Data management: unlocking strategic value from data in the Mittelstand

This article has been translated for your convenience using machine translation. Reasonable efforts have been made to provide an accurate translation. The official text is the German version of this content.

“Do more with less.”
A phrase that may sound cynical to some – often associated with growing demands and shrinking budgets – is actually a core strength of many mid-sized companies. Small and medium-sized enterprises (SMEs) have always had to think innovatively, act efficiently, and compete with limited resources. This ability isn’t just a necessity, it’s deeply embedded in their DNA and forms the foundation of their economic success.

In an era of exploding data volumes, one thing is becoming clear: those who use data strategically can truly do more with less. And that’s precisely where a significant opportunity lies for the Mittelstand.

Why data management is becoming critical for the Mittelstand

The German Mittelstand is widely regarded as the backbone of the national economy and for good reason. Around 99% of all German businesses fall into this category, providing over half of all jobs and training three out of every four apprentices.

However, this success is rooted in the past. In recent years, a crucial topic has been largely overlooked: the strategic use of data. And yet, this is where massive potential lies: to increase efficiency, drive innovation, and stay competitive in the long term.

The benefits of working with data are already measurable: 65% of data-driven SMEs outperform their competitors financially and the gap continues to grow. Those who fail to invest in professional data management risk being left behind.

What does strategic data management really mean?

Strategic data management defines how a company systematically collects, stores, structures, governs, analyzes, and utilizes data. It also clarifies which business goals are being pursued and how data contributes to achieving them.

In organizations that prioritize data and make full use of it, data management is tightly integrated into the overall business strategy. Here, data becomes a driver of efficiency, innovation, and growth.

This strategic approach differs significantly from day-to-day, operational data handling. While operational efforts may involve maintaining Excel lists or generating occasional reports, strategic data management follows a broader, long-term perspective. It integrates multiple data sources, establishes structure, and ensures that data can be accessed and used across the entire organization. This directly influences how decisions are made.

Common data sources in SMEs

From manufacturing to logistics to sales, digitalization is impacting every part of the business. Machines provide real-time data, supply chains are digitally mapped, and customer interaction is increasingly digital. The resulting data is far more than a technical byproduct, it’s a strategic asset. Failing to capture and use this data means leaving valuable insights on the table.

Typical data sources include:

• Machine and sensor data (e.g., from Industry 4.0 applications)
• CRM systems (customer information, sales activities)
• ERP systems (inventory, processes, finance)
• External sources (e.g., market data, weather information, logistics partners)
• Unstructured data (emails, PDFs, handwritten notes)

While large corporations are already developing data-driven business models, many SMEs are still playing catch-up. They often face similar challenges: data exists in silos, is unstructured, or isn’t captured at all. There's also uncertainty about whether and how this data can generate real value.

Key challenges in SME data management

The fragmented nature of many companies’ data landscapes remains a major challenge. Data is gathered in different departments, but often isn’t connected. These silos make it difficult to get a holistic view of the business and in turn, hinder strategic decision-making.

A company-wide data strategy is rarely in place. In a recent Possehl Group survey, only 2% of companies reported having any formal data strategy at all. Without clear responsibilities, long-term data initiatives are hard to implement. On top of that, legacy IT systems – often the result of years of incremental growth – are difficult and costly to integrate.

And then there’s data protection: 88% of companies cite compliance and privacy regulations as a major barrier to digital transformation. Adhering to GDPR and the German IT Security Act is essential, but for many mid-sized businesses, it can be a resource-intensive process.

From challenge to action: making data work

Rather than getting stuck in structural gaps or IT legacy issues, a shift in perspective can help: What use cases can already be implemented with the data that’s currently available?

There’s no need to jump straight into complex, digital business models. Even small, focused applications can deliver quick wins:

• Predictive maintenance in production: Sensor data can be used to forecast equipment failures, reducing downtime and lowering costs.
• Sales optimization via CRM data: A structured analysis of customer data can enable targeted offers, reveal cross- and upselling opportunities, and shorten sales cycles.
• Process improvement using ERP insights: Analyzing stock levels and throughput times can help identify bottlenecks and adjust production plans accordingly.
• Supply chain transparency: Smart integration of internal and external data sources can help identify risks early and increase supply chain resilience.

Collaborative data use: an untapped lever in the Mittelstand

Most companies focus internally when it comes to data: optimizing processes, improving customer insights, or increasing efficiency. But in the Mittelstand, there’s a powerful – and often underutilized – lever in looking beyond internal borders: using data collaboratively across companies.

Many SMEs operate in tightly integrated value chains with strong interdependencies. Machine manufacturers work closely with suppliers, retailers with producers, and logistics providers with customers. These networks generate huge volumes of data but this data is usually only used in isolated pockets.

By managing data collaboratively, new value can be created: improved supply chain transparency, co-development of products, or efficiency gains through standardization.

However, collaborative data management requires trust and clear frameworks. Data protection and IT security must be guaranteed, as must controlled access to shared information. Transparent governance is also essential.

A potential future model: Companies co-create so-called “data ecosystems” where every party contributes to and benefits from shared data, through better forecasting, automated workflows, or entirely new services.

Conclusion: from data administrator to data strategist

Data collection is not a goal in itself, it should always serve a strategic purpose. With pragmatic, focused initiatives, SMEs can turn data into a true competitive advantage.

As is so often the case, success starts with the first step. Starting small with pilot projects, learning from them, and then scaling up can be a smart approach – just like manroland Goss is demonstrating (note: the webinar recording is only available in German). Momentum increases when internal data stewards are appointed: people who keep track of progress, push projects forward, and make results visible.

If you’d like to explore this topic in more depth, we recommend our whitepaper: “Data as a Growth Driver in the Industrial Mittelstand” (note: this resource is only available in German). There, we introduce a two-stage approach that helps companies turn their data strategy into real-world implementation.