How can analysis benefit the German Mittlestand?
It may not be wrong to think that the biggest consumers of analytics services are in the insurance, financial services, retail and healthcare industries. Companies in these sectors are characterized by their size and scale and their ease of name recognition around the world. So it’s very easy to think that analysis is probably more useful for large companies than for smaller ones. The other perception that the data generated by a business grows exponentially with the size of the business and the more data you have the more it needs to be analyzed, which sort of reinforces the above hypothesis.
But nothing could be more misleading though, as we’ll find out by looking at the scope of the analysis for German small and medium-sized businesses, commonly referred to as Mittlestand. These companies are known for their innovative approach, their export orientation and their strong regional and social involvement. Since these businesses are typically family-owned, they are also not weighed down by the quarterly thinking of many large companies, which gives them leeway to think long-term, an ideal prerequisite for collecting, analyzing and exploiting meaningful data.
Business case for analysis for companies with tightly linked value chains
Mittlestand could use analysis in three areas:
- Build a better understanding of internal production processes,
- Understand the needs of customers and partners and
- Discover the relevant characteristics of local and global markets.
The above seems to contradict the typical Mittlestand characteristic – forming tightly linked value chains with strong links between customers, suppliers and other partners – like the easy flow of information in this network and the relatively larger sizes of companies. small ones that allow managers to interact frequently with workers in the shop floor, seem to avoid the need for information through analysis.
However, many times the information thus obtained from a trusted partner needs a lot of validation, contextualization, and additional analysis to become truly useful. By further processing this information using the latest technological means and enriching it with adjacent and historical information, value can be created not only for the company, but for the entire value chain.
Priority areas for analysis for Mittlestand
Data analysis to explore avenues to new markets: Mittlestand companies have very loyal customers, so retention and customer service, which is normally the priority area in large companies, maybe can wait, what couldn’t wait is the deployment of analytics for gain new market access. This is not a simple and straightforward request, but it is worth considering nonetheless. Here, internal data alone may not be immediately useful and data must be acquired from external sources.
For example, data on local market demographics or local businesses could be acquired and then analyzed with internal data creating a better list of targeted prospects allowing not only better marketing strategy and engagement, but also better product fit- market.
Regional data could also be analyzed to predict changes in consumer preferences and thus predict a shift in offerings from local businesses that could be potential customers to Mittlestand businesses. Mittlestand has traditionally been agile enough to act quickly on any spotted change in customer preferences, as the change is spotted on time. Here, carefully designed and deployed data models could truly provide Mittlestand with a competitive advantage by spotting change over time and providing the necessary details of the change.
Internet of Things (IoT) data deluge management and analysis: A 2019 study commissioned by Deutsche Telekom titled ‘The Internet of Things in German SMEs’ reveals that German SMEs are placing more emphasis on new IoT application cases. With the increasing adoption of IoT, data with businesses 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 from the above report is that Mittlestand is currently focusing its IoT investments in the area of “predictive maintenance” (33% of survey respondents said so) where the role of data analytics is. perhaps the most pronounced. The ROI of IoT could be further improved by using analytics to develop new creative ideas for IoT deployments.
Analytics for business operations: If there is one thing Mittlestand cannot take for granted, it is customer loyalty, regardless of their anchoring in a supply chain or their notoriety for the quality of their products. As word of the success of the 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 direction of Mittlestand, it is imperative for them to focus more on business operations despite relatively small sales teams. In fact, business operations such as revenue, service, and sales operations could be converted into analytical command centers to overcome the limitations of having smaller Go-To-Market teams. Mittlestand can also search for companies like Allura Analytica to run or configure their business operations and analytics command centers for them.
It’s now a cliché to say that data is the new oil, however, companies are only now starting to realize that there are many competitive advantages that can be created by focusing on building better analytics engines that can mine. the value even from the last drop of this oil. . Mittlestand can improve its traditional competitive advantage by focusing on analytics to explore and access new markets, better understand and fully automate its production processes through IoT and leverage business operations not only to better understand and serve their customers, but even understand the consumers who ultimately influence the choices their customers make. With the proliferation of cloud-based products and service providers and the advent of niche technology analytics and consulting firms like Allura Analytica It has never been easier for Mittlestand to enter the world of Analytics.
BDI fact check. (2021). The German Mittlestand – Data, figures, facts.
Bianchini, M., & Michalkova, V. (2019). Data analysis in SMEs: trends and policies.
Eclipse IoT. (2021). Open Source Software for Industry 4.0.
Schröder, C. (2017). The challenges of Industry 4.0 for small and medium-sized businesses.
Vogt, A. (2019). Das Internet der Dinge im deutschen Mittlestand: Bedeutung, Anwendungsfelder und Stand der Umsetzung.
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