Im Rahmen der Deep Dive Workshops beim DIGITAL DEMO DAY am 09.09.2021 hielt Justus Benning einen Workshop "Data Analytics: Opportunities for SMEs". In diesem Beitrag hat er alle wichtigen Insights aus dem Workshop für euch zusammengefasst.

Management Summary:

A quick introduction to the field of Data Analytics was followed by a live interactive analysis session with audience participation. Afterwards, the participants reflect on the lessons learned and possible transfer opportunities to leverage the power of data in SMEs.


Data analytics has become a fashionable term that is often used in conjunction with buzzwords such as AI and digitalization to give projects and initiatives a modern slant. Enormous economic potential is attributed to the topic. However, if one looks at the reality of German SMEs, you see that less than 6 % of companies actively use AI or learn from their data(Rammer 2020). The goal of the Deep-Dive Workshop was to provide clarity with a sober and realistic view of the topic. And thus, to promote the use of analytics in companies with a few key learnings.


The most important insight for companies on this topic is that data analytics should not be used to build technical solutions for their own sake, but to solve real business problems. A new technical solution that changes an existing process will only be adopted by employees if the new workflow significantly simplifies their daily routine. Customers will only use new digital services if they offer them added value. To realize this goal, it is best to create the analytics use cases with a customer-centric method, such as the Value Proposition Canvas by Osterwalder & Pigneur (see Figure 1).

Figure 1: Value Proposition Canvas,Osterwalder & Pigneur 2014

In this method, the daily woes of the customer(which can be external or internal, i.e. an employee) are confronted with the potentials of the solution.

The second most important insight for companies is that many analytics problems have already been solved technically and that work steps are often repeated in various use cases. Therefore, the possibility of using an analytics or machine learning platform in the company should be examined. These offer a high benefit for specialists, who can use them to scale, maintain and support their applications and models more easily. On the other hand, possibly more importantly, they democratize the model creation process through low-code and no-code functionalities. On some platforms, it is possible to create multiple predictive models without a single line of code, compare them against each other, and deploy the best one immediately (see Figure 2).

Figure 2: Platform workflow

By leveraging this technology, many of the current obstacles of implementing analytics in SMEs can be overcome. They keep maintenance cost low, lessen the gap of needed expertise, and provide visual aids to create real insights – even for non-experts.


Rammer, Christian (2020): Einsatz von Künstlicher Intelligenz in der Deutschen Wirtschaft. Stand der KI-Nutzung im Jahr 2019. Hg. v. Bundesministerium für Wirtschaft und Energie (BMWi). Online verfügbar unter

Osterwalder, A., Pigneur, Y.,Bernarda, G. and Smith, A., (2014). Value proposition design: How to create products and services customers want. John Wiley & Sons.

About the author:

Justus Benning | Group Head Information LogisticsFIR - Institute for Industrial Management at RWTH Aachen University

Having studied industrial engineering at RWTH Aachen University along with a semester abroad at Korea University, where he enhanced his knowledge in Business Analytics, Justus Benning is highly interested in finding ways to leverage the potentials of Analytics and AI in enterprises. He is currently heading the Programming Team and Group of Information Logistics at FIR, a non-profit, cross-industry research and training institution at RWTH Aachen University in the field of business organization, information logistics and corporate IT with the aim of creating the organizational foundations for the digitally networked industrial enterprise of the future.

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