More and more companies are discovering that data can fuel completely new business models. Boris Otto, director of the Fraunhofer Institute for Software and Systems Engineering (ISST) and head of its research project Industrial Data Space (IDS), explains why all this data also requires a marketplace.

Professor Otto, it’s not just the German government calling data an important raw material for the economy and predicting the rise of a whole new data industry. So why exactly do we need data marketplaces?  

Boris Otto: A key feature of the digitalization of the economy is the evolving role of data. It’s no longer just a “fulfillment agent” for business processes, but rather a strategic resource for innovative business models. This has turned data into a commodity – that is, something that can be cultivated according to cost, time and quality. Companies exchange, sell and trade not just physical goods, but also increasingly with data. This has created a market for data – and that requires a marketplace.  

What are the biggest hurdles to sharing or trading data and how do most companies tackle these challenges?  

Companies have recognized that data is now a commodity, that it has an economic value. Management approaches for the commodity data are still very unsophisticated. Data behaves a bit differently to physical goods: It can, for example, be shared without reducing its value. This presents a challenge for us. Moreover, we have to have an effective process for valuing data and controlling its use. Just like physical goods, like raw materials or finished good.  

As a member of the Industrial Data Space project, Deutsche Telekom has just launched the Data Intelligence Hub, a data marketplace with an accompanying lab for artificial intelligence. What advantages do you see in having this hub implement most of the IDS concepts?  

The IDS initiative stands for data sovereignty, that is to say the ability to decide the usage rules for your own data and supplying the data yourself. This is a crucial precondition for a data marketplace to function properly. I see Deutsche Telekom in a market leading role with the Data Intelligence Hub, because it can provide data marketplace participants a trustworthy and competent data exchange. That is a real competitive advantage.  

You are a co-founder of the International Data Spaces Association which now comprises almost 100 firms. Do your members have jump on the competition when it comes to the raw material data – conceptionally, in mindset or technology-wise?  

We see many cases where the importance of data surpasses that of physical goods, such as for 3D printing. The printing data is almost more important than the production facility and the raw materials. Data is where the action will be in the future, the success of business models will depend on it. That only works, however, when the owner of the data has control over it. This requires an international standard, which we are developing together with the IDSA. Only those involved in the process can help shape this standard. So, yes, the IDSA members have a clear competitive advantage.  

Artificial intelligence and data analytics are currently considered the best possible ways to leverage data. How should companies attempt to tap the potential benefits these provide?  

Essentially, it’s all about making better decisions by using all available data more effectively and efficiently – and that means across the entire company. That means all steps from production and procurement to processing, quality and integration all the way to usage of the data in artificial intelligence processes must be viewed as a continuous value-added chain, as a data value chain. And not a series of unconnected data processing functions.  

Where is this already being done?  

I reckon one good application example would be predictive maintenance. Companies are using it quite successfully already. Generally speaking, wherever data can be used collectively in a so-called ecosystem – that is, in collaboration with various parties for a common customer benefit – everyone profits from it. Examples for this include the usage of data for material properties across the entire product lifecycle, as well as the joint usage of relational and route data for so-called platooning on motorways, where trucks with the same destination are linked virtually.  

Which sectors can benefit most easily or the most overall from AI and data analytics?  

We can generally say that the distinctions between classic industries are fading away. Innovative areas like mobility, energy, logistic and healthcare are no longer limited to their tradition sectors, but innovation here is developing in a rather interdisciplinary fashion. That is why I believe such new ecosystem scenarios contain the greatest potential for AI and data analytics.  

How important is cross-sector data exchange?  

These so-called ecosystems have one thing in common: No party has all the data necessary to launch disruptive solutions for mobility, energy, logistic and healthcare on its own. These parties have to join forces and use the data together. And that brings us to cross-sector data exchange and data analytics.  

Professor Otto, thank you for speaking with us.