{"id":5743,"date":"2025-04-07T13:47:18","date_gmt":"2025-04-07T13:47:18","guid":{"rendered":"https:\/\/novalytica.com\/data-literacy-part-1-what-actually-is-data-literacy\/"},"modified":"2026-04-15T12:16:49","modified_gmt":"2026-04-15T12:16:49","slug":"data-literacy-part-1-what-actually-is-data-literacy","status":"publish","type":"post","link":"https:\/\/novalytica.com\/en\/data-literacy-part-1-what-actually-is-data-literacy\/","title":{"rendered":"Data literacy part 1: What actually is data literacy?"},"content":{"rendered":"\n
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Data is present in nearly every aspect of life. We are surrounded by a veritable flood of data that is growing exponentially and can take on various forms. How can this flood of data be managed in such a way that it yields information and, in turn, generates a flood of knowledge? The key to this lies in data literacy. <\/em>In our blog series on the topic of “Data Literacy”, the topic is examined using various building blocks. The first part starts with a general definition of the term and an explanation of why data literacy is also referred to as the “competence of the 21st century”. <\/em><\/p>\n<\/div>\n\n\n\n

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Definition and delimitation<\/h2>\n\n

Data literacy is a set of skills and abilities at the interface of various related competencies, such as information literacy or critical thinking. For this reason, there are various narrow and broad definitions. In this blog series, we will use the definition by Sch\u00fcller et al. (2019), which covers the entire cycle up to data-driven decision-making: <\/p>\n\n

“Data literacy is the cluster of all efficient behaviors and attitudes for theeffective execution of all process steps for value creation or decision-making from data.” (Sch\u00fcller et al., 2019, p. 26)<\/p>\n\n

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Figure 1: Data literacy and related skillshttps:\/\/www.dataliteracy.uni-jena.de\/was-ist-data-literacy<\/figcaption><\/figure>\n\n

Data literacy therefore enables data to be collected, processed, presented and analyzed in such a way that added value is created and data-driven decisions can be made.<\/p>\n\n

Data literacy includes a range of different skills and abilities that can be summarized in five areas (according to Ridsdale et al., 2015):<\/p>\n\n

Conceptual framework<\/strong>This includes general data basics, such as knowing what data is generated in the company and what makes it a valuable resource. This framework also includes an established data culture and the necessary tools and techniques to implement it. <\/p>\n\n

Collecting data<\/strong>This area includes skills for collecting, organizing and processing data. For example, the uniform collection and organization of customer data in a CRM. <\/p>\n\n

Manage data<\/strong>This area mainly includes skills and abilities for basic data analysis, interpreting data and using data tools. An example of this is importing and visualizing CRM data in a Microsoft Power BI dashboard. <\/p>\n\n

Evaluating data<\/strong>In addition to identifying problems in the use of data, evaluating data also involves visualizing and verbally presenting data and making data-based decisions. In the example with the CRM data, for example, gaps in the data, such as missing gender designations, could be a problem. CRM data can be used to derive important information for the more efficient conversion of leads to deals, and sales can be strategically aligned accordingly. <\/p>\n\n

Use data<\/strong>On the one hand, this includes evaluating the data-based decisions mentioned above. Have the strategic changes in the sales process made a difference? And if not, what needs to be adjusted? It also includes sharing and citing data, for example in an (internal) report on the results of a data analysis. This module also includes aspects of data ethics, i.e. which customer data may be collected and how it may be used. <\/p>\n\n

From data to added value<\/h2>\n\n

Data literacy is the ability to generate added value from data. But what actually is data and how can added value be created from it? <\/p>\n\n

Data can be seen as a raw material that represents the real world. Information is generated by cleansing and linking this data. Knowledge, in turn, is created by analyzing this data. However, added value, so-called “action knowledge”, is only created when the data is also interpreted and used in a meaningful way. (Sch\u00fcller et al. 2019) <\/p>\n\n

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Figure 2: Pyramid model of data value creation (own diagram based on Sch\u00fcller et al., 2019)<\/figcaption><\/figure>\n\n

From data collection to data-driven decision-making, the data passes through various stages along a cycle.<\/p>\n\n

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Figure 3: Cycle of data-driven decision-making (own diagram)<\/figcaption><\/figure>\n\n

As the broad definition has shown, data literacy cannot be reduced to one stage. In the following articles in this series, we will therefore look at the following areas of the data cycle: <\/p>\n\n