Skip to main content

Data Product Metadata Management: An Industrial Perspective

  • Conference paper
  • First Online:
Service-Oriented Computing – ICSOC 2022 Workshops (ICSOC 2022)

Abstract

Decentralised data exchanges are promising alternatives to monolithic data lakes and warehouses which are typically emerging around complex service solutions. In theory, this removes some of the bottlenecks of traditional data management solutions. In practice, the road towards achieving such goal is a long way ahead. In this work, we provide an industry perspective on the implications for such work, with a focus on metadata management; the work in question draws from an in-vivo action research approach we enacted at a major German automotive company that is transitioning to an internal decentral data market. Our results provide insight into an industry perspective on the requirements for metadata management. Additionally, we propose and validate a solution design for metadata management in decentralised data exchanges based on semantic web service technology.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.sisense.com/blog/why-businesses-fail-to-capitalize-on-their-data/.

  2. 2.

    Alternatively called data owners, (data) product providers, or (data) product developers [6].

References

  1. Catena-X: Automotive Network (2021)

    Google Scholar 

  2. Alexander, I.F., Beus-Dukic, L.: Discovering Requirements: How to Specify Products and Services. Wiley, Chichester (2009)

    Google Scholar 

  3. Dehghani, Z.: Data Mesh: Delivering Data-Driven Value at Scale, 1st edn. O’Reilly (2022)

    Google Scholar 

  4. Dibowski, H., Schmid, S.: Using knowledge graphs to manage a data lake. In: INFORMAITK 2020, Lecture Notes in Informatics (LNI), pp. 41–50 (2021)

    Google Scholar 

  5. Doan, A., Halevy, A., Ives, Z.: Principles of Data Integration, 1st edn. Elsevier, Waltham, MA (2012)

    Google Scholar 

  6. Driessen, S., Monsieur, G., Van Den Heuvel, W.: Data market design: a systematic literature review. IEEE Access 10, 33123–33153 (2022). https://doi.org/10.1109/access.2022.3161478

    Article  Google Scholar 

  7. Eichler, R., Giebler, C., Gröger, C., Hoos, E., Schwarz, H., Mitschang, B.: Enterprise-wide metadata management: an industry case on the current state and challenges. In: Business Information Systems (July), pp. 269–279 (2021). https://doi.org/10.52825/bis.v1i.47

  8. Fernandez, R.C., Subramaniam, P., Franklin, M.J.: Data market platforms: trading data assets to solve data problems. Proc. VLDB Endow. 13(12), 2150–8097 (2020)

    Article  Google Scholar 

  9. Goedgebuure, A.: Data mesh: systematic gray literature study, reference architecture, and cloud-based instantiation at ASML (2022). https://stefan-driessen.github.io/publication/data-mesh-systematic-grey-literature-study/

  10. Hevner, A., Chatterjee, S.: Design Research in Information Systems: Theory and Practice, vol. 28. Springer, NY (2010). https://doi.org/10.1007/978-1-4419-5653-8

  11. Hooshmand, Y., Resch, J., Wischnewski, P., Patil, P.: From a monolithic PLM landscape to a federated domain and data mesh. Proc. Design Soc. 2, 713–722 (2022)

    Article  Google Scholar 

  12. Koutroumpis, P., Leiponen, A., Thomas, L.: The (unfulfilled) potential of data marketplaces. ETLA Working Papers 2420(53) (2017). http://pub.etla.fi/ETLA-Working-Papers-53.pdf%0Apub.etla.fi/ETLA-Working-Papers-53.pd

  13. Koutroumpis, P., Leiponen, A., Thomas, L.D.W.: Markets for data. Ind. Corp. Chang. 29(3), 645–660 (2020). https://doi.org/10.1093/icc/dtaa002

    Article  Google Scholar 

  14. Lauesen, S.: Software Requirements-Styles and Techniques. Pearson Education (2002)

    Google Scholar 

  15. Loukiala, A., Joutsenlahti, J.-P., Raatikainen, M., Mikkonen, T., Lehtonen, T.: Migrating from a centralized data warehouse to a decentralized data platform architecture. In: Ardito, L., Jedlitschka, A., Morisio, M., Torchiano, M. (eds.) PROFES 2021. LNCS, vol. 13126, pp. 36–48. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-91452-3_3

    Chapter  Google Scholar 

  16. Narayan, S.: Products over projects (2018). https://martinfowler.com/articles/products-over-projects.html

  17. Newman, S.: Monolith to Microservices: Evolutionary Patterns to Transform Your Monolith. O’Reilly (2020). https://www.oreilly.com/library/view/monolith-to-microservices/9781492047834/

  18. O’Neil, B.T.: Failure rates for analytics, AI, and big data projects = 85% - yikes! (2019)

    Google Scholar 

  19. Otto, B., Steinbuß, S., Teuscher, A., Lohmann, S.: IDSA reference architecture model. International Data Spaces Association (April) (2019). https://internationaldataspaces.org/download/16630/

  20. Roman, D., et al.: The euBusinessGraph ontology: a lightweight ontology for harmonizing basic company information. Semantic Web 13(1), 41–68 (2021). https://doi.org/10.3233/sw-210424

    Article  Google Scholar 

  21. Spiekermann, M., Tebernum, D., Wenzel, S., Otto, B.: A metadata model for data goods. In: MKWI 2018 - Multikonferenz Wirtschaftsinformatik 2018-March, pp. 326–337 (2018)

    Google Scholar 

  22. Stach, C., Bräcker, J., Eichler, R., Giebler, C., Mitschang, B.: Demand-driven data provisioning in data lakes. In: Association for Computing Machinery, vol. 1 (2021). https://doi.org/10.1145/3487664.3487784

  23. Strengholt, P.: ABN AMRO’s data and integration mesh (2020). https://www.linkedin.com/pulse/abn-amros-data-integration-mesh-piethein-strengholt/

  24. Sweller, J.: Cognitive load during problem solving: effects on learning. Cogn. Sci. 12(2), 257–285 (1988). https://doi.org/10.1016/0364-0213(88)90023-7

    Article  Google Scholar 

  25. Dehghani, Z.: How to move beyond a monothilitic data lake to a distributed data mesh (2019). https://martinfowler.com/articles/data-monolith-to-mesh.html

  26. W3C: Semantic web - leading the web to its full potential (2015)

    Google Scholar 

  27. Wieringa, R.J.: Design Science Methodology for Information Systems and Software Engineering. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-43839-8

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Stefan Driessen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Driessen, S., Monsieur, G., van den Heuvel, WJ. (2023). Data Product Metadata Management: An Industrial Perspective. In: Troya, J., et al. Service-Oriented Computing – ICSOC 2022 Workshops. ICSOC 2022. Lecture Notes in Computer Science, vol 13821. Springer, Cham. https://doi.org/10.1007/978-3-031-26507-5_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-26507-5_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-26506-8

  • Online ISBN: 978-3-031-26507-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics