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| data quality management framework: Measuring Data Quality for Ongoing Improvement Laura Sebastian-Coleman, 2012-12-31 The Data Quality Assessment Framework shows you how to measure and monitor data quality, ensuring quality over time. You'll start with general concepts of measurement and work your way through a detailed framework of more than three dozen measurement types related to five objective dimensions of quality: completeness, timeliness, consistency, validity, and integrity. Ongoing measurement, rather than one time activities will help your organization reach a new level of data quality. This plain-language approach to measuring data can be understood by both business and IT and provides practical guidance on how to apply the DQAF within any organization enabling you to prioritize measurements and effectively report on results. Strategies for using data measurement to govern and improve the quality of data and guidelines for applying the framework within a data asset are included. You'll come away able to prioritize which measurement types to implement, knowing where to place them in a data flow and how frequently to measure. Common conceptual models for defining and storing of data quality results for purposes of trend analysis are also included as well as generic business requirements for ongoing measuring and monitoring including calculations and comparisons that make the measurements meaningful and help understand trends and detect anomalies. - Demonstrates how to leverage a technology independent data quality measurement framework for your specific business priorities and data quality challenges - Enables discussions between business and IT with a non-technical vocabulary for data quality measurement - Describes how to measure data quality on an ongoing basis with generic measurement types that can be applied to any situation |
| data quality management framework: Handbook of Data Quality Shazia Sadiq, 2013-08-13 The issue of data quality is as old as data itself. However, the proliferation of diverse, large-scale and often publically available data on the Web has increased the risk of poor data quality and misleading data interpretations. On the other hand, data is now exposed at a much more strategic level e.g. through business intelligence systems, increasing manifold the stakes involved for individuals, corporations as well as government agencies. There, the lack of knowledge about data accuracy, currency or completeness can have erroneous and even catastrophic results. With these changes, traditional approaches to data management in general, and data quality control specifically, are challenged. There is an evident need to incorporate data quality considerations into the whole data cycle, encompassing managerial/governance as well as technical aspects. Data quality experts from research and industry agree that a unified framework for data quality management should bring together organizational, architectural and computational approaches. Accordingly, Sadiq structured this handbook in four parts: Part I is on organizational solutions, i.e. the development of data quality objectives for the organization, and the development of strategies to establish roles, processes, policies, and standards required to manage and ensure data quality. Part II, on architectural solutions, covers the technology landscape required to deploy developed data quality management processes, standards and policies. Part III, on computational solutions, presents effective and efficient tools and techniques related to record linkage, lineage and provenance, data uncertainty, and advanced integrity constraints. Finally, Part IV is devoted to case studies of successful data quality initiatives that highlight the various aspects of data quality in action. The individual chapters present both an overview of the respective topic in terms of historical research and/or practice and state of the art, as well as specific techniques, methodologies and frameworks developed by the individual contributors. Researchers and students of computer science, information systems, or business management as well as data professionals and practitioners will benefit most from this handbook by not only focusing on the various sections relevant to their research area or particular practical work, but by also studying chapters that they may initially consider not to be directly relevant to them, as there they will learn about new perspectives and approaches. |
| data quality management framework: Master Data Management David Loshin, 2010-07-28 The key to a successful MDM initiative isn't technology or methods, it's people: the stakeholders in the organization and their complex ownership of the data that the initiative will affect.Master Data Management equips you with a deeply practical, business-focused way of thinking about MDM—an understanding that will greatly enhance your ability to communicate with stakeholders and win their support. Moreover, it will help you deserve their support: you'll master all the details involved in planning and executing an MDM project that leads to measurable improvements in business productivity and effectiveness. - Presents a comprehensive roadmap that you can adapt to any MDM project - Emphasizes the critical goal of maintaining and improving data quality - Provides guidelines for determining which data to master. - Examines special issues relating to master data metadata - Considers a range of MDM architectural styles - Covers the synchronization of master data across the application infrastructure |
| data quality management framework: Data Quality Management with Semantic Technologies Christian Fürber, 2015-12-11 Christian Fürber investigates the useful application of semantic technologies for the area of data quality management. Based on a literature analysis of typical data quality problems and typical activities of data quality management processes, he develops the Semantic Data Quality Management framework as the major contribution of this thesis. The SDQM framework consists of three components that are evaluated in two different use cases. Moreover, this thesis compares the framework to conventional data quality software. Besides the framework, this thesis delivers important theoretical findings, namely a comprehensive typology of data quality problems, ten generic data requirement types, a requirement-centric data quality management process, and an analysis of related work. |
| data quality management framework: Data Quality Carlo Batini, Monica Scannapieco, 2006-09-27 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. |
| data quality management framework: Corporate Data Quality Boris Otto, Hubert Österle, 2015 |
| data quality management framework: Non-Invasive Data Governance Robert S. Seiner, 2014-09-01 Data-governance programs focus on authority and accountability for the management of data as a valued organizational asset. Data Governance should not be about command-and-control, yet at times could become invasive or threatening to the work, people and culture of an organization. Non-Invasive Data Governance™ focuses on formalizing existing accountability for the management of data and improving formal communications, protection, and quality efforts through effective stewarding of data resources. Non-Invasive Data Governance will provide you with a complete set of tools to help you deliver a successful data governance program. Learn how: • Steward responsibilities can be identified and recognized, formalized, and engaged according to their existing responsibility rather than being assigned or handed to people as more work. • Governance of information can be applied to existing policies, standard operating procedures, practices, and methodologies, rather than being introduced or emphasized as new processes or methods. • Governance of information can support all data integration, risk management, business intelligence and master data management activities rather than imposing inconsistent rigor to these initiatives. • A practical and non-threatening approach can be applied to governing information and promoting stewardship of data as a cross-organization asset. • Best practices and key concepts of this non-threatening approach can be communicated effectively to leverage strengths and address opportunities to improve. |
| data quality management framework: DAMA-DMBOK Dama International, 2017 Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment. |
| data quality management framework: How to Establish a Data Quality Management Framework Accurity, 2022-05-17 A significant amount of money is lost every year to bad data. This includes time spent on correcting bad data, evaluating data sources that are not trusted, or simply the costs of mistakes due to incorrect customer identification. Why not improve your business in an area that you can directly influence? Our whitepaper helps you understand the purpose and added value of Data Quality Management, what types of common data quality issues exist, and guides you through the steps needed to establish a good Data Quality Management framework as a part of your overall data governance. In this whitepaper, you will: • Learn what data quality management is and how it helps your business • Understand what data quality is and how you can categorize data issues as data quality dimensions • Discover how bad data is produced in the first place and how to improve data quality • See what position data quality management takes in data governance • Get a step-by-step guide to the data quality management process |
| data quality management framework: The Practitioner's Guide to Data Quality Improvement David Loshin, 2010-11-22 The Practitioner's Guide to Data Quality Improvement offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. It shares the fundamentals for understanding the impacts of poor data quality, and guides practitioners and managers alike in socializing, gaining sponsorship for, planning, and establishing a data quality program. It demonstrates how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. It includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. This book is recommended for data management practitioners, including database analysts, information analysts, data administrators, data architects, enterprise architects, data warehouse engineers, and systems analysts, and their managers. - Offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. - Shows how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. - Includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. |
| data quality management framework: Executing Data Quality Projects Danette McGilvray, 2021-05-27 Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. 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. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today's data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization's standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before. - Includes concrete instructions, numerous templates, and practical advice for executing every step of The Ten Steps approach - Contains real examples from around the world, gleaned from the author's consulting practice and from those who implemented based on her training courses and the earlier edition of the book - 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 templates featured in the text, quick summaries of key ideas from the Ten Steps methodology, and other tools and information that are available online |
| data quality management framework: Performance Dashboards Wayne W. Eckerson, 2005-10-27 Tips, techniques, and trends on how to use dashboard technology to optimize business performance Business performance management is a hot new management discipline that delivers tremendous value when supported by information technology. Through case studies and industry research, this book shows how leading companies are using performance dashboards to execute strategy, optimize business processes, and improve performance. Wayne W. Eckerson (Hingham, MA) is the Director of Research for The Data Warehousing Institute (TDWI), the leading association of business intelligence and data warehousing professionals worldwide that provide high-quality, in-depth education, training, and research. He is a columnist for SearchCIO.com, DM Review, Application Development Trends, the Business Intelligence Journal, and TDWI Case Studies & Solution. |
| data quality management framework: Data Quality Assessment Arkady Maydanchik, 2007 Imagine a group of prehistoric hunters armed with stone-tipped spears. Their primitive weapons made hunting large animals, such as mammoths, dangerous work. Over time, however, a new breed of hunters developed. They would stretch the skin of a previously killed mammoth on the wall and throw their spears, while observing which spear, thrown from which angle and distance, penetrated the skin the best. The data gathered helped them make better spears and develop better hunting strategies. Quality data is the key to any advancement, whether it is from the Stone Age to the Bronze Age. Or from the Information Age to whatever Age comes next. The success of corporations and government institutions largely depends on the efficiency with which they can collect, organise, and utilise data about products, customers, competitors, and employees. Fortunately, improving your data quality does not have to be such a mammoth task. This book is a must read for anyone who needs to understand, correct, or prevent data quality issues in their organisation. Skipping theory and focusing purely on what is practical and what works, this text contains a proven approach to identifying, warehousing, and analysing data errors. Master techniques in data profiling and gathering metadata, designing data quality rules, organising rule and error catalogues, and constructing the dimensional data quality scorecard. David Wells, Director of Education of the Data Warehousing Institute, says This is one of those books that marks a milestone in the evolution of a discipline. Arkady's insights and techniques fuel the transition of data quality management from art to science -- from crafting to engineering. From deep experience, with thoughtful structure, and with engaging style Arkady brings the discipline of data quality to practitioners. |
| data quality management framework: Federal Statistics, Multiple Data Sources, and Privacy Protection National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Committee on National Statistics, Panel on Improving Federal Statistics for Policy and Social Science Research Using Multiple Data Sources and State-of-the-Art Estimation Methods, 2018-01-27 The environment for obtaining information and providing statistical data for policy makers and the public has changed significantly in the past decade, raising questions about the fundamental survey paradigm that underlies federal statistics. New data sources provide opportunities to develop a new paradigm that can improve timeliness, geographic or subpopulation detail, and statistical efficiency. It also has the potential to reduce the costs of producing federal statistics. The panel's first report described federal statistical agencies' current paradigm, which relies heavily on sample surveys for producing national statistics, and challenges agencies are facing; the legal frameworks and mechanisms for protecting the privacy and confidentiality of statistical data and for providing researchers access to data, and challenges to those frameworks and mechanisms; and statistical agencies access to alternative sources of data. The panel recommended a new approach for federal statistical programs that would combine diverse data sources from government and private sector sources and the creation of a new entity that would provide the foundational elements needed for this new approach, including legal authority to access data and protect privacy. This second of the panel's two reports builds on the analysis, conclusions, and recommendations in the first one. This report assesses alternative methods for implementing a new approach that would combine diverse data sources from government and private sector sources, including describing statistical models for combining data from multiple sources; examining statistical and computer science approaches that foster privacy protections; evaluating frameworks for assessing the quality and utility of alternative data sources; and various models for implementing the recommended new entity. Together, the two reports offer ideas and recommendations to help federal statistical agencies examine and evaluate data from alternative sources and then combine them as appropriate to provide the country with more timely, actionable, and useful information for policy makers, businesses, and individuals. |
| data quality management framework: Data Quality Jack E. Olson, 2003-01-09 Data Quality: The Accuracy Dimension is about assessing the quality of corporate data and improving its accuracy using the data profiling method. Corporate data is increasingly important as companies continue to find new ways to use it. Likewise, improving the accuracy of data in information systems is fast becoming a major goal as companies realize how much it affects their bottom line. Data profiling is a new technology that supports and enhances the accuracy of databases throughout major IT shops. Jack Olson explains data profiling and shows how it fits into the larger picture of data quality.* Provides an accessible, enjoyable introduction to the subject of data accuracy, peppered with real-world anecdotes. * Provides a framework for data profiling with a discussion of analytical tools appropriate for assessing data accuracy. * Is written by one of the original developers of data profiling technology. * Is a must-read for any data management staff, IT management staff, and CIOs of companies with data assets. |
| data quality management framework: Data and Information Quality Carlo Batini, Monica Scannapieco, 2016-03-23 This book provides a systematic and comparative description of the vast number of research issues related to the quality of data and information. It does so by delivering a sound, integrated and comprehensive overview of the state of the art and future development of data and information quality in databases and information systems. To this end, it presents an extensive description of the techniques that constitute the core of data and information quality research, including record linkage (also called object identification), data integration, error localization and correction, and examines the related techniques in a comprehensive and original methodological framework. Quality dimension definitions and adopted models are also analyzed in detail, and differences between the proposed solutions are highlighted and discussed. Furthermore, while systematically describing data and information quality as an autonomous research area, paradigms and influences deriving from other areas, such as probability theory, statistical data analysis, data mining, knowledge representation, and machine learning are also included. Last not least, the book also highlights very practical solutions, such as methodologies, benchmarks for the most effective techniques, case studies, and examples. The book has been written primarily for researchers in the fields of databases and information management or in natural sciences who are interested in investigating properties of data and information that have an impact on the quality of experiments, processes and on real life. The material presented is also sufficiently self-contained for masters or PhD-level courses, and it covers all the fundamentals and topics without the need for other textbooks. Data and information system administrators and practitioners, who deal with systems exposed to data-quality issues and as a result need a systematization of the field and practical methods in the area, will also benefit from the combination of concrete practical approaches with sound theoretical formalisms. |
| data quality management framework: Enterprise Master Data Management Allen Dreibelbis, Eberhard Hechler, Ivan Milman, Martin Oberhofer, Paul van Run, Dan Wolfson, 2008-06-05 The Only Complete Technical Primer for MDM Planners, Architects, and Implementers Companies moving toward flexible SOA architectures often face difficult information management and integration challenges. The master data they rely on is often stored and managed in ways that are redundant, inconsistent, inaccessible, non-standardized, and poorly governed. Using Master Data Management (MDM), organizations can regain control of their master data, improve corresponding business processes, and maximize its value in SOA environments. Enterprise Master Data Management provides an authoritative, vendor-independent MDM technical reference for practitioners: architects, technical analysts, consultants, solution designers, and senior IT decisionmakers. Written by the IBM ® data management innovators who are pioneering MDM, this book systematically introduces MDM’s key concepts and technical themes, explains its business case, and illuminates how it interrelates with and enables SOA. Drawing on their experience with cutting-edge projects, the authors introduce MDM patterns, blueprints, solutions, and best practices published nowhere else—everything you need to establish a consistent, manageable set of master data, and use it for competitive advantage. Coverage includes How MDM and SOA complement each other Using the MDM Reference Architecture to position and design MDM solutions within an enterprise Assessing the value and risks to master data and applying the right security controls Using PIM-MDM and CDI-MDM Solution Blueprints to address industry-specific information management challenges Explaining MDM patterns as enablers to accelerate consistent MDM deployments Incorporating MDM solutions into existing IT landscapes via MDM Integration Blueprints Leveraging master data as an enterprise asset—bringing people, processes, and technology together with MDM and data governance Best practices in MDM deployment, including data warehouse and SAP integration |
| data quality management framework: Exploratory Data Mining and Data Cleaning Tamraparni Dasu, Theodore Johnson, 2003-08-01 Written for practitioners of data mining, data cleaning and database management. Presents a technical treatment of data quality including process, metrics, tools and algorithms. Focuses on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge. Addresses methods of detecting, quantifying and correcting data quality issues that can have a significant impact on findings and decisions, using commercially available tools as well as new algorithmic approaches. Uses case studies to illustrate applications in real life scenarios. Highlights new approaches and methodologies, such as the DataSphere space partitioning and summary based analysis techniques. Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analys is and data mining. |
| data quality management framework: Multi-Domain Master Data Management Mark Allen, Dalton Cervo, 2015-03-21 Multi-Domain Master Data Management delivers practical guidance and specific instruction to help guide planners and practitioners through the challenges of a multi-domain master data management (MDM) implementation. Authors Mark Allen and Dalton Cervo bring their expertise to you in the only reference you need to help your organization take master data management to the next level by incorporating it across multiple domains. Written in a business friendly style with sufficient program planning guidance, this book covers a comprehensive set of topics and advanced strategies centered on the key MDM disciplines of Data Governance, Data Stewardship, Data Quality Management, Metadata Management, and Data Integration. - Provides a logical order toward planning, implementation, and ongoing management of multi-domain MDM from a program manager and data steward perspective. - Provides detailed guidance, examples and illustrations for MDM practitioners to apply these insights to their strategies, plans, and processes. - Covers advanced MDM strategy and instruction aimed at improving data quality management, lowering data maintenance costs, and reducing corporate risks by applying consistent enterprise-wide practices for the management and control of master data. |
| data quality management framework: Data Governance John Ladley, 2019-11-08 Managing data continues to grow as a necessity for modern organizations. There are seemingly infinite opportunities for organic growth, reduction of costs, and creation of new products and services. It has become apparent that none of these opportunities can happen smoothly without data governance. The cost of exponential data growth and privacy / security concerns are becoming burdensome. Organizations will encounter unexpected consequences in new sources of risk. The solution to these challenges is also data governance; ensuring balance between risk and opportunity. Data Governance, Second Edition, is for any executive, manager or data professional who needs to understand or implement a data governance program. It is required to ensure consistent, accurate and reliable data across their organization. This book offers an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable. This valuable resource provides comprehensive guidance to beginning professionals, managers or analysts looking to improve their processes, and advanced students in Data Management and related courses. With the provided framework and case studies all professionals in the data governance field will gain key insights into launching successful and money-saving data governance program. - Incorporates industry changes, lessons learned and new approaches - Explores various ways in which data analysts and managers can ensure consistent, accurate and reliable data across their organizations - Includes new case studies which detail real-world situations - Explores all of the capabilities an organization must adopt to become data driven - Provides guidance on various approaches to data governance, to determine whether an organization should be low profile, central controlled, agile, or traditional - Provides guidance on using technology and separating vendor hype from sincere delivery of necessary capabilities - Offers readers insights into how their organizations can improve the value of their data, through data quality, data strategy and data literacy - Provides up to 75% brand-new content compared to the first edition |
| data quality management framework: Data as a Service Pushpak Sarkar, 2015-07-31 Data as a Service shows how organizations can leverage “data as a service” by providing real-life case studies on the various and innovative architectures and related patterns Comprehensive approach to introducing data as a service in any organization A reusable and flexible SOA based architecture framework Roadmap to introduce ‘big data as a service’ for potential clients Presents a thorough description of each component in the DaaS reference architecture so readers can implement solutions |
| data quality management framework: Applied Qualitative Research Design Margaret R. Roller, Paul J. Lavrakas, 2015-02-23 This unique text provides a comprehensive framework for creating, managing, and interpreting qualitative research studies that yield valid and useful information. Examples of studies from a wide range of disciplines illustrate the strengths, limitations, and applications of the primary qualitative methods: in-depth interviews, focus group discussions, ethnography, content analysis, and case study and narrative research. Following a consistent format, chapters show students and researchers how to implement each method within a paradigm-neutral and flexible Total Quality Framework (TQF) comprising four interrelated components: Credibility, Analyzability, Transparency, and Usefulness. Unlike other texts that relegate quality issues to one or two chapters, detailed discussions of such crucial topics as construct validity, interresearcher reliability, researcher bias, and verification strategies are featured throughout. The book also addresses applications of the TQF to the writing, review, and evaluation of qualitative research proposals and manuscripts. Pedagogical Features *Summary tables that highlight important content, such as the application of a method to vulnerable or hard-to-reach populations. *Case studies that illustrate TQF standards in practice for each method. *Guidelines for effective documentation (via thick descriptions) of each type of study. *End-of-chapter discussion topics, exercises, and suggested further reading and Web resources. *Chapters open with a preview and close with a bulleted summary of key ideas. *Extensive glossary. 2021 Winner--American Association for Public Opinion Research (AAPOR) Book Award |
| data quality management framework: The DAMA Dictionary of Data Management Dama International, 2011 A glossary of over 2,000 terms which provides a common data management vocabulary for IT and Business professionals, and is a companion to the DAMA Data Management Body of Knowledge (DAMA-DMBOK). Topics include: Analytics & Data Mining Architecture Artificial Intelligence Business Analysis DAMA & Professional Development Databases & Database Design Database Administration Data Governance & Stewardship Data Management Data Modeling Data Movement & Integration Data Quality Management Data Security Management Data Warehousing & Business Intelligence Document, Record & Content Management Finance & Accounting Geospatial Data Knowledge Management Marketing & Customer Relationship Management Meta-Data Management Multi-dimensional & OLAP Normalization Object-Orientation Parallel Database Processing Planning Process Management Project Management Reference & Master Data Management Semantic Modeling Software Development Standards Organizations Structured Query Language (SQL) XML Development |
| data quality management framework: Global Perspectives on Quality Assurance and Accreditation in Higher Education Institutions Magd, Hesham, Kunjumuhammed, Siraj Kariyilaparambu, 2021-09-17 Quality accreditation in higher education institutions (HEIs) is currently a buzzword. The need to maintain high-quality education standards is a critical requirement for HEIs to remain competitive in the market and for government and regulatory bodies to ensure the quality standards of programs offered. From being an implicit requirement that is internally addressed, quality assurance activities become an explicit requirement that is regularly audited and appraised by national and international accreditation agencies. HEIs are voluntarily integrating quality management systems (QMS), institutional and program-specific, in response to the political and competitive environment in which it exists. Through its higher education department or by creating non-profitable accreditation bodies, many governments have implemented a quality framework for licensing HEIs and invigilates its adherence based on which accreditation statuses are granted for HEIs. Global Perspectives on Quality Assurance and Accreditation in Higher Education Institutions provides a comprehensive framework for HEIs to address quality assurance and quality accreditation requirements and serves as a practical tool to develop and deploy well-defined quality management systems in higher education. The book focuses on the critical aspects of quality assurance; the need to develop a concise and agile vision, mission, values, and graduate attributes; and to develop a system that effectively aligns the various activities of the HEI to the attainment of the strategic priorities listed in the institutional plans. The chapters each cover the various facets of the quality assurance framework and accreditation agencies' requirements with practical examples of each. This book is useful for HEI administrators, quality assurance specialists in HEIs, heads of academic departments, internal auditors, external auditors, and other practitioners of quality, along with stakeholders, researchers, academicians, and students interested in quality assurance and accreditation in higher education. |
| data quality management framework: Business Information Systems Witold Abramowicz, Rafael Corchuelo, 2019-06-18 The two-volume set LNBIP 353 and 354 constitutes the proceedings of the 22nd International Conference on Business Information Systems, BIS 2019, held in Seville, Spain, in June 2019. The theme of the BIS 2019 was Data Science for Business Information Systems, inspiring researchers to share theoretical and practical knowledge of the different aspects related to Data Science in enterprises. The 67 papers presented in these proceedings were carefully reviewed and selected from 223 submissions. The contributions were organized in topical sections as follows: Part I: Big Data and Data Science; Artificial Intelligence; ICT Project Management; and Smart Infrastructure. Part II: Social Media and Web-based Systems; and Applications, Evaluations and Experiences. |
| data quality management framework: Assuring Data Quality and Validity in Clinical Trials for Regulatory Decision Making Institute of Medicine, Roundtable on Research and Development of Drugs, Biologics, and Medical Devices, 1999-07-27 In an effort to increase knowledge and understanding of the process of assuring data quality and validity in clinical trials, the IOM hosted a workshop to open a dialogue on the process to identify and discuss issues of mutual concern among industry, regulators, payers, and consumers. The presenters and panelists together developed strategies that could be used to address the issues that were identified. This IOM report of the workshop summarizes the present status and highlights possible strategies for making improvements to the education of interested and affected parties as well as facilitating future planning. |
| data quality management framework: Principles of Database Management Wilfried Lemahieu, Seppe vanden Broucke, Bart Baesens, 2018-07-12 Introductory, theory-practice balanced text teaching the fundamentals of databases to advanced undergraduates or graduate students in information systems or computer science. |
| data quality management framework: The Land Governance Assessment Framework Klaus Deininger, Harris Selod, Anthony Burns, 2012 Increased global demand for land posits the need for well-designed country-level land policies to protect long-held rights, facilitate land access and address any constraints that land policy may pose for broader growth. While the implementation of land reforms can be a lengthy process, the need to swiftly identify key land policy challenges and devise responses that allow the monitoring of progress, in a way that minimizes conflicts and supports broader development goals, is clear. The Land Governance Assessment Framework (LGAF) makes a substantive contribution to the land sector by providing a quick and innovative tool to monitor land governance at the country level. The LGAF offers a comprehensive diagnostic tool that covers five main areas for policy intervention: Legal and institutional framework; Land use planning, management and taxation; Management of public land; Public provision of land information; and Dispute resolution and conflict management. The LGAF assesses these areas through a set of detailed indicators that are rated on a scale of pre-coded statements (from lack of good governance to good practice). While land governance can be highly technical in nature and tends to be addressed in a partial and sporadic manner, the LGAF posits a tool for a comprehensive assessment, taking into account the broad range of issues that land governance encompasses, while enabling those unfamiliar with land to grasp its full complexity. The LGAF will make it possible for policymakers to make sense of the technical levels of the land sector, benchmark governance, identify areas that require further attention and monitor progress. It is intended to assist countries in prioritizing reforms in the land sector by providing a holistic diagnostic review that can inform policy dialogue in a clear and targeted manner. In addition to presenting the LGAF tool, this book includes detailed case studies on its implementation in five selected countries: Peru, the Kyrgyz Republic, Ethiopia, Indonesia and Tanzania. |
| data quality management framework: Total Information Risk Management Alexander Borek, Ajith Kumar Parlikad, Jela Webb, Philip Woodall, 2013-08-30 How well does your organization manage the risks associated with information quality? Managing information risk is becoming a top priority on the organizational agenda. The increasing sophistication of IT capabilities along with the constantly changing dynamics of global competition are forcing businesses to make use of their information more effectively. Information is becoming a core resource and asset for all organizations; however, it also brings many potential risks to an organization, from strategic, operational, financial, compliance, and environmental to societal. If you continue to struggle to understand and measure how information and its quality affects your business, this book is for you. This reference is in direct response to the new challenges that all managers have to face. Our process helps your organization to understand the pain points regarding poor data and information quality so you can concentrate on problems that have a high impact on core business objectives. This book provides you with all the fundamental concepts, guidelines and tools to ensure core business information is identified, protected and used effectively, and written in a language that is clear and easy to understand for non-technical managers. - Shows how to manage information risk using a holistic approach by examining information from all sources - Offers varied perspectives of an author team that brings together academics, practitioners and researchers (both technical and managerial) to provide a comprehensive guide - Provides real-life case studies with practical insight into the management of information risk and offers a basis for broader discussion among managers and practitioners |
| data quality management framework: Big Data Computing Rajendra Akerkar, 2013-12-05 Due to market forces and technological evolution, Big Data computing is developing at an increasing rate. A wide variety of novel approaches and tools have emerged to tackle the challenges of Big Data, creating both more opportunities and more challenges for students and professionals in the field of data computation and analysis. Presenting a mix of industry cases and theory, Big Data Computing discusses the technical and practical issues related to Big Data in intelligent information management. Emphasizing the adoption and diffusion of Big Data tools and technologies in industry, the book introduces a broad range of Big Data concepts, tools, and techniques. It covers a wide range of research, and provides comparisons between state-of-the-art approaches. Comprised of five sections, the book focuses on: What Big Data is and why it is important Semantic technologies Tools and methods Business and economic perspectives Big Data applications across industries |
| data quality management framework: Registries for Evaluating Patient Outcomes Agency for Healthcare Research and Quality/AHRQ, 2014-04-01 This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. |
| data quality management framework: The Fourth Industrial Revolution Klaus Schwab, 2017-01-03 World-renowned economist Klaus Schwab, Founder and Executive Chairman of the World Economic Forum, explains that we have an opportunity to shape the fourth industrial revolution, which will fundamentally alter how we live and work. Schwab argues that this revolution is different in scale, scope and complexity from any that have come before. Characterized by a range of new technologies that are fusing the physical, digital and biological worlds, the developments are affecting all disciplines, economies, industries and governments, and even challenging ideas about what it means to be human. Artificial intelligence is already all around us, from supercomputers, drones and virtual assistants to 3D printing, DNA sequencing, smart thermostats, wearable sensors and microchips smaller than a grain of sand. But this is just the beginning: nanomaterials 200 times stronger than steel and a million times thinner than a strand of hair and the first transplant of a 3D printed liver are already in development. Imagine “smart factories” in which global systems of manufacturing are coordinated virtually, or implantable mobile phones made of biosynthetic materials. The fourth industrial revolution, says Schwab, is more significant, and its ramifications more profound, than in any prior period of human history. He outlines the key technologies driving this revolution and discusses the major impacts expected on government, business, civil society and individuals. Schwab also offers bold ideas on how to harness these changes and shape a better future—one in which technology empowers people rather than replaces them; progress serves society rather than disrupts it; and in which innovators respect moral and ethical boundaries rather than cross them. We all have the opportunity to contribute to developing new frameworks that advance progress. |
| data quality management framework: Laboratory Quality Management System World Health Organization, 2011 Achieving, maintaining and improving accuracy, timeliness and reliability are major challenges for health laboratories. Countries worldwide committed themselves to build national capacities for the detection of, and response to, public health events of international concern when they decided to engage in the International Health Regulations implementation process. Only sound management of quality in health laboratories will enable countries to produce test results that the international community will trust in cases of international emergency. This handbook was developed through collaboration between the WHO Lyon Office for National Epidemic Preparedness and Response, the United States of America Centers for Disease Control and Prevention (CDC) Division of Laboratory Systems, and the Clinical and Laboratory Standards Institute (CLSI). It is based on training sessions and modules provided by the CDC and WHO in more than 25 countries, and on guidelines for implementation of ISO 15189 in diagnostic laboratories, developed by CLSI. This handbook is intended to provide a comprehensive reference on Laboratory Quality Management System for all stakeholders in health laboratory processes, from management, to administration, to bench-work laboratorians. This handbook covers topics that are essential for quality management of a public health or clinical laboratory. They are based on both ISO 15189 and CLSI GP26-A3 documents. Each topic is discussed in a separate chapter. The chapters follow the framework developed by CLSI and are organized as the 12 Quality System Essentials. |
| data quality management framework: IT Quality Index Zdenek Kvapil, 2017-11-13 Over a long period of time we were receiving demand from different customers and stakeholders requesting our help with analyzing actual levels of IT management practices in their companies and to provide an external, objective look at IT, compare it with other organizations and develop a management summary of our overall findings. While this seems like a straight forward request and is covered by many consulting service providers, we struggled to develop answers to some very simple questions which are crucial to the different management levels from CEO level to IT professionals delivering operational support to the end user. Key questions we were requested to answer: _ How good is our IT? _ How is the quality of our IT evolving? _ How do we compare with other IT departments in similar organizations? _ How do we align the perception of quality between the business and IT? _ Can you compare the quality and costs of our IT? There are many existing assessment approaches based on ISO/IEC, capability models, maturity models, structured surveys as well as there being many different benchmarking approaches that provide detailed analytical outputs. These are typically focused and targeted internally, providing largely internal benefits to a company’s IT organization, looking mostly at the presence of documentation and processes, but none of them providing a comprehensive and straight forward answer to address the simple yet fundamental question asked by management – HOW GOOD IS OUR IT? We believe that a simple question should have a simple answer. |
| data quality management framework: Principles of Data Quality Arthur D. Chapman, 2005 |
| data quality management framework: Engineering Asset Management Dimitris Kiritsis, Christos Emmanouilidis, Andy Koronios, Joseph Mathew, 2011-02-03 Engineering Asset Management discusses state-of-the-art trends and developments in the emerging field of engineering asset management as presented at the Fourth World Congress on Engineering Asset Management (WCEAM). It is an excellent reference for practitioners, researchers and students in the multidisciplinary field of asset management, covering such topics as asset condition monitoring and intelligent maintenance; asset data warehousing, data mining and fusion; asset performance and level-of-service models; design and life-cycle integrity of physical assets; deterioration and preservation models for assets; education and training in asset management; engineering standards in asset management; fault diagnosis and prognostics; financial analysis methods for physical assets; human dimensions in integrated asset management; information quality management; information systems and knowledge management; intelligent sensors and devices; maintenance strategies in asset management; optimisation decisions in asset management; risk management in asset management; strategic asset management; and sustainability in asset management. |
| data quality management framework: Innovations in Federal Statistics National Academies of Sciences, Engineering, and Medicine, Division of Behavioral and Social Sciences and Education, Committee on National Statistics, Panel on Improving Federal Statistics for Policy and Social Science Research Using Multiple Data Sources and State-of-the-Art Estimation Methods, 2017-04-21 Federal government statistics provide critical information to the country and serve a key role in a democracy. For decades, sample surveys with instruments carefully designed for particular data needs have been one of the primary methods for collecting data for federal statistics. However, the costs of conducting such surveys have been increasing while response rates have been declining, and many surveys are not able to fulfill growing demands for more timely information and for more detailed information at state and local levels. Innovations in Federal Statistics examines the opportunities and risks of using government administrative and private sector data sources to foster a paradigm shift in federal statistical programs that would combine diverse data sources in a secure manner to enhance federal statistics. This first publication of a two-part series discusses the challenges faced by the federal statistical system and the foundational elements needed for a new paradigm. |
| data quality management framework: Project to Product Mik Kersten, 2018-11-20 As tech giants and startups disrupt every market, those who master large-scale software delivery will define the economic landscape of the 21st century, just as the masters of mass production defined the landscape in the 20th. Unfortunately, business and technology leaders are woefully ill-equipped to solve the problems posed by digital transformation. At the current rate of disruption, half of S&P 500 companies will be replaced in the next ten years. A new approach is needed. In Project to Product, Value Stream Network pioneer and technology business leader Dr. Mik Kersten introduces the Flow Framework—a new way of seeing, measuring, and managing software delivery. The Flow Framework will enable your company’s evolution from project-oriented dinosaur to product-centric innovator that thrives in the Age of Software. If you’re driving your organization’s transformation at any level, this is the book for you. |
| data quality management framework: Big Data Management Peter Ghavami, 2020-11-09 Data analytics is core to business and decision making. The rapid increase in data volume, velocity and variety offers both opportunities and challenges. While open source solutions to store big data, like Hadoop, offer platforms for exploring value and insight from big data, they were not originally developed with data security and governance in mind. Big Data Management discusses numerous policies, strategies and recipes for managing big data. It addresses data security, privacy, controls and life cycle management offering modern principles and open source architectures for successful governance of big data. The author has collected best practices from the world’s leading organizations that have successfully implemented big data platforms. The topics discussed cover the entire data management life cycle, data quality, data stewardship, regulatory considerations, data council, architectural and operational models are presented for successful management of big data. The book is a must-read for data scientists, data engineers and corporate leaders who are implementing big data platforms in their organizations. |
| data quality management framework: Managing Information Quality Martin J. Eppler, 2013-06-05 What makes information useful? This seemingly simple and yet intriguing and complicated question is discussed in this book. It examines ways in which the quality of information can be improved in knowledge-intensive processes (such as on-line communication, strategy, product development, or consulting). Based on existing information quality literature, the book proposes a conceptual framework to manage information quality for knowledge-based content. It presents four proven principles to apply the framework to a variety of information products. Five in-depth company case studies show how information quality can be managed systematically. The book uses frequent diagrams and tables, as well as diagnostic questions and summary boxes to make its content actionable. |
FCSM-20-04 A Framework for Data Quality
Tis report presents a framework for identifying data quality for all data, summarizes the current state of practice in iden-tifying threats to data quality for the components of the framework, and …
Data Quality Management - EY
Data quality issues can be mitigated in three pillars of people, processes, and technology as outlined below. By implementing these three pillars, organizations can effectively address data …
DATA QUALITY FRAMEWORK
approach to understanding data quality across the vast range of data types, sectors, and products that comprise our work to encourage collaboration and organizational learning.
PwC s data quality capabilities
PwC’s data quality capabilities 5 March 2021 Poor-quality data flowing through applications can cause a multitude of negative consequences. A few repercussions of not imple menting a …
A FRAMEWORK AND A METHODOLOGY FOR DATA QUALITY …
In this paper, we present the first result of this activity: ORME-DQ a methodology and an associated framework for the assessment of data quality in an organization. The paper is …
Data Quality - Victorian Government
Develop and maintain a Data Quality Management Plan for each critical (high value/high risk) data asset. Key considerations to include in this plan would be details about data collection, age, …
DATA QUALITY ASSESSMENT FRAMEWORK GENERIC …
5.2.1 Documentation on concepts, scope, classifications, basis of recording, data sources, and statistical techniques is available, and differences from internationally accepted standards, …
Dimensions of Data Quality (DDQ) - DAMA NL
1.2 Dimensions, data and quality The title of this report is Dimensions of Data Quality (DDQ). Dimension is defined as a measurable feature of an object (ISO 9001). The object is in this …
DAMA-DMBOK Functional Framework - Governance Foundation
This document describes Version 3.02 of the DAMA-DMBOK Functional Framework, provided by DAMA International to help formalize the best practices of our profession. The original draft of a …
A Suggested Framework for the Quality of Big Data - UNECE
The Big Data Quality framework developed here provides a structured view of quality at three phases of the business process: . Output – the reporting of quality with statistical outputs …
A Framework for Data Quality: Case Studies October 2023
The FCSM Framework for Data Quality provides a comprehensive list of quality challenges for consideration to address the following objectives: • Design new data collections, estimation …
A Management System for Data Quality
In the world of data management there are frameworks that are similar to the DQ MS explained in this paper. First, the standard ISO 8000 has existed for quite some time. This consists of 13 …
Global Fund Data Quality Improvement Framework
1. Develop a national data quality improvement plan (DQIP), including community data, following a periodic national or targeted data quality audit to address identified root-causes. 2. Adopt as …
Data Quality Management and Interoperability
Objective 3 – Data Quality Framework, aligned with EU best practice is commonly used across the Network. Sets out the principles for data quality objectives applicable across all EMRN data …
Data Quality Assessment Framework - Massachusetts Institute …
Many efforts to measure data quality focus on abstract concepts and cannot find a practical way to apply them. Or they attach to specific issues and cannot imagine measurement beyond them. …
A Management System for Data Quality - dama-nl.org
In the world of data management there are frameworks that are similar to the DQMS explained in this paper. First, the standard ISO 8000 has existed for quite some time. This consists of 13 …
Towards a Framework for Corporate Data Quality Management
This paper takes up on the first two aspects and proposes a framework for Corporate Data Quality (CDQ) management that provides an integrated reference for the implementation and …
Data Quality Framework overview - European Medicines Agency
The EU Data Quality Framework guideline seeks to address the topic of characterising the data quality of any given dataset used for medicine regulation It sets out the concepts, the …
DATA QUALITY ASSURANCE IN DATA WAREHOUSING: A …
This article presents a comprehensive framework for data quality assurance in data warehousing, addressing the critical need for maintaining data integrity, accuracy, and reliability in...
FCSM-20-04 A Framework for Data Quality
Tis report presents a framework for identifying data quality for all data, summarizes the current state of practice in iden-tifying threats to data quality for the components of the framework, and …
Data Quality Management - EY
Data quality issues can be mitigated in three pillars of people, processes, and technology as outlined below. By implementing these three pillars, organizations can effectively address data …
DATA QUALITY FRAMEWORK
approach to understanding data quality across the vast range of data types, sectors, and products that comprise our work to encourage collaboration and organizational learning.
PwC s data quality capabilities
PwC’s data quality capabilities 5 March 2021 Poor-quality data flowing through applications can cause a multitude of negative consequences. A few repercussions of not imple menting a holistic …
Data Quality Fundamentals - DAMA NY
• Policy-based data quality management • Performance management driven by data quality policies • Data quality service level agreements used for managing observance of policies
A FRAMEWORK AND A METHODOLOGY FOR DATA …
In this paper, we present the first result of this activity: ORME-DQ a methodology and an associated framework for the assessment of data quality in an organization. The paper is organized as …
Data Quality - Victorian Government
Develop and maintain a Data Quality Management Plan for each critical (high value/high risk) data asset. Key considerations to include in this plan would be details about data collection, age, …
DATA QUALITY ASSESSMENT FRAMEWORK GENERIC …
5.2.1 Documentation on concepts, scope, classifications, basis of recording, data sources, and statistical techniques is available, and differences from internationally accepted standards, …
Dimensions of Data Quality (DDQ) - DAMA NL
1.2 Dimensions, data and quality The title of this report is Dimensions of Data Quality (DDQ). Dimension is defined as a measurable feature of an object (ISO 9001). The object is in this …
DAMA-DMBOK Functional Framework - Governance …
This document describes Version 3.02 of the DAMA-DMBOK Functional Framework, provided by DAMA International to help formalize the best practices of our profession. The original draft of a …
A Suggested Framework for the Quality of Big Data - UNECE
The Big Data Quality framework developed here provides a structured view of quality at three phases of the business process: . Output – the reporting of quality with statistical outputs derived …
A Framework for Data Quality: Case Studies October 2023
The FCSM Framework for Data Quality provides a comprehensive list of quality challenges for consideration to address the following objectives: • Design new data collections, estimation …
A Management System for Data Quality
In the world of data management there are frameworks that are similar to the DQ MS explained in this paper. First, the standard ISO 8000 has existed for quite some time. This consists of 13 parts …
Global Fund Data Quality Improvement Framework
1. Develop a national data quality improvement plan (DQIP), including community data, following a periodic national or targeted data quality audit to address identified root-causes. 2. Adopt as …
Data Quality Management and Interoperability
Objective 3 – Data Quality Framework, aligned with EU best practice is commonly used across the Network. Sets out the principles for data quality objectives applicable across all EMRN data …
Data Quality Assessment Framework - Massachusetts …
Many efforts to measure data quality focus on abstract concepts and cannot find a practical way to apply them. Or they attach to specific issues and cannot imagine measurement beyond them. To …
A Management System for Data Quality - dama-nl.org
In the world of data management there are frameworks that are similar to the DQMS explained in this paper. First, the standard ISO 8000 has existed for quite some time. This consists of 13 parts …
Towards a Framework for Corporate Data Quality …
This paper takes up on the first two aspects and proposes a framework for Corporate Data Quality (CDQ) management that provides an integrated reference for the implementation and …
Data Quality Framework overview - European Medicines …
The EU Data Quality Framework guideline seeks to address the topic of characterising the data quality of any given dataset used for medicine regulation It sets out the concepts, the definitions, …
DATA QUALITY ASSURANCE IN DATA WAREHOUSING: A …
This article presents a comprehensive framework for data quality assurance in data warehousing, addressing the critical need for maintaining data integrity, accuracy, and reliability in...
