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| data management standards and guidelines: 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 management standards and guidelines: 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 management standards and guidelines: 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 management standards and guidelines: Recommendations for Database Management System Standards FIPS Task Group on Database Management System Standards (U.S.), 1979 |
| data management standards and guidelines: Climate Data Records from Environmental Satellites National Research Council, Division on Earth and Life Studies, Board on Atmospheric Sciences and Climate, Committee on Climate Data Records from NOAA Operational Satellites, 2004-08-26 The report outlines key elements to consider in designing a program to create climate-quality data from satellites. It examines historical attempts to create climate data records, provides advice on steps for generating, re-analyzing, and storing satellite climate data, and discusses the importance of partnering between agencies, academia, and industry. NOAA will use this report-the first in a two-part study-to draft an implementation plan for climate data records. |
| data management standards and guidelines: Data Governance: The Definitive Guide Evren Eryurek, Uri Gilad, Valliappa Lakshmanan, Anita Kibunguchy-Grant, Jessi Ashdown, 2021-03-08 As your company moves data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure you meet compliance. Data governance incorporates the ways that people, processes, and technology work together to support business efficiency. With this practical guide, chief information, data, and security officers will learn how to effectively implement and scale data governance throughout their organizations. You'll explore how to create a strategy and tooling to support the democratization of data and governance principles. Through good data governance, you can inspire customer trust, enable your organization to extract more value from data, and generate more-competitive offerings and improvements in customer experience. This book shows you how. Enable auditable legal and regulatory compliance with defined and agreed-upon data policies Employ better risk management Establish control and maintain visibility into your company's data assets, providing a competitive advantage Drive top-line revenue and cost savings when developing new products and services Implement your organization's people, processes, and tools to operationalize data trustworthiness. |
| data management standards and guidelines: Data Management for Researchers Kristin Briney, 2015-09-01 A comprehensive guide to everything scientists need to know about data management, this book is essential for researchers who need to learn how to organize, document and take care of their own data. Researchers in all disciplines are faced with the challenge of managing the growing amounts of digital data that are the foundation of their research. Kristin Briney offers practical advice and clearly explains policies and principles, in an accessible and in-depth text that will allow researchers to understand and achieve the goal of better research data management. Data Management for Researchers includes sections on: * The data problem – an introduction to the growing importance and challenges of using digital data in research. Covers both the inherent problems with managing digital information, as well as how the research landscape is changing to give more value to research datasets and code. * The data lifecycle – a framework for data’s place within the research process and how data’s role is changing. Greater emphasis on data sharing and data reuse will not only change the way we conduct research but also how we manage research data. * Planning for data management – covers the many aspects of data management and how to put them together in a data management plan. This section also includes sample data management plans. * Documenting your data – an often overlooked part of the data management process, but one that is critical to good management; data without documentation are frequently unusable. * Organizing your data – explains how to keep your data in order using organizational systems and file naming conventions. This section also covers using a database to organize and analyze content. * Improving data analysis – covers managing information through the analysis process. This section starts by comparing the management of raw and analyzed data and then describes ways to make analysis easier, such as spreadsheet best practices. It also examines practices for research code, including version control systems. * Managing secure and private data – many researchers are dealing with data that require extra security. This section outlines what data falls into this category and some of the policies that apply, before addressing the best practices for keeping data secure. * Short-term storage – deals with the practical matters of storage and backup and covers the many options available. This section also goes through the best practices to insure that data are not lost. * Preserving and archiving your data – digital data can have a long life if properly cared for. This section covers managing data in the long term including choosing good file formats and media, as well as determining who will manage the data after the end of the project. * Sharing/publishing your data – addresses how to make data sharing across research groups easier, as well as how and why to publicly share data. This section covers intellectual property and licenses for datasets, before ending with the altmetrics that measure the impact of publicly shared data. * Reusing data – as more data are shared, it becomes possible to use outside data in your research. This chapter discusses strategies for finding datasets and lays out how to cite data once you have found it. This book is designed for active scientific researchers but it is useful for anyone who wants to get more from their data: academics, educators, professionals or anyone who teaches data management, sharing and preservation. An excellent practical treatise on the art and practice of data management, this book is essential to any researcher, regardless of subject or discipline. —Robert Buntrock, Chemical Information Bulletin |
| data management standards and guidelines: 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 management standards and guidelines: Data Management at Scale Piethein Strengholt, 2020-07-29 As data management and integration continue to evolve rapidly, storing all your data in one place, such as a data warehouse, is no longer scalable. In the very near future, data will need to be distributed and available for several technological solutions. With this practical book, you’ll learnhow to migrate your enterprise from a complex and tightly coupled data landscape to a more flexible architecture ready for the modern world of data consumption. Executives, data architects, analytics teams, and compliance and governance staff will learn how to build a modern scalable data landscape using the Scaled Architecture, which you can introduce incrementally without a large upfront investment. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including technological developments, regulatory requirements, and privacy concerns Go deep into the Scaled Architecture and learn how the pieces fit together Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata |
| data management standards and guidelines: Data Management for Libraries Laura Krier, Carly A. Strasser, 2014 Since the National Science Foundation joined the National Institutes of Health in requiring that grant proposals include a data management plan, academic librarians have been inundated with related requests from faculty and campus-based grant consulting offices. Data management is a new service area for many library staff, requiring careful planning and implementation. This guide offers a start-to-finish primer on understanding, building, and maintaining a data management service, showing another way the academic library can be invaluable to researchers. Krier and Strasser of the California Digital Library guide readers through every step of a data management plan by Offering convincing arguments to persuade researchers to create a data management plan, with advice on collaborating with them Laying out all the foundations of starting a service, complete with sample data librarian job descriptions and data management plans Providing tips for conducting successful data management interviews Leading readers through making decisions about repositories and other infrastructure Addressing sensitive questions such as ownership, intellectual property, sharing and access, metadata, and preservation This LITA guide will help academic librarians work with researchers, faculty, and other stakeholders to effectively organize, preserve, and provide access to research data. |
| data management standards and guidelines: 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 management standards and guidelines: The DAMA Guide to the Data Management Body of Knowledge Mark Mosley, 2010 Written by over 120 data management practitioners, this is the most impressive compilation of data management principals and best practices, ever assembled. It provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure. The equivalent of the PMBOK or the BABOK, the DAMA-DMBOK provides information on: Data Governance; Data Architecture Management; Data Development; Database Operations Management; Data Security Management; Reference & Master Data Management; Data Warehousing & Business Intelligence Management; Document & Content Management; Meta Data Management; Data Quality Management; Professional Development. As an authoritative introduction to data management, the goals of the DAMA-DMBOK Guide are: To build consensus for a generally applicable view of data management functions; To provide standard definitions for commonly used data management functions, deliverables, roles, and other terminology; To document guiding principles for data management; To present a vendor-neutral overview to commonly accepted good practices, widely adopted methods and techniques, and significant alternative approaches; To clarify the scope and boundaries of data management; To act as a reference which guides readers to additional resources for further understanding. |
| data management standards and guidelines: 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 management standards and guidelines: Target-setting Methods and Data Management to Support Performance-based Resource Allocation by Transportation Agencies National Cooperative Highway Research Program, 2010 TRB's National Cooperative Highway Research Program (NCHRP) Report 666: Target Setting Methods and Data Management to Support Performance-Based Resource Allocation by Transportation Agencies - Volume I: Research Report, and Volume II: Guide for Target-Setting and Data Management provides a framework and specific guidance for setting performance targets and for ensuring that appropriate data are available to support performance-based decision-making. Volume III to this report was published separately in an electronic-only format as NCHRP Web-Only Document 154. Volume III includes case studies of organizations investigated in the research used to develop NCHRP Report 666. |
| data management standards and guidelines: 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 management standards and guidelines: Data Governance Evren Eryurek, Uri Gilad, Jessi Ashdown, Valliappa Lakshmanan, Anita Kibunguchy, 2021-04-13 As your company moves data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure you meet compliance. Data governance incorporates the ways that people, processes, and technology work together to support business efficiency. With this practical guide, chief information, data, and security officers will learn how to effectively implement and scale data governance throughout their organizations. You'll explore how to create a strategy and tooling to support the democratization of data and governance principles. Through good data governance, you can inspire customer trust, enable your organization to extract more value from data, and generate more-competitive offerings and improvements in customer experience. This book shows you how. Enable auditable legal and regulatory compliance with defined and agreed-upon data policies Employ better risk management Establish control and maintain visibility into your company's data assets, providing a competitive advantage Drive top-line revenue and cost savings when developing new products and services Implement your organization's people, processes, and tools to operationalize data trustworthiness |
| data management standards and guidelines: Data Governance and Compliance Rupa Mahanti, 2021-04-27 This book sets the stage of the evolution of corporate governance, laws and regulations, other forms of governance, and the interaction between data governance and other corporate governance sub-disciplines. Given the continuously evolving and complex regulatory landscape and the growing number of laws and regulations, compliance is a widely discussed issue in the field of data. This book considers the cost of non-compliance bringing in examples from different industries of instances in which companies failed to comply with rules, regulations, and other legal obligations, and goes on to explain how data governance helps in avoiding such pitfalls. The first in a three-volume series on data governance, this book does not assume any prior or specialist knowledge in data governance and will be highly beneficial for IT, management and law students, academics, information management and business professionals, and researchers to enhance their knowledge and get guidance in managing their own data governance projects from a governance and compliance perspective. |
| data management standards and guidelines: Quality Management and Accreditation in Hematopoietic Stem Cell Transplantation and Cellular Therapy Mahmoud Aljurf, John A. Snowden, Patrick Hayden, Kim H. Orchard, Eoin McGrath, 2021-02-19 This open access book provides a concise yet comprehensive overview on how to build a quality management program for hematopoietic stem cell transplantation (HSCT) and cellular therapy. The text reviews all the essential steps and elements necessary for establishing a quality management program and achieving accreditation in HSCT and cellular therapy. Specific areas of focus include document development and implementation, audits and validation, performance measurement, writing a quality management plan, the accreditation process, data management, and maintaining a quality management program. Written by experts in the field, Quality Management and Accreditation in Hematopoietic Stem Cell Transplantation and Cellular Therapy: A Practical Guide is a valuable resource for physicians, healthcare professionals, and laboratory staff involved in the creation and maintenance of a state-of-the-art HSCT and cellular therapy program. |
| data management standards and guidelines: Super Charge Your Data Warehouse Dan Linstedt, 2011-11-11 Do You Know If Your Data Warehouse Flexible, Scalable, Secure and Will It Stand The Test Of Time And Avoid Being Part Of The Dreaded Life Cycle? The Data Vault took the Data Warehouse world by storm when it was released in 2001. Some of the world's largest and most complex data warehouse situations understood the value it gave especially with the capabilities of unlimited scaling, flexibility and security. Here is what industry leaders say about the Data Vault The Data Vault is the optimal choice for modeling the EDW in the DW 2.0 framework - Bill Inmon, The Father of Data Warehousing The Data Vault is foundationally strong and an exceptionally scalable architecture - Stephen Brobst, CTO, Teradata The Data Vault should be considered as a potential standard for RDBMS-based analytic data management by organizations looking to achieve a high degree of flexibility, performance and openness - Doug Laney, Deloitte Analytics Institute I applaud Dan's contribution to the body of Business Intelligence and Data Warehousing knowledge and recommend this book be read by both data professionals and end users - Howard Dresner, From the Foreword - Speaker, Author, Leading Research Analyst and Advisor You have in your hands the work, experience and testing of 2 decades of building data warehouses. The Data Vault model and methodology has proven itself in hundreds (perhaps thousands) of solutions in Insurance, Crime-Fighting, Defense, Retail, Finance, Banking, Power, Energy, Education, High-Tech and many more. Learn the techniques and implement them and learn how to build your Data Warehouse faster than you have ever done before while designing it to grow and scale no matter what you throw at it. Ready to Super Charge Your Data Warehouse? |
| data management standards and guidelines: Fundamentals of Clinical Data Science Pieter Kubben, Michel Dumontier, Andre Dekker, 2018-12-21 This open access book comprehensively covers the fundamentals of clinical data science, focusing on data collection, modelling and clinical applications. Topics covered in the first section on data collection include: data sources, data at scale (big data), data stewardship (FAIR data) and related privacy concerns. Aspects of predictive modelling using techniques such as classification, regression or clustering, and prediction model validation will be covered in the second section. The third section covers aspects of (mobile) clinical decision support systems, operational excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants intending to develop and refine their skills in personalized medicine, using solutions based on large datasets from electronic health records or telemonitoring programmes. The book’s promise is “no math, no code”and will explain the topics in a style that is optimized for a healthcare audience. |
| data management standards and guidelines: Practical Guide to Clinical Data Management, Third Edition Susanne Prokscha, 2011-10-26 The management of clinical data, from its collection during a trial to its extraction for analysis, has become a critical element in the steps to prepare a regulatory submission and to obtain approval to market a treatment. Groundbreaking on its initial publication nearly fourteen years ago, and evolving with the field in each iteration since then, the third edition of Practical Guide to Clinical Data Management includes important updates to all chapters to reflect the current industry approach to using electronic data capture (EDC) for most studies. See what’s new in the Third Edition: A chapter on the clinical trial process that explains the high level flow of a clinical trial from creation of the protocol through the study lock and provides the context for the clinical data management activities that follow Reorganized content reflects an industry trend that divides training and standard operating procedures for clinical data management into the categories of study startup, study conduct, and study closeout Coverage of current industry and Food and Drug Administration (FDA) approaches and concerns The book provides a comprehensive overview of the tasks involved in clinical data management and the computer systems used to perform those tasks. It also details the context of regulations that guide how those systems are used and how those regulations are applied to their installation and maintenance. Keeping the coverage practical rather than academic, the author hones in on the most critical information that impacts clinical trial conduct, providing a full end-to-end overview or introduction for clinical data managers. |
| data management standards and guidelines: NoSQL Distilled Pramod J. Sadalage, Martin Fowler, 2013 'NoSQL Distilled' is designed to provide you with enough background on how NoSQL databases work, so that you can choose the right data store without having to trawl the whole web to do it. It won't answer your questions definitively, but it should narrow down the range of options you have to consider. |
| data management standards and guidelines: 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 management standards and guidelines: Federal Information Processing Standards Publication , 1979 |
| data management standards and guidelines: Data Governance Dimitrios Sargiotis, |
| data management standards and guidelines: Data Quality Rupa Mahanti, 2019-03-18 This is not the kind of book that youll read one time and be done with. So scan it quickly the first time through to get an idea of its breadth. Then dig in on one topic of special importance to your work. Finally, use it as a reference to guide your next steps, learn details, and broaden your perspective. from the foreword by Thomas C. Redman, Ph.D., the Data Doc Good data is a source of myriad opportunities, while bad data is a tremendous burden. Companies that manage their data effectively are able to achieve a competitive advantage in the marketplace, while bad data, like cancer, can weaken and kill an organization. In this comprehensive book, Rupa Mahanti provides guidance on the different aspects of data quality with the aim to be able to improve data quality. Specifically, the book addresses: -Causes of bad data quality, bad data quality impacts, and importance of data quality to justify the case for data quality-Butterfly effect of data quality-A detailed description of data quality dimensions and their measurement-Data quality strategy approach-Six Sigma - DMAIC approach to data quality-Data quality management techniques-Data quality in relation to data initiatives like data migration, MDM, data governance, etc.-Data quality myths, challenges, and critical success factorsStudents, academicians, professionals, and researchers can all use the content in this book to further their knowledge and get guidance on their own specific projects. It balances technical details (for example, SQL statements, relational database components, data quality dimensions measurements) and higher-level qualitative discussions (cost of data quality, data quality strategy, data quality maturity, the case made for data quality, and so on) with case studies, illustrations, and real-world examples throughout. |
| data management standards and guidelines: A Librarian's Guide to ISO Standards for Information Governance, Privacy, and Security Phyllis L. Elin, Max Rapaport, 2023-11-20 This book was written to demystify critical standards related to information security, records management privacy information management for the modern librarian and archival professional. In the digital age, librarians and archival professionals play a crucial role in safeguarding the world's knowledge. A Librarian's Guide to ISO Standards for Information Governance, Privacy, and Security is a curated resource for librarians, presenting core ISO standards related to information governance, data privacy, and security. The book provides detailed summaries of these standards, along with case studies and advice on applying them in the modern digital age. It empowers library staff and patrons to prioritize data security and privacy, ensuring trust and confidentiality in their services. The purpose is to demystify critical standards related to information security, records management privacy information management for the modern librarian and archival professional. Inside, you will find detailed summaries of the core ISO standards, descriptions, and case studies illustrating how these standards can apply to librarians in the modern digital age, advice on how to cultivate a culture of data security, and privacy awareness among library staff and patrons. |
| data management standards and guidelines: Practical Guide to Clinical Data Management Susanne Prokscha, 2011-10-26 The management of clinical data, from its collection during a trial to its extraction for analysis, has become a critical element in the steps to prepare a regulatory submission and to obtain approval to market a treatment. Groundbreaking on its initial publication nearly fourteen years ago, and evolving with the field in each iteration since then, |
| data management standards and guidelines: ITIL Intermediate Certification Companion Study Guide Helen Morris, Liz Gallacher, 2016-03-11 Complete, detailed preparation for the Intermediate ITIL Service Lifecycle exams ITIL Intermediate Certification Companion Study Guide is the ultimate supporting guide to the ITIL Service Lifecycle syllabus, with full coverage of all Intermediate ITIL Service Lifecycle exam objectives for Service Operation, Service Design, Service Transition, Continual Service Improvement, and Service Strategy. Using clear and concise language, this useful companion guides you through each Lifecycle module and each of the process areas, helping you understand the concepts that underlie each skill required for certification. Illustrative examples demonstrate how these skills are applied in real-life scenarios, helping you realize the importance of what you're learning each step of the way. Additional coverage includes service strategy principles and processes, governance, organization, implementation, and technology considerations, plus guidance toward common challenges and risks. ITIL is the most widely adopted approach for IT Service Management in the world, providing a practical, no-nonsense framework for identifying, planning, delivering, and supporting IT services to businesses. This study guide is the ultimate companion for certification candidates, giving you everything you need to know in a single informative volume. Review the information needed for all five Lifecycle exams Examine real-life examples of how these concepts are applied Gain a deeper understanding of each of the process areas Learn more about governance, organization, implementation, and more The Intermediate ITIL Service Lifecycle exams expect you to demonstrate thorough knowledge of the concepts, processes, and functions related to the modules. The certification is recognized around the world as the de facto standard for IT Service Management, and the skills it requires increase your value to any business. For complete, detailed exam preparation, ITIL Certification Companion Study Guide for the Intermediate ITIL Service Lifecycle Exams is an invaluably effective tool. |
| data management standards and guidelines: Practical Guide to Clinical Data Management Susanne Prokscha, 2006-08-01 The management of clinical data, from its collection to its extraction for analysis, has become a critical element in the steps to prepare a regulatory submission and to obtain approval to market a treatment. As its importance has grown, clinical data management (CDM) has changed from an essentially clerical task in the late 1970s and early 1980s t |
| data management standards and guidelines: A Guide to GCP for Clinical Data Management MARK. ELSLEY, 2017 |
| data management standards and guidelines: Tenth Periodic Monitoring Report On The Status Of Management Implementation Plans In Response To Board-Endorsed IEO Recommendations International Monetary Fund, 2020-03-04 The Tenth Periodic Monitoring Report (PMR) on the Status of Management Implementation Plans (MIPs) in Response to Board-Endorsed Independent Evaluation Office (IEO) Recommendations assesses the progress made over the last year on actions contained in 10 MIPs with open management actions. |
| data management standards and guidelines: Encyclopedia of Public Health Wilhelm Kirch, 2008-06-13 The Encyclopedic Reference of Public Health presents the most important definitions, principles and general perspectives of public health, written by experts of the different fields. The work includes more than 2,500 alphabetical entries. Entries comprise review-style articles, detailed essays and short definitions. Numerous figures and tables enhance understanding of this little-understood topic. Solidly structured and inclusive, this two-volume reference is an invaluable tool for clinical scientists and practitioners in academia, health care and industry, as well as students, teachers and interested laypersons. |
| data management standards and guidelines: CLOUD COMPUTING ARCHITECTURE (DESIGN, IMPLEMENTATION, AND SECURITY STRATEGIES) ASHISH KUMAR SHYAMAKRISHNA SIDDHARTH CHAMARTHY RAMYA RAMACHANDRAN RAGHAV AGARWAL, 2024-10-24 In the ever-evolving landscape of the modern world, the synergy between technology and management has become a cornerstone of innovation and progress. This book, Cloud Computing Architecture: Design, Implementation, and Security Strategies, is conceived to bridge the gap between emerging technological advancements in cloud computing and their strategic application in modern IT management. Our objective is to equip readers with the tools and insights necessary to excel in this dynamic intersection of fields. This book is structured to provide a comprehensive exploration of the methodologies and strategies that define the innovation of cloud technologies, particularly in terms of architecture, implementation, and security. From foundational theories to advanced applications, we delve into the critical aspects that drive successful cloud-based solutions in enterprise environments. We have made a concerted effort to present complex concepts in a clear and accessible manner, making this work suitable for a diverse audience, including students, IT managers, and industry professionals. In authoring this book, we have drawn upon the latest research and best practices to ensure that readers not only gain a robust theoretical understanding but also acquire practical skills that can be applied in real-world cloud computing scenarios. The chapters are designed to strike a balance between depth and breadth, covering topics ranging from technological development and cloud architecture design to the strategic management of security in cloud-based systems. Additionally, we emphasize the importance of effective communication, dedicating sections to the art of presenting innovative ideas and solutions in a precise and academically rigorous manner. The inspiration for this book arises from a recognition of the crucial role that cloud computing architecture and security strategies play in shaping the future of digital businesses. We are profoundly grateful to Chancellor Shri Shiv Kumar Gupta of Maharaja Agrasen Himalayan Garhwal University for his unwavering support and vision. His dedication to fostering academic excellence and promoting a culture of innovation has been instrumental in bringing this project to fruition. We hope this book will serve as a valuable resource and inspiration for those eager to deepen their understanding of how cloud computing technologies and management practices can be harnessed together to drive innovation. We believe that the knowledge and insights contained within these pages will empower readers to lead the way in creating secure, scalable cloud solutions that will define the future of enterprise IT. Thank you for joining us on this journey. Authors |
| data management standards and guidelines: Entity Information Life Cycle for Big Data John R. Talburt, Yinle Zhou, 2015-04-20 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 |
| data management standards and guidelines: VA Catalog of Recurring Publication Code Numbers United States. Department of Veterans Affairs, 1994 |
| data management standards and guidelines: Encyclopedia of Information Assurance - 4 Volume Set (Print) Rebecca Herold, Marcus K. Rogers, 2010-12-22 Charged with ensuring the confidentiality, integrity, availability, and delivery of all forms of an entity's information, Information Assurance (IA) professionals require a fundamental understanding of a wide range of specializations, including digital forensics, fraud examination, systems engineering, security risk management, privacy, and compliance. Establishing this understanding and keeping it up to date requires a resource with coverage as diverse as the field it covers. Filling this need, the Encyclopedia of Information Assurance presents an up-to-date collection of peer-reviewed articles and references written by authorities in their fields. From risk management and privacy to auditing and compliance, the encyclopedia’s four volumes provide comprehensive coverage of the key topics related to information assurance. This complete IA resource: Supplies the understanding needed to help prevent the misuse of sensitive information Explains how to maintain the integrity of critical systems Details effective tools, techniques, and methods for protecting personal and corporate data against the latest threats Provides valuable examples, case studies, and discussions on how to address common and emerging IA challenges Placing the wisdom of leading researchers and practitioners at your fingertips, this authoritative reference provides the knowledge and insight needed to avoid common pitfalls and stay one step ahead of evolving threats. Also Available Online This Taylor & Francis encyclopedia is also available through online subscription, offering a variety of extra benefits for researchers, students, and librarians, including: Citation tracking and alerts Active reference linking Saved searches and marked lists HTML and PDF format options Contact Taylor and Francis for more information or to inquire about subscription options and print/online combination packages. US: (Tel) 1.888.318.2367; (E-mail) e-reference@taylorandfrancis.com International: (Tel) +44 (0) 20 7017 6062; (E-mail) online.sales@tandf.co.uk |
| data management standards and guidelines: The Medical Library Association Guide to Data Management for Librarians Lisa Federer, 2016-09-15 Technological advances and the rise of collaborative, interdisciplinary approaches have changed the practice of research. The 21st century researcher not only faces the challenge of managing increasingly complex datasets, but also new data sharing requirements from funders and journals. Success in today’s research enterprise requires an understanding of how to work effectively with data, yet most researchers have never had any formal training in data management. Libraries have begun developing services and programs to help researchers meet the demands of the data-driven research enterprise, giving librarians exciting new opportunities to use their expertise and skills. The Medical Library Association Guide to Data Management for Librarians highlights the many ways that librarians are addressing researchers’ changing needs at a variety of institutions, including academic, hospital, and government libraries. Each chapter ends with “pearls of wisdom,” a bulleted list of 5-10 takeaway messages from the chapter that will help readers quickly put the ideas from the chapter into practice. From theoretical foundations to practical applications, this book provides a background for librarians who are new to data management as well as new ideas and approaches for experienced data librarians. |
| data management standards and guidelines: 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 management standards and guidelines: Data Driven Thomas C. Redman, 2008-09-22 Your company's data has the potential to add enormous value to every facet of the organization -- from marketing and new product development to strategy to financial management. Yet if your company is like most, it's not using its data to create strategic advantage. Data sits around unused -- or incorrect data fouls up operations and decision making. In Data Driven, Thomas Redman, the Data Doc, shows how to leverage and deploy data to sharpen your company's competitive edge and enhance its profitability. The author reveals: · The special properties that make data such a powerful asset · The hidden costs of flawed, outdated, or otherwise poor-quality data · How to improve data quality for competitive advantage · Strategies for exploiting your data to make better business decisions · The many ways to bring data to market · Ideas for dealing with political struggles over data and concerns about privacy rights Your company's data is a key business asset, and you need to manage it aggressively and professionally. Whether you're a top executive, an aspiring leader, or a product-line manager, this eye-opening book provides the tools and thinking you need to do that. |
Data Management Operating Procedures and Guidelines
Update DM OP-006, OP-034, and G-007 to capture new procedures for reusing and updating the Enterprise Logical Data Model (ELDM). Restructure the DM OP&G document so that each …
Federal Data Strategy Data Governance Playbook
• Data Management Policy – Develop short statements of management intent and fundamental rules for governing the creation, acquisition, privacy, integrity, security, quality, and use of data …
Data Classification Concepts and Considerations for …
8 data protection approaches by becoming more aware of data classification considerations and 9 taking them into account in business and mission use cases, such as secure data sharing, 10 …
Data Integrity and Compliance With CGMP Guidance for …
How does FDA recommend data integrity problems identified during inspections, in warning letters, or in other regulatory actions be addressed?...
Developing Effective Data Policies and Processes
Effective data governance depends on well-developed, documented, and fully implemented policies and processes. Data policies and processes direct all aspects of information asset …
DISA Data Governance Bylaws & Guidelines
Apr 30, 2025 · privacy and data standards. 2.5.2 Data Use and Analytics Outcomes ... Following established DISA, DoD and federal records management guidelines, DISA identifies data …
ENTERPRISE DATA MANAGEMENT POLICY - Homeland Security
Ensure Component compliance with DHS Enterprise Architecture, Systems Engineering Life Cycle (SELC), and enterprise data management policies and enterprise data standards.
Data governance and data policies - European Commission
Data governance sets out a framework with clear roles, and the responsibilities and interdependencies of those roles. Data policies introduce common principles, guidance and …
Data Management Best Practices - Smithsonian Libraries
These best practices are designed to improve overall management of data at each point in the lifecycle, resulting in published data that are not only easy to care for long after the project is …
Guidelines for Best Practices in Data Management – Roles and …
Understanding and implementing the roles in these Guidelines will clarify business decision‐making and encourage information sharing and integration.
Data Management Plan Guidelines and Template 1.
The Data Management Plan should help researchers manage their data during the full life cycle of a research project. This data management plan should be updated before, during and after the …
ABU DHABI GOVERNMENT DATA MANAGEMENT STANDARDS
Government has developed a core set of standards for data management based on the following principles: 1. Data shall be owned : all information used to enable the Entity’s business must …
12. Data Management
It involves the implementation of data quality standards and procedures to address and improve the accuracy, completeness, timeliness, relevance, consistency and reliability of the data.
Data Management Operating Procedures and Guidelines
Aug 24, 2004 · Data Management Operating Procedures and Guidelines The full text of operating procedures, guidelines, and standards, including their underlying rationale , and level of …
Clinical trial data management technology Guide - CDISC
relevant laws and regulations and guidelines provide specific evidence and guidance for the standardization and regulation of clinical trial data management. Such as: (21 CFR. materials.
10 40 20 - Data.gov
will guide federal data management and use via: a mission statement, ten operating principles, and a set of 40 best practices to guide agencies in leveraging the value of federal and …
Smithsonian Data Management Best Practices - Smithsonian …
Mar 1, 2021 · Any plan should at a minimum answer the following questions in bold for each stage in the data management lifecycle. More specific guidance on best practices in the data …
Research Data Management Principles and Guidance Yale …
Jun 7, 2018 · data management, a description of relevant University policies, rules, and expectations, and guidance to Yale researchers about available university resources. It …
Data Management Guidelines for Researchers - FutureTDM
These guidelines are intended to give an introduction to the principles of data management. They are aimed primarily at academic researchers who collect, create, store and share data, to give …
Guidelines for Media Sanitization - NIST
Federal Information Security Management Act of 2002 (FISMA), 44 U.S.C. § 3541 , Public Law et seq. 107-347. NIST is responsible for developing information security standards and …
Guidelines for Data Management Standards in Routine …
Guidelines for RHIS Data Management Standards ix SDMX Statistical Data and Metadata Exchange SMART specific, measurable, agreed-upon, realistic, time-bound SNNPR Southern …
Data Standards Document - dmo.ksu.edu.sa
The National Data Management Office has defined Data Management Guiding Principles anchoring the development of the National Data Management and Personal Data Protection …
Data Management Policy - City and County of San Francisco
Proper interdepartmental data management can unlock value and control costs for the City. Where data can serve multiple programs, initiatives or client groups across more than one …
Guidelines for Data Management Standards in Routine …
Guidelines for RHIS Data Management Standards ix SDMX Statistical Data and Metadata Exchange SMART specific, measurable, agreed-upon, realistic, time-bound SNNPR Southern …
ABU DHABI GOVERNMENT DATA MANAGEMENT POLICY
The Data Management Policy is supported by additional publications – most notably, the Abu Dhabi Government Data Management Standards – together with various other guides, …
ANSI/BICSI 002-2019,
ANSI/BICSI 002-2019, Data Center Design and Implementation Best Practices The foundation standard for data center design around the world, ANSI/BICSI 002-2019 continues its mission …
12. Data Management
Data Management Where data governance sets the rules of engagement for how data-related decisions are ... It involves the implementation of data quality standards and procedures to …
Annex 4 - World Health Organization
4.4. Data governance should ensure the application of ALCOA+ principles. 4.5. Senior management is responsible for providing the environment to establish, maintain and continually …
Best Practices on Digital Data Management 21 CFR Part 11 …
2 Digital Data and Content Management Best Practices on Digital Data Management 03 In Digital world, data is important asset to any organization. Data can be used to make informed …
Security and Privacy Controls for Information Systems and
data, proof of concept implementations, and technical analyses to advance the development and productive use of information technology (IT). ITL’s responsibilities include the development of …
Management of patient information - World Health …
Guidelines documents 48 Standards for indicators used to monitor health and health systems 49 Standards to identify patients 49 Individual patient data standards 49 Vocabulary standards 49 …
Intelligence Community Data Management
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Data Management Standard - ITS Office of Information …
the effectiveness of University data management practices. 3.1.2 Data Management Group The data management group is responsible for establishing operational practices and initiatives …
GUIDELINES FOR THE MANAGEMENT OF PERSONAL DATA …
his judgment or advice to the institution on data protection management; and iii. be trained in the general principles and management of personal data within 90 days of appointment. b. The …
Data Quality Guideline - Victorian Government
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Withdrawn NIST Technical Series Publication
Information Security Management Act (FISMA), Public Law (P.L.) 107-347. NIST is responsible for developing information security standards and guidelines, including minimum requirements for …
Data Entry Standards - Northwestern State University
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GUIDELINES FOR THE IMPLEMENTATION OF THE ABU DHABI …
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Guidelines on Data Analytics - Comptroller and Auditor …
for Data Management and Analytics was the first step in establishing this framework. The Guidelines for Data Analytics is a major initiative in institutionalising the practice ... Compliance …
Washington, DC 20420 Transmittal Sheet December 8
Jan 3, 2021 · standards including compliance with data quality and security requirements. d. All data in the Department shall be available for responsible sharing as provided ... organization, …
(October 2019) - World Health Organization
4.8. The data governance programme should include policies and procedures addressing data management. Elements of effective management governance should include: •management …
Data management Policy - QH-POL-279:2014 - Queensland …
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The Ultimate Guide to Data Center Compliance Standards
Compliance Standards Data center certi˜cation and compliance standards are de˜ned to reduce client risk and set a baseline for data center and cloud operations. Standards help ensure that …
IT Asset Management - NIST
The security characteristics in our IT asset management platform are derived from the best practices of standards organizations, including the Payment Card Industry Data Security …
Federal Information Processing Standards Publication: …
Specifications. Federal Information Processing Standard 31 (FIPS 31), Guidelines for Automatic Data Processing Physical Security and Risk Management, (affixed). Qualifications. The …
Institutional Data Dictionary Technical Standards and …
standards and guidelines. 1.2 Rationale The drive behind development of these standards and guidelines is to ensure data are collected in a comprehensive and consistent way as well as …
NIST SP 800-88, Guidelines for Media Santifization
test methods, reference data, proof of concept implementations and technical analysis to advance the development and productive use of information technology. ITL’s responsibilities include …
E2D(R1) POST-APPROVAL SAFETY DATA: DEFINITIONS AND …
7 on pre-approval safety data management), with consideration as to how the terms and definitions 8 should be applied in the post-approval phase of the product life cycle. Detailed …
Information security handbook: a guide for managers - NIST
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Department of Defense Chief Data Officer
policies around data management. These policies govern data management across the entire data lifecycle (from origination to disposition) and cover all types of data regardless of purpose …
Guidance for Review of Data Management Plans - National …
conduct of scientific research submit a data management plan (DMP) that includes, at a minimum: 1. a summary of activities that generate data 2. a summary of the types of data generated by …
The purpose of this document is to provide an overview of …
Federal Information Processing Standards (FIPS) Under the Information Technology Management Reform Act (Public Law 104-106), the Secretary of Commerce approves standards and …
Guidelines on security and privacy in public cloud computing
management standards and guidelines for the cost-effective security and privacy of sensitive unclassified information in Federal computer systems. This Special Publication discusses ITL’s …
Management of Data and Information in Research
data and findings’, which requires researchers to share and communicate research methodology, data and findings openly, responsibly and accurately. • Principle 7, ‘Accountability for the …
Education Management Information Systems - UNESCO
The first International Conference on Education Management Information Systems (EMIS), which was hosted by UNESCO and the Global Partnership for Education (GPE) in April 2018, led to …
Cyber Security Standards - NIST
Standards can be contrasted with another category of documents, generally referred to as guidelines. Both standards and guidelines provide guidance aimed at enhancing cyber …
Federal Zero Trust Data Security Guide - cio.gov
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Data Strategy for the U.S. Department of Justice
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GUIDELINES - kazi.go.tz
place to ensure availability of data. PART II DATA MANAGEMENT Data management policy 5. Every Scheme shall formulate and implement written data management policies and practices …
Data Quality Management In Clinical Research - National …
data and records. These standards are used by institutions, sponsors and regulatory agencies, such as the FDA, to monitor the conduct of research and data collected on trials. ... to know: …
GCF Data Standards - GOV.UK
The data dictionary provides useful guidelines for the users of the data both with regards to correctly entering data and on how to interpret it, and is a key reference point and driving force …
EQRS Data Management Guidelines - My CROWN Web
Sep 12, 2023 · EQRS Data Management Guidelines Page| 6 . EQRS Data Management Guidelines . In 2021, CMS implemented a security measure which disables the EQRS user …
Standards and Guidelines for Information Technology …
Feb 12, 2007 · management policy. This policy establishes uniform data management standards, identifies the shared responsibilities for assuring data integrity, and works to ensure that data …
CPG 235 – Managing Data Risk - Australian Prudential …
existing industry standards and guidelines. Subject to meeting APRA’s prudential requirements, a ... The management of data and associated risks is important for a broad range of business …
Data Guidelines for Produce Markets in Regional Food Systems
developed an extensive set of data standards and guidelines that was released in 2013, the “Implementation Guide for Fresh Produce Data Standards and Synchronization”. This work …
Data for health and sustainable development - Health Data …
Guidelines for Data Management Standards in Routine Health Information Systems . This guide has been supported by the President’s Emergency Plan for AIDS Relief (PEPFAR) through
Identifiable Information (PII) - NIST
methods, reference data, proof of concept implementations, and technical analysis to advance the ... physical, administrative, and management standards and guidelines for the cost-effective …
Laboratory Quality Standards - World Health Organization
guidelines become a stepping-stone to achieving the ISO standards. Establishing and maintaining laboratory quality standards are essential. This document presents a minimum set of standards …
DATA MANAGEMENT MATURITY (DMM)SM - Capability …
1.1 Data management roles are established for at least one project. LeVeL 2: ManaGeD 2.1 An approved interaction and engagement model ensures that stakeholders engage with the data …
