“It’s easy to view software as a product or service. Software instructs hardware how to process data. Data, however, is a product, though it is seldom considered so” — Larry Crosby (retired Drucker School Dean in AMA Marketing News 2019)

As digitization intensifies, there is a growing need for more software applications and the corresponding data to fuel them. This application data must be refined from raw data to ensure relevance, quality, and format required by an app. This refinement scales economically only if (a) data is treated as a product (drawing from product management principles pioneered by Procter & Gamble in the 1930s) and if (b) this process is, in turn, industrialized with ‘data factories’ (an approach pioneered by Henry Ford, who took the Motorwagen invented by Karl Benz and industrialized it into a consumer product). A fitting analogy for a data product is food. Just as any food product is meticulously labeled to offer comprehensive information about its content (ingredients) and quality (nutritional value), data products need to prioritize transparency regarding (i) information content and (ii) quality (see “Data as a product” below). In this context, a digital twin emerges as an exemplary representation of a data product. Its core logic revolves around mirroring a physical entity, thereby necessitating impeccable information content and quality (see “Digital twin examples” below).

The data problems
Most of us have run into these problems below, which can be mitigated or solved with (a) productizing data (Procter & Gamble) and (b) industrialising this process (Henry Ford).

• Sizing the data productivity crisis. Link
• How to measure data? Link
• Quantity or quality? Link
• Confusing data – hurting automotive: Schlueter Langdon, C. 2020. Metamorphosis of Auto into Mobility. IDSA Blog (2020-07-10), International Data Spaces Association, Berlin, link
• Data industrialization: Schlueter Langdon, C. 2020. IDSA on Center Stage at Data Natives of Europe. IDSA Blog (2020-05-26), International Data Spaces Association, Berlin, link

Data as a product – our R&D
Definition: “A data product is refined and ready-to-use data accessible to various software applications, analogous to a food product characterized by its informational content (akin to a food ingredients label), quality (similar to a nutritional value label), and quantifiable measure suitable for diverse use cases, much like recipes (adapted from Schlueter Langdon & Sikora 2020; also McKinsey’s Desai et al. 2022a, 2022b).”

• Guggenberger, T. M., M. Altendeitering, and C. Schlueter Langdon. 2024. Design Principles for Quality Scoring – Coping with Information Asymmetry of Data Products. Proceedings of the 57th Hawaii International Conference on System Sciences (HICSS): 4526-4535, link
• Bitkom. 2023. Best Practices zur Entwicklung von Datenprodukten (Best practices for the development of data products). Leitfaden (German only; December), Bitkom e.V., Berlin, link
• Schlueter Langdon, C., and R. Sikora. 2020. Creating a Data Factory for Data Products. In: Lang, K. R., J. J. Xu et al. (eds). Smart Business: Technology and Data Enabled Innovative Business Models and Practices. Springer Nature, Switzerland: 43-55, link
• Data factory example – refining data for bias mitigation: Sikora R., and C. Schlueter Langdon. 2019. Marketing to “Minorities”: Mitigating Class Imbalance Problems with Majority Voting Ensemble Learning. Frontiers of Marketing Data Science Journal (Fall): 27-33, link
• Crosby, L., and C. Schlueter Langdon. 2019. Data as a Product to be Managed. Marketing News, American Marketing Association (October 10th), link

Digital twin examples – our projects
Definition: “A digital twin is a digital model of an intended or actual real-world physical object, system, process or person that serves as the effectively indistinguishable digital counterpart of it for practical purposes, such as simulation, testing, and monitoring” (Wikipedia 2023, McKinsey 2023, Pettey 2017; for a narrower engineering version, see Giachetti 2023, p. 1214). Digital twins can help users simulate real situations and their outcomes, ultimately improving design and decision-making. We have been using ‘digital twins’ for simulation experiments (see “Simulation: From Impossible to Probable”) to test if new technology is improving performance of a system. Examples include use of dataspace tech (see “Dataspaces 101”) to improve systems, such as inner-city travel and human machine interaction.

Travel

• • Schlueter Langdon, C., N. Oehrlein, and D. Kerinnis. 2021. Integrated Public Transport: Quantifying user benefits – Example of Hamburg. Technical Paper ID 438, 27th ITS World Congress, Hamburg, link
  • Schlueter Langdon, C. 2020. Berlin digital twin: Yes, intermodal traffic is faster! Telekom Data Intelligence Hub Blog (2020-08-20), T-Systems International GmbH, Frankfurt, link

Consumer avatars for human machine interaction

• • Schlueter Langdon, C. 2020. Data-Centered Value Creation – From Hollywood into Your Home: The Customer Digital Twin is Coming … with “IDS Inside”. IDSA Blog (2020-06-10), International Data Spaces Association, Berlin, link
  • Schlueter Langdon, C. 2020. A human digital twin with data sovereignty: Say hello to “DaWID”. Telekom Data Intelligence Hub Blog (2020-05-06), T-Systems International GmbH, Frankfurt, link
  • Hoffmann, D. 2020. Human Digital Twins sind im Kommen/ Human digital twins are on the rise. Digital Twin Special, Automotive IT (2020-05), page 31
  • “Calculator” Powered by ML: Auto Interior & UX”

Digital twins and dataspaces

A synthesis of our case studies reveals the economic rationale and business advantages of combining both, which provides the rationale of a partnership between Bosch, the world’s largest automotive supplier by revenue, and Deutsche Telekom’s T-Systems, launched at BCW Bosch Connected World 2024 in Berlin: Research note, link

References
Desai, V., T. Fountaine, and K. Rowshankish. 2022a. How to unlock the full value of data? Manage it like a product. McKinsey Article (2022-06-14), McKinsey & Company, link

Desai, V., T. Fountaine, and K. Rowshankish. 2022b. A Better Way to Put Your Data to Work – Package it the way you would a product. Harvard Business Review (July–August 2022), link

Giachetti, R. 2023. Digital Engineering. In: SEBoK Editorial Board. 2023. The Guide to the Systems Engineering Body of Knowledge (SEBoK), v. 2.9, N. Hutchison (Editor in Chief with The International Council on Systems Engineering (INCOSE), Systems Engineering Research Center (SERC), IEEE Systems Council (IEEE-SYSC)). Hoboken, NJ: The Trustees of the Stevens Institute of Technology, link

McKinsey. 2023. What is digital twin technology? McKinsey Explainers (2023-07-12), McKinsey & Company, link

Pettey, C. 2017. Prepare for the Impact of Digital Twins – Develop new economic and business models that deliver maximum value from digital twins. Information Technology (2017-09-18), Gartner, Inc., Stamford, CT, link