The core competence of the future: Data Literacy. The growing demand for data experts has long exceeded the supply on the labour market. But how should companies work data-based if their employees do not have the appropriate expertise?
Data-based business models are experiencing an unprecedented boom. About one in seven companies wants to introduce data-driven business models in the future. In particular, larger companies and those that recognized the signs of the times early on are already employing data experts and are now increasing their staff. That is currently about 33 percent – and the trend is rising. Data scientists are more in demand than ever, and many companies come away empty-handed when looking for data experts. This is also reflected in current surveys.
The Importance of Data Literacy for the Economy
In the context of many activities from different areas of the company, data is gaining in importance. After all, 88 percent of executives surveyed by Tableau believe they can make better decisions through data analytics. According to IDC estimates, the global amount of data will grow to 143 zettabytes by 2024. In one third of German companies, annual data growth is between 31 and 60 percent. Additional data sources and data types increase complexity.
In order to be able to use the data purposefully, companies must proceed strategically. This requires a high degree of data literacy – the ability not only to use data, but also to understand, efficiently evaluate and productively apply the structures behind it.
However, a representative survey by the digital association Bitkom shows that with 96 percent, almost all of the more than 600 companies surveyed rate filling vacancies in the field of data analysis and evaluation as challenging. Bitkom therefore advises companies that want to win the "War for Talents" above all to hire skilled workers from abroad and to promote women. Funding is indeed a trend in companies: About a quarter state that they train their own employees in data science due to the shortage of skilled workers.
That's why companies train their own data experts
However, the tense situation on the labour market is not the only reason why more and more companies are relying on training instead of new hires. Many employees first have to learn how to prepare data in such a way that it can really function as a basis for decision-making. Crash courses, seminars and further training are the means of choice. But is it really that easy to build real data literacy? The fact that further education and training turns employees from different disciplines into real data experts seems at least questionable.
Data Literacy – Further training is problematic
Further education and training measures have many advantages: They promote employee satisfaction, promise quick successes and are comparatively easy to implement. But are they also the right strategy to train employees to become data experts? In fact, this approach is problematic for several reasons.
Problem 1 – Quality:
Brochures, flyers and websites of many educational institutions advertise a wide variety of offers in the areas of data analysis, evaluation and management. Their promises: to make the participants "masters" or at least fit in dealing with data. Sometimes in the training center on site, sometimes remotely from the home desk. But how good are these courses really? The quality of education providers and further education is often not apparent at first glance. Many training courses are offered directly by the manufacturers – and thus focus only on their tools and data sources and rarely on a holistic approach.
Problem 2 – Time:
In addition to the unclear quality of many training courses, there is another problem of further training closely related to this: No master has yet fallen from the sky. Real competence – and above all data literacy – can hardly be acquired in a short time. Often, even long training paths, such as a bachelor's and master's degree, are not enough, because real data experts usually have many years of professional experience behind them. No boot camp or weekend course can convey this wealth of experience in a few days, weeks or months. Above all, not all employees without previous knowledge and certainly not parallel to the already stressful day-to-day business – after all, the employees also have to do their actual job.
Problem 3 – Costs:
The financial aspect of the training endeavor should not be underestimated. Further education and training to become a data expert are expensive! Certificate training courses from renowned providers such as the Fraunhofer Academy cost around 4,000 euros for four training days. If it is to be a part-time study at a private distance learning university, the sum quickly amounts to almost five-digit amount. Even if companies are willing to invest these sums in the training of their employees, they have no guarantee that they will ultimately pay for themselves. New tools, technologies and the increasing complexity of data volumes and sources ensure that the level of knowledge is quickly outdated, even shortly after a training measure.
Problem 4 – Shortage of Skilled Workers:
Skilled workers are not only difficult to find in the field of data science. Many other positions in companies often remain unfilled for a long time – especially those that have a certain affinity for data: so-called STEM professions. Resources are already scarce, and employees are often already overloaded. If they are now increasingly used as data experts and spend the majority of their time on complex data analyses, a gap arises elsewhere. This would then have to be refilled at enormous cost and time.
Bring real data literacy into the company thanks to the data literacy platform
One thing is certain: training alone will not be enough to turn employees into data experts. However, data literacy must be available to everyone in the long term – only then can companies really benefit from data-driven business models. The most effective strategy is the use of innovative technologies that have set themselves exactly this goal: to democratize data literacy.
With a so-called data literacy platform, all the data knowledge available in the company is immediately accessible to every employee – and usable. With the help of AI-supported technology, information, structures and logic behind data become visible to everyone in the company. A cross-system search engine puts data in its context and helps to avoid redundancies, break down silos and share knowledge.
A large part of the inquiries can now be solved by the specialist departments themselves, because they have access to relevant data knowledge and can apply it directly. For data scientists and other data experts, this new technology is a relief and they have more time for their actual tasks. In addition, they have access to the work results and the wealth of experience of their colleagues, because all version histories and documentation can be found centrally in one place and can be edited directly in the code.
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 greatest competitive advantage.