Best practice for companies: How to turn data into successful business models

From intelligent failure forecasts for production lines to data-based monitoring in greenhouses and AI-controlled hand prostheses - Plattform Lernende Systeme uses a total of 13 practical examples from SMEs and industry to illustrate in its current report how new business models can be developed from data. The study shows: Collaboration and the sharing of data, technologies and skills in businessnetworks will be essential for companies in the future if they are to remain competitive in a digitalised economy. However, many companies still often lack both the willingness to do so and a secure, trustworthy infrastructure for data exchange. The results of the report were presented by Plattform Lernende Systeme and acatech in a web-talk.

Not only since the Corona Pandemic have the business models of many companies been under pressure to digitize. By linking and analyzing data, Artificial Intelligence (AI) makes it possible to generate knowledge from which new individualized products and services are created. However, small and medium-sized businesses in particular rarely have the necessary data and technologies alone to implement data-driven business models. In addition, organizations often lack expertise in the areas of data analysis and AI. Cooperation with providers of data, technologies and digital platforms can help to build up the required knowledge within so-called digital ecosystems and create added value from it.

"It is essential for the future of our economy that companies work together in digital ecosystems," says Karl-Heinz Streibich, chairman of Plattform Lernende Systeme and acatech president. "European companies today operate in a very tight corset of restrictions, which often differ from country to country, limiting their access to data. Data rooms of relevant size can only be created if companies can exchange their data across company and industry boundaries. This requires secure, open European data rooms with fair conditions for all participating companies, so-called common level playing fields.”

The cross-company networking of data, technologies and competencies is the central component of a digital business model. "However, a successful business model only emerges in this complex interplay of providers, users and operators if all those involved also benefit from concrete added value. To achieve this, we must enrich our established products with intelligent services and give people a new value proposition," said Frank Riemensperger, CEO of Accenture Germany and steering committee member of Plattform Lernende Systeme. "In one of the practical examples in our report, for example, electricity customers not only receive the kilowatt hours they need from their energy supplier, but also a transparent overview of the electricity consumption of their devices. In another case, car insured benefit because they are not assigned to an average calculated policy, but the insurance company determines a tailor-made tariff based on their individual driving style.“

The case studies outlined in the Plattform Lernende Systeme report also show the hurdles that companies often have to overcome on the way to cross-functional collaboration. In addition to the high regulatory requirements for data protection and security, many actors still lack the willingness to share their data or competencies. The study also found that the necessary technical infrastructure and standards for data exchange are often lacking.

The authors recommend creating the greatest possible transparency in the handling of data in order to build trust with all network partners. They see the evaluation of data "on edge" directly at the point of collection and the choice of a European web hoster to ensure data sovereignty as good ways of guaranteeing data security technologically. When building their IT infrastructure, network players should avoid isolated solutions and instead participate in standardization initiatives.

The report "From Data to Value Creation: Potentials of Data- and AI-based Value Creation Networks" describes 13 case studies of successful data-based value creation networks from different sectors such as industry, agriculture, energy supply or health care. The respective value-added network is visualized in a schematic representation with uniform logic in a comprehensible way. Thus, parallels across different industries become visible.

The report is available for download.

The case studies presented will be continuously supplemented by further practical examples and presented on the topic page .

Further information:

Linda Treugut / Birgit Obermeier
Press and Public Relations

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