Company-wide data quality

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Company- wide data quality considers data quality in a company-wide context. Data is as a factor of production (Engl. And shareholder value asset ) considered that needs to be managed. Certain structures must be established for this management, the definition of which is the subject of current research.

The requirement to treat information in the company as a production factor is not new. However, it is increasingly coming to the fore because the number of business drivers is growing, especially in decentralized and complex group structures , which require company-wide data management as the basis for the operational information base:

  • In the telecommunications industry, integrated end customer management is only possible if all contract and invoice data is consistent, up-to-date and available quickly - regardless of the sales channel through which it was recorded.
  • In strategic purchasing in manufacturing companies, master data on suppliers must be available consistently and correctly across various divisions and there must be transparency about hierarchical relationships both with regard to the supplier organization and with regard to the own company so that analyzes for supplier evaluation and procurement sources can be carried out reliably.
  • In many companies, information technology (IT) has developed without overarching control - with the result that it is often not known which application systems are leading in the maintenance and provision of, for example, product or material master data . This in turn prevents consolidation measures and harmonization efforts in the IT organization and in IT operations.
  • In the course of the growing number of official and legal requirements, the requirements for reporting and risk management of companies are also increasing : Registration and traceability obligations in the chemical industry cannot be implemented without high-quality data.

These examples illustrate the importance of high quality data in the company. Data is increasingly becoming a fixed asset . Compared to the management of physical facilities such as machine tools, real estate, etc. ä. which takes data management often a subordinate role. Where there are maintenance strategies for manufacturing plants and network infrastructures that maximize the usability and operational value of plants over their entire lifecycle, most companies lack preventive strategies to maintain the quality of business-critical data. Integrated approaches exist, such as Total Data Quality Management , which was developed at the Massachusetts Institute of Technology (MIT), and Total Information Quality Management , but both are rarely used across the board in companies.

Individual evidence

  1. ^ Richard Y. Wang: A Product Perspective on Total Data Quality Management . Communications of the ACM, 1998 ( acm.org ).
  2. ^ Larry P. English: Total Information Quality Management - A Complete Methodology for IQ Management . DMReview, 2003 ( dmreview.com [accessed May 20, 2008]).

Further literature

  • Larry P. English: Improving Data Warehouse and Business Information Quality . John Wiley & Sons, Inc., New York, NY 1999, ISBN 0-471-25383-9 .
  • Richard Y. Wang, Diane M. Strong: Beyond Accuracy: What Data Quality Means to Data Consumers . In: Journal of Management Information Systems 12 (4) . 1996, p. 5-34 .
  • Detlef Apel, Wolfgang Behme, Rüdiger Eberlein, Christian Merighi: Controlling data quality successfully, practical solutions for business intelligence projects . Hanser, Munich, Vienna 2009, ISBN 978-3-446-42056-4 .