How SMEs can benefit from secure data exchange
For years, both industry and the research community have been talking about the value of data. Few if any enterprises can still doubt that data has become the raw material of new business models. But while large corporations are already developing their own data strategies, many small and medium-sized enterprises remain unsure about what to do. Their caution is understandable – data is a valuable commodity, and you don’t just give it away without a second thought. Businesses have serious concerns about losing control or leaking precious know-how. However, this means that the potential to improve efficiency and create new services by working with partners often remains untapped. This is a missed opportunity, especially for SMEs, since partnering with other organizations can help them drive innovation much faster than they could on their own. This is where the Ferdinand Steinbeis Institute (FSTI) research project “DT-INFODATVO” comes in. The joint project with the University of Stuttgart’s Department of Business Administration and Information Systems II is funded by the Federal Ministry of Education and Research.
FSTI research fellow Maximilian Werling outlines one of the key challenges: “Our research has found that while businesses are undoubtedly sitting on valuable data sets, it’s not easy for them to decide whether, how and with whom to share them”. After all, not all data is equal. Some data is highly sensitive, whereas other data may be relatively uncritical. While production or customer data may be directly linked to a company’s competitive advantages, other types of data only add value when shared, for example if they provide evidence of sustainability or enable collaboration across supply chains.
When enterprises are reluctant to share data, it is often due to a lack of data evaluation structures and tools. If they are unable to say how carefully a data set needs to be protected or what value it may have for third parties, then they can’t make an informed decision about the implications of sharing it. As a result, opportunities to improve efficiency and develop new services or joint innovations often go begging.
A methodological approach to building trust
The research project “DT-INFODATVO” aims to develop a method for characterizing and evaluating data that can help enterprises and “data trustees” to create a sound basis for deciding whether or not to share data. Data trustees are independent intermediaries who match data supply and demand and ensure that the data exchange is conducted in a fair way.
“As part of the project, we have developed two key tools. The first is a data asset evaluation matrix that enables systematic recording of attributes such as type, sensitivity and purpose. The second is a data exchange process model which sets out how providers, users and data trustees can work together in a structured manner. This covers everything from describing the data and matching supply and demand to the subsequent negotiation of performance and consideration”, explains Dr. Jens Lachenmaier, Professorial Fellow at the FSTI.
A structured and comprehensive description of the data is key. Purely technical metadata such as the format or available interfaces is not enough to assess a data set’s value and the associated risks. In order to enable informed decision-making, this data must be supplemented by business metadata – i.e. information about the data’s usage context, how much value it is expected to add, or how carefully it needs to be protected. Rather than just making data technically usable, enriched metadata like this also allows it to be classified for business purposes. It enables targeted data set positioning for potential data providers, while the structured description lets potential users see whether the data on offer actually meets their needs. And data trustees also need descriptions to be as complete as possible in order to enable structured, reliable matching.
This approach opens up new opportunities, especially for small and medium-sized enterprises. The ability to systematically describe data makes it easier to position themselves in data ecosystems and join industry-wide initiatives like Catena-X or the Mobility Data Space. As well as optimizing their internal use, it means that data sets can also be offered to the rest of the value chain. Initiatives like these also give enterprises access to external data sources that can unleash innovation, for instance by enabling them to develop and market new digital products and services that they would struggle to deliver on their own. And, last but not least, transparency regarding usage rights and protection requirements creates trust between them and their partners, thereby largely alleviating concerns about loss of control.
A real-world example: spare part availability in the mechanical engineering industry
An SME in the mechanical engineering sector regularly receives customer enquiries about the availability of certain spare parts. But it often lacks up-to-the-minute data about inventory levels and supplier delivery times. If the companies don’t share data, it can take a long time to process these enquiries, and the information supplied is liable to be inaccurate because it is based on guesswork and individual research. This can cause the customer to lose confidence or switch to a competitor.
A structured approach to data sharing allows the suppliers to describe their inventory and delivery data in a way that makes it clear which data may be shared and which data must stay protected. The data to be shared can subsequently be securely exchanged via a data trustee, who is responsible for ensuring that the relevant data protection requirements are met when the exchange is made. As a result, the engineering company can increase customer satisfaction by providing its customers with real-time information, while their suppliers gain a transparent picture of current demand.
Once described and shared, the data can also be used for predictive maintenance or optimization purposes. Because the engineering company knows in advance when a part is likely to fail, it can offer the customer a replacement part and service call before this actually happens. As well as increasing customer retention, this also opens up new revenue sources, illustrating how secure data sharing can give rise to concrete business models.
Adding value through customized data sharing
Sharing your own data doesn’t automatically mean relinquishing control over it. As long as you are able to make an informed decision and the process for sharing the data is known and understood, data can be seen as a resource that can be used flexibly, even outside of your own company. It is clear that some data sets are so sensitive that they must be permanently protected, while many others can add real value if shared. Structured evaluation methods and independent intermediaries help to reduce uncertainty and open up new opportunities. Accordingly, SMEs that quickly learn to systematically classify their own data sets will put themselves in a position to actively participate in future data economies rather than watching on from the sidelines. In the latest stage of the project, the FSTI team and their project partners are developing an AI agent to support data evaluation.
SME RESERVATIONS ABOUT SHARING DATA
- Fear of losing control or leaking know-how: Many companies are worried about confidential information getting into the wrong hands and losing their competitive advantages.
- Uncertainty about the law: A lack of clarity in relation to data protection and liability issues means that enterprises prefer to play it safe and keep their data back rather than share it.
- Uncertainty about the value of data and how carefully it needs to be protected: Enterprises are often unsure about which data is truly critical and which data can be readily shared. As a result, they often choose to protect all their data across the board.
- Lack of technical implementation know-how: SMEs in particular often lack the resources and expertise to introduce secure data exchange interfaces, standards and tools.
The findings presented in this article are drawn from the research project “DT-INFODATVO” (Concept for the Characterization and Evaluation of Data and Information Protection Requirements) (funding code 16DTM238A). The project is now funded by the Federal Ministry of Research, Technology and Space (BMFTR), formerly the Federal Ministry of Education and Research (BMBF).