The 6 best knowledge sources of data literacy

Data literacy, also known as data competence in German-speaking countries, is in greater demand than ever before and is an important hiring criterion in many companies. After all, decision-makers from more and more corporate divisions are now dependent on understanding data and aligning business strategies with it. This is often not so easy and requires years of experience. These six knowledge sources provide a first insight into the topic.

Introduction: "No competitiveness without data competence" - What was dismissed as an exaggeration a few years ago is already reality today. Increasing digitalization ensures that companies collect more and more data and use it profitably to optimize their business processes. This development not only affects traditional areas such as IT, Finance or Sales, but also Marketing or HR.

Entrepreneurs and managers are now faced with the challenge of promoting data competence in their teams in order not to lose touch and to start safely into the digital future. For many, this means first obtaining an overview of the subject area themselves. After all, the success of the digital transformation in the company stands and falls with the management decisions. But where can you find practical information and resources that really help in everyday business?

Data Literacy: The best knowledge sources for decision makers summarized

The good news: Thought leaders from science and practice are already intensively addressing the topic of data literacy and have published numerous white papers, articles and guides. The information on offer is huge, but confusing. It is aimed at different target groups and does not always offer great added value for executives and data experts.

That's why we've identified six online resources to help decision makers gain a better understanding of data literacy, reflect on the value it adds to their organizations, and kick-start the shift toward a data-driven culture:

1. Many Skills, One Term: "What is Data Literacy?"

In this article, Friedrich Schiller University Jena shows in detail what data literacy means. Above all, the article deals with the extent to which skills such as data discovery and collection, data management, data evaluation and data application are part of the competence set. If you want to read up on the topic quickly and scientifically, you are in good hands here.

2. Making Competences Measurable: "Future Skills: A Framework for Data Literacy"[3]

In the workingpaper of the Higher Education Forum on Digitisation, a competence framework for data literacy is presented, which comprises five dimensions: Knowledge, Skills, Abilities, Motivation and (Value) Attitude. The aim is to transform data literacy from an abstract concept into verifiable competence goals. Building on this, the authors of the paper also highlight the advantages and disadvantages of various instruments for testing data literacy, which are also widely discussed, particularly in corporate practice.

3. Human Factor:"The Human Impact of Data Literacy".[4]

In its guide for executives, the consultancy Accenture identifies three reasons why companies are not yet fully data-driven and provides concrete recommendations for action to develop an organization-wide data strategy. The core aspect is the development of a data-driven culture, which is largely driven by people. Accordingly, employees must understand what is important for their respective functional areas and what handling of data is expected of them. The culture change thus becomes an explicit management task.

4. Self-Check: "How Data Literate Is Your Company?"[5]

Many managers ask themselves the question about the level of existing data competence in the company. After all, this significantly determines the quality of data analysis and interpretation and thus influences strategic business decisions. Since there is usually still great potential for optimizing data knowledge, the Harvard Business Review shares best practices for improving data literacy in companies. These include incentive structures that reward data-based decision-making, as well as opportunities to demonstrate data literacy in your own projects.

5. Breaking Down Silos: "How to Increase Data Literacy for Success With Analytics".[6]

The Gartner white paper also deals with strategies for increasing the data competence of employees. Closing data literacy gaps and thereby increasing the quality of data analyses is one of the key challenges facing companies in 2022, which is why the analysts recommend first determining the specific level of data literacy in the company. This is followed by recommendations for action to promote internal exchange with various stakeholders and increase collaboration on data-related projects. Change must affect the entire organization - not just one department.

6. Trends and News: The Lyntics Blog

With Lyntics, we are always where the data scene pulsates. Data literacy is our core topic and so we are constantly working on bundling the knowledge of our experts in one place. On the Lyntics blog, decision makers and data enthusiasts will find curated content around data literacy in an enterprise context. From deep dives into data tools to news, trends and best practices, you'll find relevant content on the platform to stay up to date on data literacy, analytics, marketing and more.

Real Data Literacy at the push of a button with the Data Literacy Platform

Anyone who works a lot with data in their day-to-day work is unlikely to become a data expert by reading information texts. The same applies to training and education. Because genuine competence acquisition is a lengthy process that can not be shortened - contrary to what is promised in many articles - by means of short-term training courses. [8]

Nevertheless, companies that want to remain competitive in the long term can already benefit from data literacy today. With Lyntics' Data Literacy Platform, the knowledge of real data experts is available to companies at the click of a button. They can directly access the data logics and structures they need to understand and interpret your data. So you get meaningful results in no time and a solid data basis for better business decisions.

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