Data quality: A survey of data quality dimensions
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paper_title: Methodologies for data quality assessment and improvement

paper_content:

The literature provides a wide range of techniques to assess and improve the quality of data. Due to the diversity and complexity of these techniques, research has recently focused on defining methodologies that help the selection, customization, and application of data quality assessment and improvement techniques. The goal of this article is to provide a systematic and comparative description of such methodologies. Methodologies are compared along several dimensions, including the methodological phases and steps, the strategies and techniques, the data quality dimensions, the types of data, and, finally, the types of information systems addressed by each methodology. The article concludes with a summary description of each methodology.
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paper_title: Factors influencing organizations to improve data quality in their information systems

paper_content:

Although managers consider accurate, timely and relevant information as critical to the quality of their decisions, evidence of large variations in data quality abounds. This research examines factors influencing the level of data quality within a target organization. The results indicate that management's commitment to data quality and the presence of data quality champions strongly influence data quality in the target organization. The results also show that the managers of the participating organization are committed to achieving and maintaining high data quality. However, changing work processes and establishing a data quality awareness culture are required to motivate further improvements to data quality.
---
paper_title: Methodologies for data quality assessment and improvement

paper_content:

The literature provides a wide range of techniques to assess and improve the quality of data. Due to the diversity and complexity of these techniques, research has recently focused on defining methodologies that help the selection, customization, and application of data quality assessment and improvement techniques. The goal of this article is to provide a systematic and comparative description of such methodologies. Methodologies are compared along several dimensions, including the methodological phases and steps, the strategies and techniques, the data quality dimensions, the types of data, and, finally, the types of information systems addressed by each methodology. The article concludes with a summary description of each methodology.
---
paper_title: Methodologies for data quality assessment and improvement

paper_content:

The literature provides a wide range of techniques to assess and improve the quality of data. Due to the diversity and complexity of these techniques, research has recently focused on defining methodologies that help the selection, customization, and application of data quality assessment and improvement techniques. The goal of this article is to provide a systematic and comparative description of such methodologies. Methodologies are compared along several dimensions, including the methodological phases and steps, the strategies and techniques, the data quality dimensions, the types of data, and, finally, the types of information systems addressed by each methodology. The article concludes with a summary description of each methodology.
---
paper_title: Methodologies for data quality assessment and improvement

paper_content:

The literature provides a wide range of techniques to assess and improve the quality of data. Due to the diversity and complexity of these techniques, research has recently focused on defining methodologies that help the selection, customization, and application of data quality assessment and improvement techniques. The goal of this article is to provide a systematic and comparative description of such methodologies. Methodologies are compared along several dimensions, including the methodological phases and steps, the strategies and techniques, the data quality dimensions, the types of data, and, finally, the types of information systems addressed by each methodology. The article concludes with a summary description of each methodology.
---
paper_title: Methodologies for data quality assessment and improvement

paper_content:

The literature provides a wide range of techniques to assess and improve the quality of data. Due to the diversity and complexity of these techniques, research has recently focused on defining methodologies that help the selection, customization, and application of data quality assessment and improvement techniques. The goal of this article is to provide a systematic and comparative description of such methodologies. Methodologies are compared along several dimensions, including the methodological phases and steps, the strategies and techniques, the data quality dimensions, the types of data, and, finally, the types of information systems addressed by each methodology. The article concludes with a summary description of each methodology.
---
paper_title: Data Quality: Concepts, Methodologies and Techniques

paper_content:

Poor data quality can seriously hinder or damage the efficiency and effectiveness of organizations and businesses. The growing awareness of such repercussions has led to major public initiatives like the "Data Quality Act" in the USA and the "European 2003/98" directive of the European Parliament. Batini and Scannapieco present a comprehensive and systematic introduction to the wide set of issues related to data quality. They start with a detailed description of different data quality dimensions, like accuracy, completeness, and consistency, and their importance in different types of data, like federated data, web data, or time-dependent data, and in different data categories classified according to frequency of change, like stable, long-term, and frequently changing data. The book's extensive description of techniques and methodologies from core data quality research as well as from related fields like data mining, probability theory, statistical data analysis, and machine learning gives an excellent overview of the current state of the art. The presentation is completed by a short description and critical comparison of tools and practical methodologies, which will help readers to resolve their own quality problems. This book is an ideal combination of the soundness of theoretical foundations and the applicability of practical approaches. It is ideally suited for everyone researchers, students, or professionals interested in a comprehensive overview of data quality issues. In addition, it will serve as the basis for an introductory course or for self-study on this topic.
---
paper_title: Developing a Framework for Assessing Information Quality on the World Wide Web

paper_content:

Introduction--The Big Picture Over the past decade, the [Internet.sup.1]--or World Wide Web (Technically the Internet is a huge collection of networked computers using TCP/IP protocol to exchange data. The World-wide Web (WWW) is in essence only part of this network of computers, however its visible status has meant that conceptually at least, it is often used interchangeably with "Internet" to describe the same thing.)--has established itself as the key infrastructure for information administration, exchange, and publication (Alexander & Tate, 1999), and Internet Search Engines are the most commonly used tool to retrieve that information (Wang, 2001). The deficiency of enforceable standards however, has resulted in frequent information quality problems (Eppler & Muenzenmayer, 2002). This paper is part of a research project undertaken at Edith Cowan, Wollongong and Sienna Universities, to build an Internet Focused Crawler that uses "Quality" criterion in determining returns to user queries. Such a task requires that the conceptual notions of quality be ultimately quantified into Search Engine algorithms that interact with Webpage technologies, eliminating documents that do not meet specifically determined standards of quality. The focus of this paper, as part of the wider research, is on the concepts of Quality in Information and Information Systems, specifically as it pertains to Information and Information Retrieval on the Internet. As with much of the research into Information Quality (IQ) in Information Systems, the term is interchangeable with Data Quality (DQ). What Is Information Quality? Data and Information Quality is commonly thought of as a multi-dimensional concept (Klein, 2001) with varying attributed characteristics depending on an author's philosophical view-point. Most commonly, the term "Data Quality" is described as data that is "Fit-for-use" (Wang & Strong, 1996), which implies that it is relative, as data considered appropriate for one use may not possess sufficient attributes for another use (Tayi & Ballou, 1998). IQ as a series of Dimensions Table 1 summaries 12 widely accepted IQ Frameworks collated from the last decade of IS research. While varied in their approach and application, the frameworks share a number of characteristics regarding their classifications of the dimensions of quality. An analysis of Table 1 reveals the common elements between the different IQ Frameworks. These include such traditional dimensions as accuracy, consistency, timeliness, completeness, accessibility, objectiveness and relevancy. Table 2 provides a summary of the most common dimensions and the frequency with which they are included in the above IQ Frameworks. Each dimension also includes a short definition. IQ in the context of its use In order to accurately define and measure the concept of Information quality, it is not enough to identify the common elements of IQ Frameworks as individual entities in their own right. In fact, Information Quality needs to be assessed within the context of its generation (Shanks & Corbitt, 1999) and intended use (Katerattanakul & Siau, 1999). This is because the attributes of data quality can vary depending on the context in which the data is to be used (Shankar & Watts, 2003). Defining what Information Quality is within the context of the World Wide Web and its Search Engines then, will depend greatly on whether dimensions are being identified for the producers of information, the storage and maintenance systems used for information, or for the searchers and users of information. The currently accepted view of assessing IQ, involves understanding it from the users point of view. Strong and Wang (1997) suggest that quality of data cannot be assessed independent of the people who use data. Applying this commonly to the World Wide Web has its own set of problems. …
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paper_title: Beyond accuracy: what data quality means to data consumers

paper_content:

Poor data quality (DQ) can have substantial social and economic impacts. Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus narrowly on accuracy. We believe that data consumers have a much broader data quality conceptualization than IS professionals realize. The purpose of this paper is to develop a framework that captures the aspects of data quality that are important to data consumers.A two-stage survey and a two-phase sorting study were conducted to develop a hierarchical framework for organizing data quality dimensions. This framework captures dimensions of data quality that are important to data consumers. Intrinsic DQ denotes that data have quality in their own right. Contextual DQ highlights the requirement that data quality must be considered within the context of the task at hand. Representational DQ and accessibility DQ emphasize the importance of the role of systems. These findings are consistent with our understanding that high-quality data should be intrinsically good, contextually appropriate for the task, clearly represented, and accessible to the data consumer.Our framework has been used effectively in industry and government. Using this framework, IS managers were able to better understand and meet their data consumers' data quality needs. The salient feature of this research study is that quality attributes of data are collected from data consumers instead of being defined theoretically or based on researchers' experience. Although exploratory, this research provides a basis for future studies that measure data quality along the dimensions of this framework.
---
paper_title: Data Quality: Concepts, Methodologies and Techniques

paper_content:

Poor data quality can seriously hinder or damage the efficiency and effectiveness of organizations and businesses. The growing awareness of such repercussions has led to major public initiatives like the "Data Quality Act" in the USA and the "European 2003/98" directive of the European Parliament. Batini and Scannapieco present a comprehensive and systematic introduction to the wide set of issues related to data quality. They start with a detailed description of different data quality dimensions, like accuracy, completeness, and consistency, and their importance in different types of data, like federated data, web data, or time-dependent data, and in different data categories classified according to frequency of change, like stable, long-term, and frequently changing data. The book's extensive description of techniques and methodologies from core data quality research as well as from related fields like data mining, probability theory, statistical data analysis, and machine learning gives an excellent overview of the current state of the art. The presentation is completed by a short description and critical comparison of tools and practical methodologies, which will help readers to resolve their own quality problems. This book is an ideal combination of the soundness of theoretical foundations and the applicability of practical approaches. It is ideally suited for everyone researchers, students, or professionals interested in a comprehensive overview of data quality issues. In addition, it will serve as the basis for an introductory course or for self-study on this topic.
---
paper_title: Data quality improvement using fuzzy association rules

paper_content:

The activities and decisions of organizations and companies are based on data and the information obtained from data analysis. Data quality plays a crucial role in data analysis, because the incorrect data leads to wrong decisions. Nowadays, improving the data quality manually is very difficult and in many cases is impossible as data quality is one of the complicated and non-structured concepts and data refinement process can not be done without the help of professional domain experts, and detection and correction of errors require a thorough knowledge in the related domain of the data. Thus, the necessity of using (semi-)automatic methods is discussed to find data defects and errors and solve them. Because data mining methods are designed to discover interesting patterns in datasets, we can use them efficiently to improve different dimensions of data quality. In this paper, a new method is presented to measure the accuracy dimension of data quality using fuzzy association rules. Finally, Experimental results of the proposed method show the effectiveness of the proposed method to find incorrect values in datasets.
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paper_title: Dimensions of Business Processes Quality (QoBP)

paper_content:

Conceptual modeling is an important tool for understanding and revealing weaknesses of business processes. Yet, the current practice in reengineering projects often considers simply the as-is process model as a brain-storming tool. This approach heavily relies on the intuition of the participants and misses a clear description of the quality requirements. Against this background, we identify four generic quality categories of business process quality, and populate them with quality requirements from related research. We refer to the resulting framework as the Quality of Business Process (QoBP) framework. Furthermore, we present the findings from applying the QoBP framework in a case study with a major Australian bank, showing that it helps to systematically fill the white space between as-is and to-be process modeling.
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paper_title: Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (Tm)

paper_content:

Information is currency. Recent studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. In this important and timely new book, Danette McGilvray presents her ?Ten Steps? approach to information quality, a proven method for both understanding and creating information quality in the enterprise. Her trademarked approach-in which she has trained Fortune 500 clients and hundreds of workshop attendees-applies to all types of data and to all types of organizations. ::: ::: * Includes numerous templates, detailed examples, and practical advice for executing every step of the ?Ten Steps? approach. ::: ::: * Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices. ::: ::: * A companion Web site includes links to numerous data quality resources, including many of the planning and information-gathering templates featured in the text, quick summaries of key ideas from the Ten Step methodology, and other tools and information available online. ::: ::: Table of Contents ::: ::: Introduction ::: The Reason for This Book ::: Intended Audiences ::: Structure of This Book ::: How to Use This Book ::: Acknowledgements ::: ::: Chapter 1 Overview ::: Impact of Information and Data Quality ::: About the Methodology ::: Approaches to Data Quality in Projects ::: Engaging Management ::: ::: Chapter 2 Key Concepts ::: Introduction ::: Framework for Information Quality (FIQ) ::: Information Life Cycle ::: Data Quality Dimensions ::: Business Impact Techniques ::: Data Categories ::: Data Specifications ::: Data Governance and Stewardship ::: The Information and Data Quality Improvement Cycle ::: The Ten Steps? Process ::: Best Practices and Guidelines ::: ::: Chapter 3 The Ten Steps ::: 1. Define Business Need and Approach ::: 2. Analyze Information Environment ::: 3. Assess Data Quality ::: 4. Assess Business Impact ::: 5. Identify Root Causes ::: 6. Develop Improvement Plans ::: 7. Prevent Future Data Errors ::: 8. Correct Current Data Errors ::: 9. Implement Controls ::: 10. Communicate Actions and Results ::: ::: Chapter 4 Structuring Your Project ::: Projects and The Ten Steps ::: Data Quality Project Roles ::: Project Timing ::: ::: Chapter 5 Other Techniques and Tools ::: Introduction ::: Information Life Cycle Approaches ::: Capture Data ::: Analyze and Document Results ::: Metrics ::: Data Quality Tools ::: The Ten Steps and Six Sigma ::: ::: Chapter 6 A Few Final Words ::: ::: Appendix Quick References ::: Framework for Information Quality ::: POSMAD Interaction Matrix Detail ::: POSMAD Phases and Activities ::: Data Quality Dimensions ::: Business Impact Techniques ::: The Ten Steps? Overview ::: Definitions of Data Categories
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paper_title: A Noval Data Quality Controlling and Assessing Model Based on Rules

paper_content:

As a resource, data is the base for information construction and application. According to the principle of “garbage in and garbage out”, it needs us to ensure data reliability, no errors and accurately reflect the real situation to support the right decisions. However, due to various reasons, it leads to poor quality of dirty data in existing system business, while the dirty data is an important factor which affects right decisions. For the above, in this paper, a metadata-based data quality rule base is created for improving traditional quality control model, a more practical application of the weighted assessment algorithm is proposed and a three-tier data quality assessment system model is constructed based on the study of definition and classification of quality, assessment algorithm, metadata and the control theory. This model is confirmed to achieve comprehensive quality of data management and control in oilfield practical applications.
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paper_title: Analysis of data quality and information quality problems in digital manufacturing

paper_content:

This work focuses on the increasing importance of data quality in organizations, especially in digital manufacturing companies. The paper firstly reviews related works in field of data quality, including definition, dimensions, measurement and assessment, and improvement of data quality. Then, by taking the digital manufacturing as research object, the different information roles, information manufacturing processes, influential factors of information quality, and the transformation levels and paths of the data/information quality in digital manufacturing companies are analyzed. Finally an approach for the diagnosis, control and improvement of data/information quality in digital manufacturing companies, which is the basis for further works, is proposed.
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paper_title: A Conceptual Framework and Belief Function Approach to Assessing Overall Information Quality

paper_content:

We develop an information quality model based on a user-centric view adapted from Financial Accounting Standards Board1, Wang et al.2, and Wang and Strong3. The model consists of four essential attributes (or assertions): ‘Accessibility,’ ‘Interpretability,’ ‘Relevance,’ and ‘Integrity.’ Four sub-attributes lead to an evaluation of Integrity: ‘Accuracy,’ ‘Completeness,’ ‘Consistency,’ and ‘Existence.’ These sub-attributes relating to 'Integrity' are intrinsic in nature and relate to the process of how the information was created while the first three attributes: ‘Accessibility,’ ‘Interpretability,’ and ‘Relevance’ are extrinsic in nature. We present our model as an evidential network under the belief-function framework to permit user assessment of quality parameters. Two algorithms for combining assessments into an overall IQ measure are explored, and examples in the domain of medical information are used to illustrate key concepts. We discuss two scenarios, ‘online-user’ and ‘assurance-provider,’ which reflect two likely and important aspects of IQ evaluation currently facing information users – concerns about the impact of poor quality online information, and the need for information quality assurance.
---
paper_title: Methodologies for data quality assessment and improvement

paper_content:

The literature provides a wide range of techniques to assess and improve the quality of data. Due to the diversity and complexity of these techniques, research has recently focused on defining methodologies that help the selection, customization, and application of data quality assessment and improvement techniques. The goal of this article is to provide a systematic and comparative description of such methodologies. Methodologies are compared along several dimensions, including the methodological phases and steps, the strategies and techniques, the data quality dimensions, the types of data, and, finally, the types of information systems addressed by each methodology. The article concludes with a summary description of each methodology.
---
paper_title: Fundamentals of Data Warehouses

paper_content:

From the Publisher: ::: Data warehouses have captured the attention of practitioners and researchers alike. But the design and optimization of data warehouses remains an art rather than a science. This book presents a comparative review of the state of the art and best current practice of data warehouses. It covers source and data integration, multidimensional aggregation, query optimization, update propagation, metadata management, quality assessment, and design optimization. Also, based on results of the European Data Warehouse Quality project, it offers a conceptual framework by which the architecture and quality of data warehouse efforts can be assessed and improved using enriched metadata management combined with advanced techniques from databases, business modeling, and artificial intelligence. For researchers and database professionals in academia and industry, the book offers an excellent introduction to the issues of quality and metadata usage in the context of data warehouses.
---
paper_title: Beyond accuracy: what data quality means to data consumers

paper_content:

Poor data quality (DQ) can have substantial social and economic impacts. Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus narrowly on accuracy. We believe that data consumers have a much broader data quality conceptualization than IS professionals realize. The purpose of this paper is to develop a framework that captures the aspects of data quality that are important to data consumers.A two-stage survey and a two-phase sorting study were conducted to develop a hierarchical framework for organizing data quality dimensions. This framework captures dimensions of data quality that are important to data consumers. Intrinsic DQ denotes that data have quality in their own right. Contextual DQ highlights the requirement that data quality must be considered within the context of the task at hand. Representational DQ and accessibility DQ emphasize the importance of the role of systems. These findings are consistent with our understanding that high-quality data should be intrinsically good, contextually appropriate for the task, clearly represented, and accessible to the data consumer.Our framework has been used effectively in industry and government. Using this framework, IS managers were able to better understand and meet their data consumers' data quality needs. The salient feature of this research study is that quality attributes of data are collected from data consumers instead of being defined theoretically or based on researchers' experience. Although exploratory, this research provides a basis for future studies that measure data quality along the dimensions of this framework.
---
paper_title: Methodologies for data quality assessment and improvement

paper_content:

The literature provides a wide range of techniques to assess and improve the quality of data. Due to the diversity and complexity of these techniques, research has recently focused on defining methodologies that help the selection, customization, and application of data quality assessment and improvement techniques. The goal of this article is to provide a systematic and comparative description of such methodologies. Methodologies are compared along several dimensions, including the methodological phases and steps, the strategies and techniques, the data quality dimensions, the types of data, and, finally, the types of information systems addressed by each methodology. The article concludes with a summary description of each methodology.
---
paper_title: Modeling Data and Process Quality in Multi-Input, Multi-Output Information Systems

paper_content:

This paper presents a general model to assess the impact of data and process quality upon the outputs of multi-user information-decision systems. The data flow/data processing quality control model is designed to address several dimensions of data quality at the collection, input, processing and output stages. ::: ::: Starting from a data flow diagram of the type used in structured analysis, the model yields a representation of possible errors in multiple intermediate and final outputs in terms of input and process error functions. The model generates expressions for the possible magnitudes of errors in selected outputs. This is accomplished using a recursive-type algorithm which traces systematically the propagation and alteration of various errors. These error expressions can be used to analyze the impact that alternative quality control procedures would have on the selected outputs. ::: ::: The paper concludes with a discussion of the tractability of the model for various types of information systems as well as an application to a representative scenario.
---
paper_title: Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (Tm)

paper_content:

Information is currency. Recent studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. In this important and timely new book, Danette McGilvray presents her ?Ten Steps? approach to information quality, a proven method for both understanding and creating information quality in the enterprise. Her trademarked approach-in which she has trained Fortune 500 clients and hundreds of workshop attendees-applies to all types of data and to all types of organizations. ::: ::: * Includes numerous templates, detailed examples, and practical advice for executing every step of the ?Ten Steps? approach. ::: ::: * Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices. ::: ::: * A companion Web site includes links to numerous data quality resources, including many of the planning and information-gathering templates featured in the text, quick summaries of key ideas from the Ten Step methodology, and other tools and information available online. ::: ::: Table of Contents ::: ::: Introduction ::: The Reason for This Book ::: Intended Audiences ::: Structure of This Book ::: How to Use This Book ::: Acknowledgements ::: ::: Chapter 1 Overview ::: Impact of Information and Data Quality ::: About the Methodology ::: Approaches to Data Quality in Projects ::: Engaging Management ::: ::: Chapter 2 Key Concepts ::: Introduction ::: Framework for Information Quality (FIQ) ::: Information Life Cycle ::: Data Quality Dimensions ::: Business Impact Techniques ::: Data Categories ::: Data Specifications ::: Data Governance and Stewardship ::: The Information and Data Quality Improvement Cycle ::: The Ten Steps? Process ::: Best Practices and Guidelines ::: ::: Chapter 3 The Ten Steps ::: 1. Define Business Need and Approach ::: 2. Analyze Information Environment ::: 3. Assess Data Quality ::: 4. Assess Business Impact ::: 5. Identify Root Causes ::: 6. Develop Improvement Plans ::: 7. Prevent Future Data Errors ::: 8. Correct Current Data Errors ::: 9. Implement Controls ::: 10. Communicate Actions and Results ::: ::: Chapter 4 Structuring Your Project ::: Projects and The Ten Steps ::: Data Quality Project Roles ::: Project Timing ::: ::: Chapter 5 Other Techniques and Tools ::: Introduction ::: Information Life Cycle Approaches ::: Capture Data ::: Analyze and Document Results ::: Metrics ::: Data Quality Tools ::: The Ten Steps and Six Sigma ::: ::: Chapter 6 A Few Final Words ::: ::: Appendix Quick References ::: Framework for Information Quality ::: POSMAD Interaction Matrix Detail ::: POSMAD Phases and Activities ::: Data Quality Dimensions ::: Business Impact Techniques ::: The Ten Steps? Overview ::: Definitions of Data Categories
---
paper_title: Beyond accuracy: what data quality means to data consumers

paper_content:

Poor data quality (DQ) can have substantial social and economic impacts. Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus narrowly on accuracy. We believe that data consumers have a much broader data quality conceptualization than IS professionals realize. The purpose of this paper is to develop a framework that captures the aspects of data quality that are important to data consumers.A two-stage survey and a two-phase sorting study were conducted to develop a hierarchical framework for organizing data quality dimensions. This framework captures dimensions of data quality that are important to data consumers. Intrinsic DQ denotes that data have quality in their own right. Contextual DQ highlights the requirement that data quality must be considered within the context of the task at hand. Representational DQ and accessibility DQ emphasize the importance of the role of systems. These findings are consistent with our understanding that high-quality data should be intrinsically good, contextually appropriate for the task, clearly represented, and accessible to the data consumer.Our framework has been used effectively in industry and government. Using this framework, IS managers were able to better understand and meet their data consumers' data quality needs. The salient feature of this research study is that quality attributes of data are collected from data consumers instead of being defined theoretically or based on researchers' experience. Although exploratory, this research provides a basis for future studies that measure data quality along the dimensions of this framework.
---
paper_title: A Conceptual Framework and Belief Function Approach to Assessing Overall Information Quality

paper_content:

We develop an information quality model based on a user-centric view adapted from Financial Accounting Standards Board1, Wang et al.2, and Wang and Strong3. The model consists of four essential attributes (or assertions): ‘Accessibility,’ ‘Interpretability,’ ‘Relevance,’ and ‘Integrity.’ Four sub-attributes lead to an evaluation of Integrity: ‘Accuracy,’ ‘Completeness,’ ‘Consistency,’ and ‘Existence.’ These sub-attributes relating to 'Integrity' are intrinsic in nature and relate to the process of how the information was created while the first three attributes: ‘Accessibility,’ ‘Interpretability,’ and ‘Relevance’ are extrinsic in nature. We present our model as an evidential network under the belief-function framework to permit user assessment of quality parameters. Two algorithms for combining assessments into an overall IQ measure are explored, and examples in the domain of medical information are used to illustrate key concepts. We discuss two scenarios, ‘online-user’ and ‘assurance-provider,’ which reflect two likely and important aspects of IQ evaluation currently facing information users – concerns about the impact of poor quality online information, and the need for information quality assurance.
---
paper_title: Quality-Driven Query Answering for Integrated Information Systems

paper_content:

Querying the Web.- Integrating Autonomous Information Sources.- Information Quality.- Information Quality Criteria.- Quality Ranking Methods.- Quality-Driven Query Answering.- Quality-Driven Query Planning.- Query Planning Revisited.- Completeness of Data.- Completeness-Driven Query Optimization.- Discussion.- Conclusion.
---
paper_title: Methodologies for data quality assessment and improvement

paper_content:

The literature provides a wide range of techniques to assess and improve the quality of data. Due to the diversity and complexity of these techniques, research has recently focused on defining methodologies that help the selection, customization, and application of data quality assessment and improvement techniques. The goal of this article is to provide a systematic and comparative description of such methodologies. Methodologies are compared along several dimensions, including the methodological phases and steps, the strategies and techniques, the data quality dimensions, the types of data, and, finally, the types of information systems addressed by each methodology. The article concludes with a summary description of each methodology.
---
paper_title: Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (Tm)

paper_content:

Information is currency. Recent studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. In this important and timely new book, Danette McGilvray presents her ?Ten Steps? approach to information quality, a proven method for both understanding and creating information quality in the enterprise. Her trademarked approach-in which she has trained Fortune 500 clients and hundreds of workshop attendees-applies to all types of data and to all types of organizations. ::: ::: * Includes numerous templates, detailed examples, and practical advice for executing every step of the ?Ten Steps? approach. ::: ::: * Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices. ::: ::: * A companion Web site includes links to numerous data quality resources, including many of the planning and information-gathering templates featured in the text, quick summaries of key ideas from the Ten Step methodology, and other tools and information available online. ::: ::: Table of Contents ::: ::: Introduction ::: The Reason for This Book ::: Intended Audiences ::: Structure of This Book ::: How to Use This Book ::: Acknowledgements ::: ::: Chapter 1 Overview ::: Impact of Information and Data Quality ::: About the Methodology ::: Approaches to Data Quality in Projects ::: Engaging Management ::: ::: Chapter 2 Key Concepts ::: Introduction ::: Framework for Information Quality (FIQ) ::: Information Life Cycle ::: Data Quality Dimensions ::: Business Impact Techniques ::: Data Categories ::: Data Specifications ::: Data Governance and Stewardship ::: The Information and Data Quality Improvement Cycle ::: The Ten Steps? Process ::: Best Practices and Guidelines ::: ::: Chapter 3 The Ten Steps ::: 1. Define Business Need and Approach ::: 2. Analyze Information Environment ::: 3. Assess Data Quality ::: 4. Assess Business Impact ::: 5. Identify Root Causes ::: 6. Develop Improvement Plans ::: 7. Prevent Future Data Errors ::: 8. Correct Current Data Errors ::: 9. Implement Controls ::: 10. Communicate Actions and Results ::: ::: Chapter 4 Structuring Your Project ::: Projects and The Ten Steps ::: Data Quality Project Roles ::: Project Timing ::: ::: Chapter 5 Other Techniques and Tools ::: Introduction ::: Information Life Cycle Approaches ::: Capture Data ::: Analyze and Document Results ::: Metrics ::: Data Quality Tools ::: The Ten Steps and Six Sigma ::: ::: Chapter 6 A Few Final Words ::: ::: Appendix Quick References ::: Framework for Information Quality ::: POSMAD Interaction Matrix Detail ::: POSMAD Phases and Activities ::: Data Quality Dimensions ::: Business Impact Techniques ::: The Ten Steps? Overview ::: Definitions of Data Categories
---
paper_title: Fundamentals of Data Warehouses

paper_content:

From the Publisher: ::: Data warehouses have captured the attention of practitioners and researchers alike. But the design and optimization of data warehouses remains an art rather than a science. This book presents a comparative review of the state of the art and best current practice of data warehouses. It covers source and data integration, multidimensional aggregation, query optimization, update propagation, metadata management, quality assessment, and design optimization. Also, based on results of the European Data Warehouse Quality project, it offers a conceptual framework by which the architecture and quality of data warehouse efforts can be assessed and improved using enriched metadata management combined with advanced techniques from databases, business modeling, and artificial intelligence. For researchers and database professionals in academia and industry, the book offers an excellent introduction to the issues of quality and metadata usage in the context of data warehouses.
---
paper_title: Beyond accuracy: what data quality means to data consumers

paper_content:

Poor data quality (DQ) can have substantial social and economic impacts. Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus narrowly on accuracy. We believe that data consumers have a much broader data quality conceptualization than IS professionals realize. The purpose of this paper is to develop a framework that captures the aspects of data quality that are important to data consumers.A two-stage survey and a two-phase sorting study were conducted to develop a hierarchical framework for organizing data quality dimensions. This framework captures dimensions of data quality that are important to data consumers. Intrinsic DQ denotes that data have quality in their own right. Contextual DQ highlights the requirement that data quality must be considered within the context of the task at hand. Representational DQ and accessibility DQ emphasize the importance of the role of systems. These findings are consistent with our understanding that high-quality data should be intrinsically good, contextually appropriate for the task, clearly represented, and accessible to the data consumer.Our framework has been used effectively in industry and government. Using this framework, IS managers were able to better understand and meet their data consumers' data quality needs. The salient feature of this research study is that quality attributes of data are collected from data consumers instead of being defined theoretically or based on researchers' experience. Although exploratory, this research provides a basis for future studies that measure data quality along the dimensions of this framework.
---
paper_title: Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (Tm)

paper_content:

Information is currency. Recent studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. In this important and timely new book, Danette McGilvray presents her ?Ten Steps? approach to information quality, a proven method for both understanding and creating information quality in the enterprise. Her trademarked approach-in which she has trained Fortune 500 clients and hundreds of workshop attendees-applies to all types of data and to all types of organizations. ::: ::: * Includes numerous templates, detailed examples, and practical advice for executing every step of the ?Ten Steps? approach. ::: ::: * Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices. ::: ::: * A companion Web site includes links to numerous data quality resources, including many of the planning and information-gathering templates featured in the text, quick summaries of key ideas from the Ten Step methodology, and other tools and information available online. ::: ::: Table of Contents ::: ::: Introduction ::: The Reason for This Book ::: Intended Audiences ::: Structure of This Book ::: How to Use This Book ::: Acknowledgements ::: ::: Chapter 1 Overview ::: Impact of Information and Data Quality ::: About the Methodology ::: Approaches to Data Quality in Projects ::: Engaging Management ::: ::: Chapter 2 Key Concepts ::: Introduction ::: Framework for Information Quality (FIQ) ::: Information Life Cycle ::: Data Quality Dimensions ::: Business Impact Techniques ::: Data Categories ::: Data Specifications ::: Data Governance and Stewardship ::: The Information and Data Quality Improvement Cycle ::: The Ten Steps? Process ::: Best Practices and Guidelines ::: ::: Chapter 3 The Ten Steps ::: 1. Define Business Need and Approach ::: 2. Analyze Information Environment ::: 3. Assess Data Quality ::: 4. Assess Business Impact ::: 5. Identify Root Causes ::: 6. Develop Improvement Plans ::: 7. Prevent Future Data Errors ::: 8. Correct Current Data Errors ::: 9. Implement Controls ::: 10. Communicate Actions and Results ::: ::: Chapter 4 Structuring Your Project ::: Projects and The Ten Steps ::: Data Quality Project Roles ::: Project Timing ::: ::: Chapter 5 Other Techniques and Tools ::: Introduction ::: Information Life Cycle Approaches ::: Capture Data ::: Analyze and Document Results ::: Metrics ::: Data Quality Tools ::: The Ten Steps and Six Sigma ::: ::: Chapter 6 A Few Final Words ::: ::: Appendix Quick References ::: Framework for Information Quality ::: POSMAD Interaction Matrix Detail ::: POSMAD Phases and Activities ::: Data Quality Dimensions ::: Business Impact Techniques ::: The Ten Steps? Overview ::: Definitions of Data Categories
---
paper_title: Data quality assessment

paper_content:

How good is a company's data quality? Answering this question requires usable data quality metrics. Currently, most data quality measures are developed on an ad hoc basis to solve specific problems [6, 8], and fundamental principles necessary for developing usable metrics in practice are lacking. In this article, we describe principles that can help organizations develop usable data quality metrics.
---
paper_title: Beyond accuracy: what data quality means to data consumers

paper_content:

Poor data quality (DQ) can have substantial social and economic impacts. Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus narrowly on accuracy. We believe that data consumers have a much broader data quality conceptualization than IS professionals realize. The purpose of this paper is to develop a framework that captures the aspects of data quality that are important to data consumers.A two-stage survey and a two-phase sorting study were conducted to develop a hierarchical framework for organizing data quality dimensions. This framework captures dimensions of data quality that are important to data consumers. Intrinsic DQ denotes that data have quality in their own right. Contextual DQ highlights the requirement that data quality must be considered within the context of the task at hand. Representational DQ and accessibility DQ emphasize the importance of the role of systems. These findings are consistent with our understanding that high-quality data should be intrinsically good, contextually appropriate for the task, clearly represented, and accessible to the data consumer.Our framework has been used effectively in industry and government. Using this framework, IS managers were able to better understand and meet their data consumers' data quality needs. The salient feature of this research study is that quality attributes of data are collected from data consumers instead of being defined theoretically or based on researchers' experience. Although exploratory, this research provides a basis for future studies that measure data quality along the dimensions of this framework.
---
paper_title: Dimensions of Business Processes Quality (QoBP)

paper_content:

Conceptual modeling is an important tool for understanding and revealing weaknesses of business processes. Yet, the current practice in reengineering projects often considers simply the as-is process model as a brain-storming tool. This approach heavily relies on the intuition of the participants and misses a clear description of the quality requirements. Against this background, we identify four generic quality categories of business process quality, and populate them with quality requirements from related research. We refer to the resulting framework as the Quality of Business Process (QoBP) framework. Furthermore, we present the findings from applying the QoBP framework in a case study with a major Australian bank, showing that it helps to systematically fill the white space between as-is and to-be process modeling.
---
paper_title: Data quality assessment

paper_content:

How good is a company's data quality? Answering this question requires usable data quality metrics. Currently, most data quality measures are developed on an ad hoc basis to solve specific problems [6, 8], and fundamental principles necessary for developing usable metrics in practice are lacking. In this article, we describe principles that can help organizations develop usable data quality metrics.
---
paper_title: Data quality assessment

paper_content:

How good is a company's data quality? Answering this question requires usable data quality metrics. Currently, most data quality measures are developed on an ad hoc basis to solve specific problems [6, 8], and fundamental principles necessary for developing usable metrics in practice are lacking. In this article, we describe principles that can help organizations develop usable data quality metrics.
---
paper_title: Beyond accuracy: what data quality means to data consumers

paper_content:

Poor data quality (DQ) can have substantial social and economic impacts. Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus narrowly on accuracy. We believe that data consumers have a much broader data quality conceptualization than IS professionals realize. The purpose of this paper is to develop a framework that captures the aspects of data quality that are important to data consumers.A two-stage survey and a two-phase sorting study were conducted to develop a hierarchical framework for organizing data quality dimensions. This framework captures dimensions of data quality that are important to data consumers. Intrinsic DQ denotes that data have quality in their own right. Contextual DQ highlights the requirement that data quality must be considered within the context of the task at hand. Representational DQ and accessibility DQ emphasize the importance of the role of systems. These findings are consistent with our understanding that high-quality data should be intrinsically good, contextually appropriate for the task, clearly represented, and accessible to the data consumer.Our framework has been used effectively in industry and government. Using this framework, IS managers were able to better understand and meet their data consumers' data quality needs. The salient feature of this research study is that quality attributes of data are collected from data consumers instead of being defined theoretically or based on researchers' experience. Although exploratory, this research provides a basis for future studies that measure data quality along the dimensions of this framework.
---
paper_title: Beyond accuracy: what data quality means to data consumers

paper_content:

Poor data quality (DQ) can have substantial social and economic impacts. Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus narrowly on accuracy. We believe that data consumers have a much broader data quality conceptualization than IS professionals realize. The purpose of this paper is to develop a framework that captures the aspects of data quality that are important to data consumers.A two-stage survey and a two-phase sorting study were conducted to develop a hierarchical framework for organizing data quality dimensions. This framework captures dimensions of data quality that are important to data consumers. Intrinsic DQ denotes that data have quality in their own right. Contextual DQ highlights the requirement that data quality must be considered within the context of the task at hand. Representational DQ and accessibility DQ emphasize the importance of the role of systems. These findings are consistent with our understanding that high-quality data should be intrinsically good, contextually appropriate for the task, clearly represented, and accessible to the data consumer.Our framework has been used effectively in industry and government. Using this framework, IS managers were able to better understand and meet their data consumers' data quality needs. The salient feature of this research study is that quality attributes of data are collected from data consumers instead of being defined theoretically or based on researchers' experience. Although exploratory, this research provides a basis for future studies that measure data quality along the dimensions of this framework.
---
paper_title: Methodologies for data quality assessment and improvement

paper_content:

The literature provides a wide range of techniques to assess and improve the quality of data. Due to the diversity and complexity of these techniques, research has recently focused on defining methodologies that help the selection, customization, and application of data quality assessment and improvement techniques. The goal of this article is to provide a systematic and comparative description of such methodologies. Methodologies are compared along several dimensions, including the methodological phases and steps, the strategies and techniques, the data quality dimensions, the types of data, and, finally, the types of information systems addressed by each methodology. The article concludes with a summary description of each methodology.
---
paper_title: Data quality assessment

paper_content:

How good is a company's data quality? Answering this question requires usable data quality metrics. Currently, most data quality measures are developed on an ad hoc basis to solve specific problems [6, 8], and fundamental principles necessary for developing usable metrics in practice are lacking. In this article, we describe principles that can help organizations develop usable data quality metrics.
---
paper_title: Data quality assessment

paper_content:

How good is a company's data quality? Answering this question requires usable data quality metrics. Currently, most data quality measures are developed on an ad hoc basis to solve specific problems [6, 8], and fundamental principles necessary for developing usable metrics in practice are lacking. In this article, we describe principles that can help organizations develop usable data quality metrics.
---
paper_title: Data quality assessment

paper_content:

How good is a company's data quality? Answering this question requires usable data quality metrics. Currently, most data quality measures are developed on an ad hoc basis to solve specific problems [6, 8], and fundamental principles necessary for developing usable metrics in practice are lacking. In this article, we describe principles that can help organizations develop usable data quality metrics.
---
paper_title: Dimensions of Business Processes Quality (QoBP)

paper_content:

Conceptual modeling is an important tool for understanding and revealing weaknesses of business processes. Yet, the current practice in reengineering projects often considers simply the as-is process model as a brain-storming tool. This approach heavily relies on the intuition of the participants and misses a clear description of the quality requirements. Against this background, we identify four generic quality categories of business process quality, and populate them with quality requirements from related research. We refer to the resulting framework as the Quality of Business Process (QoBP) framework. Furthermore, we present the findings from applying the QoBP framework in a case study with a major Australian bank, showing that it helps to systematically fill the white space between as-is and to-be process modeling.
---
paper_title: Developing a Framework for Assessing Information Quality on the World Wide Web

paper_content:

Introduction--The Big Picture Over the past decade, the [Internet.sup.1]--or World Wide Web (Technically the Internet is a huge collection of networked computers using TCP/IP protocol to exchange data. The World-wide Web (WWW) is in essence only part of this network of computers, however its visible status has meant that conceptually at least, it is often used interchangeably with "Internet" to describe the same thing.)--has established itself as the key infrastructure for information administration, exchange, and publication (Alexander & Tate, 1999), and Internet Search Engines are the most commonly used tool to retrieve that information (Wang, 2001). The deficiency of enforceable standards however, has resulted in frequent information quality problems (Eppler & Muenzenmayer, 2002). This paper is part of a research project undertaken at Edith Cowan, Wollongong and Sienna Universities, to build an Internet Focused Crawler that uses "Quality" criterion in determining returns to user queries. Such a task requires that the conceptual notions of quality be ultimately quantified into Search Engine algorithms that interact with Webpage technologies, eliminating documents that do not meet specifically determined standards of quality. The focus of this paper, as part of the wider research, is on the concepts of Quality in Information and Information Systems, specifically as it pertains to Information and Information Retrieval on the Internet. As with much of the research into Information Quality (IQ) in Information Systems, the term is interchangeable with Data Quality (DQ). What Is Information Quality? Data and Information Quality is commonly thought of as a multi-dimensional concept (Klein, 2001) with varying attributed characteristics depending on an author's philosophical view-point. Most commonly, the term "Data Quality" is described as data that is "Fit-for-use" (Wang & Strong, 1996), which implies that it is relative, as data considered appropriate for one use may not possess sufficient attributes for another use (Tayi & Ballou, 1998). IQ as a series of Dimensions Table 1 summaries 12 widely accepted IQ Frameworks collated from the last decade of IS research. While varied in their approach and application, the frameworks share a number of characteristics regarding their classifications of the dimensions of quality. An analysis of Table 1 reveals the common elements between the different IQ Frameworks. These include such traditional dimensions as accuracy, consistency, timeliness, completeness, accessibility, objectiveness and relevancy. Table 2 provides a summary of the most common dimensions and the frequency with which they are included in the above IQ Frameworks. Each dimension also includes a short definition. IQ in the context of its use In order to accurately define and measure the concept of Information quality, it is not enough to identify the common elements of IQ Frameworks as individual entities in their own right. In fact, Information Quality needs to be assessed within the context of its generation (Shanks & Corbitt, 1999) and intended use (Katerattanakul & Siau, 1999). This is because the attributes of data quality can vary depending on the context in which the data is to be used (Shankar & Watts, 2003). Defining what Information Quality is within the context of the World Wide Web and its Search Engines then, will depend greatly on whether dimensions are being identified for the producers of information, the storage and maintenance systems used for information, or for the searchers and users of information. The currently accepted view of assessing IQ, involves understanding it from the users point of view. Strong and Wang (1997) suggest that quality of data cannot be assessed independent of the people who use data. Applying this commonly to the World Wide Web has its own set of problems. …
---
paper_title: Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (Tm)

paper_content:

Information is currency. Recent studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. In this important and timely new book, Danette McGilvray presents her ?Ten Steps? approach to information quality, a proven method for both understanding and creating information quality in the enterprise. Her trademarked approach-in which she has trained Fortune 500 clients and hundreds of workshop attendees-applies to all types of data and to all types of organizations. ::: ::: * Includes numerous templates, detailed examples, and practical advice for executing every step of the ?Ten Steps? approach. ::: ::: * Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices. ::: ::: * A companion Web site includes links to numerous data quality resources, including many of the planning and information-gathering templates featured in the text, quick summaries of key ideas from the Ten Step methodology, and other tools and information available online. ::: ::: Table of Contents ::: ::: Introduction ::: The Reason for This Book ::: Intended Audiences ::: Structure of This Book ::: How to Use This Book ::: Acknowledgements ::: ::: Chapter 1 Overview ::: Impact of Information and Data Quality ::: About the Methodology ::: Approaches to Data Quality in Projects ::: Engaging Management ::: ::: Chapter 2 Key Concepts ::: Introduction ::: Framework for Information Quality (FIQ) ::: Information Life Cycle ::: Data Quality Dimensions ::: Business Impact Techniques ::: Data Categories ::: Data Specifications ::: Data Governance and Stewardship ::: The Information and Data Quality Improvement Cycle ::: The Ten Steps? Process ::: Best Practices and Guidelines ::: ::: Chapter 3 The Ten Steps ::: 1. Define Business Need and Approach ::: 2. Analyze Information Environment ::: 3. Assess Data Quality ::: 4. Assess Business Impact ::: 5. Identify Root Causes ::: 6. Develop Improvement Plans ::: 7. Prevent Future Data Errors ::: 8. Correct Current Data Errors ::: 9. Implement Controls ::: 10. Communicate Actions and Results ::: ::: Chapter 4 Structuring Your Project ::: Projects and The Ten Steps ::: Data Quality Project Roles ::: Project Timing ::: ::: Chapter 5 Other Techniques and Tools ::: Introduction ::: Information Life Cycle Approaches ::: Capture Data ::: Analyze and Document Results ::: Metrics ::: Data Quality Tools ::: The Ten Steps and Six Sigma ::: ::: Chapter 6 A Few Final Words ::: ::: Appendix Quick References ::: Framework for Information Quality ::: POSMAD Interaction Matrix Detail ::: POSMAD Phases and Activities ::: Data Quality Dimensions ::: Business Impact Techniques ::: The Ten Steps? Overview ::: Definitions of Data Categories
---
paper_title: Beyond accuracy: what data quality means to data consumers

paper_content:

Poor data quality (DQ) can have substantial social and economic impacts. Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus narrowly on accuracy. We believe that data consumers have a much broader data quality conceptualization than IS professionals realize. The purpose of this paper is to develop a framework that captures the aspects of data quality that are important to data consumers.A two-stage survey and a two-phase sorting study were conducted to develop a hierarchical framework for organizing data quality dimensions. This framework captures dimensions of data quality that are important to data consumers. Intrinsic DQ denotes that data have quality in their own right. Contextual DQ highlights the requirement that data quality must be considered within the context of the task at hand. Representational DQ and accessibility DQ emphasize the importance of the role of systems. These findings are consistent with our understanding that high-quality data should be intrinsically good, contextually appropriate for the task, clearly represented, and accessible to the data consumer.Our framework has been used effectively in industry and government. Using this framework, IS managers were able to better understand and meet their data consumers' data quality needs. The salient feature of this research study is that quality attributes of data are collected from data consumers instead of being defined theoretically or based on researchers' experience. Although exploratory, this research provides a basis for future studies that measure data quality along the dimensions of this framework.
---
paper_title: Dimensions of Business Processes Quality (QoBP)

paper_content:

Conceptual modeling is an important tool for understanding and revealing weaknesses of business processes. Yet, the current practice in reengineering projects often considers simply the as-is process model as a brain-storming tool. This approach heavily relies on the intuition of the participants and misses a clear description of the quality requirements. Against this background, we identify four generic quality categories of business process quality, and populate them with quality requirements from related research. We refer to the resulting framework as the Quality of Business Process (QoBP) framework. Furthermore, we present the findings from applying the QoBP framework in a case study with a major Australian bank, showing that it helps to systematically fill the white space between as-is and to-be process modeling.
---
paper_title: Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (Tm)

paper_content:

Information is currency. Recent studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. In this important and timely new book, Danette McGilvray presents her ?Ten Steps? approach to information quality, a proven method for both understanding and creating information quality in the enterprise. Her trademarked approach-in which she has trained Fortune 500 clients and hundreds of workshop attendees-applies to all types of data and to all types of organizations. ::: ::: * Includes numerous templates, detailed examples, and practical advice for executing every step of the ?Ten Steps? approach. ::: ::: * Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices. ::: ::: * A companion Web site includes links to numerous data quality resources, including many of the planning and information-gathering templates featured in the text, quick summaries of key ideas from the Ten Step methodology, and other tools and information available online. ::: ::: Table of Contents ::: ::: Introduction ::: The Reason for This Book ::: Intended Audiences ::: Structure of This Book ::: How to Use This Book ::: Acknowledgements ::: ::: Chapter 1 Overview ::: Impact of Information and Data Quality ::: About the Methodology ::: Approaches to Data Quality in Projects ::: Engaging Management ::: ::: Chapter 2 Key Concepts ::: Introduction ::: Framework for Information Quality (FIQ) ::: Information Life Cycle ::: Data Quality Dimensions ::: Business Impact Techniques ::: Data Categories ::: Data Specifications ::: Data Governance and Stewardship ::: The Information and Data Quality Improvement Cycle ::: The Ten Steps? Process ::: Best Practices and Guidelines ::: ::: Chapter 3 The Ten Steps ::: 1. Define Business Need and Approach ::: 2. Analyze Information Environment ::: 3. Assess Data Quality ::: 4. Assess Business Impact ::: 5. Identify Root Causes ::: 6. Develop Improvement Plans ::: 7. Prevent Future Data Errors ::: 8. Correct Current Data Errors ::: 9. Implement Controls ::: 10. Communicate Actions and Results ::: ::: Chapter 4 Structuring Your Project ::: Projects and The Ten Steps ::: Data Quality Project Roles ::: Project Timing ::: ::: Chapter 5 Other Techniques and Tools ::: Introduction ::: Information Life Cycle Approaches ::: Capture Data ::: Analyze and Document Results ::: Metrics ::: Data Quality Tools ::: The Ten Steps and Six Sigma ::: ::: Chapter 6 A Few Final Words ::: ::: Appendix Quick References ::: Framework for Information Quality ::: POSMAD Interaction Matrix Detail ::: POSMAD Phases and Activities ::: Data Quality Dimensions ::: Business Impact Techniques ::: The Ten Steps? Overview ::: Definitions of Data Categories
---
paper_title: Beyond accuracy: what data quality means to data consumers

paper_content:

Poor data quality (DQ) can have substantial social and economic impacts. Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus narrowly on accuracy. We believe that data consumers have a much broader data quality conceptualization than IS professionals realize. The purpose of this paper is to develop a framework that captures the aspects of data quality that are important to data consumers.A two-stage survey and a two-phase sorting study were conducted to develop a hierarchical framework for organizing data quality dimensions. This framework captures dimensions of data quality that are important to data consumers. Intrinsic DQ denotes that data have quality in their own right. Contextual DQ highlights the requirement that data quality must be considered within the context of the task at hand. Representational DQ and accessibility DQ emphasize the importance of the role of systems. These findings are consistent with our understanding that high-quality data should be intrinsically good, contextually appropriate for the task, clearly represented, and accessible to the data consumer.Our framework has been used effectively in industry and government. Using this framework, IS managers were able to better understand and meet their data consumers' data quality needs. The salient feature of this research study is that quality attributes of data are collected from data consumers instead of being defined theoretically or based on researchers' experience. Although exploratory, this research provides a basis for future studies that measure data quality along the dimensions of this framework.
---
paper_title: Beyond accuracy: what data quality means to data consumers

paper_content:

Poor data quality (DQ) can have substantial social and economic impacts. Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus narrowly on accuracy. We believe that data consumers have a much broader data quality conceptualization than IS professionals realize. The purpose of this paper is to develop a framework that captures the aspects of data quality that are important to data consumers.A two-stage survey and a two-phase sorting study were conducted to develop a hierarchical framework for organizing data quality dimensions. This framework captures dimensions of data quality that are important to data consumers. Intrinsic DQ denotes that data have quality in their own right. Contextual DQ highlights the requirement that data quality must be considered within the context of the task at hand. Representational DQ and accessibility DQ emphasize the importance of the role of systems. These findings are consistent with our understanding that high-quality data should be intrinsically good, contextually appropriate for the task, clearly represented, and accessible to the data consumer.Our framework has been used effectively in industry and government. Using this framework, IS managers were able to better understand and meet their data consumers' data quality needs. The salient feature of this research study is that quality attributes of data are collected from data consumers instead of being defined theoretically or based on researchers' experience. Although exploratory, this research provides a basis for future studies that measure data quality along the dimensions of this framework.
---
paper_title: Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (Tm)

paper_content:

Information is currency. Recent studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. In this important and timely new book, Danette McGilvray presents her ?Ten Steps? approach to information quality, a proven method for both understanding and creating information quality in the enterprise. Her trademarked approach-in which she has trained Fortune 500 clients and hundreds of workshop attendees-applies to all types of data and to all types of organizations. ::: ::: * Includes numerous templates, detailed examples, and practical advice for executing every step of the ?Ten Steps? approach. ::: ::: * Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices. ::: ::: * A companion Web site includes links to numerous data quality resources, including many of the planning and information-gathering templates featured in the text, quick summaries of key ideas from the Ten Step methodology, and other tools and information available online. ::: ::: Table of Contents ::: ::: Introduction ::: The Reason for This Book ::: Intended Audiences ::: Structure of This Book ::: How to Use This Book ::: Acknowledgements ::: ::: Chapter 1 Overview ::: Impact of Information and Data Quality ::: About the Methodology ::: Approaches to Data Quality in Projects ::: Engaging Management ::: ::: Chapter 2 Key Concepts ::: Introduction ::: Framework for Information Quality (FIQ) ::: Information Life Cycle ::: Data Quality Dimensions ::: Business Impact Techniques ::: Data Categories ::: Data Specifications ::: Data Governance and Stewardship ::: The Information and Data Quality Improvement Cycle ::: The Ten Steps? Process ::: Best Practices and Guidelines ::: ::: Chapter 3 The Ten Steps ::: 1. Define Business Need and Approach ::: 2. Analyze Information Environment ::: 3. Assess Data Quality ::: 4. Assess Business Impact ::: 5. Identify Root Causes ::: 6. Develop Improvement Plans ::: 7. Prevent Future Data Errors ::: 8. Correct Current Data Errors ::: 9. Implement Controls ::: 10. Communicate Actions and Results ::: ::: Chapter 4 Structuring Your Project ::: Projects and The Ten Steps ::: Data Quality Project Roles ::: Project Timing ::: ::: Chapter 5 Other Techniques and Tools ::: Introduction ::: Information Life Cycle Approaches ::: Capture Data ::: Analyze and Document Results ::: Metrics ::: Data Quality Tools ::: The Ten Steps and Six Sigma ::: ::: Chapter 6 A Few Final Words ::: ::: Appendix Quick References ::: Framework for Information Quality ::: POSMAD Interaction Matrix Detail ::: POSMAD Phases and Activities ::: Data Quality Dimensions ::: Business Impact Techniques ::: The Ten Steps? Overview ::: Definitions of Data Categories
---
paper_title: Developing a Framework for Assessing Information Quality on the World Wide Web

paper_content:

Introduction--The Big Picture Over the past decade, the [Internet.sup.1]--or World Wide Web (Technically the Internet is a huge collection of networked computers using TCP/IP protocol to exchange data. The World-wide Web (WWW) is in essence only part of this network of computers, however its visible status has meant that conceptually at least, it is often used interchangeably with "Internet" to describe the same thing.)--has established itself as the key infrastructure for information administration, exchange, and publication (Alexander & Tate, 1999), and Internet Search Engines are the most commonly used tool to retrieve that information (Wang, 2001). The deficiency of enforceable standards however, has resulted in frequent information quality problems (Eppler & Muenzenmayer, 2002). This paper is part of a research project undertaken at Edith Cowan, Wollongong and Sienna Universities, to build an Internet Focused Crawler that uses "Quality" criterion in determining returns to user queries. Such a task requires that the conceptual notions of quality be ultimately quantified into Search Engine algorithms that interact with Webpage technologies, eliminating documents that do not meet specifically determined standards of quality. The focus of this paper, as part of the wider research, is on the concepts of Quality in Information and Information Systems, specifically as it pertains to Information and Information Retrieval on the Internet. As with much of the research into Information Quality (IQ) in Information Systems, the term is interchangeable with Data Quality (DQ). What Is Information Quality? Data and Information Quality is commonly thought of as a multi-dimensional concept (Klein, 2001) with varying attributed characteristics depending on an author's philosophical view-point. Most commonly, the term "Data Quality" is described as data that is "Fit-for-use" (Wang & Strong, 1996), which implies that it is relative, as data considered appropriate for one use may not possess sufficient attributes for another use (Tayi & Ballou, 1998). IQ as a series of Dimensions Table 1 summaries 12 widely accepted IQ Frameworks collated from the last decade of IS research. While varied in their approach and application, the frameworks share a number of characteristics regarding their classifications of the dimensions of quality. An analysis of Table 1 reveals the common elements between the different IQ Frameworks. These include such traditional dimensions as accuracy, consistency, timeliness, completeness, accessibility, objectiveness and relevancy. Table 2 provides a summary of the most common dimensions and the frequency with which they are included in the above IQ Frameworks. Each dimension also includes a short definition. IQ in the context of its use In order to accurately define and measure the concept of Information quality, it is not enough to identify the common elements of IQ Frameworks as individual entities in their own right. In fact, Information Quality needs to be assessed within the context of its generation (Shanks & Corbitt, 1999) and intended use (Katerattanakul & Siau, 1999). This is because the attributes of data quality can vary depending on the context in which the data is to be used (Shankar & Watts, 2003). Defining what Information Quality is within the context of the World Wide Web and its Search Engines then, will depend greatly on whether dimensions are being identified for the producers of information, the storage and maintenance systems used for information, or for the searchers and users of information. The currently accepted view of assessing IQ, involves understanding it from the users point of view. Strong and Wang (1997) suggest that quality of data cannot be assessed independent of the people who use data. Applying this commonly to the World Wide Web has its own set of problems. …
---
paper_title: Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (Tm)

paper_content:

Information is currency. Recent studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. In this important and timely new book, Danette McGilvray presents her ?Ten Steps? approach to information quality, a proven method for both understanding and creating information quality in the enterprise. Her trademarked approach-in which she has trained Fortune 500 clients and hundreds of workshop attendees-applies to all types of data and to all types of organizations. ::: ::: * Includes numerous templates, detailed examples, and practical advice for executing every step of the ?Ten Steps? approach. ::: ::: * Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices. ::: ::: * A companion Web site includes links to numerous data quality resources, including many of the planning and information-gathering templates featured in the text, quick summaries of key ideas from the Ten Step methodology, and other tools and information available online. ::: ::: Table of Contents ::: ::: Introduction ::: The Reason for This Book ::: Intended Audiences ::: Structure of This Book ::: How to Use This Book ::: Acknowledgements ::: ::: Chapter 1 Overview ::: Impact of Information and Data Quality ::: About the Methodology ::: Approaches to Data Quality in Projects ::: Engaging Management ::: ::: Chapter 2 Key Concepts ::: Introduction ::: Framework for Information Quality (FIQ) ::: Information Life Cycle ::: Data Quality Dimensions ::: Business Impact Techniques ::: Data Categories ::: Data Specifications ::: Data Governance and Stewardship ::: The Information and Data Quality Improvement Cycle ::: The Ten Steps? Process ::: Best Practices and Guidelines ::: ::: Chapter 3 The Ten Steps ::: 1. Define Business Need and Approach ::: 2. Analyze Information Environment ::: 3. Assess Data Quality ::: 4. Assess Business Impact ::: 5. Identify Root Causes ::: 6. Develop Improvement Plans ::: 7. Prevent Future Data Errors ::: 8. Correct Current Data Errors ::: 9. Implement Controls ::: 10. Communicate Actions and Results ::: ::: Chapter 4 Structuring Your Project ::: Projects and The Ten Steps ::: Data Quality Project Roles ::: Project Timing ::: ::: Chapter 5 Other Techniques and Tools ::: Introduction ::: Information Life Cycle Approaches ::: Capture Data ::: Analyze and Document Results ::: Metrics ::: Data Quality Tools ::: The Ten Steps and Six Sigma ::: ::: Chapter 6 A Few Final Words ::: ::: Appendix Quick References ::: Framework for Information Quality ::: POSMAD Interaction Matrix Detail ::: POSMAD Phases and Activities ::: Data Quality Dimensions ::: Business Impact Techniques ::: The Ten Steps? Overview ::: Definitions of Data Categories
---
paper_title: Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (Tm)

paper_content:

Information is currency. Recent studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. In this important and timely new book, Danette McGilvray presents her ?Ten Steps? approach to information quality, a proven method for both understanding and creating information quality in the enterprise. Her trademarked approach-in which she has trained Fortune 500 clients and hundreds of workshop attendees-applies to all types of data and to all types of organizations. ::: ::: * Includes numerous templates, detailed examples, and practical advice for executing every step of the ?Ten Steps? approach. ::: ::: * Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices. ::: ::: * A companion Web site includes links to numerous data quality resources, including many of the planning and information-gathering templates featured in the text, quick summaries of key ideas from the Ten Step methodology, and other tools and information available online. ::: ::: Table of Contents ::: ::: Introduction ::: The Reason for This Book ::: Intended Audiences ::: Structure of This Book ::: How to Use This Book ::: Acknowledgements ::: ::: Chapter 1 Overview ::: Impact of Information and Data Quality ::: About the Methodology ::: Approaches to Data Quality in Projects ::: Engaging Management ::: ::: Chapter 2 Key Concepts ::: Introduction ::: Framework for Information Quality (FIQ) ::: Information Life Cycle ::: Data Quality Dimensions ::: Business Impact Techniques ::: Data Categories ::: Data Specifications ::: Data Governance and Stewardship ::: The Information and Data Quality Improvement Cycle ::: The Ten Steps? Process ::: Best Practices and Guidelines ::: ::: Chapter 3 The Ten Steps ::: 1. Define Business Need and Approach ::: 2. Analyze Information Environment ::: 3. Assess Data Quality ::: 4. Assess Business Impact ::: 5. Identify Root Causes ::: 6. Develop Improvement Plans ::: 7. Prevent Future Data Errors ::: 8. Correct Current Data Errors ::: 9. Implement Controls ::: 10. Communicate Actions and Results ::: ::: Chapter 4 Structuring Your Project ::: Projects and The Ten Steps ::: Data Quality Project Roles ::: Project Timing ::: ::: Chapter 5 Other Techniques and Tools ::: Introduction ::: Information Life Cycle Approaches ::: Capture Data ::: Analyze and Document Results ::: Metrics ::: Data Quality Tools ::: The Ten Steps and Six Sigma ::: ::: Chapter 6 A Few Final Words ::: ::: Appendix Quick References ::: Framework for Information Quality ::: POSMAD Interaction Matrix Detail ::: POSMAD Phases and Activities ::: Data Quality Dimensions ::: Business Impact Techniques ::: The Ten Steps? Overview ::: Definitions of Data Categories
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paper_title: Executing Data Quality Projects: Ten Steps to Quality Data and Trusted Information (Tm)

paper_content:

Information is currency. Recent studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. In this important and timely new book, Danette McGilvray presents her ?Ten Steps? approach to information quality, a proven method for both understanding and creating information quality in the enterprise. Her trademarked approach-in which she has trained Fortune 500 clients and hundreds of workshop attendees-applies to all types of data and to all types of organizations. ::: ::: * Includes numerous templates, detailed examples, and practical advice for executing every step of the ?Ten Steps? approach. ::: ::: * Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices. ::: ::: * A companion Web site includes links to numerous data quality resources, including many of the planning and information-gathering templates featured in the text, quick summaries of key ideas from the Ten Step methodology, and other tools and information available online. ::: ::: Table of Contents ::: ::: Introduction ::: The Reason for This Book ::: Intended Audiences ::: Structure of This Book ::: How to Use This Book ::: Acknowledgements ::: ::: Chapter 1 Overview ::: Impact of Information and Data Quality ::: About the Methodology ::: Approaches to Data Quality in Projects ::: Engaging Management ::: ::: Chapter 2 Key Concepts ::: Introduction ::: Framework for Information Quality (FIQ) ::: Information Life Cycle ::: Data Quality Dimensions ::: Business Impact Techniques ::: Data Categories ::: Data Specifications ::: Data Governance and Stewardship ::: The Information and Data Quality Improvement Cycle ::: The Ten Steps? Process ::: Best Practices and Guidelines ::: ::: Chapter 3 The Ten Steps ::: 1. Define Business Need and Approach ::: 2. Analyze Information Environment ::: 3. Assess Data Quality ::: 4. Assess Business Impact ::: 5. Identify Root Causes ::: 6. Develop Improvement Plans ::: 7. Prevent Future Data Errors ::: 8. Correct Current Data Errors ::: 9. Implement Controls ::: 10. Communicate Actions and Results ::: ::: Chapter 4 Structuring Your Project ::: Projects and The Ten Steps ::: Data Quality Project Roles ::: Project Timing ::: ::: Chapter 5 Other Techniques and Tools ::: Introduction ::: Information Life Cycle Approaches ::: Capture Data ::: Analyze and Document Results ::: Metrics ::: Data Quality Tools ::: The Ten Steps and Six Sigma ::: ::: Chapter 6 A Few Final Words ::: ::: Appendix Quick References ::: Framework for Information Quality ::: POSMAD Interaction Matrix Detail ::: POSMAD Phases and Activities ::: Data Quality Dimensions ::: Business Impact Techniques ::: The Ten Steps? Overview ::: Definitions of Data Categories
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paper_title: Dependency discovery in data quality

paper_content:

A conceptual framework for the automatic discovery of dependencies between data quality dimensions is described. Dependency discovery consists in recovering the dependency structure for a set of data quality dimensions measured on attributes of a database. This task is accomplished through the data mining methodology, by learning a Bayesian Network from a database. The Bayesian Network is used to analyze dependency between data quality dimensions associated with different attributes. The proposed framework is instantiated on a real world database. The task of dependency discovery is presented in the case when the following data quality dimensions are considered; accuracy, completeness, and consistency. The Bayesian Network model shows how data quality can be improved while satisfying budget constraints.
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paper_title: Methodologies for data quality assessment and improvement

paper_content:

The literature provides a wide range of techniques to assess and improve the quality of data. Due to the diversity and complexity of these techniques, research has recently focused on defining methodologies that help the selection, customization, and application of data quality assessment and improvement techniques. The goal of this article is to provide a systematic and comparative description of such methodologies. Methodologies are compared along several dimensions, including the methodological phases and steps, the strategies and techniques, the data quality dimensions, the types of data, and, finally, the types of information systems addressed by each methodology. The article concludes with a summary description of each methodology.
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paper_title: Data quality assessment

paper_content:

How good is a company's data quality? Answering this question requires usable data quality metrics. Currently, most data quality measures are developed on an ad hoc basis to solve specific problems [6, 8], and fundamental principles necessary for developing usable metrics in practice are lacking. In this article, we describe principles that can help organizations develop usable data quality metrics.
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paper_title: Analysis of data quality and information quality problems in digital manufacturing

paper_content:

This work focuses on the increasing importance of data quality in organizations, especially in digital manufacturing companies. The paper firstly reviews related works in field of data quality, including definition, dimensions, measurement and assessment, and improvement of data quality. Then, by taking the digital manufacturing as research object, the different information roles, information manufacturing processes, influential factors of information quality, and the transformation levels and paths of the data/information quality in digital manufacturing companies are analyzed. Finally an approach for the diagnosis, control and improvement of data/information quality in digital manufacturing companies, which is the basis for further works, is proposed.
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Title: Data Quality: A Survey of Data Quality Dimensions

Section 1: INTRODUCTION
Description 1: Introduce the importance of data quality in organizational activities and decision-making processes.

Section 2: DATA QUALITY STRATEGIES AND TECHNIQUES
Description 2: Discuss the strategies and techniques used for improving data quality.

Section 3: TYPES OF DATA
Description 3: Explain different classifications of data and how they relate to data quality.

Section 4: DATA QUALITY DEFINITIONS
Description 4: Describe various definitions of data quality across different fields.

Section 5: DATA QUALITY PROBLEMS CLASSIFICATION
Description 5: Classify the common problems associated with data quality to better understand their impact.

Section 6: DISCUSSION
Description 6: Analyze the dependencies between different data quality dimensions and their importance in improving process quality.

Section 7: CONCLUSION
Description 7: Summarize the findings and propose future work for assessing and improving data quality dimensions.