisition of digital business opportunities. Data literacy is also one of the most important foundations for data-driven research and innovation. For example, a data management plan is increasingly required for scientific projects as research data management becomes more of a focus.
At VRVis, we have been conducting research in the areas of data science, data analytics, as well as data visualization for over 20 years, working with corporate partners to develop custom applications and solutions for data-driven issues. A fixed focus in our research is the discussion of how people can best understand data, deal with it most efficiently, and how visualization and human-centered software can form the bridge between data and people. Similarly, in the area of immersive analytics, we are exploring how we can think analytics processes even further and free them from the traditional desktop computer. To this end, we are making data even more accessible in 3D in virtual reality environments. Another corner stone of our R&E work is our participatory approach to the development of robust data pipelines, custom-fit analytics software and visualization dashboards. In numerous multi-professional projects in the fields of infrastructure, automotive industry, climate change adaptation, medicine, life science or energy industry, we are regularly called in as a partner to cover exactly these important data areas, to set up data infrastructures, to establish data management, to improve data quality, to lead trainings or to support consortia and companies with consulting.
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Johanna Schmidt is a data expert and head of the Visual Analytics research group at VRVis. In her research she puts a focus on the topic of data literacy. In this short interview she answers questions about the best data strategies for companies.
Schmidt: The term "visualization" is in our company name - this clearly shows that it is one of our core businesses. We have been researching and developing data visualization technologies and solutions for over two decades. We can look back on successful use cases from over 10 industries. We have the necessary skills and experience with real-world data and can design data visualization to guarantee that the most important aspects can be analyzed from the data.
Schmidt: Reality shows that data quality and heterogeneity are a big problem. That is, data is often very scattered, has many duplicates, or is difficult to link together. Therefore, a key aspect is to get the data into the necessary form and to establish seamless data pipelines. We also still know too little about how people read data representations and which ones are particularly suitable for conveying information. More studies in this direction are therefore necessary and are planned by us.
Schmidt: It is becoming apparent that more and more companies are turning to web-based solutions that are more and better aligned with the company's workflows. We support companies in precisely these steps: setting up efficient data pipelines that at the same time complement existing processes and applications.