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Why data literacy is essential for your company

In our post Become a data-driven Company by Integrated Business Planning, we present our maturity model for your business planning. In this, data literacy is an important building block. Therefore, we explain here what the term encompasses and how data literacy can be consistently integrated into the company.

By 2030, data literacy is predicted to be the most demanded skill, even as fundamental as being able to use a computer these days. This were the findings of a study by Censuswide at the end of 2021, which undertook a survey of more than 1,000 C-level executives and 6,000 employees on this topic.

What exactly is behind the term data literacy, and why is this skill set to be so important?

Data literacy basically means data competence, and can be understood to mean competence with the handling and use of data. By placing the data in the right context, knowledge is generated.

Data collection versus data literacy

The difference between the straightforward collection of data and the successful application of data literacy, can be illustrated with an example. The operator of a website wishes to log important key figures, such as the access numbers or the time spent by visitors on their internet presence. This corresponds to simple collection of data. The use of data literacy, however, goes beyond this step and includes the evaluation of the data as well as the deriving of measures to improve the reach and the experience of the users. For this to succeed, it is necessary for the key figures to be understood and interpreted correctly.

Partial data literacy competences

All in all, data literacy is composed of various partial competences: working with data, reading data and communicating data.

  • The partial competence of working with data includes, among others, the ability to collect, organise, cleanse and convert data, as well as the evaluation and safeguarding of data quality.
  • The reading of data encompasses the correct use of methods and tools for data analysis, the evaluation and interpretation of data, as well as the identification of problems through the use of available data.
  • The final partial competence, the communication of data, relates to the reporting and presentation of data. This contains the citation, sharing and presentation of data, which must then take place in an appropriate manner (with the choice of an appropriate type of diagram, for example).
The partial competences of data literacy

When defining data literacy, a distinction can also be made between the individual and organisational levels. At the individual level, data competence requires individual employees to be aware of the value of the data and to have specific knowledge and skills in its management. At the organisational level, people speak of a “data culture”. A company which considers its data to be a valuable data-related asset, will provide the necessary resources and align all its processes to make use of this data-related asset and putting it to profitable use.

To be able to build data literacy in a company, it is generally important for every employee to have a basic degree of data competence related to the role of the respective employee. However, different levels of competence are required which are discussed in further detail below.

The huge gap between perception and reality

At the end of 2021, Forrester conducted two online surveys of more than 1,000 employees and decision-makers from global companies, to examine their culture in the area of data competence. Within this context, it was found that 82% of decision-makers expect data competence from their employees, and a similar share (79%) of decision-makers believe that the respective departments pass on the necessary data knowledge to the employees.

On the other hand, only two-fifths of employees stated that the company provides them with the necessary data knowledge. The employees do not feel optimally prepared for future tasks surrounding the topic of data competence. This shows there is a large gap between the perceptions of the decision-makers and the reality in the companies, which clearly states that there is a need for action in this area.

Why a company should build data literacy

Building data competence throughout the company allows the business potential to be exploited. This can ensure the quality and usability of the data. A lack of data competence when collecting or entering data can lead to poor data quality. Evaluations which are based on such data yield fewer or even incorrect results (“garbage in, garbage out” principle). The presence of data literacy also ensures that decisions are not only made based on gut feeling, but also on the basis of data. This allows operations to be optimised, errors to be avoided and time and resources to be saved. In this way, decision-making capabilities can be improved and the turnover increased. Building data literacy therefore provides a basis for the development of a data driven company. I.e. a company that consistently uses its data resources for decision-making and analysis in the interests of creating new opportunities and possibilities, thereby improving its levels of innovation and competitiveness. Evaluation of the data can also improve the customer experience. In case of a website, for example, it is possible to analyse the pages on which users stay for longer periods, which specific links they follow and where they leave the website. The goal is to redesign the website accordingly in order to offer the customer a better user experience.

Establishing a common data language is also part of the company-wide development of data literacy. In this respect, it is important for a company to have fixed definitions of terms, so that everyone uses exactly the same term during a discussion and is understood by everyone else. Dashboard design rules for uniform presentations also help with understanding diagrams better and faster and avoid misinterpretations.

Data literacy is also a key competence in the digital transformation. Digitalisation is increasing the amount of data exponentially. In addition to this, not only is structured data from ERP systems or data warehouses available, for example, but also a large amount of unstructured data from a wide variety of sources, such as sensor data, data from social networks, etc. This huge amount of data, the continuous development of digital technologies and their interconnections is providing companies with a great opportunity to obtain information from the data so they can improve their business processes and make forecasts for the future. However, if people are unable to manage the considerable amount of data competently and put it to profitable use, this development also entails the risk of failing to keep pace and suffering a competitive disadvantage.

Another important reason for building data literacy in the company is increased employee satisfaction. An increased degree of data literacy and the transparent handling of data can help employees to understand the meaning of their work , both in general and when using new tools or technologies, leading to greater levels of work-related motivation and acceptance of change. For example, the above-mentioned Forrester study found that high employee data satisfaction frequently leads to a high degree of overall satisfaction with the company. With increasing data satisfaction, the likelihood that employees will decide to stay at a company also increases.

Measures for building data literacy

An important step in building data literacy in your organisation is establishing data culture. This can be understood as a range of collective behaviours and beliefs within a company that trigger its operations, mindset, and identity to be linked with data. You should start by determining the actual situation in your company by means of surveys or interviews, i.e. the current level of data competence, before you start building the data culture.

According to Qlik and Accenture, you should follow the following seven principles when establishing a data culture. 

Seven principles of data culture


1.Develop a culture of curiosity,
as data literacy is not just about working with data, but should also be associated with learning other non-technical skills, such as critical thinking, creativity or curiosity. This can enable employees to reconsider their own assumptions about data, or adopt different perspectives, for example.

2. Convince all employees
of the importance and advantages that data competence brings for the company as well as for the employees themselves. Your company will only succeed when employees are able to handle the data critically, to use it to gain insights and to thereby make better decisions.

3. Focus on the desired results.
To be able to use the data effectively, the problem to be solved must be known. Before analysing the data, you should identify problems first and then define goals on their basis.

4. Take a systemic perspective.
Unless there are legal or other reasons that speak against it, the various departments in your company should work together and support each other so the data from all the departments should be accessible to every employee. The collaboration between departments encourages collective problem solving, which can have a positive effect on efficiency and effectiveness when working with the data.

5. Select the required forms of technology
that a department or specific employees need.
For this purpose, however, data roles must be defined first, as it is not necessary for all employees to have the same data analysis capabilities. Rather, the employees should expand their data skills in their respective areas of responsibility in order to achieve more productive and efficient results. According to CAMELOT, it is possible to distinguish between four roles in this respect. Data Believers have highly detailed business-related knowledge and understand and believe in the relevance of data. Data Translators use data on a daily basis, they are able to put the data in a business context, have basic data analysis skills and good communication skills. Data Governors, on the other hand, already have a strong degree of data analysis competence. They use statistical and analytical methods on a daily basis, but they lack the overall picture. The final data role is the Data Master Minds, who are very familiar with statistical and analytical methods as well. They are also able to put the data in a business context and have comprehensive business knowledge.

In addition to the four roles described by CAMELOT, we have added a fifth data role, Data Evangelists. They can provide mentoring for the other roles, can also inspire employees to use data and help them to be more creative and effective with the data. Data Evangelists have very strong data analysis competencies and very good communication skills.


It is also necessary for you to establish a data strategy in your company for the selection of the required technologies. This provides a roadmap for working with data and how decisions and knowledge can be derived from the data. The data strategy can be created in six steps.

At the beginning, a definition of the objectives takes place, i.e. a check is made to see where data is able to change and/or improve something. After that, the various data sources are determined and merged, before the visualisation of the data begins. In this step, diagrams are generated from the data, so that problem areas or potentials can be recognised quickly. A Data Governance policy should then be anchored in the company. This term is understood to mean compliance with the statutory requirements, such as the General Data Protection Regulation, and enabling secure data access, with the aim of achieving a uniform and consistent quality of data. Furthermore, transparency should be created by making the data available to all the relevant employees – where possible -, so that they can also make their decisions based on the same data. The final step in developing a data strategy, is to firmly anchor the use of data into processes so that decisions are made on the basis of the adjusted stock of data.

6. Offer training and/or courses.
In this context, the employees should not only be convinced of the added value of data competence and expand their data knowledge, they should also be able to apply the knowledge they have gained in practice.

7. Check the success of the measures regularly.
This allows the employee satisfaction to be taken note of and to be evaluated by means of surveys or interviews, so that stress and overwork can be avoided. By making a comparison with the initially-recorded actual situation, you can see where data has already led to savings and improvements and where potential or weak spots continue to exist, so that the strategy can be adapted accordingly.

What challenges have to be overcome?

Your organisation will face many challenges when building data literacy. This can create resistance and disinterest among employees. In this respect, you should give your employees time and space to change their behaviour surrounding the use and handling of data. Offer them consistent support, through for example regular meetings. In addition to this, the data should also be handled as transparently as possible and employees should be made aware of the advantages that a higher degree of data competence is able to offer both the employees themselves and the company.

Further challenges arise when training materials are lacking or inadequate, or when the company has a shortage of qualified employees which prevents the data competence training from being offered in the first place. In this case, external support can be offered through training or workshops. We will be happy to support you in this area. Contact us to obtain all the details!


In summary, it is possible to say that the topic of data literacy will play an essential role for companies in the future. It will be important for companies to take advantage of the opportunities created by the exponential increase in data volumes and to thereby secure a competitive advantage. In this context, building data competence throughout the company plays a major role. A data culture and a data strategy must also be developed so that the entire company, including all its employees, pursues the same goals as one team, in order to be well-prepared for the future.

The “PIKON maturity model for your business planning” provides you with initial orientation for your journey to becoming a data-driven company. It helps you with your own assessment of exactly which business planning phase your company currently occupies. 

Maturity Model for your Integrated Business Planning

Maturity Model for your Integrated Business Planning

  • Defined target states for different areas (from the management view through to business processes and up to data storage/visualisation) with various issues as an aid for own assessment.
  • Detailed development phases on the way towards the target state – aid for self-assessment
  • Information on optimisation potentials (quick wins)
  • Orientation for your own individual roadmap


We are looking forward to supporting you!



Genevieve Victor


Kevin Klesen


Tobias Haßdenteufel

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About the author
Tobias Haßdenteufel
Tobias Haßdenteufel
I am a Business Intelligence Consultant at PIKON Deutschland AG. With my work, I want to support our customers in the architecture, design and implementation of solutions based on SAP BI and SAP HANA.

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