Towards a Strategy Design Method for Corporate Data Quality Management (original) (raw)

Corporate data quality management in context

… apresentada na 15th International Conference on …, 2010

Presently, we are well aware that poor quality data is costing large amounts of money to corporations all over the world. Nevertheless, little research has been done about the way Organizations are dealing with data quality management and the strategies they are using ...

Enterprise Data Quality: A Pragmatic Approach

Information Systems Frontiers, 1999

Enterprise data—the data that is created, used and shared by a corporation in conducting business—is a critical business resource that must be analyzed, architected and managed with data quality as a guiding principle. This paper presents results, practical insights, and lessons learned from a large scale study conducted in the telecommunications industry that synthesizes data quality issues into an architectural and management approach. We describe the real life case study and show how requirements for data quality were collected, how the data quality metrics were defined, what guidelines were established for intersystem data flows, what COTS (commercial off-the-shelf) technologies were used, and what results were obtained through a prototype effort. As a result of experience gained and lessons learned, we propose a comprehensive data quality approach that combines data quality and data architectures into a single framework with a series of steps, procedures, checklists, and tools. Our approach takes into account the technology, process, and people issues and extends the extant literature on data quality.

A Maturity Model for Enterprise Data Quality Management

Enterprise Modelling and Information Systems Architectures, 2013

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 effort to deliberately design and establish organisation-wide data quality management. The model design process, which covered a period of five 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.

Identifying Critical Success Factors for Data Quality Management through a Delphi Study

2019

Organizations support their operations and decision making on the data they have at their disposal, so the quality of these data is remarkably important and Data Quality (DQ) is currently a relevant issue, the literature being unanimous in pointing out that poor DQ can result in large costs for organizations. The literature review identified and described 24 Critical Success Factors (CSF) for Data Quality Management (DQM) that were presented to a panel of experts, who ordered them according to their degree of importance, using the Delphi method with the Q-sort technique, based on an online questionnaire. The study shows that the five most important CSF for DQM are: definition of appropriate policies and standards, control of inputs, definition of a strategic plan for DQ, organizational culture focused on quality of the data and obtaining top management commitment and support.

Data quality assessment and improvement

International Journal of Business Information Systems, 2016

Data quality has significance to companies, but is an issue that can be challenging to approach and operationalise. This study focuses on data quality from the perspective of operationalisation by analysing the practices of a company that is a world leader in its business. A model is proposed for managing data quality to enable evaluation and operationalisation. The results indicate that data quality is best ensured when organisation specific aspects are taken into account. The model acknowledges the needs of different data domains, particularly those that have master data characteristics. The proposed model can provide a starting point for operationalising data quality assessment and improvement. The consequent appreciation of data quality improves data maintenance processes, IT solutions, data quality and relevant expertise, all of which form the basis for handling the origins of products.

The Impact of Data Quality Management: A Concept Paper on the Banking Industry in Oman

Sri Lanka Journal of Management , 2023

The banking industry in the contemporary world has an exigency for Data Quality Management (DQM). Accurate and timely data not only offers a competitive advantage for banks but also empowers the corporate management to make informed and sound decisions. The importance of DQM thus lies in its ability to bolster strategic positioning while enhancing the decision-making processes. Empirical evidence reveals that there is a significant lacuna in this study area. Hence, the purpose of this concept paper is to explore the attributes and dimensions, and to ascertain how such dimensions’ impact DQM. This paper while focusing on theory synthesis, was theorised and conceptualised with the Theory of Creative Destruction (TCD), the Theory of Social Constructivism (TSC) along with the DIKAR model and the Information System Success model. The paper proposes a positivist epistemology with a deductive approach. The proposed target population in the study consists of the members of the senior staff, IT staff and the regulators in the banking industry. The unit of analysis is the individual, and the proposed technique to collect data is the purposive judgmental sampling method. The paper finally discusses the envisaged theoretical, managerial and socio-economic implications along with the conclusion. Keywords: Data quality management, Theory of creative destruction, Theory of social constructivism, Regulatory compliance, Banking system architecture, Employee skill development.

Prologue: Research and Practice in Data Quality Management

Handbook of Data Quality, 2013

This handbook is motivated by the presence of diverse communities within the area of data quality management, which have individually contributed a wealth of knowledge on data quality research and practice. The chapter presents a snapshot of these contributions from both research and practice, and highlights the background and rational for the handbook.

A framework for analysis of data quality research

IEEE Transactions on Knowledge and Data Engineering, 1995

Abstiuct-Organizational databases are pervaded with data of poor quality. However, there has not been an analysis of the data quality literature that provides an overall understanding of the state-of-art research in this area. Using an analogy between product manufacturing and data manufacturing, this paper develops a framework for analyzing data quality research, and uses it as the basis for organizing the data quality literature. This framework consists of seven elements: management responsibilities, operation and assurance costs, research and development, production, distribution, personnel management, and legal function. The analysis reveals that most research efforts focus on operation and assurance costs, research and development, and production of data products. Unexplored research topics and unresolved issues are identified and directions for future research provided.