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| data lifecycle management framework: 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 lifecycle management framework: Guidebook for Managing Data from Emerging Technologies for Transportation Kelley Klaver Pecheux, Benjamin B. Pecheux, Gene Ledbetter, Chris Lambert (Systems consultant), 2020 With increased connectivity between vehicles, sensors, systems, shared-use transportation, and mobile devices, unexpected and unparalleled amounts of data are being added to the transportation domain at a rapid rate, and these data are too large, too varied in nature, and will change too quickly to be handled by the traditional database management systems of most transportation agencies. The TRB National Cooperative Highway Research Program's NCHRP Research Report 952: Guidebook for Managing Data from Emerging Technologies for Transportation provides guidance, tools, and a big data management framework, and it lays out a roadmap for transportation agencies on how they can begin to shift - technically, institutionally, and culturally - toward effectively managing data from emerging technologies. Modern, flexible, and scalable big data methods to manage these data need to be adopted by transportation agencies if the data are to be used to facilitate better decision-making. As many agencies are already forced to do more with less while meeting higher public expectations, continuing with traditional data management systems and practices will prove costly for agencies unable to shift. |
| data lifecycle management framework: DAMA-DMBOK Dama International, 2017 Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment. |
| data lifecycle management framework: Data Protection and Information Lifecycle Management Thomas D. Petrocelli, 2006 This book introduces Information Lifecycle Management (ILM), a powerful new strategy for managing enterprise information based on its value over time. The author explains emerging techniques for protecting storage systems and storage networks, and for integrating storage security into your overall security plan. He also presents new technical advances and opportunities to improve existing data-protection processes, including backup/restore, replication, and remote copy. |
| data lifecycle management framework: Life Cycle Management Guido Sonnemann, Manuele Margni, 2015-07-16 This book provides insight into the Life Cycle Management (LCM) concept and the progress in its implementation. LCM is a management concept applied in industrial and service sectors to improve products and services, while enhancing the overall sustainability performance of business and its value chains. In this regard, LCM is an opportunity to differentiate through sustainability performance on the market place, working with all departments of a company such as research and development, procurement and marketing, and to enhance the collaboration with stakeholders along a company’s value chain. LCM is used beyond short-term business success and aims at long-term achievements by minimizing environmental and socio-economic burden, while maximizing economic and social value. |
| data lifecycle management framework: Data Governance John Ladley, 2019-11-08 Managing data continues to grow as a necessity for modern organizations. There are seemingly infinite opportunities for organic growth, reduction of costs, and creation of new products and services. It has become apparent that none of these opportunities can happen smoothly without data governance. The cost of exponential data growth and privacy / security concerns are becoming burdensome. Organizations will encounter unexpected consequences in new sources of risk. The solution to these challenges is also data governance; ensuring balance between risk and opportunity. Data Governance, Second Edition, is for any executive, manager or data professional who needs to understand or implement a data governance program. It is required to ensure consistent, accurate and reliable data across their organization. This book offers an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable. This valuable resource provides comprehensive guidance to beginning professionals, managers or analysts looking to improve their processes, and advanced students in Data Management and related courses. With the provided framework and case studies all professionals in the data governance field will gain key insights into launching successful and money-saving data governance program. - Incorporates industry changes, lessons learned and new approaches - Explores various ways in which data analysts and managers can ensure consistent, accurate and reliable data across their organizations - Includes new case studies which detail real-world situations - Explores all of the capabilities an organization must adopt to become data driven - Provides guidance on various approaches to data governance, to determine whether an organization should be low profile, central controlled, agile, or traditional - Provides guidance on using technology and separating vendor hype from sincere delivery of necessary capabilities - Offers readers insights into how their organizations can improve the value of their data, through data quality, data strategy and data literacy - Provides up to 75% brand-new content compared to the first edition |
| data lifecycle management framework: Data Integration Life Cycle Management with SSIS Andy Leonard, 2017-11-17 Build a custom BimlExpress framework that generates dozens of SQL Server Integration Services (SSIS) packages in minutes. Use this framework to execute related SSIS packages in a single command. You will learn to configure SSIS catalog projects, manage catalog deployments, and monitor SSIS catalog execution and history. Data Integration Life Cycle Management with SSIS shows you how to bring DevOps benefits to SSIS integration projects. Practices in this book enable faster time to market, higher quality of code, and repeatable automation. Code will be created that is easier to support and maintain. The book teaches you how to more effectively manage SSIS in the enterprise environment by drawing on the art and science of modern DevOps practices. What You'll Learn Generate dozens of SSIS packages in minutes to speed your integration projects Reduce the execution of related groups of SSIS packages to a single command Successfully handle SSIS catalog deployments and their projects Monitor the execution and history of SSIS catalog projects Manage your enterprise data integration life cycle through automated tools and utilities Who This Book Is For Database professionals working with SQL Server Integration Services in enterprise environments. The book is especially useful to those readers following, or wishing to follow, DevOps practices in their use of SSIS. |
| data lifecycle management framework: Product Lifecycle Management Antti Saaksvuori, Anselmi Immonen, 2005-12-06 In today`s industrial manufacturing Product Lifecycle Management (PLM) is essential in order to cope with the challenges of more demanding global competition. New and more complex products must be introduced to markets faster than ever before. Companies form large collaborative networks, and the product process must flow flexibly across company borders. This first book on Product Lifecycle Management in English language is designed to introduce the reader to the basic terms and fundamentals of PLM and to give a solid foundation for starting a PLM development project. It gives ideas and examples how PLM can be utilized in various industries. In addition, it also offers an insight into how PLM can assist in creating new business opportunities and in making real eBusiness possible. |
| data lifecycle management framework: SAP Information Lifecycle Management Iwona Luther, Nicole Fernandes, 2020-08-27 Master SAP ILM, from retention management to lifecycle management for custom code. Follow step-by-step instructions and walk through all major functionality including policy creation, legal case management, data archiving, and more. Whether you're on SAP S/4HANA, SAP S/4HANA Cloud, or SAP ERP, you'll find the details you need to configure and use SAP ILM. Control and protect your data! Highlights include: 1) Retention management 2) GDPR 3) Data security 4) Blocking data 5) Data deletion 6) Archiving data 7) Legal case management 8) Data controller rule framework 9) Custom code data lifecycle 10) SAP S/4HANA 11) SAP S/4HANA Cloud 12) SAP ERP HCM |
| data lifecycle management framework: The DAMA Dictionary of Data Management Dama International, 2011 A glossary of over 2,000 terms which provides a common data management vocabulary for IT and Business professionals, and is a companion to the DAMA Data Management Body of Knowledge (DAMA-DMBOK). Topics include: Analytics & Data Mining Architecture Artificial Intelligence Business Analysis DAMA & Professional Development Databases & Database Design Database Administration Data Governance & Stewardship Data Management Data Modeling Data Movement & Integration Data Quality Management Data Security Management Data Warehousing & Business Intelligence Document, Record & Content Management Finance & Accounting Geospatial Data Knowledge Management Marketing & Customer Relationship Management Meta-Data Management Multi-dimensional & OLAP Normalization Object-Orientation Parallel Database Processing Planning Process Management Project Management Reference & Master Data Management Semantic Modeling Software Development Standards Organizations Structured Query Language (SQL) XML Development |
| data lifecycle management framework: Digital Transformation of the Design, Construction and Management Processes of the Built Environment Bruno Daniotti, Marco Gianinetto, Stefano Della Torre, 2019-12-30 This open access book focuses on the development of methods, interoperable and integrated ICT tools, and survey techniques for optimal management of the building process. The construction sector is facing an increasing demand for major innovations in terms of digital dematerialization and technologies such as the Internet of Things, big data, advanced manufacturing, robotics, 3D printing, blockchain technologies and artificial intelligence. The demand for simplification and transparency in information management and for the rationalization and optimization of very fragmented and splintered processes is a key driver for digitization. The book describes the contribution of the ABC Department of the Polytechnic University of Milan (Politecnico di Milano) to R&D activities regarding methods and ICT tools for the interoperable management of the different phases of the building process, including design, construction, and management. Informative case studies complement the theoretical discussion. The book will be of interest to all stakeholders in the building process – owners, designers, constructors, and faculty managers – as well as the research sector. |
| data lifecycle management framework: The Analytics Lifecycle Toolkit Gregory S. Nelson, 2018-03-07 An evidence-based organizational framework for exceptional analytics team results The Analytics Lifecycle Toolkit provides managers with a practical manual for integrating data management and analytic technologies into their organization. Author Gregory Nelson has encountered hundreds of unique perspectives on analytics optimization from across industries; over the years, successful strategies have proven to share certain practices, skillsets, expertise, and structural traits. In this book, he details the concepts, people and processes that contribute to exemplary results, and shares an organizational framework for analytics team functions and roles. By merging analytic culture with data and technology strategies, this framework creates understanding for analytics leaders and a toolbox for practitioners. Focused on team effectiveness and the design thinking surrounding product creation, the framework is illustrated by real-world case studies to show how effective analytics team leadership works on the ground. Tools and templates include best practices for process improvement, workforce enablement, and leadership support, while guidance includes both conceptual discussion of the analytics life cycle and detailed process descriptions. Readers will be equipped to: Master fundamental concepts and practices of the analytics life cycle Understand the knowledge domains and best practices for each stage Delve into the details of analytical team processes and process optimization Utilize a robust toolkit designed to support analytic team effectiveness The analytics life cycle includes a diverse set of considerations involving the people, processes, culture, data, and technology, and managers needing stellar analytics performance must understand their unique role in the process of winnowing the big picture down to meaningful action. The Analytics Lifecycle Toolkit provides expert perspective and much-needed insight to managers, while providing practitioners with a new set of tools for optimizing results. |
| data lifecycle management framework: The Canadian Health Information Management Lifecycle CHIMA, 2017-05-09 This HIM lifecycle resource will be useful to a wide range of jurisdictions that manage health information. The document will provide a summary of the recommended leading practices and principles related to managing health information throughout its lifecycle, regardless of the type of jurisdiction or information media. -- Publisher's website. |
| data lifecycle management framework: Managing the Dynamics of New Product Development Processes Arie Karniel, Yoram Reich, 2011-07-28 Managing the Dynamics of New-Product Development Processes merges product-based planning, process modelling, process execution, probabilistic simulations, and simulation based decision-making into one framework called the Dynamic new-Product Development Process. It provides readers with a means of improving the management of product development through enhanced methods and tools that are specifically tailored to the characteristics and challenges of such processes. It calls for a new Product Lifecycle Management paradigm of utilizing the managed product data for management of the product's development process. Within the framework, the methods used are enhanced or modified to fit the new-product development process requirements. Each specific method is exhaustively analyzed, from the basic definition of terms through a description of the state of the art of that topic and its limitations. Then, the method enhancements are illustrated by many examples, and discussed while suggesting further research directions. Finally, the enhanced methods are integrated and demonstrated by a test case. The main two methods described are the design structure matrix (DSM) and Petri nets, which are merged into a novel concept entitled DSM nets. Managing the Dynamics of New Product Development Processes provides algorithms, proofs, and practical examples that can be used for general study of the issues concerned. The main concepts presented are applicable to systems engineering and can be used by practitioners of product development processes, such as designers, product managers, and process managers, as well as developers of process management tools for systems with dynamically changing process structures. |
| data lifecycle management framework: 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 lifecycle management framework: Data Mesh Zhamak Dehghani, 2022-03-08 Many enterprises are investing in a next-generation data lake, hoping to democratize data at scale to provide business insights and ultimately make automated intelligent decisions. In this practical book, author Zhamak Dehghani reveals that, despite the time, money, and effort poured into them, data warehouses and data lakes fail when applied at the scale and speed of today's organizations. A distributed data mesh is a better choice. Dehghani guides architects, technical leaders, and decision makers on their journey from monolithic big data architecture to a sociotechnical paradigm that draws from modern distributed architecture. A data mesh considers domains as a first-class concern, applies platform thinking to create self-serve data infrastructure, treats data as a product, and introduces a federated and computational model of data governance. This book shows you why and how. Examine the current data landscape from the perspective of business and organizational needs, environmental challenges, and existing architectures Analyze the landscape's underlying characteristics and failure modes Get a complete introduction to data mesh principles and its constituents Learn how to design a data mesh architecture Move beyond a monolithic data lake to a distributed data mesh. |
| data lifecycle management framework: Beginning Application Lifecycle Management Joachim Rossberg, 2014-09-22 Beginning Application Lifecycle Management is a guide to an area of rapidly growing interest within the development community: managing the entire cycle of building software. ALM is an area that spans everything from requirements specifications to retirement of an IT-system or application. Because its techniques allow you to deal with the process of developing applications across many areas of responsibility and across many different disciplines, the benefits and effects of ALM techniques used on your project can be wide-ranging and pronounced. In this book, author Joachim Rossberg will show you what ALM is and why it matters. He will also show you how you can assess your current situation and how you can use this assessment to create the road ahead for improving or implementing your own ALM process across all of your team's development efforts. Beginning Application Lifecycle Management can be implemented on any platform. This book will use Microsoft Team Foundation Server as a foundation in many examples, but the key elements are platform independent and you'll find the book written in a platform agnostic way. In this book, you'll learn: What application lifecycle management is and why it matters. The steps necessary for implementing an ALM process. Tips and techniques you can use to gain control of your development efforts. How to implement an agile framework into your ALM process How to achieve traceability and visibility in your projects How to automate your ALM process |
| data lifecycle management framework: 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 lifecycle management framework: Knowledge Sharing in the Integrated Enterprise Peter Bernus, Mark Fox, 2006-03-09 Enterprise Architects, in their endeavor to achieve Enterprise Integration, have limited guidance on how best to use Enterprise Models and Modeling Tools to support their practice. It is widely recognized that the practice of engineering enterprises needs a number of models, but how to maintain the relation between these models with ease is still a problem. Model interoperability is an issue on multiople counts: - How to interchange models between enterprise modeling tools? - How to maintain the interdependencies between models - whether they describe the enterprise on the same level (but from different points of view), or from the same point of view (but on different levels of abstraction and granularity)? - How to maintain a coherent and evolving set of enterprise models in support onf continuous change processes? - How to use and reuse enterprise models as a knowledge resource? The answers to these questions are of great importance to anyone who is implementing ISO9001:2000 requirements, whether through using enterprise architecture practice or not - although it can be argued that a well executed architecture practice should satisfy ISO9001 without additional effort. This volume attacks the problem on three fronts: 1. Authors working in international standardisation and tool development as well as in enterprise modeling research present the latest developments in semantic integration; 2. Authors who are practitioners of, or conducting active research in, enterprise architecting methodologies give an account on the latest developments and strategic directions in architecture frameworks and methodologies; 3. Authors who use or develop information integration infrastructures present best practice and future trends of this aspect of enterprise integration. Chapters of this book include contributions to the International Conference on Enterprise Integration and Modelling Technology (ICEIMT'04), and those presented at the Design of Information Infrastructure Systems for Manufacturing (DIISM'04) Workshop. While DIISM is traditionally oriented at supporting manufacturing practice, the results have a far greater domain of applicability. |
| data lifecycle management framework: Executing Data Quality Projects Danette McGilvray, 2021-05-27 Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today's data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization's standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before. - Includes concrete instructions, numerous templates, and practical advice for executing every step of The Ten Steps approach - Contains real examples from around the world, gleaned from the author's consulting practice and from those who implemented based on her training courses and the earlier edition of the book - Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices - A companion Web site includes links to numerous data quality resources, including many of the templates featured in the text, quick summaries of key ideas from the Ten Steps methodology, and other tools and information that are available online |
| data lifecycle management framework: Product Lifecycle Management for a Global Market Shuichi Fukuda, Alain Bernard, Balan Gurumoorthy, Abdelaziz Bouras, 2014-12-17 This book constitutes the refereed post-proceedings of the 11th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2014, held in Yokohama, Japan, in July 2014. The 51 full papers presented were carefully reviewed and selected from 77 submissions. They are organized in the following topical sections: BIM operations, maintenance, and renovation; BIM concepts and lifecycle management; design and education; naval engineering and shipbuilding; aeronautical and automotive engineering; industry and consumer products; interoperability, integration, configuration, systems engineering; change management and maturity; knowledge engineering; knowledge management; service and manufacturing; and new PLM. |
| data lifecycle management framework: Enterprise Master Data Management Allen Dreibelbis, Eberhard Hechler, Ivan Milman, Martin Oberhofer, Paul van Run, Dan Wolfson, 2008-06-05 The Only Complete Technical Primer for MDM Planners, Architects, and Implementers Companies moving toward flexible SOA architectures often face difficult information management and integration challenges. The master data they rely on is often stored and managed in ways that are redundant, inconsistent, inaccessible, non-standardized, and poorly governed. Using Master Data Management (MDM), organizations can regain control of their master data, improve corresponding business processes, and maximize its value in SOA environments. Enterprise Master Data Management provides an authoritative, vendor-independent MDM technical reference for practitioners: architects, technical analysts, consultants, solution designers, and senior IT decisionmakers. Written by the IBM ® data management innovators who are pioneering MDM, this book systematically introduces MDM’s key concepts and technical themes, explains its business case, and illuminates how it interrelates with and enables SOA. Drawing on their experience with cutting-edge projects, the authors introduce MDM patterns, blueprints, solutions, and best practices published nowhere else—everything you need to establish a consistent, manageable set of master data, and use it for competitive advantage. Coverage includes How MDM and SOA complement each other Using the MDM Reference Architecture to position and design MDM solutions within an enterprise Assessing the value and risks to master data and applying the right security controls Using PIM-MDM and CDI-MDM Solution Blueprints to address industry-specific information management challenges Explaining MDM patterns as enablers to accelerate consistent MDM deployments Incorporating MDM solutions into existing IT landscapes via MDM Integration Blueprints Leveraging master data as an enterprise asset—bringing people, processes, and technology together with MDM and data governance Best practices in MDM deployment, including data warehouse and SAP integration |
| data lifecycle management framework: Product Lifecycle Management: Driving the Next Generation of Lean Thinking Michael Grieves, 2005-11-16 Product Lifecycle Management (PLM) is the newest wave in productivity. This revolutionary approach is an outcome of lean thinking; however, PLM eliminates waste and efficiency across all aspects of a product's life--from design to deployment--not just in its manufacture. By using people, product information, processes, and technology to reduce wasted time, energy, and material across an organization and into the supply chain, PLM drives the next generation of lean thinking. Now PLM pioneer Michael Grieves offers everyone from Six Sigma and lean practitioners to supply chain managers, product developers, and consultants a proven framework for adopting this information-driven approach. Product Lifecycle Management shows you how to greatly enhance your firm's productivity by integrating the efforts of your entire organization. Most companies are seeing the returns of their efforts in lean methods diminishing, as the most fruitful applications have already been addressed. Here, Grieves reveals how PLM gives you an opportunity to make improvements both within and across functional areas in order to increase agility, optimize efficiency, and reduce costs across the board. He gives you the most comprehensive view of PLM available, fully outlining its characteristics, method, and tools and helping you assess your organizational readiness. There's also proven examples from the field, where PLM is being widely adopted by leading companies, including General Motors, General Electric, and Dell, that are widely adopting the approach. You'll see how PLM has saved these companies billions in unnecessary costs and shaved as much as 60% off cycle times. With this book you'll learn how to: Develop and implement your PLM strategy to support your corporate objectives Engage all your employees in using information to eliminate waste Enable improved information flow Better organize and utilize your intellectual capital Foster an environment that drives PLM Lean manufacturing can only take your organization so far. To bring your productivity to the next level and save remarkable amounts of time, money, and resources, Product Lifecycle Management is your one-stop, hands-on guide to implementing this powerful methodology. |
| data lifecycle management framework: Agile Application Lifecycle Management Bob Aiello, Leslie Sachs, 2016-06-01 Integrate Agile ALM and DevOps to Build Better Software and Systems at Lower Cost Agile Application Lifecycle Management (ALM) is a comprehensive development lifecycle that encompasses essential Agile principles and guides all activities needed to deliver successful software or other customized IT products and services. Flexible and robust, Agile ALM offers “just enough process” to get the job done efficiently and utilizes the DevOps focus on communication and collaboration to enhance interactions among all participants. Agile Application Lifecycle Management offers practical advice and strategies for implementing Agile ALM in your complex environment. Leading experts Bob Aiello and Leslie Sachs show how to fully leverage Agile benefits without sacrificing structure, traceability, or repeatability. You’ll find realistic guidance for managing source code, builds, environments, change control, releases, and more. The authors help you support Agile in organizations that maintain traditional practices, conventional ALM systems, or siloed, non-Agile teams. They also show how to scale Agile ALM across large or distributed teams and to environments ranging from cloud to mainframe. Coverage includes Understanding key concepts underlying modern application and system lifecycles Creating your best processes for developing your most complex software and systems Automating build engineering, continuous integration, and continuous delivery/deployment Enforcing Agile ALM controls without compromising productivity Creating effective IT operations that align with Agile ALM processes Gaining more value from testing and retrospectives Making ALM work in the cloud, and across the enterprise Preparing for the future of Agile ALM Today, you need maximum control, quality, and productivity, and this guide will help you achieve these capabilities by combining the best practices found in Agile ALM, Configuration Management (CM), and DevOps. |
| data lifecycle management framework: A Comprehensive Guide to the NIST Cybersecurity Framework 2.0 Jason Edwards, 2024-12-23 Learn to enhance your organization’s cybersecurit y through the NIST Cybersecurit y Framework in this invaluable and accessible guide The National Institute of Standards and Technology (NIST) Cybersecurity Framework, produced in response to a 2014 US Presidential directive, has proven essential in standardizing approaches to cybersecurity risk and producing an efficient, adaptable toolkit for meeting cyber threats. As these threats have multiplied and escalated in recent years, this framework has evolved to meet new needs and reflect new best practices, and now has an international footprint. There has never been a greater need for cybersecurity professionals to understand this framework, its applications, and its potential. A Comprehensive Guide to the NIST Cybersecurity Framework 2.0 offers a vital introduction to this NIST framework and its implementation. Highlighting significant updates from the first version of the NIST framework, it works through each of the framework’s functions in turn, in language both beginners and experienced professionals can grasp. Replete with compliance and implementation strategies, it proves indispensable for the next generation of cybersecurity professionals. A Comprehensive Guide to the NIST Cybersecurity Framework 2.0 readers will also find: Clear, jargon-free language for both beginning and advanced readers Detailed discussion of all NIST framework components, including Govern, Identify, Protect, Detect, Respond, and Recover Hundreds of actionable recommendations for immediate implementation by cybersecurity professionals at all levels A Comprehensive Guide to the NIST Cybersecurity Framework 2.0 is ideal for cybersecurity professionals, business leaders and executives, IT consultants and advisors, and students and academics focused on the study of cybersecurity, information technology, or related fields. |
| data lifecycle management framework: 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 lifecycle management framework: Big Data Management Peter Ghavami, 2020-11-09 Data analytics is core to business and decision making. The rapid increase in data volume, velocity and variety offers both opportunities and challenges. While open source solutions to store big data, like Hadoop, offer platforms for exploring value and insight from big data, they were not originally developed with data security and governance in mind. Big Data Management discusses numerous policies, strategies and recipes for managing big data. It addresses data security, privacy, controls and life cycle management offering modern principles and open source architectures for successful governance of big data. The author has collected best practices from the world’s leading organizations that have successfully implemented big data platforms. The topics discussed cover the entire data management life cycle, data quality, data stewardship, regulatory considerations, data council, architectural and operational models are presented for successful management of big data. The book is a must-read for data scientists, data engineers and corporate leaders who are implementing big data platforms in their organizations. |
| data lifecycle management framework: Navigating the Labyrinth Laura Sebastian-Coleman, An Executive Guide to Data Management |
| data lifecycle management framework: Linked Open Data -- Creating Knowledge Out of Interlinked Data Sören Auer, Volha Bryl, Sebastian Tramp, 2014-07-31 Linked Open Data (LOD) is a pragmatic approach for realizing the Semantic Web vision of making the Web a global, distributed, semantics-based information system. This book presents an overview on the results of the research project “LOD2 -- Creating Knowledge out of Interlinked Data”. LOD2 is a large-scale integrating project co-funded by the European Commission within the FP7 Information and Communication Technologies Work Program. Commencing in September 2010, this 4-year project comprised leading Linked Open Data research groups, companies, and service providers from across 11 European countries and South Korea. The aim of this project was to advance the state-of-the-art in research and development in four key areas relevant for Linked Data, namely 1. RDF data management; 2. the extraction, creation, and enrichment of structured RDF data; 3. the interlinking and fusion of Linked Data from different sources and 4. the authoring, exploration and visualization of Linked Data. |
| data lifecycle management framework: 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 lifecycle management framework: Data Governance Dimitrios Sargiotis, |
| data lifecycle management framework: Data Lineage from a Business Perspective Irina Steenbeek, 2021-10 Data lineage has become a daily demand. However, data lineage remains an abstract/ unknown concept for many users. The implementation is complex and resource-consuming. Even if implemented, it is not used as expected. This book uncovers different aspects of data lineage for data management and business professionals. It provides the definition and metamodel of data lineage, demonstrates best practices in data lineage implementation, and discusses the key areas of data lineage usage. Several groups of professionals can use this book in different ways: Data management and business professionals can develop ideas about data lineage and its application areas. Professionals with a technical background may gain a better understanding of business needs and requirements for data lineage. Project management professionals can become familiar with the best practices of data lineage implementation. |
| data lifecycle management framework: The Data Management Toolkit: A Step-By-Step Implementation Guide for the Pioneers of Data Management Irina Steenbeek, 2019-03-09 Eight years ago, I joined a new company. My first challenge was to develop an automated management accounting reporting system. A deep analysis of the existing reports showed us the high necessity to implement a singular reporting platform, and we opted to implement a data warehouse. At the time, one of the consultants came to me and said, I heard that we might need data management. I don't know what it is. Check it out. So I started Googling Data management...This book is for professionals who are now in the same position I found myself in eight years ago and for those who want to become a data management pro of a medium sized company.It is a collection of hands-on knowledge, experience and observations on how to implement data management in an effective, feasible and to-the-point way. |
| data lifecycle management framework: ADKAR Jeff Hiatt, 2006 In his first complete text on the ADKAR model, Jeff Hiatt explains the origin of the model and explores what drives each building block of ADKAR. Learn how to build awareness, create desire, develop knowledge, foster ability and reinforce changes in your organization. The ADKAR Model is changing how we think about managing the people side of change, and provides a powerful foundation to help you succeed at change. |
| data lifecycle management framework: Enterprise Interoperability III Kai Mertins, Rainer Ruggaber, Keith Popplewell, Xiaofei Xu, 2008-03-25 Interoperability: the ability of a system or a product to work with other systems or products without special effort from the user is a key issue in manufacturing and industrial enterprise generally. It is fundamental to the production of goods and services quickly and at low cost at the same time as maintaining levels of quality and customisation. Composed of over 50 papers, Enterprise Interoperability III ranges from academic research through case studies to industrial and administrative experience of interoperability. The international nature of the authorship continues to broaden. Many of the papers have examples and illustrations calculated to deepen understanding and generate new ideas. A concise reference to the state of the art in software interoperability, Enterprise Interoperability III will be of great value to engineers and computer scientists working in manufacturing and other process industries and to software engineers and electronic and manufacturing engineers working in the academic environment. |
| data lifecycle management framework: Multi-Domain Master Data Management Mark Allen, Dalton Cervo, 2015-03-21 Multi-Domain Master Data Management delivers practical guidance and specific instruction to help guide planners and practitioners through the challenges of a multi-domain master data management (MDM) implementation. Authors Mark Allen and Dalton Cervo bring their expertise to you in the only reference you need to help your organization take master data management to the next level by incorporating it across multiple domains. Written in a business friendly style with sufficient program planning guidance, this book covers a comprehensive set of topics and advanced strategies centered on the key MDM disciplines of Data Governance, Data Stewardship, Data Quality Management, Metadata Management, and Data Integration. - Provides a logical order toward planning, implementation, and ongoing management of multi-domain MDM from a program manager and data steward perspective. - Provides detailed guidance, examples and illustrations for MDM practitioners to apply these insights to their strategies, plans, and processes. - Covers advanced MDM strategy and instruction aimed at improving data quality management, lowering data maintenance costs, and reducing corporate risks by applying consistent enterprise-wide practices for the management and control of master data. |
| data lifecycle management framework: 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 lifecycle management framework: The Data Science Framework Juan J. Cuadrado-Gallego, Yuri Demchenko, 2020-10-01 This edited book first consolidates the results of the EU-funded EDISON project (Education for Data Intensive Science to Open New science frontiers), which developed training material and information to assist educators, trainers, employers, and research infrastructure managers in identifying, recruiting and inspiring the data science professionals of the future. It then deepens the presentation of the information and knowledge gained to allow for easier assimilation by the reader. The contributed chapters are presented in sequence, each chapter picking up from the end point of the previous one. After the initial book and project overview, the chapters present the relevant data science competencies and body of knowledge, the model curriculum required to teach the required foundations, profiles of professionals in this domain, and use cases and applications. The text is supported with appendices on related process models. The book can be used to develop new courses in data science, evaluate existing modules and courses, draft job descriptions, and plan and design efficient data-intensive research teams across scientific disciplines. |
| data lifecycle management framework: Master Data Management David Loshin, 2010-07-28 The key to a successful MDM initiative isn't technology or methods, it's people: the stakeholders in the organization and their complex ownership of the data that the initiative will affect.Master Data Management equips you with a deeply practical, business-focused way of thinking about MDM—an understanding that will greatly enhance your ability to communicate with stakeholders and win their support. Moreover, it will help you deserve their support: you'll master all the details involved in planning and executing an MDM project that leads to measurable improvements in business productivity and effectiveness. - Presents a comprehensive roadmap that you can adapt to any MDM project - Emphasizes the critical goal of maintaining and improving data quality - Provides guidelines for determining which data to master. - Examines special issues relating to master data metadata - Considers a range of MDM architectural styles - Covers the synchronization of master data across the application infrastructure |
| data lifecycle management framework: Building a Second Brain Tiago Forte, 2022-06-14 Building a second brain is getting things done for the digital age. It's a ... productivity method for consuming, synthesizing, and remembering the vast amount of information we take in, allowing us to become more effective and creative and harness the unprecedented amount of technology we have at our disposal-- |
DISA Data Governance Bylaws & Guidelines
Apr 30, 2025 · Executive Summary Effective Data Lifecycle Management (DLM) is crucial for DISA to optimize the value of their data assets, ensure compliance with regulatory standards, …
Cloud Data Management Capabilities Framework - EDM Council
The Data Lifecycle component is a set of capabilities for defining and applying a data lifecycle management framework and ensuring that data quality in cloud environments is managed …
DaLiF: a data lifecycle framework for data-driven governments
This data lifecycle consists of the following phases, data creation, data selec-tion, data analysis, data curation, data publishing, data discovery, data exploration, data storage, and data …
DAMA DMBOK Functonal Framework - Governance Foundation
Data Development – The data-focused activities within the system development lifecycle (SDLC), including data modeling and data requirements analysis, design, implementation and …
Intelligence Community Data Management
Establish an IC data management planning framework that includes the collecting, acquiring, creating, processing, disseminating, using, storing, and disposing of data, as well as …
Data Life Cycle Models and Concepts CEOS Version 1
Data discovery, access, use, and dissemination - Provide systems, tools, procedures, and capacity for discovery of data by specialist and non-specialist users, access to data through …
Principle Category Data Lifecycle Govern and Manage Data as …
Governance Structures: Prioritize data governance within federal agencies by establishing and supporting Data Councils/Boards to coordinate the effective development, : Maintain an …
ASEAN Data Management Framework
It is aimed at helping all businesses operating in ASEAN participate in the digital economy and practice data governance across all data types within an organisation throughout the data …
DOD Data Strategy - U.S. Department of Defense
DoD will assign data stewards, data custodians, and a set of functional data managers to achieve accountability throughout the entire data lifecycle. Data stewards establish policies...
Data Lifecycle Management Use Case - Utility - Data …
A Data Strategy that utilizes Use Case management to prioritize critical organizational data, and manages the data lifecycle from Suppliers through Consumers, is a more strategic way to …
Lifecycle Management (LCM) Framework
The Framework describes the key activities, deliverables and reviews an IT solution moves through over the course of its lifecycle. The Framework is designed to coordinate and integrate …
Management of the master data lifecycle: a framework for …
Purpose – The purpose of the paper is to propose a reference model describing a holistic view of the master data lifecycle, including strategic, tactical and operational aspects. The Master...
Data Lifecycle Management - NEC Australia
Adopting a data-driven culture means that data should be treated as a resource that must be identified, owned and managed throughout its useful lifecycle. Data Lifecycle Management …
12. Data Management
the data lifecycle. Data security and privacy measures are implemented to protect agencies’ critical, personal or otherwise sensitive data from unauthorised access and use, and ensure …
RDaF Preliminary Framework Core - National Institute of …
Lifecycle considerations: living documents or static proposals? The tactical management positioning in an organization for effective research data management throughout the research …
10 40 20 FDS - Data.gov
Assess and Balance the Needs of Stakeholders: Identify and engage stake-holders throughout the data lifecycle to identify stakeholder needs and to incorporate stakeholder feedback into …
DaLiF: a data lifecycle framework for data‐driven governments
provide data, which is complete, precise, readable, and accessible to data users. A data lifecycle provides a high-level framework to plan, organ-ize and manage all aspects of data...
Getting the Most out of IoT with an Effective Data Lifecycle …
The aim of the data lifecycle management strategy is to establish a standardized data management framework to help organizations derive maximum value from an IoT ecosystem.
NSW Infrastructure Data Management Framework (IDMF)
The State Infrastructure Strategy 2018-2038 (SIS) identified data as a key asset for the NSW Government that needs to be effectively managed across the asset lifecycle as it is critical to …
Demystifying Data Governance - PwC
PwC’s Data Governance Framework encompasses the various layers around data for an organisation, including enterprise data architecture, enterprise data management, information …
DISA Data Governance Bylaws & Guidelines
Apr 30, 2025 · Executive Summary Effective Data Lifecycle Management (DLM) is crucial for DISA to optimize the value of their data assets, ensure …
Cloud Data Management Capabilities Framework - E…
The Data Lifecycle component is a set of capabilities for defining and applying a data lifecycle management framework and ensuring that data quality in cloud …
DaLiF: a data lifecycle framework for data-driven …
This data lifecycle consists of the following phases, data creation, data selec-tion, data analysis, data curation, data publishing, data discovery, data …
DAMA DMBOK Functonal Framework - Governance F…
Data Development – The data-focused activities within the system development lifecycle (SDLC), including data modeling and data requirements …
Intelligence Community Data Management
Establish an IC data management planning framework that includes the collecting, acquiring, creating, processing, disseminating, using, …
