Information and Database Quality

As noted above, while most organizations claim that “data (and information) are among our most important assets,” they simply do ... both inside and outside the organization, are not held accountable for the quality of data they create.

Information and Database Quality

In a global and increasingly competitive market, where organizations are driven by information, the search for ways to transform data into true knowledge is critical to a business's success. Few companies, however, have effective methods of managing the quality of this information. Because quality is a multidimensional concept, its management must consider a wide variety of issues related to information and data quality. Information and Database Quality is a compilation of works from research and industry that examines these issues, covering both the organizational and technical aspects of information and data quality. Information and Database Quality is an excellent reference for both researchers and professionals involved in any aspect of information and database research.

Contemporary Issues in Database Design and Information Systems Development

The general definition of data quality is data that is fit for use by data consumers (Huang et al., 1999). Data quality dimensions refer to issues that are important to information consumers (people who use information).

Contemporary Issues in Database Design and Information Systems Development

"This book presents the latest research ideas and topics on databases and software development. It provides a representation of top notch research in all areas of database and information systems development"--Provided by publisher.

Entity Information Life Cycle for Big Data

The discussion of the ISO 8000 standard is an occasion where it can be helpful to separate the concepts of data quality and information quality. In 2010, the International Association for Information and Data Quality (IAIDQ) undertook ...

Entity Information Life Cycle for Big Data

Entity Information Life Cycle for Big Data walks you through the ins and outs of managing entity information so you can successfully achieve master data management (MDM) in the era of big data. This book explains big data’s impact on MDM and the critical role of entity information management system (EIMS) in successful MDM. Expert authors Dr. John R. Talburt and Dr. Yinle Zhou provide a thorough background in the principles of managing the entity information life cycle and provide practical tips and techniques for implementing an EIMS, strategies for exploiting distributed processing to handle big data for EIMS, and examples from real applications. Additional material on the theory of EIIM and methods for assessing and evaluating EIMS performance also make this book appropriate for use as a textbook in courses on entity and identity management, data management, customer relationship management (CRM), and related topics. Explains the business value and impact of entity information management system (EIMS) and directly addresses the problem of EIMS design and operation, a critical issue organizations face when implementing MDM systems Offers practical guidance to help you design and build an EIM system that will successfully handle big data Details how to measure and evaluate entity integrity in MDM systems and explains the principles and processes that comprise EIM Provides an understanding of features and functions an EIM system should have that will assist in evaluating commercial EIM systems Includes chapter review questions, exercises, tips, and free downloads of demonstrations that use the OYSTER open source EIM system Executable code (Java .jar files), control scripts, and synthetic input data illustrate various aspects of CSRUD life cycle such as identity capture, identity update, and assertions

Information Quality

Failure to manage information properly, or inaccurate data, costs businesses billions of dollars each year. This volume presents cutting-edge research on information quality.

Information Quality

Organizations today have access to vast stores of data that come in a wide variety of forms and may be stored in places ranging from file cabinets to databases, and from library shelves to the Internet. The enormous growth in the quantity of data, however, has brought with it growing problems with the quality of information, further complicated by the struggles many organizations are experiencing as they try to improve their systems for knowledge management and organizational memory. Failure to manage information properly, or inaccurate data, costs businesses billions of dollars each year. This volume presents cutting-edge research on information quality. Part I seeks to understand how data can be measured and evaluated for quality. Part II deals with the problem of ensuring quality while processing data into information a company can use. Part III presents case studies, while Part IV explores organizational issues related to information quality. Part V addresses issues in information quality education.

Statistical Methods and the Improvement of Data Quality

In this paper the aim is to point out how EDA can be useful in providing to modelers better information about data quality and model fit. This information, in turn, can be used to improve the overall quality of modeling and forecasting.

Statistical Methods and the Improvement of Data Quality

Statistical Methods and the Improvement of Data Quality contains the proceedings of The Small Conference on the Improvement of the Quality of Data Collected by Data Collection Systems, held on November 11-12, 1982, in Oak Ridge, Tennessee. The conference provided a forum for discussing the use of statistical methods to improve data quality, with emphasis on the problems of data collection systems and how to handle them using state-of-the-art techniques. Comprised of 16 chapters, this volume begins with an overview of some of the limitations of surveys, followed by an annotated bibliography on frames from which the probability sample is selected. The reader is then introduced to sample designs and methods for collecting data over space and time; response effects to behavior and attitude questions; and how to develop and use error profiles. Subsequent chapters focus on principles and methods for handling outliers in data sets; influence functions, outlier detection, and data editing; and application of pattern recognition techniques to data analysis. The use of exploratory data analysis as an aid in modeling and statistical forecasting is also described. This monograph is likely to be of primary benefit to students taking a general course in survey sampling techniques, and to individuals and groups who deal with large data collection systems and are constantly seeking ways to improve the overall quality of their data.

Advances in Data Science and Information Engineering

D. Ardagna, C. Cappiello, W. Samá, M. Vitali, Context-aware data quality assessment for big data. Future Gener. Comput. Syst. 89, 548–562 (2018) 4. O. Azeroual, M. Abuosba, Improving the data quality in the research information systems ...

Advances in Data Science and Information Engineering

The book presents the proceedings of two conferences: the 16th International Conference on Data Science (ICDATA 2020) and the 19th International Conference on Information & Knowledge Engineering (IKE 2020), which took place in Las Vegas, NV, USA, July 27-30, 2020. The conferences are part of the larger 2020 World Congress in Computer Science, Computer Engineering, & Applied Computing (CSCE'20), which features 20 major tracks. Papers cover all aspects of Data Science, Data Mining, Machine Learning, Artificial and Computational Intelligence (ICDATA) and Information Retrieval Systems, Information & Knowledge Engineering, Management and Cyber-Learning (IKE). Authors include academics, researchers, professionals, and students. Presents the proceedings of the 16th International Conference on Data Science (ICDATA 2020) and the 19th International Conference on Information & Knowledge Engineering (IKE 2020); Includes papers on topics from data mining to machine learning to informational retrieval systems; Authors include academics, researchers, professionals and students.

Handbook of Research on Information Technology Management and Clinical Data Administration in Healthcare

The quality of such data can be problematic as multitude of threats exist that can hamper data quality. In this chapter, practices to establish data quality suitable to support research studies were illustrated. Good data is important ...

Handbook of Research on Information Technology Management and Clinical Data Administration in Healthcare

"This book presents theoretical and empirical research on the value of information technology in healthcare"--Provided by publisher.

Data Quality

Written with the goal to provide an overview of the cumulated research results from the MIT TDQM research perspective as it relates to database research, this book is an excellent introduction to Ph.D. who wish to further pursue their ...

Data Quality

Data Quality provides an exposé of research and practice in the data quality field for technically oriented readers. It is based on the research conducted at the MIT Total Data Quality Management (TDQM) program and work from other leading research institutions. This book is intended primarily for researchers, practitioners, educators and graduate students in the fields of Computer Science, Information Technology, and other interdisciplinary areas. It forms a theoretical foundation that is both rigorous and relevant for dealing with advanced issues related to data quality. Written with the goal to provide an overview of the cumulated research results from the MIT TDQM research perspective as it relates to database research, this book is an excellent introduction to Ph.D. who wish to further pursue their research in the data quality area. It is also an excellent theoretical introduction to IT professionals who wish to gain insight into theoretical results in the technically-oriented data quality area, and apply some of the key concepts to their practice.

Ethical Data and Information Management

Information quality management is about more than just measuring the quality characteristics of your data. You need to be sure you are addressing the right issues, and you need to be certain you are tackling the root causes of ...

Ethical Data and Information Management

Information and how we manage, process and govern it is becoming increasingly important as organizations ride the wave of the big data revolution. Ethical Data and Information Management offers a practical guide for people in organizations who are tasked with implementing information management projects. It sets out, in a clear and structured way, the fundamentals of ethics, and provides practical and pragmatic methods for organizations to embed ethical principles and practices into their management and governance of information. Written by global experts in the field, Ethical Data and Information Management is an important book addressing a topic high on the information management agenda. Key coverage includes how to build ethical checks and balances into data governance decision making; using quality management methods to assess and evaluate the ethical nature of processing during design; change methods to communicate ethical values; how to avoid common problems that affect ethical action; and how to make the business case for ethical behaviours.

Journey to Data Quality

This practical guide, based on rigorous research and informed by real-world examples, describes the challenges of data management and provides the principles, strategies, tools, and techniques necessary to meet them.

Journey to Data Quality

All organizations today confront data quality problems, both systemic and structural. Neither ad hoc approaches nor fixes at the systems level—installing the latest software or developing an expensive data warehouse—solve the basic problem of bad data quality practices. Journey to Data Quality offers a roadmap that can be used by practitioners, executives, and students for planning and implementing a viable data and information quality management program. This practical guide, based on rigorous research and informed by real-world examples, describes the challenges of data management and provides the principles, strategies, tools, and techniques necessary to meet them. The authors, all leaders in the data quality field for many years, discuss how to make the economic case for data quality and the importance of getting an organization's leaders on board. They outline different approaches for assessing data, both subjectively (by users) and objectively (using sampling and other techniques). They describe real problems and solutions, including efforts to find the root causes of data quality problems at a healthcare organization and data quality initiatives taken by a large teaching hospital. They address setting company policy on data quality and, finally, they consider future challenges on the journey to data quality.

National Conference on Improving the Quality of Criminal History Records

... of our own information and data quality requirements . We have written and funded the development of software which has automated police departments and probation offices . We initially developed some prosecution management systems ...

National Conference on Improving the Quality of Criminal History Records


Handbook of Financial Data and Risk Information II

27.2.13 Data quality processes Quality data is the key to effective decision-making and planning. The purpose of data governance is to have an integrated, single source of data truth to be used across the enterprise in operations, ...

Handbook of Financial Data and Risk Information II

A comprehensive resource for understanding the issues involved in collecting, measuring and managing data in the financial services industry.

Symposium 97

Tax data could serve as auxiliary information for estimation and / or mass imputation . levels ? ... surveys program is being established by pulling together available information on data quality for reference years 1995 and 1996 .

Symposium 97

Symposium 97 was the fourtheenth international symposium on methodological issues sponsored by Statistics Canada. Each year, the symposium focuses on a particular theme. This year's theme was on new directions in surveys and censuses. The 1997 symposium attracted over 500 people who met over three days at the Palais des Congrès in Hull to listen to over 70 presentations by experts from various statistical and other government agencies, universities and the private sector. Aside from translation and Aormatting, the papers submitted by the presenters have been reproduced in these proceedings.

Information Technology and Data in Healthcare

Finally, of increasing importance in today's data environments, the whole question of authorization including data security, privacy, encryption, etc. is a primary focus. From a quality perspective, security is more related to getting ...

Information Technology and Data in Healthcare

Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization’s day-to-day operations is becoming vital for hospitals and health systems to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it. This book addresses several topics important to the understanding and use of data in healthcare. First, it provides a formal explanation based on epistemology (theory of knowledge) of what data actually is, what we can know about it, and how we can reason with it. The culture of data is also covered and where it fits into healthcare. Then, data quality is addressed, with a historical appreciation, as well as new concepts and insights derived from the author’s 35 years of experience in technology. The author provides a description of what healthcare data analysis is and how it is changing in the era of abundant data. Just as important is the topic of infrastructure and how it provides capability for data use. The book also describes how healthcare information infrastructure needs to change in order to meet current and future needs. The topics of artificial intelligence (AI) and machine learning in healthcare are also addressed. The author concludes with thoughts on the evolution of the role and use of data and information going into the future.

Executing Data Quality Projects

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 ...

Executing Data Quality Projects

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.

Data and Information in Online Environments

However, it is important to consider that in order to aggregate data from different sources, they must first be organized to ensure favorable conditions for diversified high-quality analyses, integrated searches and possibilities for ...

Data and Information in Online Environments

This book constitutes the refereed post-conference proceedings of the Second International Conference on Data Information in Online Environments, DIONE 2021, which took place in March 2021. Due to COVID-19 pandemic the conference was held virtually. DIONE 2021 presents theoretical proposals and practical solutions in the treatment, processing and study of data and information produced in online environments, the latest trends in the analysis of network information, media metrics social, data processing technologies and open science. The 40 revised full papers were carefully reviewed and selected from 86 submissions. The papers are grouped in thematical sessions on evaluation of science in social networking environment; scholarly publishing and online communication; and education in online environments.

Data Requirement Descriptions Index Index of Technical and Management Information Specifications for Use on NASA Programs

TITLE KEYWORDS KEYWORD INFORMATION SPECIFICATION TITLE NUMIER DATE SOURCE QUALIFICATION - PROCEDURE , QUALIFICATION ... DATA QUALITY ASSURANCE REVIEW - DMOOOH 05-27-66 MSFC RA219K 01-30-68 KSC PLAN , OPERATING RELIABILITY AND QUALITY ...

Data Requirement Descriptions Index  Index of Technical and Management Information Specifications for Use on NASA Programs


Translating Data into Information to Improve Teaching and Learning

Data Quality Also Quality Data. Accurate, timely, meaningful, and complete data. Data Quality Campaign (DQC) The Data Quality Campaign (DQC) isa national, collaborative effort to encourage and support state policymakers to improve the ...

Translating Data into Information to Improve Teaching and Learning

Here it is ... the latest from best-selling author Victoria Bernhardt. This book helps educators think through the selection of the data elements and data tools needed to support quality decisions for improving teaching and learning. It shows you how to use data to help make decisions about strategies to improve student achievement.

Financial Management Information Systems and Open Budget Data

Scope and Presentation Quality of Public Finance Information The scope and presentation quality of PF data published in government websites were analyzed using four indicators (I-7 to I-10) derived from two questions (Q4 and Q5).

Financial Management Information Systems and Open Budget Data

This study is the first attempt to explore the effects of Financial Management Information Systems on publishing open budget data and improving budget transparency, and develop some guidelines on relevant aspects. The findings of the study are expected to provide a comprehensive view of the current government practices.

Knowledge Discovery and Data Mining

Data auditing (evaluating data quantity and quality). To build a robust model, we need data of sufficient quantity and quality. If the number of available records is too small, no information-theoretic network will be built due to the ...

Knowledge Discovery and Data Mining

This book presents a specific and unified approach to Knowledge Discovery and Data Mining, termed IFN for Information Fuzzy Network methodology. Data Mining (DM) is the science of modelling and generalizing common patterns from large sets of multi-type data. DM is a part of KDD, which is the overall process for Knowledge Discovery in Databases. The accessibility and abundance of information today makes this a topic of particular importance and need. The book has three main parts complemented by appendices as well as software and project data that are accessible from the book's web site (http://www.eng.tau.ac.iV-maimonlifn-kdg£). Part I (Chapters 1-4) starts with the topic of KDD and DM in general and makes reference to other works in the field, especially those related to the information theoretic approach. The remainder of the book presents our work, starting with the IFN theory and algorithms. Part II (Chapters 5-6) discusses the methodology of application and includes case studies. Then in Part III (Chapters 7-9) a comparative study is presented, concluding with some advanced methods and open problems. The IFN, being a generic methodology, applies to a variety of fields, such as manufacturing, finance, health care, medicine, insurance, and human resources. The appendices expand on the relevant theoretical background and present descriptions of sample projects (including detailed results).