The Impact Of Data Quality Metadata On Decision Making
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| Author | : Yu Cai |
| Publisher | : |
| Total Pages | : 312 |
| Release | : 2007 |
| Genre | : |
| ISBN | : |
Abstract: The quality of the data used in a decision process has a direct impact on the quality of the decisions. Unfortunately, data quality (DQ) issues are inherent in information systems. Quality metadata is data that describes the quality of the data. It can inform decision makers about the quality of the data used in the decision process and can potentially enhance decision performance. While research has shown that quality metadata can change decision outcomes, it has not shown whether it improves decision performance. This dissertation first investigates whether and how DQ metadata improves decision performance in structured decision environments. When the DQ metadata is available, integrating it into the decision process requires allocating extra information processing capacity to it. This may impact overall decision performance differently, subject to other factors like task complexity and decision makers' experience. Using a set of experiments, we explore those important relationships. The findings support our general proposition that when there is sufficient amount of cognitive capacity available, the provision of DQ metadata will improve the overall quality of decision-making. However, when the complexity of the decision task increases, thereby reducing the cognitive capacity that the decision-maker has to spare, the provision of DQ metadata can negatively affect decision performance. This research further investigates the use of data visualization to reduce this cognitive load. Building upon the findings from the first part, we develop a prototype decision support system that utilizes visualization to reduce the cognitive effort in integrating quality metadata into the decision process. Using an experimental setup, we assess the impact of data visualization on decision performance using a common type of decision task, structured choice. The results confirm the benefits of visualization for integrating DQ metadata into the decision process. This research has important implications for decision-making, data management and decision support systems. The findings can help (a) identify the conditions under which the provision of DQ metadata is beneficial for decision making, (b) justify the need of including DQ metadata in data management, and (c) shed light on designing decision support systems that integrate DQ metadata for effective decision-making.
| Author | : Christoph Samitsch |
| Publisher | : Springer |
| Total Pages | : 70 |
| Release | : 2014-12-01 |
| Genre | : Business & Economics |
| ISBN | : 3658082003 |
Christoph Samitsch investigates whether decision-making efficiency is being influenced by the quality of data and information. Results of the research provide evidence that defined data quality dimensions have an effect on decision-making performance as well as the time it takes to make a decision.
| Author | : Darmont, Jrme |
| Publisher | : IGI Global |
| Total Pages | : 433 |
| Release | : 2006-03-31 |
| Genre | : Computers |
| ISBN | : 1591406579 |
"This book provides an overall view of the emerging field of complex data processing, highlighting the similarities between the different data, issues and approaches"--Provided by publisher.
| Author | : Frada Burstein |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 886 |
| Release | : 2008-01-22 |
| Genre | : Computers |
| ISBN | : 3540487131 |
Decision support systems have experienced a marked increase in attention and importance over the past 25 years. The aim of this book is to survey the decision support system (DSS) field – covering both developed territory and emergent frontiers. It will give the reader a clear understanding of fundamental DSS concepts, methods, technologies, trends, and issues. It will serve as a basic reference work for DSS research, practice, and instruction. To achieve these goals, the book has been designed according to a ten-part structure, divided in two volumes with chapters authored by well-known, well-versed scholars and practitioners from the DSS community.
| Author | : David Loshin |
| Publisher | : Elsevier |
| Total Pages | : 423 |
| Release | : 2010-11-22 |
| Genre | : Computers |
| ISBN | : 0080920349 |
The Practitioner's Guide to Data Quality Improvement offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. It shares the fundamentals for understanding the impacts of poor data quality, and guides practitioners and managers alike in socializing, gaining sponsorship for, planning, and establishing a data quality program. It demonstrates how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. It includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. This book is recommended for data management practitioners, including database analysts, information analysts, data administrators, data architects, enterprise architects, data warehouse engineers, and systems analysts, and their managers. Offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. Shows how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. Includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning.
| Author | : Thomas C. Redman |
| Publisher | : Digital Press |
| Total Pages | : 264 |
| Release | : 2001 |
| Genre | : Computers |
| ISBN | : 9781555582517 |
Can any subject inspire less excitement than "data quality"? Yet a moment's thought reveals the ever-growing importance of quality data. From restated corporate earnings, to incorrect prices on the web, to the bombing of the Chinese Embassy, the media reports the impact of poor data quality on a daily basis. Every business operation creates or consumes huge quantities of data. If the data are wrong, time, money, and reputation are lost. In today's environment, every leader, every decision maker, every operational manager, every consumer, indeed everyone has a vested interest in data quality. Data Quality: The Field Guide provides the practical guidance needed to start and advance a data quality program. It motivates interest in data quality, describes the most important data quality problems facing the typical organization, and outlines what an organization must do to improve. It consists of 36 short chapters in an easy-to-use field guide format. Each chapter describes a single issue and how to address it. The book begins with sections that describe why leaders, whether CIOs, CFOs, or CEOs, should be concerned with data quality. It explains the pros and cons of approaches for addressing the issue. It explains what those organizations with the best data do. And it lays bare the social issues that prevent organizations from making headway. "Field tips" at the end of each chapter summarize the most important points. Allows readers to go directly to the topic of interest Provides web-based material so readers can cut and paste figures and tables into documents within their organizations Gives step-by-step instructions for applying most techniques and summarizes what "works"
| Author | : Lei Chen |
| Publisher | : Springer |
| Total Pages | : 383 |
| Release | : 2009-08-27 |
| Genre | : Computers |
| ISBN | : 3642042058 |
DASFAA is an annual international database conference, located in the Asia- Paci?cregion,whichshowcasesstate-of-the-artR & Dactivities in databases- tems and their applications. It provides a forum for technical presentations and discussions among database researchers, developers and users from academia, business and industry. DASFAA 2009, the 14th in the series, was held during April 20-23, 2009 in Brisbane, Australia. In this year, we carefully selected six workshops, each focusing on speci?c research issues that contribute to the main themes of the DASFAA conference. Thisvolumecontainsthe?nalversionsofpapersacceptedforthesesixworkshops that were held in conjunction with DASFAA 2009. They are: – First International Workshop on Benchmarking of XML and Semantic Web Applications (BenchmarX 2009) – Second International Workshop on Managing Data Quality in Collaborative Information Systems (MCIS 2009) – First International Workshop on Data and Process Provenance (WDPP 2009) – First International Workshop on Privacy-Preserving Data Analysis (PPDA 2009) – FirstInternationalWorkshoponMobileBusinessCollaboration(MBC2009) – DASFAA 2009 PhD Workshop All the workshops were selected via a public call-for-proposals process. The workshop organizers put a tremendous amount of e?ort into soliciting and - lecting papers with a balance of high quality, new ideas and new applications. We asked all workshops to follow a rigid paper selection process, including the procedure to ensure that any Program Committee members are excluded from the paper review process of any paper they are involved with. A requirement about the overall paper acceptance rate of no more than 50% was also imposed on all the workshops.
| Author | : David Marco |
| Publisher | : John Wiley & Sons |
| Total Pages | : 482 |
| Release | : 2004-03-25 |
| Genre | : Computers |
| ISBN | : 0764571591 |
* The heart of the book provides the complete set of models that will support most of an organization's core business functions, including universal meta models for enterprise-wide systems, business meta data and data stewardship, portfolio management, business rules, and XML, messaging, and transactions * Developers can directly adapt these models to their own businesses, saving countless hours of development time * Building effective meta data repositories is complicated and time-consuming, and few IT departments have the necessary expertise to do it right-which is why this book is sure to find a ready audience * Begins with a quick overview of the Meta Data Repository Environment and the business uses of meta data, then goes on to describe the technical architecture followed by the detailed models
| Author | : Rupa Mahanti |
| Publisher | : Quality Press |
| Total Pages | : 526 |
| Release | : 2019-03-18 |
| Genre | : Business & Economics |
| ISBN | : 0873899776 |
This is not the kind of book that youll read one time and be done with. So scan it quickly the first time through to get an idea of its breadth. Then dig in on one topic of special importance to your work. Finally, use it as a reference to guide your next steps, learn details, and broaden your perspective. from the foreword by Thomas C. Redman, Ph.D., the Data Doc Good data is a source of myriad opportunities, while bad data is a tremendous burden. Companies that manage their data effectively are able to achieve a competitive advantage in the marketplace, while bad data, like cancer, can weaken and kill an organization. In this comprehensive book, Rupa Mahanti provides guidance on the different aspects of data quality with the aim to be able to improve data quality. Specifically, the book addresses: -Causes of bad data quality, bad data quality impacts, and importance of data quality to justify the case for data quality-Butterfly effect of data quality-A detailed description of data quality dimensions and their measurement-Data quality strategy approach-Six Sigma - DMAIC approach to data quality-Data quality management techniques-Data quality in relation to data initiatives like data migration, MDM, data governance, etc.-Data quality myths, challenges, and critical success factorsStudents, academicians, professionals, and researchers can all use the content in this book to further their knowledge and get guidance on their own specific projects. It balances technical details (for example, SQL statements, relational database components, data quality dimensions measurements) and higher-level qualitative discussions (cost of data quality, data quality strategy, data quality maturity, the case made for data quality, and so on) with case studies, illustrations, and real-world examples throughout.
| Author | : John W. Payne |
| Publisher | : Cambridge University Press |
| Total Pages | : 352 |
| Release | : 1993-05-28 |
| Genre | : Education |
| ISBN | : 9780521425261 |
The Adaptive Decision Maker argues that people use a variety of strategies to make judgments and choices. The authors introduce a model that shows how decision makers balance effort and accuracy considerations and predicts which strategy a person will use in a given situation. A series of experiments testing the model are presented, and the authors analyse how the model can lead to improved decisions and opportunities for further research.
