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Data-driven business and data privacy: Challenges and measures for product-based companies

Author

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  • Schäfer, Fabian
  • Gebauer, Heiko
  • Gröger, Christoph
  • Gassmann, Oliver
  • Wortmann, Felix

Abstract

To leverage the opportunities provided by the Internet of Things (IoT), product-based companies are exploring new data-driven business opportunities. They may miss these same opportunities, however, owing to data-privacy challenges. These challenges start with the customers of product-based companies, extend to the wider business ecosystem, and continue with the companies themselves. This article identifies 12 data-privacy challenges and introduces 12 measures to address them. These include intuitive recommendations, such as enabling cross-product consent collection, as well as less intuitive measures, such as fostering a can-do attitude in legal units, closing the gap between legal and business initiatives, or implementing a clear process for well-reasoned risk-taking. The following four principles were found to support companies in implementing these measures: (1) letting privacy and data-driven business go hand in hand, (2) putting customers first and turning their privacy preferences into opportunities, (3) aligning risk-management activities with the process of digital service development, and (4) using technology to professionalize legal processes.

Suggested Citation

  • Schäfer, Fabian & Gebauer, Heiko & Gröger, Christoph & Gassmann, Oliver & Wortmann, Felix, 2023. "Data-driven business and data privacy: Challenges and measures for product-based companies," Business Horizons, Elsevier, vol. 66(4), pages 493-504.
    • Handle: RePEc:eee:bushor:v:66:y:2023:i:4:p:493-504
    DOI: 10.1016/j.bushor.2022.10.002
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    References listed on IDEAS

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    Cited by:

    1. Michael Fruhwirth & Viktoria Pammer-Schindler & Stefan Thalmann, 2024. "Knowledge Leaks in Data-Driven Business Models? Exploring Different Types of Knowledge Risks and Protection Measures," Schmalenbach Journal of Business Research, Springer, vol. 76(3), pages 357-396, September.
    2. Osama AlQahtani, 2025. "AI-powered network optimization for next-generation wireless connectivity: exploring 5G/6G networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 88(3), pages 1-14, September.
    3. Dabestani, Reza & Solaimani, Sam & Ajroemjan, Gazar & Koelemeijer, Kitty, 2025. "Exploring the enablers of data-driven business models: A mixed-methods approach," Technological Forecasting and Social Change, Elsevier, vol. 213(C).

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