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How Analytics Can Benefit the German Mittelstand?

How Analytics Can Benefit the German Mittelstand?

It is perhaps not wrong to think that the biggest consumers of analytics services are found in Insurance, Financial Services, Retail and Healthcare sectors. Companies in these sectors are characterized by their size and scale and their easy name recognition worldwide. So, it is very easy to gravitate towards thinking that analytics probably is more useful to big corporations rather than the smaller ones. The other perception that the data generated by a company grows exponentially with the size of the company and the more data one has the more is the requirement to analyze it, kind of bolster the above assumption.

But nothing could be more misleading though, as we will find out by examining the scope of analytics for German small and medium-sized companies popularly known as the Mittelstand. These companies are known for their innovative approach, export orientation, and strong regional and social involvement. Since these companies are generally family-owned, they are also not burdened by the quarter-to-quarter thinking of many larger corporations giving them leeway to think long term, an ideal condition for gathering, analyzing and leveraging meaningful data.


Business Case for Analytics for Companies with tightly knit Value Chains

Mittelstand could use analytics in three areas – 

  • Building a better understanding of internal production processes,
  • Understanding needs of clients and partners and 
  • Discovering the relevant characteristics of local and global markets. 

The above seems at odds with the typical characteristic of Mittlestand – to form tightly knit value chains with close connections between clients, suppliers and other partners – as the easy information flow in this network and relatively smaller sizes of companies which enable managers to frequently interact with workers at the shop floor, seem to obviate the need for insights through analytics.

However, many times, the information thus obtained from howsoever trusted a partner needs lots of validation, contextualization and further analysis to become really useful. By further processing this information through the latest technological means and by enriching it with adjacent and historical information, value can be created for not just the company but the entire value chain. 

Figure 1: Analytics in a typical Mittlestand Value Chain

Priority Areas for Analytics for Mittlestand

Data Analytics for Exploring Paths to New Markets: Mittlestand firms have highly loyal customers so customer loyalty and customer service, which is normally the priority area in big corporations, perhaps can wait, what couldn’t wait is deploying analytics in gaining new market access. This is not an easy and straightforward ask but worth considering nonetheless. Here in house data alone may not be immediately useful and data needs to be acquired from external sources.

For example, data about local market demographics or local businesses could be acquired and then analyzed along with internal data creating a better list of targeted prospects enabling not only a better marketing strategy and engagement but also a better product-market fit.

Regional data could also be analyzed to predict the shift in consumer preferences and thereby predicting a shift in offerings from local businesses who could be potential customers to Mittlestand firms. Mittlestand had been traditionally nimble enough to quickly act on any shift spotted in customer preferences given the shift is spotted in time. Here thoughtfully designed and deployed data models could really provide a competitive edge to Mittlestand by spotting the shift in time and by providing necessary details of the shift.

Figure 2: Finding a Pathway to a New Market

Managing and Analyzing Deluge of Data from Internet of Things (IoT): A 2019 study commissioned by Deutsche Telekom entitled “The Internet of Things in German SMEs” reveals that SMEs in Germany are placing an increased emphasis on new IoT application cases. With increased adoption of IoT, the data with the companies will grow exponentially and it will become imperative to find innovative ways to manage and analyze this data.

Many cloud data platforms like Record Evolution, Snowflake, Dataiku, Panoply are launched keeping in mind the needs of SMEs to host and analyze their IoT data in the cloud. Another important finding of the above report is that the Mittlestand are currently concentrating their IoT investments in the area of “Predictive Maintenance” (33% of the survey respondents said so) where the role of data analytics is perhaps the most pronounced.  IoT RoI could be further enhanced by using analytics for developing new creative ideas for IoT deployments.

Figure 3: IoT Analytics

Analytics for Commercial Operations: If there is one thing Mittlestand can’t take for granted then that is customer loyalty, howsoever entrenched they may be in a supply chain or howsoever well known they may be for the quality of their products. As the word about success of Mittlestand model spreads around the world they can only expect more intense competition from those who will emulate them. In this environment, knowing the long-term orientation of the Mittlestand, it’s imperative for them to focus more on commercial operations despite having relatively smaller sales teams. In fact, commercial operations like revenue, service and sales operations could be converted into analytics command centers to overcome the limitations of having smaller Go-To-Market teams. Mittlestand can also look for firms like Allura Analytica to run or setup their commercial operations and analytics command centers for them.

Figure 4: Analytics Command Center Led by Commercial Operations


It is now a cliché to say that data is the new oil, however, companies are beginning to realize only now that they can create lots of competitive advantage by focusing on building better analytics engines which can extract value from even the last drop of this oil. Mittlestand can enhance their traditional competitive advantage manifold by focusing on analytics to explore and access new markets, better understand and fully automate their production processes through IoT and leverage commercial operations to not only better understand and serve their customers but even understand consumers who ultimately influence the choices their customers make. Given the proliferation of cloud-based products and service providers and the advent of niche analytics and technology consulting firms like Allura Analytica it was never so easy for Mittlestand to take the plunge into the world of Analytics.

See Also
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BDI Fact Check. (2021). The German Mittlestand -Data, numbers, facts.

Bianchini, M., & Michalkova, V. (2019). Data Analytics in SMEs: Trends and Policies.

Eclipse IoT. (2021). Open Source Software for Industry 4.0.

Schroder, C. (2017). The Challenges of Industry 4.0 for Small and Medium-sized Enterprises.

Vogt, A. (2019). Das Internet der Dinge im deutschen Mittlestand: Bedeutung, Anwendungsfelder und Stand der Umsetzung.

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