The interviews aim to collect and describe the experts’ idea and their experiences about Big Data and related topics. We also invite people with the competence to participate in this scientific activity by simply contact us via Email.

Dr. Johannes Winter


Dr. Johannes Winter
Managing Director at Lernende Systeme – Germany’s Artificial Intelligence Platform &
Head of Technology Department at acatech – National Academy of Science and Engineering

Twitter: @jw4null
Johannes Winter is Managing Director of “Lernende Systeme – Germany’s Artificial Intelligence Platform” and Head of Technology at acatech – National Academy of Science and Engineering. Previously he was the Head of Economic & Science Relations and Personal Assistant to Professor Henning Kagermann, the father of Industrie 4.0. Johannes Winter holds a PhD in regional economics from University of Cologne and has served as lecturer at University of Applied Sciences and Economics in Munich. His prior work experience includes positions in automotive industry, consulting as well as academic research.


1. How is digital transformation changing the economy?

In history, industrial production was transformed several times: first by steam power, then by electricity and nearly 50 years ago by automation. Over the last decade significant progress was made in various fields like microelectronics, robotics, photonic, machine learning, cloud computing and real-time analytics which led to the next industrial revolution. Some years ago, my colleagues from acatech – National Academy of Science and Engineering looked for solutions to secure Germany´s competitiveness and published in this context the concepts so called Industrie 4.0. What does it mean? In a nutshell, the fourth industrial revolution, or Industrie 4.0, can be characterized by three acronyms: smart, hyperconnected and autonomous. The Internet of Things is entering the factory connecting smart products, smart machines and worker equipped with smart devices.

2. Why is big data important?

In this context data become more and more independent economic goods, have a value and are base of innovative and profitable business models. Once they have left factory, smart products are still connected via the internet and exchange massive volumes of data during their use. These big data are refined into smart data, which can then be used to control, maintain or enhance and improve smart products and services. They generate the knowledge that forms the basis of new business models.
The consolidation and refinement via real-time analytics and artificial intelligence is usually done in data-rich digital platforms, which will soon be the predominant marketplace.

3. Which are the most significant challenges and opportunities related to big data technologies in your opinion?

Quite a few companies have already connected smart products to the internet and have started collecting and evaluating data. Ideally those platforms should combine device management with easy connectivity, data storage systems and an App Store open for customized data-driven services provided by an open digital ecosystem. The quality of the digital innovation ecosystem and how fast it can be established will be crucial for a successful implementation of new digital business models. In addition, several challenges must be answered regarding financing, reliability, data security, IPR-protection, and finally standardization.

4. How do big data technologies impact the Future of Work and industry 4.0?

The digital transformation will enable companies to react faster and more precisely to changing customer needs and new market conditions. It is already well understood that a fast implementation of data-based business models and a high level of flexibility, adaptability, and willingness to change among organizations and its employees are crucial for success in the face of global competition. Key factors in the successful introduction of big data include the acceptance of new technologies by employees and the design of attractive forms of work. At the same time, the higher degree of flexibility, in turn, opens the opportunity for workers to also achieve a higher level of work-life-balance and to safeguard their long-term employability by personalized re- and up-skilling measures. In this context, the ability of workers to learn (and retrain) throughout the span of their careers is key to ensuring their future employability (lifelong learning). Companies share the responsibility by providing the corresponding education and training, and their employees obviously benefit from these measures.

5. In your opinion how can big data be combined with AI and what can be the resulting benefits?

Data are receiving a monetary value – which is what inspires some to speak of the data economy or data capitalism. The required data are merged, analyzed, and interpreted on digital, usually cloud-based technology platforms, with the help of AI and machine-learning methods and tools. Autonomous software systems such as self-learning robot advisers or assistance systems contribute to a personalized and convenient user experience. Reconfiguration is no longer a manual process but autonomous and dynamic. This provides us with highly adaptable processes on all organizational levels for the first time: from the factory floor to the business level, which is often referred to as a new wave of business process reengineering. As a result, the collection and use of data will become omnipresent. Self-learning and autonomous systems driven by artificial intelligence use that to make independent decisions, also building on their own learning processes. These developments represent a challenge, but above all an opportunity for Germany and the US. The guiding principle of action here should be that digitalization is primarily shaped by people, for people. In order to design self-learning systems according to the needs of humans and society, the German Federal Ministry of Science and Education (BMBF) and acatech have launched Germany’s AI Platform “Lernende Systeme” in 2017. The Platform brings together leading expertise from science, industry and society and consolidates the current state of knowledge about big data and artificial intelligence. They point out developments in industry and society, analyze the skills which will be needed in the future and use real application scenarios to demonstrate the benefit of data-based systems.

6. Would you be interested in collaborating with USA institutions (companies and academia) for projects in the domain of big data?

Yes, of course. Innovation needs stronger collaboration, between scientific disciplines, all societal groups and on an international level, in particular when it comes to common regulation, industrial standards and markets. Just think of new data-driven business models. They will not be created by single companies but in digital ecosystems cross different industries and countries with large multinationals as well as SMEs and start-ups. In any case, international networking and open co-innovation are the critical success factors for the innovation system of the future.