| data lifecycle management refers to what: 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 refers to what: 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 refers to what: 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 refers to what: 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 refers to what: Product Lifecycle Management Anselmi Immonen, Antti Saaksvuori, 2013-06-05 This is the first English-language book on Product Lifecycle Management (PLM) that introduces the reader to the basic terms and fundamentals of PLM. The text provides 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 refers to what: 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 refers to what: 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 refers to what: 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 refers to what: 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 management refers to what: Designing and Operating a Data Reservoir Mandy Chessell, Nigel L Jones, Jay Limburn, David Radley, Kevin Shank, IBM Redbooks, 2015-05-26 Together, big data and analytics have tremendous potential to improve the way we use precious resources, to provide more personalized services, and to protect ourselves from unexpected and ill-intentioned activities. To fully use big data and analytics, an organization needs a system of insight. This is an ecosystem where individuals can locate and access data, and build visualizations and new analytical models that can be deployed into the IT systems to improve the operations of the organization. The data that is most valuable for analytics is also valuable in its own right and typically contains personal and private information about key people in the organization such as customers, employees, and suppliers. Although universal access to data is desirable, safeguards are necessary to protect people's privacy, prevent data leakage, and detect suspicious activity. The data reservoir is a reference architecture that balances the desire for easy access to data with information governance and security. The data reservoir reference architecture describes the technical capabilities necessary for a system of insight, while being independent of specific technologies. Being technology independent is important, because most organizations already have investments in data platforms that they want to incorporate in their solution. In addition, technology is continually improving, and the choice of technology is often dictated by the volume, variety, and velocity of the data being managed. A system of insight needs more than technology to succeed. The data reservoir reference architecture includes description of governance and management processes and definitions to ensure the human and business systems around the technology support a collaborative, self-service, and safe environment for data use. The data reservoir reference architecture was first introduced in Governing and Managing Big Data for Analytics and Decision Makers, REDP-5120, which is available at: http://www.redbooks.ibm.com/redpieces/abstracts/redp5120.html. This IBM® Redbooks publication, Designing and Operating a Data Reservoir, builds on that material to provide more detail on the capabilities and internal workings of a data reservoir. |
| data lifecycle management refers to what: 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 management refers to what: 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 refers to what: 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 refers to what: 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 refers to what: 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 management refers to what: 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 management refers to what: System Lifecycle Management Martin Eigner, 2021-08-09 Years of experience in the area of Product Lifecycle Management (PLM) in industry, research and education form the basis for this overview. The author covers the development from PDM via PLM to SysLM (System Lifecycle Management) in the form commonly used today, which are necessary prerequisites for the sustainable development and implementation of IoT/IoS, Industry 4.0 and Engineering 4.0 concepts. The building blocks and properties of future-proof systems for the successful implementation of the concepts of Engineering 4.0 are thereby dedicated to holistic considerations, which also inform in detail. SysLM functions and processes in mechatronic development and design as well as across the entire product lifecycle - from requirements management to the Digital Twin - are covered as examples. SysLM trends such as low code development, cloud, disruptive business models, and bimodality provide an outlook on future developments. The author dedicates the treatment of the agile SysLM introduction to the implementation in the enterprise. The basics are deepened with examples of a concrete SysLM system. |
| data lifecycle management refers to what: Product Lifecycle Management (Volume 1) John Stark, 2015-04-10 This third edition updates and adds to the successful second edition and gives the reader a thorough description of PLM, providing them with a full understanding of the theory and the practical 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 second edition. Describing the environment in which products are developed, manufactured and supported, before addressing the Five Pillars of PLM: business processes, product data, PLM applications, Organisational Change Management (OCM) and Project Management, this book explains what Product Lifecycle Management is, and why it’s needed. The final part of the book addresses the PLM timeline, showing the typical steps and activities of a PLM project or initiative. “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. |
| data lifecycle management refers to what: Product Lifecycle Management John Stark, 2006-03-30 Product Lifecycle Management (PLM), a new paradigm for product manufacturing, enables a company to manage its products all the way across their lifecycles in the most effective way. It helps companies get products to market faster, provide better support for their use, and manage end-of-life better. In today’s highly competitive global markets, companies must meet the increasing demands of customers to rapidly and continually improve their products and services. PLM meets these needs, extending and bringing together previously separate fields such as Computer Aided Design, Product Data Management, Sustainable Development, Enterprise Resource Planning, Life Cycle Analysis and Recycling. Product Lifecycle Management: 21st century Paradigm for Product Realisation explains the importance of PLM, from both the business and technical viewpoints, supported by examples showing how world-class engineering and manufacturing companies are implementing PLM successfully. The book: - introduces PLM, a unique holistic view of product development, support, use and disposal for industry worldwide, based on experience with internationally renowned companies; - shows you how to take full advantage of PLM, how to prepare people to work in the PLM environment, how to choose the best solution for your situation; - provides deep understanding, nurturing the skills you will need to successfully implement PLM and achieve world-class product development and support performance; and - gives access to a companion www site containing further material. |
| data lifecycle management refers to what: 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 refers to what: 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 management refers to what: LIFECYCLE MANAGEMENT: Emerging Paradigm B.M. KAPOOR, 2009-12 This book covers the fundamentals and concepts of product lifecycle management and terminology. It considers the different roles of information processing systems within the company from the biewpoint of product information management. It survey the deployment and completion of implementation projects for PLM systems; case examples concretize the use of systems in compaines making different kinds of products. It envisages PLM concept from a markedly wider perspective thinking in terms of the development of the business and also considers the significance of cooperation or collaboration between companies and the role of PLM in this. |
| data lifecycle management refers to what: Product Lifecycle Management (Volume 6) John Stark, |
| data lifecycle management refers to what: Strategic Blueprint for Enterprise Analytics Liang Wang, |
| data lifecycle management refers to what: Data Engineering for Machine Learning Pipelines Pavan Kumar Narayanan, |
| data lifecycle management refers to what: Azure Modern Data Architecture Anouar BEN ZAHRA, Key Features Discover the key drivers of successful Azure architecture Practical guidance Focus on scalability and performance Expert authorship Book Description This book presents a guide to design and implement scalable, secure, and efficient data solutions in the Azure cloud environment. It provides Data Architects, developers, and IT professionals who are responsible for designing and implementing data solutions in the Azure cloud environment with the knowledge and tools needed to design and implement data solutions using the latest Azure data services. It covers a wide range of topics, including data storage, data processing, data analysis, and data integration. In this book, you will learn how to select the appropriate Azure data services, design a data processing pipeline, implement real-time data processing, and implement advanced analytics using Azure Databricks and Azure Synapse Analytics. You will also learn how to implement data security and compliance, including data encryption, access control, and auditing. Whether you are building a new data architecture from scratch or migrating an existing on premises solution to Azure, the Azure Data Architecture Guidelines are an essential resource for any organization looking to harness the power of data in the cloud. With these guidelines, you will gain a deep understanding of the principles and best practices of Azure data architecture and be equipped to build data solutions that are highly scalable, secure, and cost effective. What You Need to Use this Book? To use this book, it is recommended that readers have a basic understanding of data architecture concepts and data management principles. Some familiarity with cloud computing and Azure services is also helpful. The book is designed for data architects, data engineers, data analysts, and anyone involved in designing, implementing, and managing data solutions on the Azure cloud platform. It is also suitable for students and professionals who want to learn about Azure data architecture and its best practices. |
| data lifecycle management refers to what: Creating Integrated IBM WebSphere Solutions using Application Lifecycle Management Emrah Barkana, Antonella Bertoletti, Stefano Bussaglia, Ernest Calalang, Sebastian Kapciak, Leonardo Olivera, Sergio Polastri, Fabio Silva, IBM Redbooks, 2014-12-21 This IBM® Redbooks® publication demonstrates, through a practical solution and step-by-step implementation instructions, how customers can use the IBM Rational® Application Lifecycle Management (ALM) portfolio to build and manage an integrated IBM WebSphere® Application. Building a business application (mobile and desktop) that uses WebSphere Application Server, IBM MQ, IBM Integration Bus (IIB), Business Process Management (BPM), Operational Decision Management (ODM), and Mobile. IBM RedpaperTM publication, Rapid deployment of integrated WebSphere solutions in your cloud, REDP-5132, is an extension to this IBM Redbooks publication. Using the same practical solution covered in this Redbooks publication, REDP-5132 demonstrates how the IBM PureApplication® System is a logical extension versus a whole new world, covering PureApplication Patterns and the new PureApplication as a service on Softlayer. The intended audience for this book is architects, developers, administrators, and DevOps personnel. |
| data lifecycle management refers to what: Privacy for Software-defined Battery Electric Vehicles Umar Zakir, Abdul Hamid, 2024-06-17 The integration of software-defined approaches with software-defined battery electric vehicles brings forth challenges related to privacy regulations, such as European Union’s General Data Protection Regulation and Data Act, as well as the California Consumer Privacy Act. Compliance with these regulations poses barriers for foreign brands and startups seeking entry into these markets. Car manufacturers and suppliers, particularly software suppliers, must navigate complex privacy requirements when introducing vehicles to these regions. Privacy for Software-defined Battery Electric Vehicles aims to educate practitioners across different market regions and fields. It seeks to stimulate discussions for improvements in processes and requirements related to privacy aspects regarding these vehicles. The report covers the significance of privacy, potential vulnerabilities and risks, technical challenges, safety risks, management and operational challenges, and the benefits of compliance with privacy standards within the software-defined battery electric vehicle realm. Click here to access the full SAE EDGETM Research Report portfolio. https://doi.org/10.4271/EPR2024012 |
| data lifecycle management refers to what: 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 refers to what: Product Lifecycle Management for Digital Transformation of Industries Ramy Harik, Louis Rivest, Alain Bernard, Benoit Eynard, Abdelaziz Bouras, 2017-03-15 This book constitutes the refereed proceedings of the 13th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2016, held in Columbia, SC, USA, in July 2016. The 57 revised full papers presented were carefully reviewed and selected from 77 submissions. The papers are organized in the following topical sections: knowledge sharing, re-use and preservation; collaborative development architectures; interoperability and systems integration; lean product development and the role of PLM; PLM and innovation; PLM tools; cloud computing and PLM tools; traceability and performance; building information modeling; big data analytics and business intelligence; information lifecycle management; industry 4.0; metrics, standards and regulation; and product, service and systems. |
| data lifecycle management refers to what: Product Lifecycle Management to Support Industry 4.0 Paolo Chiabert, Abdelaziz Bouras, Frédéric Noël, José Ríos, 2018-12-08 This book constitutes the refereed post-conference proceedings of the 15th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2018, held in Turin, Spain, in July 2018. The 72 revised full papers presented were carefully reviewed and selected from 82 submissions. The papers are organized in the following topical sections: building information modeling; collaborative environments and new product development; PLM for digital factories and cyber physical systems; ontologies and data models; education in the field of industry 4.0; product-service systems and smart products; lean organization for industry 4.0; knowledge management and information sharing; PLM infrastructure and implementation; PLM maturity, implementation and adoption; 3D printing and additive manufacturing; and modular design and products and configuration and change management. |
| data lifecycle management refers to what: 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 refers to what: 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 refers to what: Product Lifecycle Management for Society Alain Bernard, Louis Rivest, Debasish Dutta, 2013-11-09 This book constitutes the refereed proceedings of the 10th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2013, held in Nantes, France, in July 2013. The 63 full papers presented together with 2 keynote talks were carefully reviewed and selected from 91 submissions. They are organized in the following topical sections: PLM for sustainability, traceability and performance; PLM infrastructure and implementation processes; capture and reuse of product and process information; PLM and knowledge management; enterprise system integration; PLM and influence of/from social networks; PLM maturity and improvement concepts; PLM and collaborative product development; PLM virtual and simulation environments; and building information modeling. |
| data lifecycle management refers to what: Product Lifecycle Management. PLM in Transition Times: The Place of Humans and Transformative Technologies Frédéric Noël, Felix Nyffenegger, Louis Rivest, Abdelaziz Bouras, 2023-01-31 This book constitutes the refereed proceedings of the 19th IFIP WG 5.1 International Conference, PLM 2022, Grenoble, France, July 10–13, 2022, Revised Selected Papers. The 67 full papers included in this book were carefully reviewed and selected from 94 submissions. They were organized in topical sections as follows: Organisation: Knowledge Management, Business Models, Sustainability, End-to-End PLM, Modelling tools: Model-Based Systems Engineering, Geometric modelling, Maturity models, Digital Chain Process, Transversal Tools: Artificial Intelligence, Advanced Visualization and Interaction, Machine learning, Product development: Design Methods, Building Design, Smart Products, New Product Development, Manufacturing: Sustainable Manufacturing, Lean Manufacturing, Models for Manufacturing. |
| data lifecycle management refers to what: Product Lifecycle Management (Volume 7) John Stark, |
| data lifecycle management refers to what: 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 refers to what: Secure Data Management Willem Jonker, Milan Petković, 2014-05-14 This book constitutes the refereed proceedings of the 10th VLDB Workshop on Secure Data Management held in Trento, Italy, on August 30, 2013. The 15 revised full papers and one keynote paper presented were carefully reviewed and selected from various submissions. The papers are organized in technical papers and 10 vision papers which address key challenges in secure data management and indicate interesting research questions. |
| data lifecycle management refers to what: Enterprise Content Management in Information Systems Research Jan vom Brocke, Alexander Simons, 2013-11-04 This book collects ECM research from the academic discipline of Information Systems and related fields to support academics and practitioners who are interested in understanding the design, use and impact of ECM systems. It also provides a valuable resource for students and lecturers in the field. “Enterprise content management in Information Systems research – Foundations, methods and cases” consolidates our current knowledge on how today’s organizations can manage their digital information assets. The business challenges related to organizational information management include reducing search times, maintaining information quality, and complying with reporting obligations and standards. Many of these challenges are well-known in information management, but because of the vast quantities of information being generated today, they are more difficult to deal with than ever. Many companies use the term “enterprise content management” (ECM) to refer to the management of all forms of information, especially unstructured information. While ECM systems promise to increase and maintain information quality, to streamline content-related business processes, and to track the lifecycle of information, their implementation poses several questions and challenges: Which content objects should be put under the control of the ECM system? Which processes are affected by the implementation? How should outdated technology be replaced? Research is challenged to support practitioners in answering these questions. |
| data lifecycle management refers to what: MAPPING: MAnagement and Processing of Images for Population ImagiNG Michel Dojat, Wiro Niessen, David N. Kennedy, 2017-09-04 Several recent papers underline methodological points that limit the validity of published results in imaging studies in the life sciences and especially the neurosciences (Carp, 2012; Ingre, 2012; Button et al., 2013; Ioannidis, 2014). At least three main points are identified that lead to biased conclusions in research findings: endemic low statistical power and, selective outcome and selective analysis reporting. Because of this, and in view of the lack of replication studies, false discoveries or solutions persist. To overcome the poor reliability of research findings, several actions should be promoted including conducting large cohort studies, data sharing and data reanalysis. The construction of large-scale online databases should be facilitated, as they may contribute to the definition of a “collective mind” (Fox et al., 2014) facilitating open collaborative work or “crowd science” (Franzoni and Sauermann, 2014). Although technology alone cannot change scientists’ practices (Wicherts et al., 2011; Wallis et al., 2013, Poldrack and Gorgolewski 2014; Roche et al. 2014), technical solutions should be identified which support a more “open science” approach. Also, the analysis of the data plays an important role. For the analysis of large datasets, image processing pipelines should be constructed based on the best algorithms available and their performance should be objectively compared to diffuse the more relevant solutions. Also, provenance of processed data should be ensured (MacKenzie-Graham et al., 2008). In population imaging this would mean providing effective tools for data sharing and analysis without increasing the burden on researchers. This subject is the main objective of this research topic (RT), cross-listed between the specialty section “Computer Image Analysis” of Frontiers in ICT and Frontiers in Neuroinformatics. Firstly, it gathers works on innovative solutions for the management of large imaging datasets possibly distributed in various centers. The paper of Danso et al. describes their experience with the integration of neuroimaging data coming from several stroke imaging research projects. They detail how the initial NeuroGrid core metadata schema was gradually extended for capturing all information required for future metaanalysis while ensuring semantic interoperability for future integration with other biomedical ontologies. With a similar preoccupation of interoperability, Shanoir relies on the OntoNeuroLog ontology (Temal et al., 2008; Gibaud et al., 2011; Batrancourt et al., 2015), a semantic model that formally described entities and relations in medical imaging, neuropsychological and behavioral assessment domains. The mechanism of “Study Card” allows to seamlessly populate metadata aligned with the ontology, avoiding fastidious manual entrance and the automatic control of the conformity of imported data with a predefined study protocol. The ambitious objective with the BIOMIST platform is to provide an environment managing the entire cycle of neuroimaging data from acquisition to analysis ensuring full provenance information of any derived data. Interestingly, it is conceived based on the product lifecycle management approach used in industry for managing products (here neuroimaging data) from inception to manufacturing. Shanoir and BIOMIST share in part the same OntoNeuroLog ontology facilitating their interoperability. ArchiMed is a data management system locally integrated for 5 years in a clinical environment. Not restricted to Neuroimaging, ArchiMed deals with multi-modal and multi-organs imaging data with specific considerations for data long-term conservation and confidentiality in accordance with the French legislation. Shanoir and ArchiMed are integrated into FLI-IAM1, the national French IT infrastructure for in vivo imaging. |
Data Management Life Cycle Final report - Texas A&M …
Researchers developed the data management life cycle to organize data, characterize its nature and value over time, and identify policy implications of cross-cutting data management issues.
The Data Life Cycle
Oct 4, 2019 · To put data science in context, we present phases of the data life cycle, from data generation to data interpretation. These phases transform raw bits into value for the end user.
DATA LIFECYCLE MANAGEMENT (DLM) EXPLAINED
Think of DLM as the set of governing principles that defines and automates the stages of useful life, and determines prioritization. In more simple terms, data lifecycle management is the …
Data Lifecycle and Analytics in the AWS Cloud - Amazon Web …
What is the Data Lifecycle? As data is generated, it moves from its raw form to a processed version, to outputs that end users need to make better decisions. All data goes through this …
The Research Data Lifecycle and Data Management - ASME …
Oct 17, 2017 · RDM is the planning, organization, sharing, and preservation of data, from when it enters the research lifecycle through to the dissemination and archiving of results. It is …
Research Data Management 101: The Lifecycle of a Dataset
Metadata, or “data about data” explains your dataset and allows you to document important information for: • Finding the data later • Understanding what the data is later • Sharing the …
Managing Data Lifecycle Effectively: Best Practices for Data
Data Lifecycle Management (DLM) refers to the comprehensive approach of managing an organization's data flow from its initial creation and storage to its eventual archival and deletion …
DISA Data Governance Bylaws & Guidelines
Apr 30, 2025 · The Data Lifecycle Management (DLM) Guidebook is intended for a broad spectrum of DISA stakeholders who play essential roles in the creation, handling, oversight, …
Research Data Management An Overview - okanagan.bc.ca
• RDM refers to the processes applied throughout the lifecycle of a research project to guide the collection, documentation, storage, sharing, and preservation of research data. • RDM …
Data Life Cycle: Introduction, Definitions and Considerations
•Data (management) life-cycle broad elements - –Acquisition: Process of recording or generating a concrete artefact from the concept (see transduction) –Curation: The activity of managing the …
The Three Pillars of Security Multi-Factor Authentication …
Data lifecycle management refers to the process of managing data via a tiered approach. While the terminology for each tier can vary, here’s what the life cycle of data generally involves: This …
Data Management Considerations for the Data Life Cycle
• Data Life Cycle : The data life cycle is a term coined to represent the entire process of data management. • It starts with concept study and data collection, but importantly has no end, as …
The research data life cycle, legacy data, and dilemmas in …
THE RESEARCH-DATA LIFECYCLE Unlike domain-specific fields that leverage data to answer research questions, the iFields focus on knowledge organization and representation (Bates, …
Terms and definitions ICFA Data Lifecycle Panel - indico.cern.ch
A data lifecycle refers to the sequence of stages that a particular unit of data goes through, from its initial creation or collection to its eventual archiving or deletion. The key stages in the data …
Research Data Management - gfz-potsdam.de
Research data management refers to the process of administering this data throughout its lifecycle, from planning, production, selection and evaluation to storage and processing for the …
12. Data Management
• Lifecycle management: the quality of data is proactively managed across the data lifecycle, from collection through to disposal. • Root-cause remediation : problems with data quality are …
Data and Information Lifecycle Management Procedure
Data and Information Lifecycle Management Procedure Section 1 - Context (1) This procedure documents the required steps for the effective management of RMIT Group data and …
DATA GOVERNANCE AND DATA MANAGEMENT WHITE PAPER
What is “Data Management?” The Data Management Book of Knowledge (DMBOK) published by DAMA International, the professional organization for those in the data management …
Data LifeCycle Management Method Research Based on …
It is urgent to start from the "data life cycle management" idea of the data center and adopt a data life cycle management model to realize the full life cycle management of data from generation …
Joint Cybersecurity Information
In its Data Management Lexicon, [1] the Intelligence Community ... Securing data throughout the AI system lifecycle Data security is a critical enabler that spans all phases of the AI system …
Data Management Life Cycle Final report - Texas A&M U…
Researchers developed the data management life cycle to organize data, characterize its nature and value over time, and identify policy implications …
GOOD PRACTICES FOR DATA MANAGEMENT AND INTEGR…
5.1.2 The data lifecycle refers to how data is generated, processed, reported, checked, used for decision-making, stored and finally discarded at the end …
The Data Life Cycle
Oct 4, 2019 · To put data science in context, we present phases of the data life cycle, from data generation to data interpretation. These phases …
DATA LIFECYCLE MANAGEMENT (DLM) EXPL…
Think of DLM as the set of governing principles that defines and automates the stages of useful life, and determines prioritization. In more simple terms, …
Data Lifecycle and Analytics in the AWS Cloud - Amazo…
What is the Data Lifecycle? As data is generated, it moves from its raw form to a processed version, to outputs that end users need to make better decisions. …
