Martin Ofner

Towards a maturity model for corporate data quality management

High-quality corporate data is a prerequisite for world-wide business process harmonization, global spend analysis, integrated service management, and compliance with regulatory and legal requirements. Corporate Data Quality Management (CDQM) describes the quality oriented organization and control of a companys key data assets such as material, customer, and vendor data. With regard to the aforementioned business drivers, companies demand an instrument to assess the progress and performance of their CDQM initiative. This paper proposes a reference model for CDQM maturity assessment. The model is intended to be used for supporting the build process of CDQM. A case study shows how the model has been successfully implemented in a real-world scenario.

Integrating a data quality perspective into business process management

– The purpose of this paper is to conceptualize data quality (DQ) in the context of business process management and to propose a DQ oriented approach for business process modeling. The approach is based on key concepts and metrics from the data quality management domain and supports decision‐making in process re‐design projects on the basis of process models., – The paper applies a design oriented research approach, in the course of which a modeling method is developed as a design artifact. To do so, method engineering is used as a design technique. The artifact is theoretically founded and incorporates DQ considerations into process re‐design. Furthermore, the paper uses a case study to evaluate the suggested approach., – The paper shows that the DQ oriented process modeling approach facilitates and improves managerial decision‐making in the context of process re‐design. Data quality is considered as a success factor for business processes and is conceptualized using a rule‐based approach., – The paper presents design research and a case study. More research is needed to triangulate the findings and to allow generalizability of the results., – The paper supports decision‐makers in enterprises in taking a DQ perspective in business process re‐design initiatives., – The paper reports on integrating DQ considerations into business process management in general and into process modeling in particular, in order to provide more comprehensive decision‐making support in process re‐design projects. The paper represents one of the first contributions to literature regarding a contemporary phenomenon of high practical and scientific relevance.

Management of the master data lifecycle: a framework for analysis

– 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., – 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., – 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., – 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 organizations 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., – The paper shows how the reference model enables practitioners to analyze the master data lifecycle and how it helps identify areas of improvement., – 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.

A Maturity Model for Enterprise Data Quality Management

Enterprises need high-quality data in order to meet a number of strategic business requirements. Permanent maintenance and sustainable improvement of data quality can be achieved by an enterprise-wide approach only. The paper presents a Maturity Model for Enterprise Data Quality Management (Enterprise DQM), which aims at supporting enterprises in their eff ort to deliberately design and establish organisation-wide data quality management. The model design process, which covered a period of fi ve years, included several iterations of multiple design and evaluation cycles and intensive collaboration with practitioners. The Maturity Model is a hierarchical model comprising, on its most detailed level, 30 practices and 56 measures that can be used as concrete assessment elements during an appraisal. Besides being used for determining the level of maturity of Enterprise DQM in organisations, the results of the paper contribute to the ongoing discussion in the information systems (IS) community about maturity model design in general.