Data Literacy: How can companies act in a data driven way?

Table of Contents

We have arrived in a golden age – an age of data. Many companies already rely on actively using data, data analytics and they make decisions based on this data. This development is very positive, because data driven, and thus evidence-based, decision-making enables risk minimization and at the same time offers great potential for identifying optimization and innovation – provided that the relevant data literacy has been built up in the company as a whole. Because all too quickly, the potential companies can hope for can be wiped out if the workforce is not considered and prepared to work with data.

In this article, we want to discuss the question of how companies can effectively make data-driven decisions and take action, what obstacles they need to consider, and we’ll provide concrete recommendations for action to ensure that your implementation is successful.

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Fact-based innovation instead of flying blind

By now, almost everyone is aware that data is a key factor for success when it comes to business decisions. Some even consider data to be the oil of the 21st century. Data has always been important to most businesses, and resourceful businesspeople have used it when making decisions long before computers became commonplace. But the more data a company collects, the harder it seems to become for some decision makers to align their strategy around it. That’s perfectly understandable. The key word is data literacy – admittedly not an easy concept to grasp, but an essential one.

We use data far too rarely as a starting point for making our own decisions in everyday life. The reason for this is not a lack of tools or a lack of data experts, but rather a lack of the right mindset. There is a lack of the basic attitude of wanting to think, work and make decisions in a data-driven way. Relying on gut instinct, past experience or traditional processes is still the norm in most boardrooms.

So far, this concept has worked wonderfully for many companies, but gradually more and more corporations are facing a flood of data that they no longer know how to deal with. The sheer volume of data available today, both internally and externally, opens up completely new possibilities. Companies are at a crossroads: do they leave everything as it is or do they take advantage of the enormous potential for growth and innovation?

It’s obvious: it’s time to seize this opportunity. For years, companies have been collecting data to improve products and services, increase customer satisfaction, and avoid bad decisions. The work behind this is done by a few, much-needed and hard-to-find data experts.

In the past, the search for and preparation of data and the insights derived from it required special data experts. Without experts who were familiar with the complicated BI tools, nothing worked. But in today’s world, where innovations like big data, AI and IoT are changing all industries and only those companies that adapt quickly will be successful, data experts are no longer enough.

Non-technical employees must also be able to access and interact with data. This is exactly why skills such as data literacy are coming to the forefront right now. So far, individual employees in the technical departments are hardly noticing these changing requirements. This is unfortunate because data literacy is actually not just in demand at work, but it will also play an increasingly important role in our private lives and in our society. It is the key to the next stage of data-driven companies and a democratic data culture.

Earlier this year, the German government issued its data strategy, making data literacy not only a buzzword in Germany as well, but declaring it an integral part of basic education. This undoubtedly presents many companies with a challenge. Even if the value of active data use has been recognized by executives, employees are often still unclear about how to deal with data.

The prevailing fear of contact with data can cause many companies’ implementation of data and digital strategies to fizzle out. One major reason for this is easy to identify. Companies are still reluctant to promote data literacy among their own employees. The misconception that data skills are only relevant to experts is firmly entrenched, turning a stepping stone into a stumbling block. It is therefore all the more important that data literacy is included on more and more L&D roadmaps in 2021.

Data Literacy – an indispensable hard skill?

Data literacy is considered one of the most important and fundamental concepts of the decade. For experts, it is an indisputable success factor in a data-driven world, but for many, the term is new. Literacy implies that the skill is on par with learning to read and write. This may sound drastic, but it gives an important indication of the direction in which we are heading socially and economically. Similar to other kinds of literacy, data literacy is about being able to read, work with, analyze, and communicate data.

Today, no one can escape the decisions made by companies, insurers or official institutions based on their own personal and sometimes sensitive data. It is not without reason that the concept of the transparent citizen is repeatedly criticized, because data does not always allow for clear conclusions to be drawn. This is what makes data literacy so important.

If everyone in our digital society is able to deal critically with data, this will ensure greater security and transparency. Companies are also responsible in this regard. Data protection can only be adhered to if employees understand data. Although this has been the hobbyhorse of IT and data departments so far, data literacy must now be carried into other specialist departments as well. It sounds like a lot of effort, but it will bring great benefits, especially for companies.

Many companies are very aware of the added value of data analytics, but even though they are using data more and more, this does not automatically mean that employees’ data literacy itself has increased. An Accenture study from 2020 was able to prove that out of four employees, only one feels competent enough to actively use data. Conversely, this means that three quarters of employees feel overwhelmed when they are asked to work with data. This means that managers urgently need to take action to ensure that this overburdening does not have an impact on performance as data use increases.

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In a study by the Data Literacy Project (DLP), 60 percent of respondents admitted to preferring to make decisions on the job based on gut instinct rather than using specific data. One in three even shies away from working with data to the point that they avoid working with data altogether and even called in sick due to IT, data and information issues.

Whether your own employees become roadblocks or enablers for data-driven companies and companies who want to start using data more depends on whether they are involved in the journey. But there is some good news: The DLP study also showed that 37 percent of employees surveyed felt that data literacy training would increase their data literacy and productivity. Executives and L&D managers now have a responsibility to integrate data literacy into their training plans and promote data among their workforce.

How investing in data competence pays off

The McKinsey Global Institute was able to demonstrate clear competitive advantages in a study on data-driven companies. Data-driven companies:

  • Acquire new customers 23 times more quickly
  • Are 6 times more likely to retain customers long-term
  • Are 19 times more likely to be profitable

Data is a true gamechanger for companies, provided they know how to use their data for decision-making and embed it into their strategy.

On the path to becoming a data-driven company, decision-makers face a number of challenges. They quickly realize that data and intelligent software alone cannot make a company more successful. What’s really needed is a fundamental shift in thinking and training for employees and managers. The type of training can depend on factors such as department, career level, prior knowledge, and yet the entire workforce from marketing to IT should have access to data knowledge.

The focus here should be on on-demand, scalable training rather than “one-size-fits-all” training. The advantage of training programs that are customized to employees’ needs is that they retain what they learn longer because it ideally reflects their real-life work and they can apply these new skills directly.

To the starting line

The starting point needs to be well thought out; after all, a functioning data culture won’t just build itself. In the case of a company-wide transformation, it is advisable to start at executive level or with higher management and from there move on to the specialist departments. To support this transformation, on the one hand there must be a clear vision and companies should discuss what goals they want to achieve; on the other hand, the executive floor and senior management must act as role models.

Data-driven management means making business decisions based on available data and communicating them in this way. It is advisable to form a project team. If possible, this should represent and include all stakeholder and departmental perspectives, which will enable a reliable needs assessment and the required support and power users for each stakeholder group can be identified.

It’s not just businesses and customers that need to benefit from the new capabilities, either. They can also add value to employees’ day-to-day work. By taking a training course in Python, simple, repetitive and therefore often boring tasks can be completely automated. This is an additional incentive for employees, who now have more time at their disposal. So, if you show them what is concretely possible by using data and learning programming languages, an initial obligation becomes an intrinsic motivation.

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Data experts among themselves

Hardly any profession is currently more in demand on the job market than data analysts and data scientists. Companies are literally courting good candidates, but once they have brought them on board, they are often cut off from other departments. As a result, departments fall far behind in their data expertise, and poor briefings from these departments then slow down the data teams. Providing access to data and strengthening data literacy across all departments would lead to more concrete and useful questions for data analytics, and thus put less strain on data team resources, leaving more valuable time for optimizing business strategies.

However, there is a simple solution to this challenge: There are already some pioneers who are ensuring that their data experts move closer to the core business and that the entire company is trained in data literacy in return. This doesn’t require every employee to be able to program and create machine learning algorithms, but they do need to learn to understand the language of data and derive insights. If data literacy is promoted company-wide, then departments will be empowered to perform simple analyses themselves using dashboards, and data experts will be able to focus on where their time is most wisely spent.

Data scientists in particular add tremendous value through their ability to automate processes and handle complex issues. Data analysts should be able to work very closely with business departments and management to properly understand and advise them. It is advisable to make the boundaries between data teams and specialist departments more permeable and to promote company-wide data competence in order to benefit at several levels.

Apples or oranges?

Knowing what you want to measure and what to do with the results is the most important basic requirement for data-driven decision-making. Quite a few companies collect data for which they have never determined a use. In order to handle data prudently and also not to overload your own data warehouse, it is therefore advisable to consider in advance exactly which data is really useful for collection. After all, data is only valuable if the conclusions it allows provide real added value for the company, its customers and, in the best case, society. Some might run the risk of establishing themselves as data-driven just to serve a trend they can take credit for.

But collecting and processing data haphazardly will only cost valuable resources if there is no well-considered question behind it, the answer to which will really move the company forward. Companies should be aware here of how and for what purpose they want to use this valuable resource and make such decisions thoughtfully and with foresight.

Decisions with a common thread

Transparency and traceability form the basis for data-driven decisions. In practice, this means that others must be able to understand business decisions made from the data at hand and that these decisions are communicated internally. In most cases, there is more than one possible way to address challenges. When decisions and approaches are made transparent, stakeholders can contribute their perspectives. This prevents potentially untrue assumptions from going undetected as the basis for decisions.

Too often, we rely on our habits when making decisions, especially when other courses of action are unknown or seem risky. For example, it might look like this: A leader would take half an hour before a meeting to review a list of courses of action and their supporting facts. In the process, data-based suggestions for action can be submitted by employees. The executive can evaluate the suggestions against the supporting data and go into the subsequent meeting informed and able to justify business decisions based on the data.

These behaviors also trickle downward, as employees discover that the proposals for action they submitted are actually implemented based on good, evidence-based arguments. This has the further positive side effect of making employees feel taken seriously, reinforcing data-based reasoning, and increasing overall motivation. Of course, you take a risk when you communicate your decisions transparently. The risk of intelligent questions. If you take this risk, the gain is that you gain new insights and thus increase your employees’ confidence in the business strategy.

Data Literacy – a make or buy question?

It is very tempting to simply buy in the required skills, subject matter experts and software and internally check off the issue of data literacy. Of course, this can be a part of the solution that we don’t want to neglect, but companies cannot rely on this alone. The shortage of data experts is increasing every year, and the labor market is already failing to meet the demand. As the importance and use of data increases, so will the shortage of trained and experienced data experts. This is one of the reasons why the trend is for companies to train their own employees to become data analysts, data scientists and in complementary, job-relevant skills. In this way, they solve the problems of the labor market internally and can develop employees in-house.

Software solutions are also helpful to give non-technical departments access to data and let them perform simple analyses on their own. But an important prerequisite for this is that data literacy in the company is advanced enough that these employees are able to operate the software and convert the results of the analyses into insights, communicate them accordingly and act on them. This is not a matter of course and requires not only prior knowledge, but also an ability to think analytically in preference to going by gut feeling.

This make-or-buy question therefore cannot be answered unequivocally. First and foremost, a company that wants to act successfully in a data-driven manner must not leave its workforce out in the cold. After all, at the end of the day it is the specialist department that must not only be able to understand the data analyses but should also base its actions around them.

Self-service analyses also play an important role in success. They allow all employees to base their decision-making on data. What starts with lightbulb moments develops into a corporate culture. Regulated access to data, the trust of senior management and the empowerment of the company’s own workforce are key. The impact of this change is usually seen through cost savings, faster decision making, streamlined processes, and stronger customer and partner engagement.

For this change to be sustainable, new capabilities, processes and behaviors will be needed across the company, even if a self-service analytics solution is also deployed. Executives, in particular, play a critical role in advocating and spreading company-wide data literacy. If management truly believes that every employee can provide the next breakthrough by gaining key insights from data, it will support positive change. Managers should be aware of the following factors and should communicate them to their workforce:

  • Data must be classified as a type of capital in its own right.
  • All business units must be trained to use data actively and correctly.
  • Employees must understand and experience the benefits for the company, the customers, but also their own work reality.
  • Curiosity and voluntariness should be encouraged through freely available training, info material and mentoring.
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How to make your company data-driven

For companies considering the shift toward being data-driven, the following questions can help determine your own needs and identify hurdles:

  • Is there already a data strategy?
  • What challenges does the company face that predictions from data can help with?
  • Is senior leadership committed to putting data at the center of business decision making?
  • Is there an understanding of what data is being collected?
  • Have employees received basic information in the form of data literacy training?
  • What are the gaps in data literacy at different business levels and in different departments?
  • What knowledge standards need to be put in place to ensure consistent practices can be followed?

For some, the question may now arise whether now, of all times, is really the best time to become data driven. To that end, many companies are currently facing and addressing this very challenge. Regardless of company size and sector – many are making this change at the same time. As a training provider, we offer the support these companies need. We have developed practical training courses for companies and employees from all areas and at all career levels, together with data experts from various specialist areas, which are applied to real business cases.

This enables participants to apply the knowledge they have gained directly to their own work processes. From online courses such as “Data Analyst” and “Data Scientist” or training for managers and employees in skills such as “Data Literacy”, “Data-Driven Management” or “Data Storytelling”, we have every qualification requirement covered. The course content is scalable and supports the demand-oriented development of data skills, which are so crucial for data-driven change in companies.

Do you think it is important to establish data and analytics as the basis for strategic business decisions? Then find out about our courses, funding opportunities and get more useful tips on building data skills.


German Government: „Datenstrategie der Bundesregierung” [05.02.2021]

The Data Literacy Project: “The Data Literacy Project” [05.02.2021]

StackFuel: “Data Literacy: Wie wichtig sind Daten-Skills für Unternehmen und Gesellschaft“ [01.04.2021]

McKinsey Global Institute: “Five facts: How customer analytics boosts corporate performance” [31.03.2021]

Tableau: “How to build a data-driven organization” [31.03.2021]

Accenture: “New Research from Accenture and Qlik Shows the Data Skills Gap is Costing Organizations Billions in Lost Productivity” [05.02.2021]

Louisa Krützfeldt
Louisa Krützfeldt
Louisa is a Berlin native and Junior Data Scientist at StackFuel. She studied biochemistry in her hometown and bioinformatics in nearby Potsdam. After graduating, she worked in medical research, where she was particularly involved in analyzing medical data to research cancer prognosis in children. Analyzing the vast amounts of data was very valuable to Louisa and she fondly remembers the feeling when she was able to extract relevant, actionable information from the data. Louisa wants to bring this valuable skill and her excitement for data analysis to learners at StackFuel and make it a fun experience.

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