Advertisement
| data lifecycle management goals: 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 goals: 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 goals: Data Lifecycles Roger Reid, Gareth Fraser-King, W. David Schwaderer, 2007-01-11 Businesses now rely almost entirely on applications and databases, causing data and storage needs to increase at astounding rates. It is therefore imperative for a company to optimize and simplify the complexity of managing its data resources. Plenty of storage products are now available, however the challenge remains for companies to proactively manage their storage assets and align the resources to the various departments, divisions, geographical locations and business processes to achieve improved efficiency and profitability. Data Lifecycles identifies ways to incorporate an intelligent service platform to manage and map the storage of data. The authors give an overview of the latest trends and technologies in storage networking and cover critical issues such as world-wide compliance. Data Lifecycles: Provides a single-source guide to data and storage methodologies, processes, technologies and compliance issues. Addresses the need of an encompassing intelligent data and storage management platform for modern businesses. Gives an overview of the latest data technologies and concepts such as utility computing and information lifecycle management. Clearly defines and describes lifecycle management and strategies to ensure growth of critical business data. Shows how to dramatically reduce the total cost of storage ownership and provide rapid return on investment. Enables customers to make decisions directed toward the purchase of storage tools and storage management solutions. This text is an ideal introduction to modern data lifecycle management for network managers, system administrators, storage/system architects, network managers, information management directors as well as CIO/CTOs and their teams, senior IT managers and decision makers, and database administrators. |
| data lifecycle management goals: Data Governance Dimitrios Sargiotis, |
| data lifecycle management goals: 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 goals: Information Governance Principles and Practices for a Big Data Landscape Chuck Ballard, Cindy Compert, Tom Jesionowski, Ivan Milman, Bill Plants, Barry Rosen, Harald Smith, IBM Redbooks, 2014-03-31 This IBM® Redbooks® publication describes how the IBM Big Data Platform provides the integrated capabilities that are required for the adoption of Information Governance in the big data landscape. As organizations embark on new use cases, such as Big Data Exploration, an enhanced 360 view of customers, or Data Warehouse modernization, and absorb ever growing volumes and variety of data with accelerating velocity, the principles and practices of Information Governance become ever more critical to ensure trust in data and help organizations overcome the inherent risks and achieve the wanted value. The introduction of big data changes the information landscape. Data arrives faster than humans can react to it, and issues can quickly escalate into significant events. The variety of data now poses new privacy and security risks. The high volume of information in all places makes it harder to find where these issues, risks, and even useful information to drive new value and revenue are. Information Governance provides an organization with a framework that can align their wanted outcomes with their strategic management principles, the people who can implement those principles, and the architecture and platform that are needed to support the big data use cases. The IBM Big Data Platform, coupled with a framework for Information Governance, provides an approach to build, manage, and gain significant value from the big data landscape. |
| data lifecycle management goals: 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 lifecycle management goals: Practical DataOps Harvinder Atwal, 2019-12-09 Gain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making. Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data producers, data consumers, and the rest of the organization through the adoption of lean thinking and agile software development principles. This book helps you to improve the speed and accuracy of analytical application development through data management and DevOps practices that securely expand data access, and rapidly increase the number of reproducible data products through automation, testing, and integration. The book also shows how to collect feedback and monitor performance to manage and continuously improve your processes and output. What You Will LearnDevelop a data strategy for your organization to help it reach its long-term goals Recognize and eliminate barriers to delivering data to users at scale Work on the right things for the right stakeholders through agile collaboration Create trust in data via rigorous testing and effective data management Build a culture of learning and continuous improvement through monitoring deployments and measuring outcomes Create cross-functional self-organizing teams focused on goals not reporting lines Build robust, trustworthy, data pipelines in support of AI, machine learning, and other analytical data products Who This Book Is For Data science and advanced analytics experts, CIOs, CDOs (chief data officers), chief analytics officers, business analysts, business team leaders, and IT professionals (data engineers, developers, architects, and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals who want to overcome challenges of long delivery time, poor data quality, high maintenance costs, and scaling difficulties in getting data science output and machine learning into customer-facing production. |
| data lifecycle management goals: Data Management courseware based on CDMP Fundamentals Alliance BV And More Group BV, 1970-01-01 Besides the courseware publication (ISBN: 9789401811491), you are advised to obtain the DAMA DMBOK publication (ISBN: 9781634622349). Optionally, you can use the publication Data management: a gentle introduction (ISBN: 9789401805506) as inspiration for examples and quotes about the field of data management. This material is intended to prepare participants for the CDMP exam by DAMA International. The courseware can only be ordered by partners and is based on the current version of the DAMA DMBOK. The material will be updated when new versions of DMBOK are published. DAMA DMBOK is the industry reference for data management. It is published by DAMA International and is currently in its second version. The DMBOK is developed by professionals and can be seen as a collection of best practices. The domain of data management is divided into functional areas which are discussed in terms of definitions (what is it), goals (what are we trying to achieve), steps (what are typical activities), inputs/outputs, and participating roles. Developing and sustaining an effective data management function is far from an easy task. The DMBOK framework is adopted by many organizations as the foundation for their data management function: standardized language and good practices speed up the learning process. After the training, you have an overview of the field of data management, its terminology, and current best practices. |
| data lifecycle management goals: Big Scientific Data Management Jianhui Li, Xiaofeng Meng, Ying Zhang, Wenjuan Cui, Zhihui Du, 2019-08-06 This book constitutes the refereed proceedings of the First International Conference on Big Scientific Data Management, BigSDM 2018, held in Beijing, Greece, in November/December 2018. The 24 full papers presented together with 7 short papers were carefully reviewed and selected from 86 submissions. The topics involved application cases in the big scientific data management, paradigms for enhancing scientific discovery through big data, data management challenges posed by big scientific data, machine learning methods to facilitate scientific discovery, science platforms and storage systems for large scale scientific applications, data cleansing and quality assurance of science data, and data policies. |
| data lifecycle management goals: Navigating the Labyrinth Laura Sebastian-Coleman, An Executive Guide to Data Management |
| data lifecycle management goals: Decision Management: Concepts, Methodologies, Tools, and Applications Management Association, Information Resources, 2017-01-30 The implementation of effective decision making protocols is crucial in any organizational environment in modern society. Emerging advancements in technology and analytics have optimized uses and applications of decision making systems. Decision Management: Concepts, Methodologies, Tools, and Applications is a compendium of the latest academic material on the control, support, usage, and strategies for implementing efficient decision making systems across a variety of industries and fields. Featuring comprehensive coverage on numerous perspectives, such as data visualization, pattern analysis, and predictive analytics, this multi-volume book is an essential reference source for researchers, academics, professionals, managers, students, and practitioners interested in the maintenance and optimization of decision management processes. |
| data lifecycle management goals: FY2020–FY2022 Medium-Term Budget International Monetary Fund. Office of Budget and Planning, 2019-05-07 FY2020–FY2022 Medium-Term Budget |
| data lifecycle management goals: Data Stewardship David Plotkin, 2020-10-31 Data stewards in any organization are the backbone of a successful data governance implementation because they do the work to make data trusted, dependable, and high quality. Since the publication of the first edition, there have been critical new developments in the field, such as integrating Data Stewardship into project management, handling Data Stewardship in large international companies, handling big data and Data Lakes, and a pivot in the overall thinking around the best way to align data stewardship to the data—moving from business/organizational function to data domain. Furthermore, the role of process in data stewardship is now recognized as key and needed to be covered.Data Stewardship, Second Edition provides clear and concise practical advice on implementing and running data stewardship, including guidelines on how to organize based on organizational/company structure, business functions, and data ownership. The book shows data managers how to gain support for a stewardship effort, maintain that support over the long-term, and measure the success of the data stewardship effort. It includes detailed lists of responsibilities for each type of data steward and strategies to help the Data Governance Program Office work effectively with the data stewards. - Includes an enhanced section on data governance/stewardship structure for companies that do business internationally, including the structure of business terms to account for country differences - Outlines the advantages and disadvantages of data domains, details on suggested data domains and data domain structures, as well as data governance by data domains - Integrates data governance into Project methodology, defining roles on a project, adding Data Governance tasks to the Work Breakdown Structure, as well as advantages of working closely with the Project management Office - Covers the data stewardship involved in implementing national and international data privacy regulations |
| data lifecycle management goals: Mastering Data security and governance Cybellium Ltd, A Blueprint for Safeguarding Data in a Connected World In an era where data breaches and privacy concerns make headlines, the importance of robust data security and effective governance cannot be overstated. Mastering Data Security and Governance serves as your comprehensive guide to understanding and implementing strategies that protect sensitive information while ensuring compliance and accountability in today's interconnected landscape. About the Book: In a world where data is a valuable currency, organizations must prioritize data security and governance to build trust with their customers, partners, and stakeholders. Mastering Data Security and Governance delves into the critical concepts, practices, and technologies required to establish a resilient data protection framework while maintaining transparency and adhering to regulatory requirements. Key Features: Security Fundamentals: Lay the foundation with a clear explanation of data security principles, including encryption, access controls, authentication, and more. Understand the threats and vulnerabilities that can compromise data integrity and confidentiality. Governance Frameworks: Explore the intricacies of data governance, including data ownership, classification, and policies. Learn how to establish a governance framework that fosters responsible data management and usage. Compliance and Regulations: Navigate the complex landscape of data regulations and compliance standards, such as GDPR, HIPAA, and CCPA. Discover strategies for aligning your data practices with legal requirements. Risk Management: Learn how to assess and mitigate risks related to data breaches, cyberattacks, and unauthorized access. Develop incident response plans to minimize the impact of security incidents. Data Privacy: Dive into the realm of data privacy, understanding the rights of individuals over their personal information. Explore techniques for anonymization, pseudonymization, and ensuring consent-based data processing. Cloud Security: Explore the unique challenges and solutions for securing data in cloud environments. Understand how to leverage cloud security services and best practices to protect your data. Identity and Access Management: Delve into identity management systems, role-based access controls, and multi-factor authentication to ensure only authorized users have access to sensitive data. Emerging Technologies: Stay ahead of the curve by exploring how AI, blockchain, and other emerging technologies are impacting data security and governance. Understand their potential benefits and challenges. Why This Book Matters: As the digital landscape expands, so do the risks associated with data breaches and mismanagement. Mastering Data Security and Governance empowers businesses, IT professionals, and security practitioners to fortify their defenses against data threats, establish transparent governance practices, and navigate the evolving regulatory landscape. Secure Your Data Future: Data is the lifeblood of the digital age, and its security and responsible management are paramount. Mastering Data Security and Governance equips you with the knowledge and tools needed to build a robust security posture and establish effective governance, ensuring that your data remains safe, compliant, and trustworthy in an increasingly interconnected world. Your journey to safeguarding valuable data begins here. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com |
| data lifecycle management goals: Good Informatics Practices (GIP) Module: Data Management Robert Barr, Vizma Carver, Kim Green, Nishant Jain, Anthony Omosule, Steven Owens, Mark Vilicich MS, CSM, Ford Winslow, Nigel Wright, |
| data lifecycle management goals: 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 goals: Diverse Applications and Transferability of Maturity Models Katuu, Shadrack, 2018-10-19 Previously, professionals had to make judgment calls based on subjective criteria, including their own acumen, in their decision making. In order to combat this subjectivity, maturity models can be implemented to allow organizations a means of assessing everyday processes and to offer a path towards advancement using transparent objective criteria. Diverse Applications and Transferability of Maturity Models is a pivotal reference source that provides vital research on the application of maturity models in organizational development in a variety of work environments. While highlighting topics such as open government, archives and records management, enterprise content management, and digital economy, this publication explores methods to help organizations effectively implement plans in any given management system. This book is ideally designed for professionals and researchers seeking current research on a variety of social science and applied science fields including business studies, computer science, digital preservation, information governance, information science, information systems, public administration, records management, and project management. |
| data lifecycle management goals: Mastering Cloud Data Cybellium Ltd, 2023-09-06 Cybellium Ltd is dedicated to empowering individuals and organizations with the knowledge and skills they need to navigate the ever-evolving computer science landscape securely and learn only the latest information available on any subject in the category of computer science including: - Information Technology (IT) - Cyber Security - Information Security - Big Data - Artificial Intelligence (AI) - Engineering - Robotics - Standards and compliance Our mission is to be at the forefront of computer science education, offering a wide and comprehensive range of resources, including books, courses, classes and training programs, tailored to meet the diverse needs of any subject in computer science. Visit https://www.cybellium.com for more books. |
| data lifecycle management goals: Data Governance John Ladley, 2019-11-08 Managing data continues to grow as a necessity for modern organizations. There are seemingly infinite opportunities for organic growth, reduction of costs, and creation of new products and services. It has become apparent that none of these opportunities can happen smoothly without data governance. The cost of exponential data growth and privacy / security concerns are becoming burdensome. Organizations will encounter unexpected consequences in new sources of risk. The solution to these challenges is also data governance; ensuring balance between risk and opportunity. Data Governance, Second Edition, is for any executive, manager or data professional who needs to understand or implement a data governance program. It is required to ensure consistent, accurate and reliable data across their organization. This book offers an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable. This valuable resource provides comprehensive guidance to beginning professionals, managers or analysts looking to improve their processes, and advanced students in Data Management and related courses. With the provided framework and case studies all professionals in the data governance field will gain key insights into launching successful and money-saving data governance program. - Incorporates industry changes, lessons learned and new approaches - Explores various ways in which data analysts and managers can ensure consistent, accurate and reliable data across their organizations - Includes new case studies which detail real-world situations - Explores all of the capabilities an organization must adopt to become data driven - Provides guidance on various approaches to data governance, to determine whether an organization should be low profile, central controlled, agile, or traditional - Provides guidance on using technology and separating vendor hype from sincere delivery of necessary capabilities - Offers readers insights into how their organizations can improve the value of their data, through data quality, data strategy and data literacy - Provides up to 75% brand-new content compared to the first edition |
| data lifecycle management goals: Data Governance and Strategies Mr.Desidi Narsimha Reddy, 2024-09-05 Mr.Desidi Narsimha Reddy, Data Consultant (Data Governance, Data Analytics: Enterprise Performance Management, AI & ML), Soniks consulting LLC, 101 E Park Blvd Suite 600, Plano, TX 75074, United States. |
| data lifecycle management goals: Executing Data Quality Projects Danette McGilvray, 2021-05-27 Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today's data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization's standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before. - Includes concrete instructions, numerous templates, and practical advice for executing every step of The Ten Steps approach - Contains real examples from around the world, gleaned from the author's consulting practice and from those who implemented based on her training courses and the earlier edition of the book - Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices - A companion Web site includes links to numerous data quality resources, including many of the templates featured in the text, quick summaries of key ideas from the Ten Steps methodology, and other tools and information that are available online |
| data lifecycle management goals: ADVANCED DATA WAREHOUSING STRATEGIES: BUILDING SCALABLE AND HIGH-PERFORMANCE DATA STORAGE SOLUTIONS ROHAN VISWANATHA PRASAD SWATHI GARUDASU SMITA RAGHAVENDRA BHAT PROF. (DR) PUNIT GOEL, 2024-11-09 In the ever-evolving landscape of the modern world, the synergy between technology and management has become a cornerstone of innovation and progress. This book, Advanced Data Warehousing Strategies: Building Scalable and High-Performance Data Storage Solutions, is conceived to bridge the gap between emerging technological advancements in data warehousing and their strategic application in building efficient, scalable, and high-performance data storage systems. Our objective is to equip readers with the tools and insights necessary to excel in this dynamic intersection of fields. This book is structured to provide a comprehensive exploration of the methodologies and strategies that define the innovation of data warehousing technologies, particularly focusing on techniques and applications relevant to modern data storage solutions. From foundational theories to advanced applications, we delve into the critical aspects that drive successful innovation in large-scale data systems. We have made a concerted effort to present complex concepts in a clear and accessible manner, making this work suitable for a diverse audience, including students, developers, and industry professionals. In authoring this book, we have drawn upon the latest research and best practices to ensure that readers not only gain a robust theoretical understanding but also acquire practical skills that can be applied in real-world data warehousing scenarios. The chapters are designed to strike a balance between depth and breadth, covering topics ranging from data warehousing fundamentals and optimization techniques to the strategic management of scalable storage systems. Additionally, we emphasize the importance of high performance and data integrity, dedicating sections to the art of developing data solutions that deliver efficiency, scalability, and resilience. The inspiration for this book arises from a recognition of the crucial role that data warehousing systems play in shaping the future of digital interactions and business intelligence. We are profoundly grateful to Chancellor Shri Shiv Kumar Gupta of Maharaja Agrasen Himalayan Garhwal University for his unwavering support and vision. His dedication to fostering academic excellence and promoting a culture of innovation has been instrumental in bringing this project to fruition. We hope this book will serve as a valuable resource and inspiration for those eager to deepen their understanding of how data warehousing strategies can be harnessed to drive innovation. We believe that the knowledge and insights contained within these pages will empower readers to lead the way in creating high-performance data storage solutions that will define the future of enterprise data management. Thank you for joining us on this journey. Authors |
| data lifecycle management goals: 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 management goals: Building Cloud Data Platforms Solutions Anouar BEN ZAHRA, Building Cloud Data Platforms Solutions: An End-to-End Guide for Designing, Implementing, and Managing Robust Data Solutions in the Cloud comprehensively covers a wide range of topics related to building data platforms in the cloud. This book provides a deep exploration of the essential concepts, strategies, and best practices involved in designing, implementing, and managing end-to-end data solutions. The book begins by introducing the fundamental principles and benefits of cloud computing, with a specific focus on its impact on data management and analytics. It covers various cloud services and architectures, enabling readers to understand the foundation upon which cloud data platforms are built. Next, the book dives into key considerations for building cloud data solutions, aligning business needs with cloud data strategies, and ensuring scalability, security, and compliance. It explores the process of data ingestion, discussing various techniques for acquiring and ingesting data from different sources into the cloud platform. The book then delves into data storage and management in the cloud. It covers different storage options, such as data lakes and data warehouses, and discusses strategies for organizing and optimizing data storage to facilitate efficient data processing and analytics. It also addresses data governance, data quality, and data integration techniques to ensure data integrity and consistency across the platform. A significant portion of the book is dedicated to data processing and analytics in the cloud. It explores modern data processing frameworks and technologies, such as Apache Spark and serverless computing, and provides practical guidance on implementing scalable and efficient data processing pipelines. The book also covers advanced analytics techniques, including machine learning and AI, and demonstrates how these can be integrated into the data platform to unlock valuable insights. Furthermore, the book addresses an aspects of data platform monitoring, security, and performance optimization. It explores techniques for monitoring data pipelines, ensuring data security, and optimizing performance to meet the demands of real-time data processing and analytics. Throughout the book, real-world examples, case studies, and best practices are provided to illustrate the concepts discussed. This helps readers apply the knowledge gained to their own data platform projects. |
| data lifecycle management goals: Quality in the Era of Industry 4.0 Kai Yang, 2024-01-24 Enables readers to use real-world data from connected devices to improve product performance, detect design vulnerabilities, and design better solutions Quality in the Era of Industry 4.0 provides an insightful guide in harnessing user performance and behavior data through AI and other Industry 4.0 technologies. This transformative approach enables companies not only to optimize products and services in real-time, but also to anticipate and mitigate likely failures proactively. In a succinct and lucid style, the book presents a pioneering framework for a new paradigm of quality management in the Industry 4.0 landscape. It introduces groundbreaking techniques such as utilizing real-world data to tailor products for superior fit and performance, leveraging connectivity to adapt products to evolving needs and use-cases, and employing cutting-edge manufacturing methods to create bespoke, cost-effective solutions with greater efficiency. Case examples featuring applications from the automotive, mobile device, home appliance, and healthcare industries are used to illustrate how these new quality approaches can be used to benchmark the product’s performance and durability, maintain smart manufacturing, and detect design vulnerabilities. Written by a seasoned expert with experience teaching quality management in both corporate and academic settings, Quality in the Era of Industry 4.0 covers sample topics such as: Evolution of quality through industrial revolutions, from ancient times to the first and second industrial revolutions Quality by customer value creation, explaining differences in producers, stakeholders, and customers in the new digital age, along with new realities brought by Industry 4.0 Data quality dimensions and strategy, data governance, and new talents and skill sets for quality professionals in Industry 4.0 Automated product lifecycle management, predictive quality control, and defect prevention using technologies like smart factories, IoT, and sensors Quality in the Era of Industry 4.0 is a highly valuable resource for product engineers, quality managers, quality engineers and quality consultants, industrial engineers, and systems engineers who wish to make a participatory approach towards data-driven design, economical mass-customization, and late differentiation. |
| data lifecycle management goals: 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 management goals: Smarter Data Science Neal Fishman, Cole Stryker, 2020-04-09 Organizations can make data science a repeatable, predictable tool, which business professionals use to get more value from their data Enterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast majority fail. These expensive quagmires can be avoided, and this book explains precisely how. Data science is emerging as a hands-on tool for not just data scientists, but business professionals as well. Managers, directors, IT leaders, and analysts must expand their use of data science capabilities for the organization to stay competitive. Smarter Data Science helps them achieve their enterprise-grade data projects and AI goals. It serves as a guide to building a robust and comprehensive information architecture program that enables sustainable and scalable AI deployments. When an organization manages its data effectively, its data science program becomes a fully scalable function that’s both prescriptive and repeatable. With an understanding of data science principles, practitioners are also empowered to lead their organizations in establishing and deploying viable AI. They employ the tools of machine learning, deep learning, and AI to extract greater value from data for the benefit of the enterprise. By following a ladder framework that promotes prescriptive capabilities, organizations can make data science accessible to a range of team members, democratizing data science throughout the organization. Companies that collect, organize, and analyze data can move forward to additional data science achievements: Improving time-to-value with infused AI models for common use cases Optimizing knowledge work and business processes Utilizing AI-based business intelligence and data visualization Establishing a data topology to support general or highly specialized needs Successfully completing AI projects in a predictable manner Coordinating the use of AI from any compute node. From inner edges to outer edges: cloud, fog, and mist computing When they climb the ladder presented in this book, businesspeople and data scientists alike will be able to improve and foster repeatable capabilities. They will have the knowledge to maximize their AI and data assets for the benefit of their organizations. |
| data lifecycle management goals: Data Governance Neera Bhansali, 2013-06-17 As organizations deploy business intelligence and analytic systems to harness business value from their data assets, data governance programs are quickly gaining prominence. And, although data management issues have traditionally been addressed by IT departments, organizational issues critical to successful data management require the implementation of enterprise-wide accountabilities and responsibilities. Data Governance: Creating Value from Information Assets examines the processes of using data governance to manage data effectively. Addressing the complete life cycle of effective data governance—from metadata management to privacy and compliance—it provides business managers, IT professionals, and students with an integrated approach to designing, developing, and sustaining an effective data governance strategy. Explains how to align data governance with business goals Describes how to build successful data stewardship with a governance framework Outlines strategies for integrating IT and data governance frameworks Supplies business-driven and technical perspectives on data quality management, metadata management, data access and security, and data lifecycle The book summarizes the experiences of global experts in the field and addresses critical areas of interest to the information systems and management community. Case studies from healthcare and financial sectors, two industries that have successfully leveraged the potential of data-driven strategies, provide further insights into real-time practice. Facilitating a comprehensive understanding of data governance, the book addresses the burning issue of aligning data assets to both IT assets and organizational strategic goals. With a focus on the organizational, operational, and strategic aspects of data governance, the text provides you with the understanding required to leverage, derive, and sustain maximum value from the informational assets housed in your IT infrastructure. |
| data lifecycle management goals: Practitioner's Guide to Operationalizing Data Governance Mary Anne Hopper, 2023-05-09 Discover what does—and doesn’t—work when designing and building a data governance program In A Practitioner’s Guide to Operationalizing Data Governance, veteran SAS and data management expert Mary Anne Hopper walks readers through the planning, design, operationalization, and maintenance of an effective data governance program. She explores the most common challenges organizations face during and after program development and offers sound, hands-on advice to meet tackle those problems head-on. Ideal for companies trying to resolve a wide variety of issues around data governance, this book: Offers a straightforward starting point for companies just beginning to think about data governance Provides solutions when company employees and leaders don’t—for whatever reason—trust the data the company has Suggests proven strategies for getting a data governance program that’s gone off the rails back on track Complete with visual examples based in real-world case studies, A Practitioner’s Guide to Operationalizing Data Governance will earn a place in the libraries of information technology executives and managers, data professionals, and project managers seeking a one-stop resource to help them deliver practical data governance solutions. |
| data lifecycle management goals: 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 goals: Fundamentals of Data Engineering Joe Reis, Matt Housley, 2022-06-22 Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You'll understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, and governance that are critical in any data environment regardless of the underlying technology. This book will help you: Get a concise overview of the entire data engineering landscape Assess data engineering problems using an end-to-end framework of best practices Cut through marketing hype when choosing data technologies, architecture, and processes Use the data engineering lifecycle to design and build a robust architecture Incorporate data governance and security across the data engineering lifecycle |
| data lifecycle management goals: Handbook of Data Quality Shazia Sadiq, 2013-08-13 The issue of data quality is as old as data itself. However, the proliferation of diverse, large-scale and often publically available data on the Web has increased the risk of poor data quality and misleading data interpretations. On the other hand, data is now exposed at a much more strategic level e.g. through business intelligence systems, increasing manifold the stakes involved for individuals, corporations as well as government agencies. There, the lack of knowledge about data accuracy, currency or completeness can have erroneous and even catastrophic results. With these changes, traditional approaches to data management in general, and data quality control specifically, are challenged. There is an evident need to incorporate data quality considerations into the whole data cycle, encompassing managerial/governance as well as technical aspects. Data quality experts from research and industry agree that a unified framework for data quality management should bring together organizational, architectural and computational approaches. Accordingly, Sadiq structured this handbook in four parts: Part I is on organizational solutions, i.e. the development of data quality objectives for the organization, and the development of strategies to establish roles, processes, policies, and standards required to manage and ensure data quality. Part II, on architectural solutions, covers the technology landscape required to deploy developed data quality management processes, standards and policies. Part III, on computational solutions, presents effective and efficient tools and techniques related to record linkage, lineage and provenance, data uncertainty, and advanced integrity constraints. Finally, Part IV is devoted to case studies of successful data quality initiatives that highlight the various aspects of data quality in action. The individual chapters present both an overview of the respective topic in terms of historical research and/or practice and state of the art, as well as specific techniques, methodologies and frameworks developed by the individual contributors. Researchers and students of computer science, information systems, or business management as well as data professionals and practitioners will benefit most from this handbook by not only focusing on the various sections relevant to their research area or particular practical work, but by also studying chapters that they may initially consider not to be directly relevant to them, as there they will learn about new perspectives and approaches. |
| data lifecycle management goals: Data Governance for Managers Lars Michael Bollweg, 2022-05-13 Professional data management is the foundation for the successful digital transformation of traditional companies. Unfortunately, many companies fail to implement data governance because they do not fully understand the complexity of the challenge (organizational structure, employee empowerment, change management, etc.) and therefore do not include all aspects in the planning and implementation of their data governance. This book explains the driving role that a responsive data organization can play in a company's digital transformation. Using proven process models, the book takes readers from the basics, through planning and implementation, to regular operations and measuring the success of data governance. All the important decision points are highlighted, and the advantages and disadvantages are discussed in order to identify digitization potential, implement it in the company, and develop customized data governance. The book will serve as a useful guide for interested newcomers as well as for experienced managers. |
| data lifecycle management goals: 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 goals: Big Data Governance: Managing and Protecting Data Assets Michael Roberts, Big Data Governance: Managing and Protecting Data Assets offers a comprehensive exploration of the principles, practices, and strategies essential for governing vast volumes of data in the digital age. As organizations increasingly rely on big data to drive decision-making and innovation, effective governance becomes paramount to ensure data quality, security, and compliance. This book delves into key topics such as data governance frameworks, privacy regulations, risk management, and ethical considerations. Whether you're a data steward, IT manager, compliance officer, or business leader, this handbook equips you with the insights and tools to establish robust data governance frameworks and safeguard your organization's most valuable asset—its data. |
| data lifecycle management goals: Business Process Management Forum Mathias Weske, Marco Montali, Ingo Weber, Jan vom Brocke, 2018-08-31 This book constitutes the proceedings of the BPM Forum held during the 16th International Conference on Business Process Management, BPM 2018, which took place in Sydney, Australia, in September 2018. The BPM Forum hosts innovative research which has a high potential of stimulating discussions. The papers selected for the forum are expected to showcase fresh ideas from exciting and emerging topics in BPM, even if they are not yet as mature as the regular papers at the conference. The 14 papers presented in this volume were carefully reviewed and selected from a total of 113 submissions. They were organized according to the tracks of the conference: foundations; engineering; management. |
| data lifecycle management goals: 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 goals: Introduction to Marketing Analytics Cybellium, Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com |
| data lifecycle management goals: Product Lifecycle Management Enabling Smart X Felix Nyffenegger, José Ríos, Louis Rivest, Abdelaziz Bouras, 2020-11-19 This book constitutes the refereed post-conference proceedings of the 17th IFIP WG 5.1 International Conference on Product Lifecycle Management, PLM 2020, held in Rapperswil, Switzerland, in July 2020. The conference was held virtually due to the COVID-19 crisis. The 60 revised full papers presented together with 2 technical industrial papers were carefully reviewed and selected from 80 submissions. The papers are organized in the following topical sections: smart factory; digital twins; Internet of Things (IoT, IIoT); analytics in the order fulfillment process; ontologies for interoperability; tools to support early design phases; new product development; business models; circular economy; maturity implementation and adoption; model based systems engineering; artificial intelligence in CAx, MBE, and PLM; building information modelling; and industrial technical contributions. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
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 to …
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 …
