Digital data play an essential role in the design, planning and control of industrial processes. As well as being needed as input, data are also generated and output in large quantities and can be utilized to develop new products, services, and business models. Fraunhofer collaborates with numerous companies in projects to develop concepts and methods that will enable the digital treasure trove to be utilized in a secure environment, thereby extracting more added value from industrial processes.
New business models
Björn Heinze is in a hurry as usual. He’s been called to a last-minute meeting in Cologne that starts in two hours, which leaves him very little time to get there from his engineering studio in Bonn. Heinze doesn’t own a car: he sold his last one a while ago, when parking became almost impossible in the downtown areas of the city. Instead he picks up his smartphone and books an autonomous electric vehicle via a car-sharing platform. In next to no time, his order has been processed and the car rolls up to his doorstep, guided there by the user profile he previously registered on the service provider’s website. The journey would normally be a test of patience, because the roads to Cologne are frequently congested, but the self-driving vehicle’s onboard navigation system quickly finds an alternative route that brings Heinze to his destination relaxed and on time.
Admittedly, this is a fictive scenario that doesn’t quite work out like this at present, but the technology on which it is based is very real. Many of the processes involved already exist – scheduling the meeting, booking a vehicle and paying the rental fee, geolocation, finding alternative routes, locating a charging station in Cologne to recharge the vehicle’s batteries for the homeward journey (because there might not be enough juice left to cover the whole distance), and invoicing the amount of electricity consumed. The protagonist in this story might not be aware of it, but huge amounts of data flow back and forth in the background, shuttling information between the car-sharing website, the parking facility operator, the utility company and its charging infrastructure, the traffic information service, the rental vehicle, and of course Björn Heinze himself.
The economy of data
The mobility revolution is in full swing. Auto manufacturers have woken up to the idea that the old business model of selling cars is becoming obsolete, and that they need to rebrand as mobility providers. People in the automotive industry are beginning to ask themselves whether the value they create by providing digital services might one day soon exceed the value they create by selling vehicles. Similar reflections are gaining ground in many other industries, for instance in medical and mechanical engineering, where the cost of devices and machines is falling steadily and an increasing share of value creation is accounted for by services such as predictive maintenance. The economy of things is gradually giving way to an economy of data – with far-reaching consequences. What these consequences are, and how industry can draw the greatest benefit from them, are questions currently being investigated by numerous Fraunhofer Institutes.
The rise of data-driven business models is forcing companies to completely rethink their corporate structures and the way they organize their internal and external supply chains. Whereas, in the past, systems were painstakingly configured by highly trained engineers, built-in sensors have now taken over their job – using systematically collected and recorded human know-how, which by rights ought to belong to the originator of that knowledge and, in many cases, would normally constitute an industrial secret. On the other side of the equation, online commerce has created a situation in which the open sharing and linking of data creates new assets. This is tantamount to a paradigm shift in the concept of data management:
- Data have become a commodity that can be freely mined and traded, but also be manipulated or stolen.
- Many market players share jointly generated data instead of keeping it to themselves.
- Data ownership and usage rights are often undetermined, and can only be clarified after long negotiations between the parties concerned.
- Platforms combine data from many different sources, and may include personal or other data that can be traced back to an individual person or group of persons.
Data are a valuable asset for companies because they increasingly serve as the basis for new business models, but at the same time represent a new and increasing cost factor due to the elevated consumption of resources, such as the energy required to supply the processing power for the high-performance servers and databases so essential in this age of big data. So what is the best way of attributing a monetary value to data? The Fraunhofer Institute for Software and Systems Engineering ISST in Dortmund has spent many years looking into this question.
Companies can measure the value of their data assets on the basis of:
- The cost of registering ownership rights and updating proprietary databases.
- The value in use of the data. Predictive maintenance is a pertinent example because it results in cost savings by detecting faults before they become acute and thereby avoiding downtime.
- Market value. One way to determine this is by offering user data for sale and seeing how much buyers are willing to pay.
But what are the implications in the case of personal data, which are subject to strict privacy laws in Germany and other countries? How can a monetary value be placed on medical records held by hospitals and doctors’ surgeries, when people’s health is at stake? What special rules need to be applied in respect of official data gathered in the context of public administration processes? Can data concerning critical infrastructures such as the power grid or the Internet be treated as a saleable commodity without running the risk of triggering the collapse of entire market sectors or society as we know it? As the long list of questions shows, many different factors and legal issues can influence the value of data and the conditions under which data may or may not be exploited. It’s also sometimes necessary to look at certain issues from an international point of view, because differing interests may need to be reconciled.
Digital treasure: Find it, keep it safe, exploit it
Since the advent of Industrie 4.0, many industrial companies have adopted a data lake strategy, i.e. digging up all the data they can find within the company, tipping it into a vast repository or “lake,” and only afterward deciding what to do with it. But this type of strategy doesn’t necessarily produce the desired results because it leaves the field open for outsiders to step in and make money from data they didn’t even produce themselves. It’s easy to guess who these outsiders might be: first in line are the giant U.S.-based IT corporations, especially Google and Amazon. They are busily expanding into other sectors, backed by their expertise in the smart processing of large quantities of information. Google, for example, exploits freely available data and is avidly buying up companies that generate data. The best-known example is Nest, a manufacturer of smart heating control systems. Google’s interest doesn’t lie in the hardware but in gaining access to the data transmitted to and from the heating systems and the users’ smartphones, and other smart-home applications in the future, in order to sell digital services. All that without having to pay for the data on which the services are based.