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| data lifecycle program management: 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 program management: 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 program management: 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 program management: Steps to Facilitate Principal-Investigator-Led Earth Science Missions National Research Council, Division on Engineering and Physical Sciences, Space Studies Board, Committee on Earth Studies, 2004-04-21 Principal-investigator (PI) Earth science missions are small, focused science projects involving relatively small spacecraft. The selected PI is responsible for the scientific and programmatic success of the entire project. A particular objective of PI-led missions has been to help develop university-based research capacity. Such missions, however, pose significant challenges that are beyond the capabilities of most universities to manage. To help NASA's Office of Earth Science determine how best to address these, the NRC carried out an assessment of key issues relevant to the success of university-based PI-led Earth observation missions. This report presents the result of that study. In particular, the report provides an analysis of opportunities to enhance such missions and recommendations about whether and, if so, how they should be used to build university-based research capabilities. |
| data lifecycle program management: 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 program management: Product Lifecycle Management John Stark, 2011-08-12 Product Lifecycle Management (2nd edition) explains what Product Lifecycle Management (PLM) is, and why it's needed. It describes the environment in which products are developed, realised and supported, before looking at the basic components of PLM, such as the product, processes, applications, and people. The final part addresses the implementation of PLM, showing the steps of a project or initiative, and typical activities. This new and expanded edition of Product Lifecycle Management is fully updated to reflect the many advances made in PLM since the release of the first edition. It includes descriptions of PLM technologies and examples of implementation projects in industry. Product Lifecycle Management will broaden the reader’s understanding of PLM, nurturing the skills needed to implement PLM successfully and to achieve world-class product performance across the lifecycle. “A 20-year veteran of PLM, I highly recommend this book. A clear and complete overview of PLM from definition to implementation. Everything is there - reasons, resources, strategy, implementation and PLM project management.” Achim Heilmann, Manager, Global Technical Publications, Varian Medical Systems “Product Lifecycle Management is an important technology for European industry. This state-of-the art book is a reference for those implementing and researching PLM.” Dr. Erastos Filos, Head of Sector Intelligent Manufacturing Systems, European Commission “This book, written by one of the best experts in this field, is an ideal complement for PLM courses at Bachelor and Master level, as well as a well-founded reference book for practitioners.” Prof. Dr.-Ing. Dr. h.c. Sandor Vajna, University of Magdeburg, Germany “This comprehensive book can help drive an understanding of PLM at all levels – from CEOs to CIOs, and from professors to students – that will help this important industry continue to expand and thrive.” James Heppelmann, President and Chief Executive Officer, PTC “PLM is a mission-critical decision-making system leveraged by the world’s most innovative companies to transform their process of innovation on a continuous basis. That is a powerful value proposition in a world where the challenge is to get better products to the market faster than ever before. That is the power of PLM.” Tony Affuso, Chairman and CEO, Siemens PLM Software |
| data lifecycle program management: 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 program management: Program Management Ginger Levin, 2016-04-19 Program management is a rapidly emerging offshoot of project management. So much so that AT&T, IBM, and other organizations, both large and small in all sectors, have initiated a push to certify program managers. And, although universities offer courses in program management, there are few books available to guide program managers through this |
| data lifecycle program management: Managing Data Science Kirill Dubovikov, 2019-11-12 Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key FeaturesLearn the basics of data science and explore its possibilities and limitationsManage data science projects and assemble teams effectively even in the most challenging situationsUnderstand management principles and approaches for data science projects to streamline the innovation processBook Description Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learnUnderstand the underlying problems of building a strong data science pipelineExplore the different tools for building and deploying data science solutionsHire, grow, and sustain a data science teamManage data science projects through all stages, from prototype to productionLearn how to use ModelOps to improve your data science pipelinesGet up to speed with the model testing techniques used in both development and production stagesWho this book is for This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book. |
| data lifecycle program management: 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 program management: Program Management Al Zeitoun, 2023-10-10 Program Management Unique and adaptable approach to program management, offering key insights needed for professionals and business leaders to drive strategic change Program Management links business purpose, strategy, program stakeholders, benefits realization, and transformative change-making to provide a uniquely integrated view and use of program management, offering practicing initiative leaders the skills and mindset shifts needed to effectively communicate and champion programs to stakeholders. The text includes key insights into strategy execution excellence and designing risk-based governance strategies that empower a learning culture within the PMO and across the business, guidance that is customizable to the nature of strategic initiatives and change efforts at the individual and organizational level, and customization that is driven by the emphasis on the potential use of programs and projects as learning labs for different levels of complexity, organizational maturity, and diverse business contexts. Written by a highly qualified author with more than 30 years of experience in the field, Program Management covers critical topics such as: Origin of programs, program management definitions and concepts, the role of program manager vs. project manager, and the importance of value focus across the program life cycle. How leaders need to be agile, navigate political waters, manage incredible complexity, and align diverse stakeholders. Envisioning a Program Roadmap that fits context and inspires commitment to continuously achieving value. The culture for change making and the attributes for a healthy “change culture” including the future Power Skills. Also addressing the value proposition of program professionals in the future. Impact of digitalization and Artificial Intelligence (AI) on the future of programs. Creating the value-driven way of working and developing the value mindset coupled with the role of benefits management in programs and projects. The PMO as the learning engine for the enterprise and the changing role of the program offices. Becoming a Change Scientist, the maturing of value and strategic metrics in programs, and how to achieve the right metrics design and mix. For program and project managers, practitioners, PMO leaders, students in project and program management courses, and those studying for project and program management certifications, Program Management is an essential mindset, skillset, and toolset for executing a strategic plan and providing synergy, consistency in managing change, and a greater focus on achieving what matters to customers and stakeholders. |
| data lifecycle program management: 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 program management: Managing Data in Motion April Reeve, 2013-02-26 Managing Data in Motion describes techniques that have been developed for significantly reducing the complexity of managing system interfaces and enabling scalable architectures. Author April Reeve brings over two decades of experience to present a vendor-neutral approach to moving data between computing environments and systems. Readers will learn the techniques, technologies, and best practices for managing the passage of data between computer systems and integrating disparate data together in an enterprise environment. The average enterprise's computing environment is comprised of hundreds to thousands computer systems that have been built, purchased, and acquired over time. The data from these various systems needs to be integrated for reporting and analysis, shared for business transaction processing, and converted from one format to another when old systems are replaced and new systems are acquired. The management of the data in motion in organizations is rapidly becoming one of the biggest concerns for business and IT management. Data warehousing and conversion, real-time data integration, and cloud and big data applications are just a few of the challenges facing organizations and businesses today. Managing Data in Motion tackles these and other topics in a style easily understood by business and IT managers as well as programmers and architects. - Presents a vendor-neutral overview of the different technologies and techniques for moving data between computer systems including the emerging solutions for unstructured as well as structured data types - Explains, in non-technical terms, the architecture and components required to perform data integration - Describes how to reduce the complexity of managing system interfaces and enable a scalable data architecture that can handle the dimensions of Big Data |
| data lifecycle program management: Pharmaceutical Lifecycle Management Tony Ellery, Neal Hansen, 2012-06-05 A comprehensive guide to optimizing the lifecycle management of pharmaceutical brands The mounting challenges posed by cost containment policies and the prevalence of generic alternatives make optimizing the lifecycle management (LCM) of brand drugs essential for pharmaceutical companies looking to maximize the value of their products. Demonstrating how different measures can be combined to create winning strategies, Pharmaceutical Lifecycle Management: Making the Most of Each and Every Brand explores this increasingly important field to help readers understand what they can—and must—do to get the most out of their brands. Offering a truly immersive introduction to LCM options for pharmaceuticals, the book incorporates numerous real-life case studies that demonstrate successful and failed lifecycle management initiatives, explaining the key takeaway of each example. Filled with practical information on the process of actually writing and presenting an LCM plan, as well as how to link corporate, portfolio, and individual brand strategies, the book also offers a look ahead to predict which LCM strategies will continue to be effective in the future. While the development of new drugs designed to address unmet patient needs remains the single most important goal of any pharmaceutical company, effective LCM is invaluable for getting the greatest possible value from existing brands. Pharmaceutical Lifecycle Management walks you through the process step by step, making it indispensable reading for pharmaceutical executives and managers, as well as anyone working in the fields of drug research, development, and regulation. |
| data lifecycle program management: 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 program management: The Data Warehouse Lifecycle Toolkit Ralph Kimball, Margy Ross, Warren Thornthwaite, Joy Mundy, Bob Becker, 2008-01-10 A thorough update to the industry standard for designing, developing, and deploying data warehouse and business intelligence systems The world of data warehousing has changed remarkably since the first edition of The Data Warehouse Lifecycle Toolkit was published in 1998. In that time, the data warehouse industry has reached full maturity and acceptance, hardware and software have made staggering advances, and the techniques promoted in the premiere edition of this book have been adopted by nearly all data warehouse vendors and practitioners. In addition, the term business intelligence emerged to reflect the mission of the data warehouse: wrangling the data out of source systems, cleaning it, and delivering it to add value to the business. Ralph Kimball and his colleagues have refined the original set of Lifecycle methods and techniques based on their consulting and training experience. The authors understand first-hand that a data warehousing/business intelligence (DW/BI) system needs to change as fast as its surrounding organization evolves. To that end, they walk you through the detailed steps of designing, developing, and deploying a DW/BI system. You'll learn to create adaptable systems that deliver data and analyses to business users so they can make better business decisions. |
| data lifecycle program management: 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 program management: Data Warehousing in the Age of Big Data Krish Krishnan, 2013-05-02 Data Warehousing in the Age of the Big Data will help you and your organization make the most of unstructured data with your existing data warehouse. As Big Data continues to revolutionize how we use data, it doesn't have to create more confusion. Expert author Krish Krishnan helps you make sense of how Big Data fits into the world of data warehousing in clear and concise detail. The book is presented in three distinct parts. Part 1 discusses Big Data, its technologies and use cases from early adopters. Part 2 addresses data warehousing, its shortcomings, and new architecture options, workloads, and integration techniques for Big Data and the data warehouse. Part 3 deals with data governance, data visualization, information life-cycle management, data scientists, and implementing a Big Data–ready data warehouse. Extensive appendixes include case studies from vendor implementations and a special segment on how we can build a healthcare information factory. Ultimately, this book will help you navigate through the complex layers of Big Data and data warehousing while providing you information on how to effectively think about using all these technologies and the architectures to design the next-generation data warehouse. - Learn how to leverage Big Data by effectively integrating it into your data warehouse. - Includes real-world examples and use cases that clearly demonstrate Hadoop, NoSQL, HBASE, Hive, and other Big Data technologies - Understand how to optimize and tune your current data warehouse infrastructure and integrate newer infrastructure matching data processing workloads and requirements |
| data lifecycle program management: 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 program management: 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 lifecycle program management: Pro Web Project Management Justin Emond, Chris Steins, 2012-01-13 Pro Web Project Management is a collection of hard-won lessons the authors have learned managing modern web projects with small and medium budgets in a consulting environment. This isn’t a book about project management theory. Pro Web Project Management tells how to create real deliverables, get answers from indecisive clients, manage wayward programmers, and use checklists to wow clients. This book is made up of real examples, real lessons, real documents, and real tips woven together into a step-by-step walkthrough of a project's life cycle. Pro Web Project Management is written for both the full-time project manager and the aspiring project manager who might have a role that blends client support, web development, and project management. The project budget sweet spot for this book is $50,000 to $500,000. If you manage a project in this space, reading this book will make you a better project manager. Learn how to manage a modern web project with a budget of $50,000 to $500,000 Get actionable tips on dealing with real project management challenges Learn the simple, defined process—refined over the years—to take simple and complex projects from proposal to successful launch |
| data lifecycle program management: 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 program management: 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 program management: Product Lifecycle Management: Towards Knowledge-Rich Enterprises Louis Rivest, Abdelaziz Bouras, Borhen Louhichi, 2012-12-22 This book constitutes the refereed post-proceedings of the 9th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2012, held in Montreal, Canada, in July 2012. The 58 full papers presented were carefully reviewed and selected from numerous submissions. They cover a large range of topics such as collaboration in PLM, tools and methodologies for PLM, modeling for PLM, and PLM implementation issues. |
| data lifecycle program management: Climate Data Records from Environmental Satellites National Research Council, Division on Earth and Life Studies, Board on Atmospheric Sciences and Climate, Committee on Climate Data Records from NOAA Operational Satellites, 2004-08-26 The report outlines key elements to consider in designing a program to create climate-quality data from satellites. It examines historical attempts to create climate data records, provides advice on steps for generating, re-analyzing, and storing satellite climate data, and discusses the importance of partnering between agencies, academia, and industry. NOAA will use this report-the first in a two-part study-to draft an implementation plan for climate data records. |
| data lifecycle program management: 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 program management: Product Lifecycle Management with SAP Gerd Hartmann, Ulrich Schmidt, 2005 |
| data lifecycle program management: The Data Warehouse Lifecycle Toolkit Ralph Kimball, 1998-08-27 A comprehensive, thoughtful, and detailed book that will be of inestimable value to anyone struggling with the complex details of designing, building, and maintaining an enterprise-wide decision support system. Highly recommended. -Robert S. Craig, Vice President, Application Architectures, Hurwitz Group, Inc. In his bestselling book, The Data Warehouse Toolkit, Ralph Kimball showed you how to use dimensional modeling to design effective and usable data warehouses. Now, he carries these techniques to the larger issues of delivering complete data marts and data warehouses. Drawing upon their experiences with numerous data warehouse implementations, he and his coauthors show you all the practical details involved in planning, designing, developing, deploying, and growing data warehouses. Important topics include: * The Business Dimensional Lifecycle(TM) approach to data warehouse project planning and management * Techniques for gathering requirements more effectively and efficiently * Advanced dimensional modeling techniques to capture the most complex business rules * The Data Warehouse Bus Architecture and other approaches for integrating data marts into super-flexible data warehouses * A framework for creating your technical architecture * Techniques for minimizing the risks involved with data staging * Aggregations and other effective ways to boost data warehouse performance * Cutting-edge, Internet-based data warehouse security techniques The CD-ROM supplies you with: * Complete data warehouse project plan tasks and responsibilities * A set of sample models that demonstrate the Bus Architecture * Blank versions of the templates and tools described in the book * Checklists to use at key points in the project |
| data lifecycle program management: Product Lifecycle Management (Volume 1) John Stark, 2022-05-03 This fifth edition of “Product Lifecycle Management” updates and adds to the successful fourth edition, the most frequently cited PLM publication. It gives the reader a thorough explanation of Product Lifecycle Management (PLM) and provides them with a full understanding and the skills to implement PLM within their own business environment. This new and expanded edition is fully updated to reflect the many technological and management advances made in PLM since the release of the fourth edition. “Product Lifecycle Management” will broaden the reader’s understanding of PLM, nurturing the skills needed to implement PLM successfully and to achieve world-class product performance across the lifecycle. Among the components of PLM described are product-related business processes, product data, product data management (PDM) systems, other PLM applications, best practices, company objectives and organisation. This book also describes the relationships of PLM with the Internet of Things, Industry 4.0, Digital Twins and Digital Threads. “Product Lifecycle Management” (5th edition) explains what PLM is, and why it is needed. It describes the environment in which products are ideated, developed, manufactured, supported and retired, before addressing the main components of PLM and PLM Initiatives. Key activities in PLM Initiatives described include organisational change management (OCM) and project management. The final part of the book addresses the PLM Initiative, showing the typical steps and activities of a PLM project or initiative. |
| data lifecycle program management: DW 2.0: The Architecture for the Next Generation of Data Warehousing W.H. Inmon, Derek Strauss, Genia Neushloss, 2010-07-28 DW 2.0: The Architecture for the Next Generation of Data Warehousing is the first book on the new generation of data warehouse architecture, DW 2.0, by the father of the data warehouse. The book describes the future of data warehousing that is technologically possible today, at both an architectural level and technology level. The perspective of the book is from the top down: looking at the overall architecture and then delving into the issues underlying the components. This allows people who are building or using a data warehouse to see what lies ahead and determine what new technology to buy, how to plan extensions to the data warehouse, what can be salvaged from the current system, and how to justify the expense at the most practical level. This book gives experienced data warehouse professionals everything they need in order to implement the new generation DW 2.0. It is designed for professionals in the IT organization, including data architects, DBAs, systems design and development professionals, as well as data warehouse and knowledge management professionals. - First book on the new generation of data warehouse architecture, DW 2.0 - Written by the father of the data warehouse, Bill Inmon, a columnist and newsletter editor of The Bill Inmon Channel on the Business Intelligence Network - Long overdue comprehensive coverage of the implementation of technology and tools that enable the new generation of the DW: metadata, temporal data, ETL, unstructured data, and data quality control |
| data lifecycle program management: Executing Data Quality Projects Danette McGilvray, 2008-09-01 Information is currency. Recent studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. In this important and timely new book, Danette McGilvray presents her Ten Steps approach to information quality, a proven method for both understanding and creating information quality in the enterprise. Her trademarked approach—in which she has trained Fortune 500 clients and hundreds of workshop attendees—applies to all types of data and to all types of organizations.* Includes numerous templates, detailed examples, and practical advice for executing every step of the Ten Steps approach.* Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices.* A companion Web site includes links to numerous data quality resources, including many of the planning and information-gathering templates featured in the text, quick summaries of key ideas from the Ten Step methodology, and other tools and information available online. |
| data lifecycle program management: Entity Information Life Cycle for Big Data John R. Talburt, Yinle Zhou, 2015-04-20 Entity Information Life Cycle for Big Data walks you through the ins and outs of managing entity information so you can successfully achieve master data management (MDM) in the era of big data. This book explains big data's impact on MDM and the critical role of entity information management system (EIMS) in successful MDM. Expert authors Dr. John R. Talburt and Dr. Yinle Zhou provide a thorough background in the principles of managing the entity information life cycle and provide practical tips and techniques for implementing an EIMS, strategies for exploiting distributed processing to handle big data for EIMS, and examples from real applications. Additional material on the theory of EIIM and methods for assessing and evaluating EIMS performance also make this book appropriate for use as a textbook in courses on entity and identity management, data management, customer relationship management (CRM), and related topics. - Explains the business value and impact of entity information management system (EIMS) and directly addresses the problem of EIMS design and operation, a critical issue organizations face when implementing MDM systems - Offers practical guidance to help you design and build an EIM system that will successfully handle big data - Details how to measure and evaluate entity integrity in MDM systems and explains the principles and processes that comprise EIM - Provides an understanding of features and functions an EIM system should have that will assist in evaluating commercial EIM systems - Includes chapter review questions, exercises, tips, and free downloads of demonstrations that use the OYSTER open source EIM system - Executable code (Java .jar files), control scripts, and synthetic input data illustrate various aspects of CSRUD life cycle such as identity capture, identity update, and assertions |
| data lifecycle program management: 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 lifecycle program management: Residential Construction Management Joseph A. Griffin, 2010-03-15 Residential Construction Management will provide construction managers a concise and practical guide to managing residential construction projects. One of the fundamental reasons residential contractors fail to prosper is that they are poor managers. By presenting project management tools in their appropriate context of the project lifecycle—initiation, planning, execution, monitoring and controlling, and closing, readers will more clearly understand the iterative nature of construction management, which is a key to successfully managing a construction project. You can't afford to be without this indispensible working tool and its step-by-step instructions, project management templates, and real-world case studies. Residential Construction Management provides construction managers a concise and practical guide to managing residential construction projects. One of the fundamental reasons residential contractors fail to prosper is that they are poor managers. By presenting project management tools in their appropriate context of the project lifecycle — initiation, planning, execution, monitoring and controlling, and closing — you will more clearly understand the true nature of construction management, which is a key to successfully managing a construction project. You can't afford to be without this indispensible working tool and its step-by-step instructions, project management templates, and real world case studies.Key Features ¦ Walks you through the entire project management lifecycle resulting in a better understanding of the iterative processes of construction management ¦ Offers the information and real world tools needed to successfully apply to a planned or current project ¦ Shows how various knowledge areas and project management tools interact when doing a project providing you with the knowledge to create your own project plan ¦ Offers a downloadable building specification form, change order authorization form, construction schedule, sample budget, construction flowchart, a guide to working with bankers for spec home loans, and much more — available from the Web Added Value™ Download Resource Center at www.jrosspub.com |
| data lifecycle program management: Navigating the Labyrinth Laura Sebastian-Coleman, An Executive Guide to Data Management |
| data lifecycle program management: APM Body of Knowledge , 2012 The APM Body of Knowledge 6th edition provides the foundation for the successful delivery of projects, programmes and portfolios across all sectors and industries. Written by the profession for the profession it offers the key to successful project management and is an essential part of the APM Five Dimensions of Professionalism. It is a scope statement for the profession and a sourcebook for all aspiring, new and experienced project professionals offering common definitions, references and a comprehensive glossary of terms. |
| data lifecycle program management: Engineering Asset Management Dimitris Kiritsis, Christos Emmanouilidis, Andy Koronios, Joseph Mathew, 2011-02-03 Engineering Asset Management discusses state-of-the-art trends and developments in the emerging field of engineering asset management as presented at the Fourth World Congress on Engineering Asset Management (WCEAM). It is an excellent reference for practitioners, researchers and students in the multidisciplinary field of asset management, covering such topics as asset condition monitoring and intelligent maintenance; asset data warehousing, data mining and fusion; asset performance and level-of-service models; design and life-cycle integrity of physical assets; deterioration and preservation models for assets; education and training in asset management; engineering standards in asset management; fault diagnosis and prognostics; financial analysis methods for physical assets; human dimensions in integrated asset management; information quality management; information systems and knowledge management; intelligent sensors and devices; maintenance strategies in asset management; optimisation decisions in asset management; risk management in asset management; strategic asset management; and sustainability in asset management. |
| data lifecycle program management: Sharing Clinical Trial Data Institute of Medicine, Board on Health Sciences Policy, Committee on Strategies for Responsible Sharing of Clinical Trial Data, 2015-04-20 Data sharing can accelerate new discoveries by avoiding duplicative trials, stimulating new ideas for research, and enabling the maximal scientific knowledge and benefits to be gained from the efforts of clinical trial participants and investigators. At the same time, sharing clinical trial data presents risks, burdens, and challenges. These include the need to protect the privacy and honor the consent of clinical trial participants; safeguard the legitimate economic interests of sponsors; and guard against invalid secondary analyses, which could undermine trust in clinical trials or otherwise harm public health. Sharing Clinical Trial Data presents activities and strategies for the responsible sharing of clinical trial data. With the goal of increasing scientific knowledge to lead to better therapies for patients, this book identifies guiding principles and makes recommendations to maximize the benefits and minimize risks. This report offers guidance on the types of clinical trial data available at different points in the process, the points in the process at which each type of data should be shared, methods for sharing data, what groups should have access to data, and future knowledge and infrastructure needs. Responsible sharing of clinical trial data will allow other investigators to replicate published findings and carry out additional analyses, strengthen the evidence base for regulatory and clinical decisions, and increase the scientific knowledge gained from investments by the funders of clinical trials. The recommendations of Sharing Clinical Trial Data will be useful both now and well into the future as improved sharing of data leads to a stronger evidence base for treatment. This book will be of interest to stakeholders across the spectrum of research-from funders, to researchers, to journals, to physicians, and ultimately, to patients. |
| data lifecycle program management: 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-- |
| data lifecycle program management: The Data and Analytics Playbook Lowell Fryman, Gregory Lampshire, Dan Meers, 2016-08-12 The Data and Analytics Playbook: Proven Methods for Governed Data and Analytic Quality explores the way in which data continues to dominate budgets, along with the varying efforts made across a variety of business enablement projects, including applications, web and mobile computing, big data analytics, and traditional data integration. The book teaches readers how to use proven methods and accelerators to break through data obstacles to provide faster, higher quality delivery of mission critical programs. Drawing upon years of practical experience, and using numerous examples and an easy to understand playbook, Lowell Fryman, Gregory Lampshire, and Dan Meers discuss a simple, proven approach to the execution of multiple data oriented activities. In addition, they present a clear set of methods to provide reliable governance, controls, risk, and exposure management for enterprise data and the programs that rely upon it. In addition, they discuss a cost-effective approach to providing sustainable governance and quality outcomes that enhance project delivery, while also ensuring ongoing controls. Example activities, templates, outputs, resources, and roles are explored, along with different organizational models in common use today and the ways they can be mapped to leverage playbook data governance throughout the organization. - Provides a mature and proven playbook approach (methodology) to enabling data governance that supports agile implementation - Features specific examples of current industry challenges in enterprise risk management, including anti-money laundering and fraud prevention - Describes business benefit measures and funding approaches using exposure based cost models that augment risk models for cost avoidance analysis and accelerated delivery approaches using data integration sprints for application, integration, and information delivery success |
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 …
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 …
Belmont Forum Adopts Open Data Principles for Environme…
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 …
Belmont Forum Data Accessibility Statement an…
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. …
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 …
