DatenMarktplatz.NRW — The most promising use cases and business models

Dieser Artikel ist auch auf Deutsch verfügbar.

Co-Authors: Johannes Mayer (Laboratory for Machine Tools and Production Engineering (WZL) of RWTH Aachen University)

The senseering GmbH is a company founded in 2018 that has focused on opening up the bridge between plant technology (operational technology (OT)) and information technology (IT) since its inception. In addition to the digitization of (industrial) hardware, the start-up enables participation in a sovereign and self-determined data exchange (according to GAIA-X) between companies, which ensures a seamless traceable digital value creation. For this purpose, senseering has developed a multi-sided platform based on the IOTA Tangle.

The vision behind this platform is to become reality using the example of the DatenMarketplace.NRW, a then globally scalable B2x data network for the monetization of data and data-driven services (Figure 1). The #DigitalEconomyNRW as a pioneer in a data economy.

In cooperation with Aachen University of Applied Sciences, a project/internship was initiated in the winter semester 2020/2021 in which students had to identify potential pilot sectors for the DatenMarketplaces.NRW and evaluate their suitability in terms of achieving a critical mass of stakeholders. Furthermore, the existing business model of a platform operator was to be analyzed.

Acknowledgement

Special thanks to Team 1 (Ilias Achoukhi, Andreas Braining, Lois Jacobs, Ramón Piotrowski, Jonas Wardaschka), Team 12 (Stefan Terbrack, Thomas Gatzweiler, Jonas Brede, Olivier Bormann, Jan-Erik Wirtz, Tobias Cronenbroeck, Constantin Peters), Team 6 (Johannes Halaoui, Joschua Schulte-Tigges, Marin Jukic, Rick Hermans, Yannic Gerads), Team 8 (Cindy Mund, Felix Aßmann, Joshua Dall, Oliver Töbermann, Niklas Karow, Jan-Niklas Martens), Team 2 (Erik Mues, Clemens Rüttermann, Tim Wiedenmann, Teddy Cedric Leonel Tchakote Tabeth, David Merlin Strothotte, André Fleischhacker), Team 9 (Jérome Comoth, Patrick Groß, Jeremy Hoffmann, Paul Kowalewski, Thomas Krude, Florian Lange), Team 7 (Ömer Dölek, Carsten Gassen, Alexander Haase, Hakan Köse, Dominik Nowak, Alexander Reuter, Jonathan Schumann), whose work content and results are presented in this article. and results were presented in this article, among others. Great work, a lot of fun!

A special thank you also goes to Prof. Marco Motullo and Annette Schmahl of the Digital Marketing & Management department for their trust in senseering and the implementation of the event!

Contents

• 1. What is a data marketplace?
• 2. Use Cases and Business Models
• 2.1. Use Case 1: Smart Cities
• 2.2. Use Case 2: Auditing/Certification
• 2.3. Use Case 3: Big Data and AI
• 2.4. Use Case 4: Smart Weather
• 2.5. Use Case 5: Food industry
• 3. References

1. What is a data marketplace?

In the era of the Data Economy, benefited by the Internet of Things (IoT), the availability and use of a holistic database is considered to have great economic potential [1]. Novel digital service offerings such as Artificial Intelligence (AI) methods are taking the importance of data to a new level. The greatest benefit of data for business processes and supply chains results from the exchange and trade of data across company boundaries with other organizations.

Data marketplaces describe digital platforms for trading data as an economic good [2]. As intermediaries, they link various stakeholders for data exchange and create a new business model across sectors and industries by offering raw and processed data and data-related services (data science).

The main stakeholders of a data marketplace, regardless of whether they are individuals or companies, are

• the platform operator,
• the data providers/sellers, and
• the data consumers/buyers [3].

The technical design of a trading platform should allow a large number of potentially registered customers to upload and manage data. Data usage and access are defined with the help of programmed rules.

The greatest challenge for data marketplaces and the resulting success of all stakeholders involved is a correspondingly large, available and high-quality database. Only by means of appropriate incentive mechanisms for compliance with a defined quality standard and just after a critical quantity of data providers and buyers has been exceeded does a data marketplace have a data pool of this kind, which enables economic optimization.

As a relatively new phenomenon, however, digital data marketplaces are currently still lacking acceptance and insight into their potential benefits in reality. Companies from established sectors, such as the automotive industry or mechanical engineering, are sometimes difficult to acquire. Often, the focus is on the aspect of trust in data security and the fear of losing know-how/IP and competitive advantages.

In the following, use cases for a data marketplace are described, which enable the entry into the topic due to the trade with little sensitive data and thus can reveal the potential of a data marketplace for traditionally thinking industries.

Some of the use cases are already realized on the DatenMarktplatz.NRW!

2. Use Case and business models

In the following, a summary of the results of the students’ work is given, sorted by use cases.

2.1. Use Case 1: Smart Cities

Ilias Achoukhi, Andreas Braining, Lois Jacobs, Ramón Piotrowski, Jonas Wardaschka. Stefan Terbrack, Thomas Gatzweiler, Jonas Brede, Olivier Bormann, Jan-Erik Wirtz, Tobias Cronenbroeck, Constantin Peters

In addition to the industrial sectors, applications from the Smart Cities sector (cities, municipalities, etc.), such as smart grid, smart health care, smart building etc., experience increasing growth. The possibility of new business models in various service sectors leads us to expect an increase in the turnover of this industry from currently EUR 36 billion (2020) to EUR 47 billion in 2030 [4].

Within Germany, there are 81 large cities [5] and 621 medium-sized cities [6] that can generate the necessary critical mass as potential stakeholders of a data marketplace. One of the main tasks within the Smart City sector, here using municipalities as an example, is to promote the attractiveness of a city in order to create living space and quality of life, as well as to increase sustainability. In 2020 alone, delays of around 87 hours resulted in EUR 405 million in congestion costs for the city of Munich [7].

A particular focus here is on local public transport (LPT) and its tasks in timetable and route management. Delays of the means of transport, the resulting costs as well as the loss of image among customers are considered are the core problems in public transport.

A data marketplace involving cities, traffic control system manufacturers, and automobile manufacturers can trade data in real time for efficient traffic operations, better customer understanding, and more cost-effective customer transportation (Figure 2).

Intelligent traffic light and radar systems, parking garages, and transportation companies offer the potential as data sources for the design of efficient timetable and route management. The analysis of the provided data enables the identification of possible delays, traffic junctions as well as weak points within the timetable by recording the cause-effect relationships of the traffic volume. If location or destination data of the traffic participants is provided, there is also the possibility of intelligent road routing and optimized traffic light switching.

2.2. Use Case 2: Auditing/Certification

Johannes Halaoui, Joschua Schulte-Tigges, Marin Jukic, Rick Hermans, Yannic Gerads, Cindy Mund, Felix Aßmann, Joshua Dall, Oliver Töbermann, Niklas Karow, Jan-Niklas Martens

Auditing, the basis for certification, is considered to be a standardized process that is used to test and demonstrate compliance with defined requirements [8]. They exist both in the private education sector (e.g. knowledge of a foreign language) and in the industrial sector (e.g. quality condition of a product). However, they present different problems depending on their nature.

Education-related certifications are subject to regional and supra-regional variations in quality and data protection standards. Complicating matters further is the fact that certificate deficiencies are difficult to identify due to the gathering of small groups of participants.

In the manufacturing environment, standards are demanded within the company’s own production and from suppliers. These standards are checked by auditors at regular intervals [9] and, in the case of a certification audit, confirmed by means of a certificate [10]. An essential part of this audit is the analysis of a detailed and valid data and document basis by the auditor. This task requires a high level of communication and effort to check the quality and analyze the data. Poor communication can delay the availability of data, and a lack of expertise in analyzing large amounts of data can even jeopardize the entire certification process.

A data marketplace provides a remedy for the certification challenges of both sectors (Figure 3). In private continuing education, deficiencies can be identified more quickly and shared among each other so that consistent and accurate certification can take place. Due to the lack of competition between education-related certification bodies, the willingness to share data exists.

In the production environment, this platform provides the basis for more objective and resource-efficient remote auditing. Auditing data is available without communication effort to any company requiring auditing/certification and can be used to verify compliance with standards. Data analysis tools and dashboard applications with configurable visualizations can be used to quickly identify irregularities, errors, and trends, and to optimize processes in a targeted manner.

2.3. Use Case 3: Big Data and AI

Erik Mues, Clemens Rüttermann, Tim Wiedenmann, Teddy Cedric Leonel Tchakote Tabeth, David Merlin Strothotte, André Fleischhacker

The need for data analysis using novel methods is indispensable for the economic success of a company [11]. Nevertheless, the expertise is often not available within the own company due to the rarity of the job description data scientist [12]. Outsourcing the data to external parties seems to be the only option to perform data analysis without in-house Data Science experience.

However, many companies have great reservations about data sharing, which goes beyond their own company boundaries. Despite the reservations, AI startups and other data analytics service providers depend on the willingness to share data to optimize existing products/processes or develop new ones.

The GAIA-X-compliant data marketplace based on distributed ledger technology, for example, creates the necessary foundations for tamper-proof, trust-creating data trading in accordance with the DSGVO standard and thus offers a platform for networking data-providing stakeholders with little or no data analysis expertise and data scientists (Figure 4).

With the consent of the data providers, data sets from different stakeholders can be combined in one model to achieve better results for the participants. The reason for this is the correlation between the number of underlying, high-quality data and the quality of the AI model. It would be advantageous if a uniform data format were defined in advance for sharing the data on the data marketplace, as well as a score for measuring the quality of the data. The effort of data preparation can be minimized by this procedure.

2.4. Use Case 4: Smart Weather

Jérome Comoth, Patrick Groß, Jeremy Hoffmann, Paul Kowalewski, Thomas Krude, Florian Lange

Since March 2020, it has been regulated by law [13] that services of the state weather service provider Deutscher Wetterdienst (DWD), apart from warnings of severe weather or radioactivity as well as services for disaster control, must be made available against payment in order to protect competition law and the market [14]. The DWD can continue to provide weather data that it has collected itself free of charge.

For weather service providers, the use of high-quality weather data, such as temperature, air pressure, precipitation, and wind strengths, is essential to produce weather forecasts. The predictability of weather goes hand in hand with the underlying database. The data from private measuring stations, buildings and wind turbines, for example, offer untapped potential.

A data marketplace for trading weather data among government and private weather service providers, agriculture and forestry operations, and individuals offers the opportunity to increase forecast quality [15] and generate new revenue streams [16] (Figure 5).

The linking of data from a large number of weather sensors enables a more precise forecast based on the locality and forecast accuracy, as well as a forecast that is better tailored to the customer (energy and agriculture, private individual) [17].

2.5. Use Case 5: Food industry

Ömer Dölek, Carsten Gassen, Alexander Haase, Hakan Köse, Dominik Nowak, Alexander Reuter, Jonathan Schumann

The German food industry also holds great potential for a data marketplace [18]. Almost 5,570 companies [19] currently generate sales of EUR 163 billion per year, and the trend is rising [20]. High demands on the handling of the procurement process as well as an invisible supply chain make it difficult to check compliance with quality standards oneself and to prove it to third parties.

Based on the collection, analysis and evaluation of data along the supply chain, the food industry can optimize its own products and their quality, save costs and improve its image by verifiably meeting all requirements (Figure 6).

The analysis of transport data contributes to sustainability through more efficient energy consumption and minimized transport distances. A data marketplace promotes responsiveness to supply/demand changes, transportation disruptions, and price fluctuations in the market.

3. References

[1] Lange, J.; Stahl, F.; Vossen, G.: Datenmarktplätze in verschiedenen Forschungsdisziplinen: Eine Übersicht; Springer Verlag Berlin Heidelberg 2017; DOI 10.1007/s00287–017–1044–3

[2] Meisel L.; Spiekermann M.: Datenmarktplätze — Plattformen für Datenaustausch und Datenmonetarisierung in der Data Economy; ISSN 0943–1624; 2019

[3] Täuscher, K.; Hilbig, R.; Abdelkafi, N.: Geschäftsmodellelemente mehrseitiger Plattformen; 2017; DOI: 10.1007/978–3–658–12388–8_7

[4] marktforschung.de. Boom bei Smart Cities. https://www.marktforschung.de/aktuelles/marktforschung/boom-bei-smart-cities/

[5] statista. Einwohnerzahl der größten Städte in Deutschland am 31. Dezember 2019. URL https://de.statista.com/statistik/daten/studie/1353/umfrage/einwohnerzahlen-der-grossstaedte-deutschlands/

[6] Wikipedia. Liste der Groß- und Mittelstädte in Deutschland. URL: https://de.wikipedia.org/wiki/Liste_der_Gro%C3%9F-_und_Mittelst%C3%A4dte_in_Deutschland

[7] Handelsblatt. STAUSTATISTIK — München ist Deutschlands Stauhauptstadt. URL: https://www.handelsblatt.com/auto/nachrichten/staustatistik-muenchen-ist-deutschlands-stauhauptstadt/25624756.html?ticket=ST-6980889-UebWUdgxGYfmv4Cit3X0-ap2

[8] DIN EN ISO 9001

[9] DIN EN 9104

[10] DIN 18200

[11] Pennekamp, J.; Glebke, R.; Henze, M.; Meisen, T.; Quix, C.; Hai, R.; Gleim, L.; Niemietz, P.; Rudack, M.; Knape, S.; Epple, A.; Trauth, D.; Vroomen, U.; Bergs, T.; Brecher, C.; Buhrig-Polaczek, A.; Jarke, M.; Wehrle, K.: Towards an Infrastructure Enabling the Internet of Production. In: Proceedings 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS 2019). Taipei, Taiwan, 6.–9. Mai 2019. Piscataway, USA: IEEE, S. 31–37.

[12] Schürmann, H.: Datenanalysten sind rar. URL: https://www.vdi-nachrichten.com/karriere/datenanalysten-sind-rar

[13] Düsseldorfer Institut für Wettbewerbsökonomie, Ordnungspolitische Perspektiven — Wettbewerbssituation auf dem Markt für Wetterdienstleistungen, Januar 2018. http://dupress.de/fileadmin/redaktion/DUP/PDF-Dateien_/DICE/Ordnungspolitische_Perspektiven/93_OP_Haucap_Loebert.pdf

[14] Verband Deutscher Wetterdienstleister. URL: https://www.wetterverband.de/

[15] Düsseldorfer Institut für Wettbewerbsökonomie, Ordnungspolitische Perspektiven — Wettbewerbssituation auf dem Markt für Wetterdienstleistungen, Januar 2018. URL: http://dupress.de/fileadmin/redaktion/DUP/PDF-Dateien_/DICE/Ordnungspolitische_Perspektiven/93_OP_Haucap_Loebert.pdf

[16] Verband Deutscher Wetterdienstleister. URL: https://www.wetterverband.de/

[17] Scinexx das wissensmagazin. URL: https://www.scinexx.de/news/geowissen/corona-macht-wettervorhersagen-ungenauer/

[18] Statistisches Bundesamt. Anzahl der Betriebe in der Lebensmittelindustrie in Deutschland in den Jahren 2008 bis 2019. URL: https://de.statista.com/statistik/daten/studie/321182/umfrage/betriebe-inder-lebensmittelindustrie-in-deutschland/

[19] Statistisches Bundesamt. Umsatz der Lebensmittelindustrie in Deutschland in den Jahren 2008 bis 2019. URL: https://de.statista.com/statistik/daten/studie/164959/umfrage/umsatz-dernahrungsmittelindustrie- in-deutschland-seit-2005/

[20] Statistisches Bundesamt. Prognostizierte Umsatzentwicklung Ernährungsindustrie in Deutschland in den Jahren 2011 bis 2023. URL: https://de.statista.com/statistik/daten/studie/247972/umfrage/prognosezum-umsatz-in-der-ernaehrungsindustrie-in-deutschland/

About senseering

The senseering GmbH is a company founded in September 2018 that was awarded the RWTH Aachen University Spin-Off-Award. The core competence of senseering GmbH is the development and implementation of systems for the digitalization and networking of industrial and production facilities. Likewise, senseering GmbH advises on strategic corporate issues, in particular digital transformation, distributed-leger technologies, edge vs. cloud computing architectures for AI-based real-time control of industrial processes, digital business model innovation and the introduction of digital business processes such as home office, Azure or Microsoft365. Senseering is one of the winners of the first and largest AI innovation competition of the BMWi with the project www.spaicer.de.

Daniel Trauth (CEO) | www.senseering.de | E-Mail: mail@senseering.de