Management of the master data lifecycle: a framework for analysis

Martin Ofner · Kevin Straub · Boris Otto · Hubert Oesterle

40 citations0/0 files storedJournal of Enterprise Information Management

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Data Quality and Management Information Technology Governance and Strategy Big Data and Business Intelligence

Abstract

Purpose The purpose of the paper is to propose a reference model describing a holistic view of the master data lifecycle, including strategic, tactical and operational aspects. The Master Data Lifecycle Management (MDLM) map provides a structured approach to analyze the master data lifecycle. Design/methodology/approach Embedded in a design oriented research process, the paper applies the Component Business Model (CBM) method and suggests a reference model which identifies the business components required to manage the master data lifecycle. CBM is a patented IBM method to analyze the key components of a business domain. The paper uses a participative case study to evaluate the suggested model. Findings Based on a participative case study, the paper shows how the reference model makes it possible to analyze the master data lifecycle on a strategic, a tactical and an operational level, and how it helps identify areas of improvement. Research limitations/implications The paper presents design work and a participative case study. The reference model is grounded in existing literature and represents a comprehensive framework forming the foundation for future analysis of the master data lifecycle. Furthermore, the model represents an abstraction of an organization's master data lifecycle. Hence, it forms a “theory for designing”. More research is needed in order to more thoroughly evaluate the presented model in a variety of real‐life settings. Practical implications The paper shows how the reference model enables practitioners to analyze the master data lifecycle and how it helps identify areas of improvement. Originality/value The paper reports on an attempt to establish a holistic view of the master data lifecycle, including strategic, tactical and operational aspects, in order to provide more comprehensive support for its analysis and improvement.

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References

2004Design Science in Information Systems Research1 2004Design science in information systems research 2007A Design Science Research Methodology for Information Systems Research 1996Beyond Accuracy: What Data Quality Means to Data Consumers 1995Design and natural science research on information technology 2006The Nature of Theory in Information Systems1 1993When to Use Focus Groups and Why 1998A product perspective on total data quality management

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