Dealing with data, evaluating it, interpreting it and using it as a basis for decision-making requires a decisive core competence: data literacy.
Do you know how much data is generated per day worldwide? Even today, the volume of data exceeds human imagination. And it continues to grow steadily: In 2025, 181 zettabytes are expected to be added daily. In order to deal profitably with this enormous amount of data, data experts are needed in almost all business areas from marketing to HR to product development. In this context, the keyword data literacy is increasingly used. But what does this buzzword actually mean?
Understanding data structures
Data literacy means "data literacy" in direct translation from English, but is mainly called data literacy in this country. The term refers to the ability not only to want to use data, but also to understand, evaluate and apply the structures behind it. In the golden age of tech, it is therefore also regarded as the new core competence.
Specifically, in addition to data analysis, the presentation of analysis results and their interpretation as well as communication are part of data competence. Fundamental for this are statistical and/or mathematics or computer science skills, but a corresponding study is not absolutely necessary. Data literacy can – to a certain extent – also be acquired autodidactically, because the required skills have high overlaps with other fields of competence. For this reason, many career changers with previous knowledge (for example, from sociology) opt for a specialization in the data environment.
Understand data and interpret analysis results
The skills and abilities associated with the term data literacy are diverse. Typically, these include:
- Identify and clean up relevant data
- Dealing with data analysis methods and tools
- Recognize and understand structures in data
- Contextualize data
- To evaluate data for a specific purpose
- Interpret, explain and visualize analysis results (keyword: data storytelling)
- Classify and question information
Depending on the specific occupational or field of activity, the focus on individual of these skills varies. The precise and careful handling of data, on the other hand, is fundamental for all activities in the data environment – just like analytical and logical thinking.
How companies benefit from data literacy
There are many concrete application examples and advantages of effective use of data in companies. For example, the access data to an online shop can be used to find out how long users stay on the page, when and where they jump off and which internal links they follow. Evaluating and interpreting this data offers many opportunities, for example in terms of improving the customer journey. This data therefore provides many starting points to pursue a more targeted acquisition of new customers, but also to gain a deeper understanding of the existing clientele and to measure the success of the improvement measures.
Data literacy also enables the optimization of internal company processes and can contribute to more efficient workflows and optimized recruiting. This makes it clear that data literacy should not only be available in the IT sector, as it is also promising for HR, for example.
However, if a company does not have the resources to develop data literacy, this carries some risks. Misunderstandings in data interpretation can lead to wrong decisions with sometimes serious consequences and shake trust in data. So that the path to a "data-driven" company does not end in a dead end, it is necessary to support the expansion of data literacy actively. This can be done in different ways.
Promoting data literacy in the company
Get all employees on board
Data literacy does not only affect those who work with data in everyday working life anyway. In order to support the change to a data culture, employees of all hierarchical levels and departments should first understand the value of data as a new currency as well as the added value of data-based decisions for their field of work.
Breaking down silo structures
In order for business-critical data to be used profitably, opportunities are needed to access it across departments. Data records are often stored decentrally and without documentation, which is why many companies have no overview of what data they actually collect. Transparency is therefore a decisive success factor.
Extracting data from silo structures is only the first step. However, in addition to the origin, data literacy also focuses on the meaning of the data, its relationships and the entrepreneurial added value that can be generated from it.
Closing the data literacy gap
According to a study by Accenture, only about 32 percent of the executives surveyed are currently convinced that they can generate measurable added value from data. For companies that want to remain successful in the long term, the motto is to increase the skills of their employees in terms of data analysis and evaluation, across all business areas.
Training and education are not enough
This is also shown by a recent study by the digital association Bitkom. 58 percent of the companies surveyed find it difficult to find good data experts, 38 percent even very difficult. That's why many of them rely on training and further education: 28 percent stated that they train their own employees for data-driven business models. However, data literacy can only be learned to a limited extent – and is usually a time-consuming and cost-intensive process that takes several years. That's why training alone won't be enough to turn employees into data experts.
The corresponding measures will only be truly effective in combination with innovative technologies that make data literacy available to everyone and everyone. There are already software solutions that make the knowledge of less immediately applicable and searchable for all employees in the company. They collect all existing work results across departmental and company boundaries and extract, store and visualize the logics, relationships and information contained.
These tools will become a decisive competitive advantage in the future. After all, whether companies remain successful in the long term depends largely on the data literacy of their workforce. If only a few experts are able to evaluate, understand and apply data effectively, data-based business models will be difficult to scale. But data knowledge that is available to everyone is a real game changer – and the biggest success factor.
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