How do millions of individual data records become an instrument that accurately reflects the economic situation of German small and medium-sized enterprises? And what does it take for operational data to generate genuine economic insights—insights that do not merely describe developments but help us understand them?

The presentation “From Individual Data Records to an Economic Indicator – the DATEV SME Index” takes the audience on precisely this journey: from the underlying data base to meaningful economic insight. The focus is not on the sheer volume of data, but on its structure, consistency, and ability to make economic reality visible.

Because data does not derive its value from scale alone. What truly matters is whether data can be semantically structured, meaningfully linked, and transferred into a robust analytical context. In practice, this step is often the greatest challenge. Data is rarely available in an ideal form. It is distributed across organically grown system landscapes, originates from different application logics, and follows historically evolved structures. Bringing these heterogeneous data sets together, harmonizing them, and translating them into a common analytical language is complex—but indispensable.

This is precisely where the DATEV SME Index comes into play. As an IT service provider for the tax advisory profession, DATEV has access to highly granular data processed in its own data centers. Building on an initiative launched by Robert Mayr, CEO of DATEV eG, a project was initiated to systematically unlock these data for cross-cutting economic analysis for the first time. The result is a business cycle indicator based on real company data, enabling a timely, data-driven view of the economic situation of the German SME sector.

The presentation retraces this journey in a transparent and structured manner—from the initial conceptual considerations, through data exploration and technical as well as methodological processing, to the interpretation of results. It becomes clear that such an indicator is far more than a statistical product: it is the outcome of numerous methodological decisions, architectural choices, and organizational coordination efforts.

A central focus is the question of how granular company-level information can be transformed into robust macroeconomic statements. Which aggregation logics are appropriate? How can distortions be avoided? And how can different industries, regions, and time periods be made comparable without compromising the informational value of the data?

Through concrete insights into the methodology of the DATEV SME Index, it becomes tangible how economic developments are made visible—and where the methodological limits lie.
Another key emphasis of the presentation is the set of challenges inherent in this kind of data work. Data quality, methodological robustness, and, in particular, issues of data protection and anonymization play a crucial role. The responsible handling of sensitive company data is not only a regulatory requirement, but also a prerequisite for trust and acceptance.

The DATEV SME Index serves as an illustrative example of how complex data assets can be transformed into a reliable economic leading indicator. It reveals what would otherwise remain hidden at scale: dynamics within the SME sector, structural shifts, and economic trends—precise, timely, and data-driven.

In the end, the key insight is clear: large volumes of data are not an end in themselves. Their value only emerges when they are intelligently modeled, responsibly processed, and placed into a meaningful context. This is where data becomes true orientation.