Data-based decision-making: What it means and how it works in SMEs
Executives make dozens of decisions every day. From production planning to marketing budgets to personnel strategy. Many of these decisions are still based on experience, intuition, or simply on what has always worked. But in a market environment that is changing faster than ever before, this is often no longer enough.
Data-based decision making, also known as data-driven decision making, offers a way out of this dilemma. It doesn't completely replace gut feeling, but it adds an important component to it: objective facts. This is an enormous opportunity for medium-sized companies. Because while corporations have long been relying on business intelligence and predictive analytics, many SMEs are not yet exploiting this potential.
What does data-based decision-making mean in practice?
In essence, the term means something very practical. Companies systematically collect data from their business processes, analyze them and derive insights from them that substantiate their decisions. This could be sales figures, customer behavior, production data, market trends or customer feedback.
The decisive difference from conventional procedures is that decisions are made on the basis of measurable facts rather than on subjective assessments. This does not mean that experience and industry knowledge are becoming superfluous. On the contrary, the best results are achieved where data and human expertise work together.
Only 6 percent of German companies fully utilize the potential of their data, 31 percent use it heavily, 42 percent use it less and 18 percent don't use it at all. Accordingly, only 7 percent see themselves as pioneers in data-driven business models.
Why SMEs in particular benefit from data-based decision-making
For medium-sized companies, data-based decision-making offers strategic added value.
Agility as a trump card
SMEs often have leaner structures and shorter decision-making processes. As a result, findings from data analyses can be translated into concrete strategies and measures more quickly than in large corporations with their complex coordination processes. As a medium-sized company, anyone who combines this agility with data-based precision gives themselves a real competitive advantage.
Resource pressure as a driver
Data-based decision-making helps to use budgets more efficiently, avoid bad investments and identify potential where it is greatest. One BME survey this is confirmed among medium-sized buyers: 65 percent of respondents see the improvement of data analysis and decision-making as one of the driving forces behind their digitization efforts.
Customer centricity as an opportunity
SMEs are often closer to their customers than large corporations. If you combine this proximity with systematic data analysis, you can record customer needs even more precisely and make better business decisions.
The prerequisites for data-based decisions
The journey to data-driven decision-making doesn't start with the purchase of expensive software. It starts with the right foundation, which is often already available but is not used. Who is concerned with the topic Collect, structure, and use data has dealt with, knows the starting position. In most companies, valuable data is lying dormant in ERP systems, CRM databases, Excel lists and e-mail mailboxes.
It is crucial to make this data accessible and usable. This requires three essential components.
Data quality as a foundation
Data is only as valuable as its quality. Incomplete customer data, outdated product information or inconsistent recording standards lead to incorrect conclusions and therefore to poor decisions. Many quality problems can be resolved with manageable effort. Uniform data formats, clear responsibilities and regular adjustment processes create the necessary basis.
The right tools
Tools such as Power BI, Tableau, or Google Looker Studio also enable smaller teams to visualize and analyze data and identify patterns. Business intelligence is now also accessible to SMEs. More important than the tool itself is the question of which data is actually relevant to decision-making.
People with data literacy
The biggest challenge is often not technology but competence. Data-based decision-making requires employees and decision makers who can read, interpret and translate data into recommendations for action. That doesn't have to mean that everyone has to become a data scientist. But a basic understanding of data analysis and data literacy should be available in relevant areas.
In the master classes at the data:unplugged festival, experts show how data literacy can be built up in companies. The formats are practical and teach methods that can be used directly.
From theory to practice: How to get started
Data-based decision-making doesn't have to start with a major project. On the contrary: The most successful approaches start small, deliver quick results and build on them.
- Analyze existing decision-making processes. Where are important decisions currently being made, and on what basis? What information is regularly missing? Where do you rely on assumptions that you could actually verify? This inventory shows where the biggest lever lies.
- Define relevant goals and key figures. Not every number that can be measured is relevant. It is crucial to identify KPIs that are actually related to company goals. If you want to attract more new customers, you need different indicators than someone who wants to increase efficiency in production.
- Tapping into initial data sources and making them visible. A simple dashboard that shows the most important key figures at a glance is often sufficient. The psychological effect cannot be underestimated. When figures suddenly become visible, it changes the way problems and opportunities are discussed.
If you want to deepen these processes, you will find in the article on Data analysis in medium-sized companies further practical approaches.
Typical pitfalls and how to avoid them
The journey to data-driven decision making is not without challenges. Some mistakes are repeated in many companies.
Data alone doesn't make the difference
Data shows connections, patterns, and trends, but interpretation requires human judgment. Anyone who blindly trusts data without considering the context sometimes makes worse decisions than someone who relies on intuition and experience.
The flood of data
With increasing digitization, the amount of available data is growing exponentially. This can quickly become overwhelming. Successful companies focus on a few but meaningful key figures instead of trying to analyze everything.
Resistance in the company
Data-based decision-making means change — and change opens up great opportunities. With continuous persuasion and initial rapid successes, confidence in the new approach grows. This makes the added value clearly visible and the acceptance of data-based decisions is constantly increasing.
AI as an accelerator for data-based decisions
Where complex manual analyses were previously required, AI tools can now identify patterns, create forecasts and generate recommendations for action in seconds. For medium-sized companies, this means that it is easier to get started with data-based decision-making.
AI-powered co-pilots, which help decision makers analyze data, are particularly interesting. They also enable people without a technical background to use natural language to gain insights from company data and make well-founded decisions. It is important to critically examine AI results and combine them with human expertise. In this way, AI processes large amounts of data quickly, while humans contribute experience and judgment. This collaboration will help companies make even better data-based decisions.
The key lies in the combination. AI takes over data processing, analyses large amounts of data and provides suggestions. People contribute context, experience, and critical thinking.
Practitioners on the Mittelstands Stage at the data:unplugged festival 2026 will show how this combination of technology and human expertise works in reality. There, companies share their specific experiences with AI-supported decision-making.
Cultural change: More than just technology
Data-based decision-making is primarily a cultural challenge. It requires a corporate culture that focuses on objective facts, even though they can be unpleasant. A culture that makes it possible to critically question hypotheses and in which learning from mistakes is more important than sticking to best practices.
This change starts at the top. When managers set an example that they base their decisions on data and facts and are open to new insights, this is transferred to the entire organization.
Building a data-oriented culture takes time. It requires patience, continuous training, and celebrating small successes. But it pays off. Companies that systematically integrate data into their decision-making processes achieve significantly better results in improving their decision quality.
Conclusion: The right time is now
Data-based decision making is a change in the way companies are run. For SMEs, it offers the opportunity to act on equal footing with larger competitors. The start doesn't have to be perfect, it just needs to take place: a small pilot project, an initial dashboard, training for the most important decision makers.
You can find out how other SMEs are successfully taking the path to data-based decision-making at data:unplugged festival 2026 on March 26 & 27 in Münster. In master classes, on the SME stage and in direct exchange, you will learn how other companies make well-founded decisions based on data. Practical, honest and on equal footing.
Data-based decision-making affects all areas of the company. For effective implementation, it is crucial to involve key people in your company, train them and positively prepare them for deployment. data:unplugged stands for a broad and well-founded transfer of knowledge from which the entire business team benefits. Get a ticket for yourself and your core team now!
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