Measuring Master Data Quality: Findings from a Case Study

Data quality management plays a critical role in all kinds of organizations. Data is one of the most important criteria for strategic business decisions within organizations and the foundation for the execution of business processes. For the assessment of a company’s data quality, to ensure the process execution and to monitor the effectiveness of data quality initiatives, data quality has to be measured in the same way over a certain period of time. This can be achieved by implementing a measurement system. By now, the implementation of such a system to measure data quality is realized in very few organizations. This paper presents a case study describing the implementation process of a Master Data Quality Cockpit as well as the system used for measuring. The study assesses organizational, process-related, and system level changes as well as success factors necessary to implement such a tool

An Economics-Driven Decision Model for Data Quality Improvement – A Contribution to Data Currency

As poor data quality usually leads to high costs, managing data quality is essential for organizations. Thereby, comparing thecurrent with the required data quality level is necessary for an effective and economics-driven data quality management.Otherwise decision makers might decide in favor of unsuitable or inefficient data quality improvement measures with respectto cost and benefit. Existing methodologies for assessing and improving data quality often neglect providing methods fordetermining the required data quality level or argue on a managerial rather than an operational level. As a consequence, aneconomics-driven and context-dependent decision model for updating data at the level of attribute values is presented. Thismodel contains a metric for currency, errors and error costs, and a currency threshold for attribute values. The decision modelis illustrated using a direct marketing example

Controlling Customer Master Data Quality: Findings from a Case Study

Data quality management plays a critical role in all kinds of organizations. High-quality data is one of the most important prerequisites for making strategic business decisions and executing business processes. In order to be able to assess data quality, ensure efficient process execution, and verify the effectiveness of data quality initiatives, data quality has to be monitored and controlled. This can be achieved by implementing a comprehensive controlling system for data quality. However, only few organizations have managed to implement such a system. This paper presents a single-case study describing the process of implementing a comprehensive data quality controlling system. The study focuses on controlling activities defined in the field of business management

Data Quality Management in Corporate Practice

The 21st century is characterized by a rising quantity and importance of Data and Infor-mation. Companies utilize these in order to gain and maintain competitive advantages. Therefore, the Data and Information is required both in high quantity as well as quality. But while the amount of Data collected is steadily increasing, this does not necessarily mean the same is true for Data Quality. In order to assure high Data Quality, the concept of Data Quality Management (DQM) has been established, incorporating such elements as the assessment of Data Quality as well as its improvement. In order to discuss the issue of Data Quality Management, this paper pursues the following goals: (1) Systematic literature search for publications regarding Data Quality Management (Scientific contributions, Practice reports etc.) (2) Provision of a structured overview of the identified references and the research mate-rial (3) Analysis and evaluation of the scientific contributions with regards to methodology and theoretical foundation (4) Current expression of DQM in practice, differentiated by organization type and in-dustry (based upon the entire research material) as well as assessment of the situation (how well are the design recommendations based upon research results) (5) Summary of unresolved issues and challenges, based upon the research materia

Framework para avaliação de ferramentas MDM (Master Data Management)

Mestrado em Gestão de Sistemas de InformaçãoA importância dos dados e a sua gestão são, hoje em dia, um dos maiores desafios das organizações. Nos últimos anos temos assistido a impressionantes desenvolvimentos dos Sistemas de Informação, nesta área. Por outro lado, a seleção do software adequado a cada finalidade é também uma tarefa difícil com que as organizações têm de lidar. Tendo em conta estas duas questões o presente trabalho propõe uma Framework para comparação e avaliação de ferramentas de mercado de MDM. A investigação será conduzida de acordo com o método do Design Science (Hevner et al., 2004) e uma bateria de dez Tópicos de Análise chave serão identificados, com base nas expectativas das organizações para a adoção deste tipo de ferramentas. Estes dez Tópicos de Análise serão depois caracterizados de acordo com a sua relação com as seguintes disciplinas de referência teórica: Arquitetura de Dados; Gestão do Ciclo de Vida dos Dados; Governo dos Dados; Qualidade de Dados e Navegabilidade e Reporting, que representam o Modelo Teórico da Framework. Adicionalmente, Domínios de Avaliação serão definidos, para cada um dos tópicos propostos, com o objetivo de constituir o Modelo Operacional.The importance of data and its management are nowadays one of the major challenges of the organizations. In the last years we have witnessed great developments of the Information Systems on this area. On the other hand, the selection of the accurate software to each purpose is also a difficult task which organizations have to deal with. Taking both issues into consideration, the current work will propose a Framework for comparing and assessing MDM market tools. The research will be carried out by the Design Science method (Hevner et al., 2004) and a set of ten key Analysis Topics will be identified, based on the expectations of the organizations for the adoption of this kind of tools. These ten Analysis Topics will be further characterized according to its relation with the following academic subjects of reference: Data Architecture, Data Management Life Cycle, Data Governance, Data Quality and Navigability & Reporting, which represents the Theoretical Model of the Framework. Additionally, Evaluation Domains will be defined, to each topic proposed, with the aim of constitute the Operational Model

Framework para a avaliação de ferramentas de Master Data Management

Mestrado em Gestão de Sistemas de InformaçãoExiste atualmente no mercado de software uma grande variedade de aplicações de Master Data Management e tem vindo a ser conduzido um processo de consolidação entre os fabricantes daquelas ferramentas. Um dos grandes desafios, no que diz respeito à tecnologia para o Master Data Management, está relacionado com o seu grande espectro de aplicabilidade e utilização. Esta situação configura um problema para as equipas de Tecnologias de Informação na avaliação e pesquisa da ferramenta de Master Data Management mais adequada para a sua organização. O presente trabalho de dissertação tem como objetivo determinar quais as características mais significativas a considerar na avaliação de uma ferramenta de Master Data Management, tendo em vista a construção de uma framework que permita a avaliação qualitativa daquele tipo de software. Para a concretização desse objetivo, foi realizado um estudo orientado por uma metodologia baseada no paradigma do Design Science, tendo sido executada apenas uma iteração do processo. Não obstante ter sido possível elencar um conjunto de vinte e duas características fundamentais das aplicações de Master Data Management com base na literatura disponível sobre o tema e, a partir delas, conceptualizar uma framework de avaliação, o estudo realizado aponta para a necessidade de introdução de melhorias no modelo construído, quer ao nível do detalhe dos parâmetros de avaliação e respetivos valores possíveis, quer ao nível do âmbito de análise da própria framework. O processo de validação da framework proposta sugere ainda a construção de uma matriz que permita uma aplicação mais facilitada do modelo e o aumento da abrangência da framework de forma a contemplar outros fatores de análise, tais como a vertente financeira, TCO, ROI, perfil do fabricante, portfólio de serviços e a análise de risco.Currently, it exists in the software market a wide variety of Master Data Management applications and it has been conducted a process of consolidation among the manufacturers of those tools. One of the major challenges with regard to technology for Master Data Management is related to its wide range of applicability and use. This sets up a problem for the IT teams in evaluation and research of most appropriate Master Data Management tool for the organization. This dissertation work aims to determine the most important features to consider in evaluating a Master Data Management tool and build a framework with these features that allow the qualitative assessment of that kind of software. To achieve this goal, a study guided by a methodology based on the paradigm of Design Science was performed, considering only a single iteration of the process. It was possible to list a set of twenty-two fundamental characteristics of Master Data Management applications, based on the available literature on the subject and, with them, was conceptualized an evaluation framework. However, the study points to the need to introduce improvements in the model built, both at the level of detail of the assessment parameters and their possible values, and in the scope of analysis of the framework itself. The validation process of the proposed framework also suggests building a matrix that allows an easier application of the model and increasing the scope of the framework considering other analysis factors such as the financial slope, TCO, ROI, manufacturer's profile, services portfolio and risk analysis

Data, Technology, and People: Demystifying Master Data Management

With the amount of data constantly increasing, better practices are needed to manage it. Master data management (MDM) is an organizationally horizontal flow of activities aimed at managing core business data (i.e., master data). MDM differs from traditional data management practices as an organization-wide function. The idea of managing an organization’s most important data is impossible to achieve if MDM is simply treated as a data management practice or a technology-driven phenomenon. Establishing an MDM function involves introducing changes to an organization, which can relate to people and their ways of working, or technology and how it is used. If only a certain aspect is emphasized, the function will not deliver desired results.The object of this thesis is to study MDM not as a straightforward IT project, but as a complicated and multi-dimensional function. The goal is to understand the factors that should be taken into account in the development of an MDM function. The empirical part of this study is an ethnographic case study in a public sector organization, where MDM development was in early phases when the observation began. Altogether, the two data collection periods lasted for 32 months and during this, two MDM development projects were carried out, and MDM development became rooted as part of the organization’s routine operations.MDM development was analyzed as an ensemble that includes social and material components. Its theorization begins with understanding the role of master data in an organization’s information landscape and continues to examine the different views of MDM. Theories of change assist in understanding how change should be observed, understood, and managed.The study indicates that MDM effects multiple levels of an organization. Many organizational factors influence its development, and extensive dependencies exist between these factors. Especially in terms of ownership, other roles and responsibilities assume key positions. By understanding these factors and their roles in MDM development, it is easier to manage them.The study sheds light on the strong alignment between the complex concept of MDM and the organization. MDM literature is scarce and literature of public sector MDM is almost nonexistent. This dissertation contributes to research by widening the understanding of MDM in the public sector context, and by presenting a framework for establishing an MDM function as an organizational function that is closely linked with technology

Multikonferenz Wirtschaftsinformatik 2010 : Göttingen, 23. - 25. Februar 2010 ; Kurzfassungen der Beiträge

Dieser Band enthält Kurzfassungen der Beiträge zur MKWI 2010. Die Vollversionen der Beiträge sind auf dem wissenschaftlichen Publikationenserver (GoeScholar) der Georg-August-Universität Göttingen und über die Webseite des Universitätsverlags unter http://webdoc.sub.gwdg.de/univerlag/2010/mkwi/ online verfügbar und in die Literaturnachweissysteme eingebunden