Quality of Material Master Data and Its Effect on the Usefulness of Distributed ERP Systems

  • Gerhard F. Knolmayer
  • Michael Röthlin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4231)


Master data is a main component of most information systems. In distributed and heterogeneous organizations, problems of data quality may arise if several Enterprise Resource Planning (ERP) systems, customized with respect to local business needs and objectives, use subsets of common master data. In this paper we describe data management issues in a large organization, running 10 instances of the SAP R/3 system. For coordinating purposes, com mon elements of materials master data are entered via a centralized application and subsequently distributed to the affected instances. However, this master data management approach did not avoid massive data quality problems, which are, for instance, hampering the computation of informative key performance values and the effective realization of inventory reduction programs. The paper discusses possible approaches for improving data quality in this situation and in other cases of distributed ERP systems.


Data Quality Information Quality Material Master Data Enterprise Resource Planning Systems Master Data Management 


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  1. 1.
    Angeli, A., Streit, U., Gonfalonieri, R.: The SAP R/3 Guide to EDI and Interfaces, 2nd edn. Vieweg, Braunschweig/Wiesbaden (2001)Google Scholar
  2. 2.
    Ballou, D.P., Pazer, H.L.: Modeling Data and Process Quality in Multi-Input, Multi-Output Information Systems. Management Science 31(2), 150–162 (1985)CrossRefGoogle Scholar
  3. 3.
    Brackett, M.H.: Data Resource Quality. In: Turning Bad Habits into Good Practices. Addison-Wesley, Boston (2000)Google Scholar
  4. 4.
    Brodie, M.L.: Data Quality in Information Systems. Information and Management 3(6), 245–258 (1980)CrossRefGoogle Scholar
  5. 5.
    Broeckelmann, R.G.: Inventory Classification Innovation. St. Lucie Press/APICS, Boca Raton/Falls Church (1999)Google Scholar
  6. 6.
    CFO Research Services/Deloitte Consulting: IQ Matters: Senior Finance and IT Executives Seek to Boost Information Quality. CFO Publishing, Boston (2005),
  7. 7.
    Davenport, T.H.: Putting the Enterprise into the Enterprise System. Harvard Business Review 76(4), 21–131 (1998)Google Scholar
  8. 8.
    Druker, D., Rich, R.: Master Data Management. DB2 Magazine 10(3), 33–36 (2005)Google Scholar
  9. 9.
    English, L.P.: Improving Data Warehouse and Business Information Quality. Wiley, New York (1999)Google Scholar
  10. 10.
    Galway, L.A., Hanks, C.H.: Data Quality Problems in Army Logistics: Classification, Examples, and Solutions. RAND, Santa Monica (1996)Google Scholar
  11. 11.
    Gattiker, T.F., Goodhue, D.L.: Understanding the local-level costs and benefits of ERP through organizational information processing theory. Information & Management 41(4), 431–443 (2004)CrossRefGoogle Scholar
  12. 12.
    Griffin, J.: Overcoming Challenges to Master Data Management Implementation. DM Review Magazine (April 2006),
  13. 13.
    Hayler, A.: Are You Master of Your Data or Its Slave? DM Direct Newsletter, March 4 (2005),
  14. 14.
    Khanduja, D.: Master Data Management: Three Approaches to Consolidating Master Data. DM Direct Newsletter (October 14, 2005),
  15. 15.
    Kirsche, T., Baumann, G., Schanzenberger, A.: Alignment of Product Master Data. In: Cremers, et al. (eds.) INFORMATIK 2005: Informatik LIVE! Proceedings of the 35th Annual Conference of the Gesellschaft für Informatik, Bonn, vol. 2, pp. 449–453 (2005)Google Scholar
  16. 16.
    Klaus, H., Rosemann, M., Gable, G.G.: What is ERP? Information Systems Frontiers 2(2), 141–162 (2000)Google Scholar
  17. 17.
    Kremers, M., van Dissel, H.: ERP System Migrations. Comm. ACM 43(4), 53–56 (2000)CrossRefGoogle Scholar
  18. 18.
    Loshin, D.: Enterprise Knowledge Management – The Data Quality Approach. Morgan Kaufmann, San Diego (2001)Google Scholar
  19. 19.
    McKnight, W.: Justifying and Implementing Master Data Management. DM Review Magazine (April 2006),
  20. 20.
    Olson, J.E.: Data Quality – The Accuracy Dimension. Elsevier, Amsterdam (2003)Google Scholar
  21. 21.
    Piasecki, D.J.: Inventory Accuracy: People, Processes, & Technology. OPS Publishing, Kenosha (2003)Google Scholar
  22. 22.
    Redman, T.C.: Data Quality for the Information Age. Artech House, Norwood (1996)Google Scholar
  23. 23.
    Röthlin, M.: An Exploratory Study of Data Quality Management Practices in the ERP Software Systems Context. In: Dadam, P., Reichert, M. (eds.) INFORMATIK 2004, Proceedings of the 34th Annual Conference of the Gesellschaft für Informatik, Bonn, vol. 1, pp. 254–258 (2004)Google Scholar
  24. 24.
    Sherman, R.: Seven misconceptions about data quality. Software World 35(6), 13–14 (2004)MathSciNetGoogle Scholar
  25. 25.
    Walsh, T.: Master Data Management: Cross-System Assessment. DM Review Magazine (January 2004),
  26. 26.
    Wand, Y., Wang, R.Y.: Anchoring data quality dimensions in ontological foundations. Comm. ACM 39(11), 86–95 (1996)CrossRefGoogle Scholar
  27. 27.
    Wittebrock, T.: Master data – Everyone Needs it, but No-one Wants to Maintain it. SAP INFO international, September 15 (2003),
  28. 28.
    Xu, H., Nord, J.H., Brown, N., Nord, G.D.: Data quality issues in implementing an ERP. Industrial Management & Data Systems 102(1), 47–58 (2002)CrossRefGoogle Scholar
  29. 29.
    Zornes, A.: Dispelling Master Data Management Myths. DM Direct Special Report, October 20 (2005),

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Gerhard F. Knolmayer
    • 1
  • Michael Röthlin
    • 2
  1. 1.Institute of Information SystemsUniversity of BernBernSwitzerland
  2. 2.Department of Engineering and Information TechnologyBern University of Applied SciencesBiel/BienneSwitzerland

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