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| data management policies examples: 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 policies examples: 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 policies examples: 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 policies examples: 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 policies examples: 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 policies examples: 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 policies examples: 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 policies examples: IT Governance: Policies and Procedures, 2023 Edition Wallace, Webber, |
| data management policies examples: Data Governance Dimitrios Sargiotis, |
| data management policies examples: 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 policies examples: 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 policies examples: 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 management policies examples: Research Data Management Joyce M. Ray, 2014 It has become increasingly accepted that important digital data must be retained and shared in order to preserve and promote knowledge, advance research in and across all disciplines of scholarly endeavor, and maximize the return on investment of public funds. To meet this challenge, colleges and universities are adding data services to existing infrastructures by drawing on the expertise of information professionals who are already involved in the acquisition, management and preservation of data in their daily jobs. Data services include planning and implementing good data management practices, thereby increasing researchers' ability to compete for grant funding and ensuring that data collections with continuing value are preserved for reuse. This volume provides a framework to guide information professionals in academic libraries, presses, and data centers through the process of managing research data from the planning stages through the life of a grant project and beyond. It illustrates principles of good practice with use-case examples and illuminates promising data service models through case studies of innovative, successful projects and collaborations. |
| data management policies examples: Managing Information in Organizations Sharon A. Cox, 2017-09-16 New core text for Managing Information modules examining the issue of information management from both a business and an IT perspective. Grounded in the theory, it takes a practical, problem-solving approach that provides students with tools and insights to understand how to formulate and implement information management strategies. |
| data management policies examples: Data Management and Statistical Analysis Techniques Ronin Myers, 2019-05-19 |
| data management policies examples: 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 policies examples: 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 policies examples: Managing Research Data Graham Pryor, 2012-01-20 This title defines what is required to achieve a culture of effective data management offering advice on the skills required, legal and contractual obligations, strategies and management plans and the data management infrastructure of specialists and services. Data management has become an essential requirement for information professionals over the last decade, particularly for those supporting the higher education research community, as more and more digital information is created and stored. As budgets shrink and funders of research demand evidence of value for money and demonstrable benefits for society, there is increasing pressure to provide plans for the sustainable management of data. Ensuring that important data remains discoverable, accessible and intelligible and is shared as part of a larger web of knowledge will mean that research has a life beyond its initial purpose and can offer real utility to the wider community. This edited collection, bringing together leading figures in the field from the UK and around the world, provides an introduction to all the key data issues facing the HE and information management communities. Each chapter covers a critical element of data management: • Why manage research data? • The lifecycle of data management • Research data policies: principles, requirements and trends • Sustainable research data • Data management plans and planning • Roles and responsibilities – libraries, librarians and data • Research data management: opportunities and challenges for HEIs • The national data centres • Contrasting national research data strategies: Australia and the USA • Emerging infrastructure and services for research data management and curation in the UK and Europe Readership: This is essential reading for librarians and information professionals working in the higher education sector, the research community, policy makers and university managers. It will also be a useful introduction for students taking courses in information management, archivists and national library services. |
| data management policies examples: 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 policies examples: Distributed Data Management for Grid Computing Michael Di Stefano, 2005-09-15 Discover grid computing-how to successfully build, implement, and manage widely distributed computing architecture With technology budgets under increasing scrutiny and system architecture becoming more and more complex, many organizations are rethinking how they manage and use technology. Keeping a strong business focus, this publication clearly demonstrates that the current ways of tying applications to dedicated hardware are no longer viable in today's competitive, bottom line-oriented environment. This evolution in distributed computing is leading a paradigm shift in leveraging widely distributed architectures to get the most processing power per IT dollar. Presenting a solid foundation of data management issues and techniques, this practical book delves into grid architecture, services, practices, and much more, including: * Why businesses should adopt grid computing * How to master the fundamental concepts and programming techniques and apply them successfully to reach objectives * How to maximize the value of existing IT investments The author has tailored this publication for two distinct audiences. Business professionals will gain a better understanding of how grid computing improves productivity and performance, what impact it can have on their organization's bottom line, and the technical foundations necessary to discuss grid computing with their IT colleagues. Following the author's expert guidance and practical examples, IT professionals, architects, and developers will be equipped to initiate and carry out successful grid computing projects within their own organizations. |
| data management policies examples: 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 policies examples: 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 policies examples: The "Orange" Model of Data Management Irina Steenbeek, 2019-10-21 *This book is a brief overview of the model and has only 24 pages.*Almost every data management professional, at some point in their career, has come across the following crucial questions:1. Which industry reference model should I use for the implementation of data managementfunctions?2. What are the key data management capabilities that are feasible and applicable to my company?3. How do I measure the maturity of the data management functions and compare that withthose of my peers in the industry4. What are the critical, logical steps in the implementation of data management?The Orange (meta)model of data management provides a collection of techniques and templates for the practical set up of data management through the design and implementation of the data and information value chain, enabled by a set of data management capabilities.This book is a toolkit for advanced data management professionals and consultants thatare involved in the data management function implementation.This book works together with the earlier published The Data Management Toolkit. The Orange model assists in specifying the feasible scope of data management capabilities, that fits company's business goals and resources. The Data Management Toolkit is a practical implementation guide of the chosen data management capabilities. |
| data management policies examples: Data Governance Ismael Caballero, Mario Piattini, 2024-01-28 This book presents a set of models, methods, and techniques that allow the successful implementation of data governance (DG) in an organization and reports real experiences of data governance in different public and private sectors. To this end, this book is composed of two parts. Part I on “Data Governance Fundamentals” begins with an introduction to the concept of data governance that stresses that DG is not primarily focused on databases, clouds, or other technologies, but that the DG framework must be understood by business users, systems personnel, and the systems themselves alike. Next, chapter 2 addresses crucial topics for DG, such as the evolution of data management in organizations, data strategy and policies, and defensive and offensive approaches to data strategy. Chapter 3 then details the central role that human resources play in DG, analysing the key responsibilities of the different DG-related roles and boards, while chapter 4 discusses the most common barriers to DG in practice. Chapter 5 summarizes the paradigm shifts in DG from control to value creation. Subsequently chapter 6 explores the needs, characteristics and key functionalities of DG tools, before this part ends with a chapter on maturity models for data governance. Part II on “Data Governance Applied” consists of five chapters which review the situation of DG in different sectors and industries. Details about DG in the banking sector, public administration, insurance companies, healthcare and telecommunications each are presented in one chapter. The book is aimed at academics, researchers and practitioners (especially CIOs, Data Governors, or Data Stewards) involved in DG. It can also serve as a reference for courses on data governance in information systems. |
| data management policies examples: 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 policies examples: Practitioner's Guide to Operationalizing Data Governance Mary Anne Hopper, 2023-05-09 Discover what does—and doesn’t—work when designing and building a data governance program In A Practitioner’s Guide to Operationalizing Data Governance, veteran SAS and data management expert Mary Anne Hopper walks readers through the planning, design, operationalization, and maintenance of an effective data governance program. She explores the most common challenges organizations face during and after program development and offers sound, hands-on advice to meet tackle those problems head-on. Ideal for companies trying to resolve a wide variety of issues around data governance, this book: Offers a straightforward starting point for companies just beginning to think about data governance Provides solutions when company employees and leaders don’t—for whatever reason—trust the data the company has Suggests proven strategies for getting a data governance program that’s gone off the rails back on track Complete with visual examples based in real-world case studies, A Practitioner’s Guide to Operationalizing Data Governance will earn a place in the libraries of information technology executives and managers, data professionals, and project managers seeking a one-stop resource to help them deliver practical data governance solutions. |
| data management policies examples: IT Modernization using Catalogic ECX Copy Data Management and IBM Spectrum Storage Jon Tate, Christian Burns, Kamlesh Lad, Prashant Jagannathan, Peter Eicher, IBM Redbooks, 2016-04-05 Data is the currency of the new economy, and organizations are increasingly tasked with finding better ways to protect, recover, access, share, and use data. Traditional storage technologies are being stretched to the breaking point. This challenge is not because of storage hardware performance, but because management tools and techniques have not kept pace with new requirements. Primary data growth rates of 35% to 50% annually only amplify the problem. Organizations of all sizes find themselves needing to modernize their IT processes to enable critical new use cases such as storage self-service, Development and Operations (DevOps), and integration of data centers with the Cloud. They are equally challenged with improving management efficiencies for long established IT processes such as data protection, disaster recovery, reporting, and business analytics. Access to copies of data is the one common feature of all these use cases. However, the slow, manual processes common to IT organizations, including a heavy reliance on labor-intensive scripting and disparate tool sets, are no longer able to deliver the speed and agility required in today's fast-paced world. Copy Data Management (CDM) is an IT modernization technology that focuses on using existing data in a manner that is efficient, automated, scalable, and easy to use, delivering the data access that is urgently needed to meet the new use cases. Catalogic ECX, with IBM® storage, provides in-place copy data management that modernizes IT processes, enables key use cases, and does it all within existing infrastructure. This IBM Redbooks® publication shows how Catalogic Software and IBM have partnered together to create an integrated solution that addresses today's IT environment. |
| data management policies examples: Information Security Policies, Procedures, and Standards Douglas J. Landoll, 2017-03-27 Information Security Policies, Procedures, and Standards: A Practitioner's Reference gives you a blueprint on how to develop effective information security policies and procedures. It uses standards such as NIST 800-53, ISO 27001, and COBIT, and regulations such as HIPAA and PCI DSS as the foundation for the content. Highlighting key terminology, policy development concepts and methods, and suggested document structures, it includes examples, checklists, sample policies and procedures, guidelines, and a synopsis of the applicable standards. The author explains how and why procedures are developed and implemented rather than simply provide information and examples. This is an important distinction because no two organizations are exactly alike; therefore, no two sets of policies and procedures are going to be exactly alike. This approach provides the foundation and understanding you need to write effective policies, procedures, and standards clearly and concisely. Developing policies and procedures may seem to be an overwhelming task. However, by relying on the material presented in this book, adopting the policy development techniques, and examining the examples, the task will not seem so daunting. You can use the discussion material to help sell the concepts, which may be the most difficult aspect of the process. Once you have completed a policy or two, you will have the courage to take on even more tasks. Additionally, the skills you acquire will assist you in other areas of your professional and private life, such as expressing an idea clearly and concisely or creating a project plan. |
| data management policies examples: 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 policies examples: Effective Document and Data Management Bob Wiggins, 2016-04-29 Effective Document and Data Management illustrates the operational and strategic significance of how documents and data are captured, managed and utilized. Without a coherent and consistent approach the efficiency and effectiveness of the organization may be undermined by less poor management and use of its information. The third edition of the book is restructured to take this broader view and to establish an organizational context in which information is management. Along the way Bob Wiggins clarifies the distinction between information management, data management and knowledge management; helps make sense of the concept of an information life cycle to present and describe the processes and techniques of information and data management, storage and retrieval; uses worked examples to illustrate the coordinated application of data and process analysis; and provides guidance on the application of appropriate project management techniques for document and records management projects. The book will benefit a range of organizations and people, from those senior managers who need to develop coherent and consistent business and IT strategies; to information professionals, such as records managers and librarians who will gain an appreciation of the impact of the technology and of how their particular areas of expertise can best be applied; to system designers, developers and implementers and finally to users. The author can be contacted at curabyte@gmail.com for further information. |
| data management policies examples: Data Strategy in Colleges and Universities Kristina Powers, 2019-10-16 This valuable resource helps institutional leaders understand and implement a data strategy at their college or university that maximizes benefits to all creators and users of data. Exploring key considerations necessary for coordination of fragmented resources and the development of an effective, cohesive data strategy, this book brings together professionals from different higher education experiences and perspectives, including academic, administration, institutional research, information technology, and student affairs. Focusing on critical elements of data strategy and governance, each chapter in Data Strategy in Colleges and Universities helps higher education leaders address a frustrating problem with much-needed solutions for fostering a collaborative, data-driven strategy. |
| data management policies examples: Trustworthy Policies for Distributed Repositories Reagan W. Moore, Hao XU, Mike Conway, Arcot Rajasekar, Jon Crabtree, 2022-05-31 A trustworthy repository provides assurance in the form of management documents, event logs, and audit trails that digital objects are being managed correctly. The assurance includes plans for the sustainability of the repository, the accession of digital records, the management of technology evolution, and the mitigation of the risk of data loss. A detailed assessment is provided by the ISO-16363:2012 standard, Space data and information transfer systems—Audit and certification of trustworthy digital repositories. This book examines whether the ISO specification for trustworthiness can be enforced by computer actionable policies. An implementation of the policies is provided and the policies are sorted into categories for procedures to manage externally generated documents, specify repository parameters, specify preservation metadata attributes, specify audit mechanisms for all preservation actions, specify control of preservation operations, and control preservation properties as technology evolves. An application of the resulting procedures is made to enforce trustworthiness within National Science Foundation data management plans. |
| data management policies examples: Data Management for Social Scientists Nils B. Weidmann, 2023-03-09 Equips social scientists with the tools and techniques to conduct quantitative research in the age of big data. |
| data management policies examples: Introduction to Information Systems R. Kelly Rainer, Brad Prince, 2021-08-17 Introduction to Information Systems, 9th Edition teaches undergraduate business majors how to use information technology to master their current or future jobs. Students develop a working understanding of information systems and information technology and learn how to apply concepts to successfully facilitate business processes. This course demonstrates that IT is the backbone of any business, whether a student is majoring in accounting, finance, marketing, human resources, production/operations management, or MIS. |
| data management policies examples: Government 3.0 – Next Generation Government Technology Infrastructure and Services Adegboyega Ojo, Jeremy Millard, 2017-10-26 Historically, technological change has had significant effect on the locus of administrative activity, cost of carrying out administrative tasks, the skill sets needed by officials to effectively function, rules and regulations, and the types of interactions citizens have with their public authorities. Next generation Public Sector Innovation will be “Government 3.0” powered by innovations related to Open and big data, administrative and business process management, Internet-of-Things and blockchains for public sector innovation to drive improvements in service delivery, decision and policy making and resource management. This book provides fresh insights into this transformation while also examining possible negative side effects of the increasing ope nness of governments through the adoption of these new innovations. The goal is for technology policy makers to engage with the visions of Government 3.0 . Researchers should be able to critically examine some of the innovations described in the book as the basis for developing research agendas related to challenges associated with the adoption and use of some of the associated technologies. The book serves as a rich source of materials from leading experts in the field that enables Public administration practitioners to better understand how these new technologies impact traditional public administration paradigms. The book is suitable for graduate courses in Public Sector Innovation, Innovation in Public Administration, E-Government and Information Systems. Public sector technology policy makers, e-government, information systems and public administration researchers and practitioners should all benefit from reading this book. |
| data management policies examples: 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 policies examples: Data Management Margaret E. Henderson, 2016-10-25 Libraries organize information and data is information, so it is natural that librarians should help people who need to find, organize, use, or store data. Organizations need evidence for decision making; data provides that evidence. Inventors and creators build upon data collected by others. All around us, people need data. Librarians can help increase the relevance of their library to the research and education mission of their institution by learning more about data and how to manage it. Data Management will guide readers through: Understanding data management basics and best practices. Using the reference interview to help with data management Writing data management plans for grants. Starting and growing a data management service. Finding collaborators inside and outside the library. Collecting and using data in different disciplines. |
| data management policies examples: Practical Tips for Facilitating Research Moira J. Bent, 2016-03-16 This practical guide offers innovative tips and reliable best practice to enable new and experienced library and information professionals to evaluate their current provision and develop their service to meet the evolving needs of the research community. Interacting effectively with information is at the heart of all research, consequently information professionals have a key role to play in facilitating the development of researchers who are able to operate confidently and successfully in the information world. Grounded in current theory and informed by practitioners from around the world, this practical book offers a wide range of ideas and methods to assist library and information professionals in developing and managing their role in the research environment. Part of the Practical Tips for Library and Information Professionals series, the book is organised into eight sections: landscapes and models structures and strategies places and spaces library staff roles collections specific interventions in the research process or lifecycle teaching approaches information literacy skills workshops and programmes. Practical Tips for Facilitating Research will be essential reading for academic liaison librarians, research support librarians and all library and information professionals who work with research staff and students. |
| data management policies examples: Data Governance and Data Management Rupa Mahanti, 2021-09-08 This book delves into the concept of data as a critical enterprise asset needed for informed decision making, compliance, regulatory reporting and insights into trends, behaviors, performance and patterns. With good data being key to staying ahead in a competitive market, enterprises capture and store exponential volumes of data. Considering the business impact of data, there needs to be adequate management around it to derive the best value. Data governance is one of the core data management related functions. However, it is often overlooked, misunderstood or confused with other terminologies and data management functions. Given the pervasiveness of data and the importance of data, this book provides comprehensive understanding of the business drivers for data governance and benefits of data governance, the interactions of data governance function with other data management functions and various components and aspects of data governance that can be facilitated by technology and tools, the distinction between data management tools and data governance tools, the readiness checks to perform before exploring the market to purchase a data governance tool, the different aspects that must be considered when comparing and selecting the appropriate data governance technologies and tools from large number of options available in the marketplace and the different market players that provide tools for supporting data governance. This book combines the data and data governance knowledge that the author has gained over years of working in different industrial and research programs and projects associated with data, processes and technologies with unique perspectives gained through interviews with thought leaders and data experts. This book is highly beneficial for IT students, academicians, information management and business professionals and researchers to enhance their knowledge and get guidance on implementing data governance in their own data initiatives. |
| data management policies examples: 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 and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
