| data management capability assessment model: DAMA-DMBOK Dama International, 2017 Defining a set of guiding principles for data management and describing how these principles can be applied within data management functional areas; Providing a functional framework for the implementation of enterprise data management practices; including widely adopted practices, methods and techniques, functions, roles, deliverables and metrics; Establishing a common vocabulary for data management concepts and serving as the basis for best practices for data management professionals. DAMA-DMBOK2 provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure, based on these principles: Data is an asset with unique properties; The value of data can be and should be expressed in economic terms; Managing data means managing the quality of data; It takes metadata to manage data; It takes planning to manage data; Data management is cross-functional and requires a range of skills and expertise; Data management requires an enterprise perspective; Data management must account for a range of perspectives; Data management is data lifecycle management; Different types of data have different lifecycle requirements; Managing data includes managing risks associated with data; Data management requirements must drive information technology decisions; Effective data management requires leadership commitment. |
| data management capability assessment model: The "Orange" Model of Data Management Irina Steenbeek, 2019-10-21 *This book is a brief overview of the model and has only 24 pages.*Almost every data management professional, at some point in their career, has come across the following crucial questions:1. Which industry reference model should I use for the implementation of data managementfunctions?2. What are the key data management capabilities that are feasible and applicable to my company?3. How do I measure the maturity of the data management functions and compare that withthose of my peers in the industry4. What are the critical, logical steps in the implementation of data management?The Orange (meta)model of data management provides a collection of techniques and templates for the practical set up of data management through the design and implementation of the data and information value chain, enabled by a set of data management capabilities.This book is a toolkit for advanced data management professionals and consultants thatare involved in the data management function implementation.This book works together with the earlier published The Data Management Toolkit. The Orange model assists in specifying the feasible scope of data management capabilities, that fits company's business goals and resources. The Data Management Toolkit is a practical implementation guide of the chosen data management capabilities. |
| data management capability assessment model: IQM-CMM: Information Quality Management Capability Maturity Model Sasa Baskarada, 2010-04-03 Saša Baškarada presents a capability maturity model for information quality management process assessment and improvement. The author employed six exploratory case studies and a four round Delphi study to gain a better understanding of the research problem and to build the preliminary model, which he then applied in seven international case studies for further enhancement and external validation. |
| data management capability assessment model: The Cyber Patient Rebecca Mendoza Saltiel Busch, 2019-03-28 With the use of electronic health records (EHR) transforming the healthcare industry, the use of information technology in the maintenance of personal health records poses a range of issues and opportunities for every medical organization, The Cyber Patient expertly walks readers through the elements required for an efficient, well-run healthcare record management system, while reflecting the U.S. government’s goal of achieving widespread adoption of interoperable electronic health records to improve the quality and efficiency of healthcare while maintaining the levels of security and privacy that consumers expect. The author also provides an update as to where the industry stands in their push of interoperability and the increased use of data as an analytic tools. Providing an application readers can adopt as a model, this important book examines the infrastructure of electronic health records and how government criteria have impacted and will continue to impact both private and public marketplaces. This valuable resource also addresses how auditors, controllers, and healthcare providers can keep up with the market’s continued move towards an interoperable e-health world, without neglecting clinical and financial accountability in the delivery of healthcare. As e-health continues to develop and transform, The Cyber Patient thoughtfully prepares professionals to plan and implement an effective EHR as wel as internal controls system within any clinical setting. |
| data management capability assessment model: A Practitioner's Guide to Data Governance Uma Gupta, San Cannon, 2020-07-08 Data governance looks simple on paper, but in reality it is a complex issue facing organizations. In this practical guide, data experts Uma Gupta and San Cannon look to demystify data governance through pragmatic advice based on real-world experience and cutting-edge academic research. |
| data management capability assessment model: Data Management Fundamentals (DMF) - CDMP exam preparation Paul Rakké, 1970-01-01 Besides this Data Management Fundamentals (DMF) CDMP exam preparation book, you are advised to obtain the publication the Data Management courseware based on CDMP Fundamentals - Revised edition (ISBN: 9789401811491) for your preparation for your Certified Data Management Professional (CDMP) certification. This CDMP certification based on the DAMA DMBok (Data Management Body of Knowledge) is a globally recognized credential that validates the knowledge and skills required in the field of data management.This exam preparation book is a well-balanced guide to help you pass the CDMP exam and earn your certification. All the knowledge areas as described in the related courseware and/or DAMA-DMBOK (2nd edition) of the well-known study book plus extra topics as described in the book too, will be treated with exam-like questions. The number of questions per topic can differ, depending on the weights as used in the formal exam composition. All the questions are newly defined questions by the author. Separately the correct answers and guiding explanations with references to the DAMA-DMBOK book are provided. Besides the set of questions per topic which consist of a set of 140 questions, also a set of 100 extra questions with the same weights per topic is provided to give you the opportunity to prepare yourself on the exam with this similar exam. So this 240 new questions provided in this book make your road to the CDMP certification complete. |
| data management capability assessment model: The Data Management Toolkit: A Step-By-Step Implementation Guide for the Pioneers of Data Management Irina Steenbeek, 2019-03-09 Eight years ago, I joined a new company. My first challenge was to develop an automated management accounting reporting system. A deep analysis of the existing reports showed us the high necessity to implement a singular reporting platform, and we opted to implement a data warehouse. At the time, one of the consultants came to me and said, I heard that we might need data management. I don't know what it is. Check it out. So I started Googling Data management...This book is for professionals who are now in the same position I found myself in eight years ago and for those who want to become a data management pro of a medium sized company.It is a collection of hands-on knowledge, experience and observations on how to implement data management in an effective, feasible and to-the-point way. |
| data management capability assessment model: Modern Data Strategy Mike Fleckenstein, Lorraine Fellows, 2018-02-12 This book contains practical steps business users can take to implement data management in a number of ways, including data governance, data architecture, master data management, business intelligence, and others. It defines data strategy, and covers chapters that illustrate how to align a data strategy with the business strategy, a discussion on valuing data as an asset, the evolution of data management, and who should oversee a data strategy. This provides the user with a good understanding of what a data strategy is and its limits. Critical to a data strategy is the incorporation of one or more data management domains. Chapters on key data management domains—data governance, data architecture, master data management and analytics, offer the user a practical approach to data management execution within a data strategy. The intent is to enable the user to identify how execution on one or more data management domains can help solve business issues. This book is intended for business users who work with data, who need to manage one or more aspects of the organization’s data, and who want to foster an integrated approach for how enterprise data is managed. This book is also an excellent reference for students studying computer science and business management or simply for someone who has been tasked with starting or improving existing data management. |
| data management capability assessment model: Data Governance Ismael Caballero, Mario Piattini, 2024-01-28 This book presents a set of models, methods, and techniques that allow the successful implementation of data governance (DG) in an organization and reports real experiences of data governance in different public and private sectors. To this end, this book is composed of two parts. Part I on “Data Governance Fundamentals” begins with an introduction to the concept of data governance that stresses that DG is not primarily focused on databases, clouds, or other technologies, but that the DG framework must be understood by business users, systems personnel, and the systems themselves alike. Next, chapter 2 addresses crucial topics for DG, such as the evolution of data management in organizations, data strategy and policies, and defensive and offensive approaches to data strategy. Chapter 3 then details the central role that human resources play in DG, analysing the key responsibilities of the different DG-related roles and boards, while chapter 4 discusses the most common barriers to DG in practice. Chapter 5 summarizes the paradigm shifts in DG from control to value creation. Subsequently chapter 6 explores the needs, characteristics and key functionalities of DG tools, before this part ends with a chapter on maturity models for data governance. Part II on “Data Governance Applied” consists of five chapters which review the situation of DG in different sectors and industries. Details about DG in the banking sector, public administration, insurance companies, healthcare and telecommunications each are presented in one chapter. The book is aimed at academics, researchers and practitioners (especially CIOs, Data Governors, or Data Stewards) involved in DG. It can also serve as a reference for courses on data governance in information systems. |
| data management capability assessment model: The Project Risk Maturity Model Mr Martin Hopkinson, 2012-09-28 Top businesses recognise risk management as a core feature of their project management process and approach to the governance of projects. However, a mature risk management process is required in order to realise its benefits; one that takes into account the design and implementation of the process and the skills, experience and culture of the people who use it. To be mature in the way you manage risk you need an accepted framework to assess your risk management maturity, allowing you to benchmark against a recognised standard. A structured pathway for improvement is also needed, not just telling you where you are now, but describing the steps required to reach the next level. The Project Risk Maturity Model detailed here provides such an assessment framework and development pathway. It can be used to benchmark your project risk processes and support the introduction of effective in-house project risk management. Using this model, implementation and improvement of project risk management can be managed effectively to ensure that the expected benefits are achieved in a way that is appropriate to the needs of each organisation. Martin Hopkinson has developed The Project Risk Maturity Model into a robust framework, and this book allows you to access and apply his insights and experience. A key feature is a CD containing a working copy of the QinetiQ Project Risk Maturity Model (RMM). This will enable you to undertake maturity assessments for as many projects as you choose. The RMM has been proven over a period of 10 years, with at least 250 maturity assessments on projects and programmes with a total value exceeding £60 billion. A case study in the book demonstrates how it has been used to deliver significant and measurable benefits to the performance of major projects. |
| data management capability assessment model: Data Governance and Data Management Rupa Mahanti, 2021-09-08 This book delves into the concept of data as a critical enterprise asset needed for informed decision making, compliance, regulatory reporting and insights into trends, behaviors, performance and patterns. With good data being key to staying ahead in a competitive market, enterprises capture and store exponential volumes of data. Considering the business impact of data, there needs to be adequate management around it to derive the best value. Data governance is one of the core data management related functions. However, it is often overlooked, misunderstood or confused with other terminologies and data management functions. Given the pervasiveness of data and the importance of data, this book provides comprehensive understanding of the business drivers for data governance and benefits of data governance, the interactions of data governance function with other data management functions and various components and aspects of data governance that can be facilitated by technology and tools, the distinction between data management tools and data governance tools, the readiness checks to perform before exploring the market to purchase a data governance tool, the different aspects that must be considered when comparing and selecting the appropriate data governance technologies and tools from large number of options available in the marketplace and the different market players that provide tools for supporting data governance. This book combines the data and data governance knowledge that the author has gained over years of working in different industrial and research programs and projects associated with data, processes and technologies with unique perspectives gained through interviews with thought leaders and data experts. This book is highly beneficial for IT students, academicians, information management and business professionals and researchers to enhance their knowledge and get guidance on implementing data governance in their own data initiatives. |
| data management capability assessment model: SOA Source Book The Open Group, 2020-06-11 Software services are established as a programming concept, but their impact on the overall architecture of enterprise IT and business operations is not well-understood. This has led to problems in deploying SOA, and some disillusionment. The SOA Source Book adds to this a collection of reference material for SOA. It is an invaluable resource for enterprise architects working with SOA.The SOA Source Book will help enterprise architects to use SOA effectively. It explains: What SOA is How to evaluate SOA features in business terms How to model SOA How to use The Open Group Architecture Framework (TOGAF ) for SOA SOA governance This book explains how TOGAF can help to make an Enterprise Architecture. Enterprise Architecture is an approach that can help management to understand this growing complexity. |
| data management capability assessment model: ADKAR Jeff Hiatt, 2006 In his first complete text on the ADKAR model, Jeff Hiatt explains the origin of the model and explores what drives each building block of ADKAR. Learn how to build awareness, create desire, develop knowledge, foster ability and reinforce changes in your organization. The ADKAR Model is changing how we think about managing the people side of change, and provides a powerful foundation to help you succeed at change. |
| data management capability assessment model: Project Management Capability Assessment Peter T. Davis, Barry D. Lewis, 2019 Using ISO 21500 as the project management reference point and ISO 15504/33000 as the capability assessment reference, this book shows you how to assess whether your projects are being run according to a specific capability level or support them to reach higher levels of capability. -- |
| data management capability assessment model: The Capability Maturity Model Mark C. Paulk, 1995 Principal Contributors and Editors: Mark C. Paulk, Charles V. Weber, Bill Curtis, Mary Beth Chrissis In every sense, the CMM represents the best thinking in the field today... this book is targeted at anyone involved in improving the software process, including members of assessment or evaluation teams, members of software engineering process groups, software managers, and software practitioners... From the Foreword by Watts Humphrey The Capability Maturity Model for Software (CMM) is a framework that demonstrates the key elements of an effective software process. The CMM describes an evolutionary improvement path for software development from an ad hoc, immature process to a mature, disciplined process, in a path laid out in five levels. When using the CMM, software professionals in government and industry can develop and improve their ability to identify, adopt, and use sound management and technical practices for delivering quality software on schedule and at a reasonable cost. This book provides a description and technical overview of the CMM, along with guidelines for improving software process management overall. It is a sequel to Watts Humphrey's important work, Managing the Software Process, in that it structures the maturity framework presented in that book more formally. Features: Compares the CMM with ISO 9001 Provides an overview of ISO's SPICE project, which is developing international standards for software process improvement and capability determination Presents a case study of IBM Houston's Space Shuttle project, which is frequently referred to as being at Level 5 0201546647B04062001 |
| data management capability assessment model: The DAMA Dictionary of Data Management Dama International, 2011 A glossary of over 2,000 terms which provides a common data management vocabulary for IT and Business professionals, and is a companion to the DAMA Data Management Body of Knowledge (DAMA-DMBOK). Topics include: Analytics & Data Mining Architecture Artificial Intelligence Business Analysis DAMA & Professional Development Databases & Database Design Database Administration Data Governance & Stewardship Data Management Data Modeling Data Movement & Integration Data Quality Management Data Security Management Data Warehousing & Business Intelligence Document, Record & Content Management Finance & Accounting Geospatial Data Knowledge Management Marketing & Customer Relationship Management Meta-Data Management Multi-dimensional & OLAP Normalization Object-Orientation Parallel Database Processing Planning Process Management Project Management Reference & Master Data Management Semantic Modeling Software Development Standards Organizations Structured Query Language (SQL) XML Development |
| data management capability assessment model: Project Management Capability Assessment Peter T. Davis, Barry D. Lewis, 2018-09-13 Learn how to perform project management according to international standards of compliance using capability assessment processes. This book compares and contrasts the approach to project management using ISO 21500 against the more direct ISO 33000 Capability Assessment. It shows how to assess projects adequately for process improvement or how well an organization performs against a standard, measurable framework. Using ISO 21500 as the project management reference point and ISO 15504/33000 as the capability assessment reference, the book shows you how to assess whether your projects are being run according to a specific capability level or support them to reach higher levels of capability. |
| data management capability assessment model: Meeting the Challenges of Data Quality Management Laura Sebastian-Coleman, 2022-01-25 Meeting the Challenges of Data Quality Management outlines the foundational concepts of data quality management and its challenges. The book enables data management professionals to help their organizations get more value from data by addressing the five challenges of data quality management: the meaning challenge (recognizing how data represents reality), the process/quality challenge (creating high-quality data by design), the people challenge (building data literacy), the technical challenge (enabling organizational data to be accessed and used, as well as protected), and the accountability challenge (ensuring organizational leadership treats data as an asset). Organizations that fail to meet these challenges get less value from their data than organizations that address them directly. The book describes core data quality management capabilities and introduces new and experienced DQ practitioners to practical techniques for getting value from activities such as data profiling, DQ monitoring and DQ reporting. It extends these ideas to the management of data quality within big data environments. This book will appeal to data quality and data management professionals, especially those involved with data governance, across a wide range of industries, as well as academic and government organizations. Readership extends to people higher up the organizational ladder (chief data officers, data strategists, analytics leaders) and in different parts of the organization (finance professionals, operations managers, IT leaders) who want to leverage their data and their organizational capabilities (people, processes, technology) to drive value and gain competitive advantage. This will be a key reference for graduate students in computer science programs which normally have a limited focus on the data itself and where data quality management is an often-overlooked aspect of data management courses. - Describes the importance of high-quality data to organizations wanting to leverage their data and, more generally, to people living in today's digitally interconnected world - Explores the five challenges in relation to organizational data, including Big Data, and proposes approaches to meeting them - Clarifies how to apply the core capabilities required for an effective data quality management program (data standards definition, data quality assessment, monitoring and reporting, issue management, and improvement) as both stand-alone processes and as integral components of projects and operations - Provides Data Quality practitioners with ways to communicate consistently with stakeholders |
| data management capability assessment model: A Primer in Financial Data Management Martijn Groot, 2017-05-10 A Primer in Financial Data Management describes concepts and methods, considering financial data management, not as a technological challenge, but as a key asset that underpins effective business management. This broad survey of data management in financial services discusses the data and process needs from the business user, client and regulatory perspectives. Its non-technical descriptions and insights can be used by readers with diverse interests across the financial services industry. The need has never been greater for skills, systems, and methodologies to manage information in financial markets. The volume of data, the diversity of sources, and the power of the tools to process it massively increased. Demands from business, customers, and regulators on transparency, safety, and above all, timely availability of high quality information for decision-making and reporting have grown in tandem, making this book a must read for those working in, or interested in, financial management. - Focuses on ways information management can fuel financial institutions' processes, including regulatory reporting, trade lifecycle management, and customer interaction - Covers recent regulatory and technological developments and their implications for optimal financial information management - Views data management from a supply chain perspective and discusses challenges and opportunities, including big data technologies and regulatory scrutiny |
| data management capability assessment model: Patient's Healthcare Portfolio Rebecca Mendoza Saltiel Busch, 2017-04-07 As the healthcare market moves toward an era of patient engagement, population health and patient advocates, practitioners need a practical how-to guide that helps facilitate their teaching with patients and family members on not just writing down or accumulating a personal health record, but an interactive process that converts the personal health information into a data-driven decision-making process. Through numerous forms, templates and real life examples, the author provides the tools to help patients gather critical health information while minimizing their exposure to medical and financial errors. |
| data management capability assessment model: Principles of Data Fabric Sonia Mezzetta, 2023-04-06 Apply Data Fabric solutions to automate Data Integration, Data Sharing, and Data Protection across disparate data sources using different data management styles. Purchase of the print or Kindle book includes a free PDF eBook Key Features Learn to design Data Fabric architecture effectively with your choice of tool Build and use a Data Fabric solution using DataOps and Data Mesh frameworks Find out how to build Data Integration, Data Governance, and Self-Service analytics architecture Book Description Data can be found everywhere, from cloud environments and relational and non-relational databases to data lakes, data warehouses, and data lakehouses. Data management practices can be standardized across the cloud, on-premises, and edge devices with Data Fabric, a powerful architecture that creates a unified view of data. This book will enable you to design a Data Fabric solution by addressing all the key aspects that need to be considered. The book begins by introducing you to Data Fabric architecture, why you need them, and how they relate to other strategic data management frameworks. You'll then quickly progress to grasping the principles of DataOps, an operational model for Data Fabric architecture. The next set of chapters will show you how to combine Data Fabric with DataOps and Data Mesh and how they work together by making the most out of it. After that, you'll discover how to design Data Integration, Data Governance, and Self-Service analytics architecture. The book ends with technical architecture to implement distributed data management and regulatory compliance, followed by industry best practices and principles. By the end of this data book, you will have a clear understanding of what Data Fabric is and what the architecture looks like, along with the level of effort that goes into designing a Data Fabric solution. What you will learn Understand the core components of Data Fabric solutions Combine Data Fabric with Data Mesh and DataOps frameworks Implement distributed data management and regulatory compliance using Data Fabric Manage and enforce Data Governance with active metadata using Data Fabric Explore industry best practices for effectively implementing a Data Fabric solution Who this book is for If you are a data engineer, data architect, or business analyst who wants to learn all about implementing Data Fabric architecture, then this is the book for you. This book will also benefit senior data professionals such as chief data officers looking to integrate Data Fabric architecture into the broader ecosystem. |
| data management capability assessment model: Data Governance Success Rupa Mahanti, 2021-12-13 While good data is an enterprise asset, bad data is an enterprise liability. Data governance enables you to effectively and proactively manage data assets throughout the enterprise by providing guidance in the form of policies, standards, processes and rules and defining roles and responsibilities outlining who will do what, with respect to data. While implementing data governance is not rocket science, it is not a simple exercise. There is a lot confusion around what data governance is, and a lot of challenges in the implementation of data governance. Data governance is not a project or a one-off exercise but a journey that involves a significant amount of effort, time and investment and cultural change and a number of factors to take into consideration to achieve and sustain data governance success. Data Governance Success: Growing and Sustaining Data Governance is the third and final book in the Data Governance series and discusses the following: • Data governance perceptions and challenges • Key considerations when implementing data governance to achieve and sustain success• Strategy and data governance• Different data governance maturity frameworks• Data governance – people and process elements• Data governance metrics This book shares the combined knowledge related to data and data governance that the author has gained over the years of working in different industrial and research programs and projects associated with data, processes, and technologies and unique perspectives of Thought Leaders and Data Experts through Interviews conducted. This book will be highly beneficial for IT students, academicians, information management and business professionals and researchers to enhance their knowledge to support and succeed in data governance implementations. This book is technology agnostic and contains a balance of concepts and examples and illustrations making it easy for the readers to understand and relate to their own specific data projects. |
| data management capability assessment model: Data Governance Dimitrios Sargiotis, |
| data management capability assessment model: Handbook on Business Process Management and Digital Transformation Paul Grefen, Irene Vanderfeesten, 2024-08-06 Many organizations are currently undertaking digital transformation to improve their business processes and better achieve their goals. This Handbook provides a comprehensive overview of contemporary trends and research at the point where business process management and digital transformation meet. Presenting a multidisciplinary approach, it demonstrates the close link between these two fields through engagement with theory and practice. |
| data management capability assessment model: Master Data Management David Loshin, 2010-07-28 The key to a successful MDM initiative isn't technology or methods, it's people: the stakeholders in the organization and their complex ownership of the data that the initiative will affect.Master Data Management equips you with a deeply practical, business-focused way of thinking about MDM—an understanding that will greatly enhance your ability to communicate with stakeholders and win their support. Moreover, it will help you deserve their support: you'll master all the details involved in planning and executing an MDM project that leads to measurable improvements in business productivity and effectiveness. - Presents a comprehensive roadmap that you can adapt to any MDM project - Emphasizes the critical goal of maintaining and improving data quality - Provides guidelines for determining which data to master. - Examines special issues relating to master data metadata - Considers a range of MDM architectural styles - Covers the synchronization of master data across the application infrastructure |
| data management capability assessment model: Project Management Maturity Model J. Kent Crawford, 2006-07-24 Assisting organizations in improving their project management processes, the Project Management Maturity Model defines the industry standard for measuring project management maturity.Project Management Maturity Model, Second Edition provides a roadmap showing organizations how to move to higher levels of organizational behavior, improving |
| data management capability assessment model: 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 management capability assessment model: Lean Digital Thinking V. Srinivasa Rao, 2021-10-18 The effective digitalization of business can make you a business leader; however, if not executed accurately, it can destroy your business too. Around 70 per cent of digital transformation projects have been failing. Even successful digitalization projects have become white elephants or expensive during the operations phase. Lean Digital Thinking introduces the '12-12-5 model'-12 lean digital thinking principles, 12 digital business building blocks and 5 lean digitalization phases-a brilliant guide that will enable business executives to become digital business champions. Leading digital expert, author VSR, as he is popularly known, introduces the world's first lean digital thinking philosophy with 12 principles to acquire a new digital mindset and throws in critical questions: Why digitalize? Where to digitalize? What to digitalize? And how at all to digitalize? He provides lean digital methods, templates and frameworks for digitalizing 12 business building blocks at an optimal cost. Further, new business models, products, services, processes, digital workplaces and operating models, driven by digital technologies, have been discussed with insights on how to leverage digitalization to get ready for the new normal that has emerged with the COVID-19 pandemic. An effective practitioner's guide, this is a must-read for business and technology executives and anyone wishing to master the art of digital business. |
| data management capability assessment model: Systems, Software and Services Process Improvement Murat Yilmaz, Paul Clarke, Richard Messnarz, Bruno Wöran, 2022-08-25 This volume constitutes the refereed proceedings of the 29th European Conference on Systems, Software and Services Process Improvement, EuroSPI 2022, held in Salzburg, Austria, in August-September 2022. The 49 full papers and 8 short papers presented were carefully reviewed and selected from 110 submissions. The papers are organized according to the following topical sections: SPI and emerging and multidisciplinary approaches to software engineering; digitalisation of industry, infrastructure and e-mobility; SPI and good/bad SPI practices in improvement; SPI and functional safety and cybersecurity; SPI and agile; SPI and standards and safety and security norms; SPI and team skills and diversity; SPI and recent innovations; virtual reality and augmented reality. |
| data management capability assessment model: Adaptive Enterprise Architecture As Information: Architecting Intelligent Enterprises Asif Qumar Gill, 2022-07-15 This compendium discusses the adaptive enterprise architecture (AEA) as information to support decisions and actions for desired efficiency and innovation (outcomes and impacts). This comprehensive information-driven approach uses data, analytics, and intelligence (AI/ML) for architecting intelligent enterprises.The unique reference text includes practical artefacts and vivid examples based on both practice and research. It benefits chief information officers, chief data officers, chief enterprise architects, enterprise architects, business architects, information architects, data architects, and anyone who has an interest in adaptive and digital enterprise architecture. |
| data management capability assessment model: The Data Management Cookbook Irina Steenbeek, 2018-03-16 A lot of companies realize that data is an invaluable asset and has to be managed accordingly. They would also like to get value from data. Everyone wants to be 'data-driven' these days. What lies beneath this idea, is the wish to make the decision-making process easier and more effective. It means delivering the required data of acceptable quality to the relevant decision makers when and where they need it. In short: a lot of companies have the necessity to manage their data properly. The main question is: how do you put this in practice? Knowing the potential of your data, and managing it correctly is the key to an effective and successful business. As a result of well-implemented data management, you will be able to reduce risks and costs, increase efficiency, ensure business continuity and successful growth. In this book, we invite you for a five-course dinner. During each course we will explain the steps of our 5-step programme which guarantees successful implementation of data management. |
| data management capability assessment model: Fail Fast, Learn Faster Randy Bean, 2021-08-31 Explore why — now more than ever — the world is in a race to become data-driven, and how you can learn from examples of data-driven leadership in an Age of Disruption, Big Data, and AI In Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data, and AI, Fortune 1000 strategic advisor, noted author, and distinguished thought leader Randy Bean tells the story of the rise of Big Data and its business impact – its disruptive power, the cultural challenges to becoming data-driven, the importance of data ethics, and the future of data-driven AI. The book looks at the impact of Big Data during a period of explosive information growth, technology advancement, emergence of the Internet and social media, and challenges to accepted notions of data, science, and facts, and asks what it means to become data-driven. Fail Fast, Learn Faster includes discussions of: The emergence of Big Data and why organizations must become data-driven to survive Why becoming data-driven forces companies to think different about their business The state of data in the corporate world today, and the principal challenges Why companies must develop a true data culture if they expect to change Examples of companies that are demonstrating data-driven leadership and what we can learn from them Why companies must learn to fail fast and learn faster to compete in the years ahead How the Chief Data Officer has been established as a new corporate profession Written for CEOs and Corporate Board Directors, data professional and practitioners at all organizational levels, university executive programs and students entering the data profession, and general readers seeking to understand the Information Age and why data, science, and facts matter in the world in which we live, Fail Fast, Learn Faster p;is essential reading that delivers an urgent message for the business leaders of today and of the future. |
| data management capability assessment model: Practical Data Quality Robert Hawker, 2023-09-29 Identify data quality issues, leverage real-world examples and templates to drive change, and unlock the benefits of improved data in processes and decision-making Key Features Get a practical explanation of data quality concepts and the imperative for change when data is poor Gain insights into linking business objectives and data to drive the right data quality priorities Explore the data quality lifecycle and accelerate improvement with the help of real-world examples Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionPoor data quality can lead to increased costs, hinder revenue growth, compromise decision-making, and introduce risk into organizations. This leads to employees, customers, and suppliers finding every interaction with the organization frustrating. Practical Data Quality provides a comprehensive view of managing data quality within your organization, covering everything from business cases through to embedding improvements that you make to the organization permanently. Each chapter explains a key element of data quality management, from linking strategy and data together to profiling and designing business rules which reveal bad data. The book outlines a suite of tried-and-tested reports that highlight bad data and allow you to develop a plan to make corrections. Throughout the book, you’ll work with real-world examples and utilize re-usable templates to accelerate your initiatives. By the end of this book, you’ll have gained a clear understanding of every stage of a data quality initiative and be able to drive tangible results for your organization at pace.What you will learn Explore data quality and see how it fits within a data management programme Differentiate your organization from its peers through data quality improvement Create a business case and get support for your data quality initiative Find out how business strategy can be linked to processes, analytics, and data to derive only the most important data quality rules Monitor data through engaging, business-friendly data quality dashboards Integrate data quality into everyday business activities to help achieve goals Avoid common mistakes when implementing data quality practices Who this book is for This book is for data analysts, data engineers, and chief data officers looking to understand data quality practices and their implementation in their organization. This book will also be helpful for business leaders who see data adversely affecting their success and data teams that want to optimize their data quality approach. No prior knowledge of data quality basics is required. |
| data management capability assessment model: Data Stewardship in Action Pui Shing Lee, 2024-02-16 Take your organization's data maturity to the next level by operationalizing data governance Key Features Develop the mindset and skills essential for successful data stewardship Apply practical advice and industry best practices, spanning data governance, quality management, and compliance, to enhance data stewardship Follow a step-by-step program to develop a data operating model and implement data stewardship effectively Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn the competitive data-centric world, mastering data stewardship is not just a requirement—it's the key to organizational success. Unlock strategic excellence with Data Stewardship in Action, your guide to exploring the intricacies of data stewardship and its implementation for maximum efficiency. From business strategy to data strategy, and then to data stewardship, this book shows you how to strategically deploy your workforce, processes, and technology for efficient data processing. You’ll gain mastery over the fundamentals of data stewardship, from understanding the different roles and responsibilities to implementing best practices for data governance. You’ll elevate your data management skills by exploring the technologies and tools for effective data handling. As you progress through the chapters, you’ll realize that this book not only helps you develop the foundational skills to become a successful data steward but also introduces innovative approaches, including leveraging AI and GPT, for enhanced data stewardship. By the end of this book, you’ll be able to build a robust data governance framework by developing policies and procedures, establishing a dedicated data governance team, and creating a data governance roadmap that ensures your organization thrives in the dynamic landscape of data management.What you will learn Enhance your job prospects by understanding the data stewardship field, roles, and responsibilities Discover how to develop a data strategy and translate it into a functional data operating model Develop an effective and efficient data stewardship program Gain practical experience of establishing a data stewardship initiative Implement purposeful governance with measurable ROI Prioritize data use cases with the value and effort matrix Who this book is for This book is for professionals working in the field of data management, including business analysts, data scientists, and data engineers looking to gain a deeper understanding of the data steward role. Senior executives who want to (re)establish the data governance body in their organizations will find this resource invaluable. While accessible to both beginners and professionals, basic knowledge of data management concepts, such as data modeling, data warehousing, and data quality, is a must to get started. |
| data management capability assessment model: Data Lineage from a Business Perspective Irina Steenbeek, 2021-10 Data lineage has become a daily demand. However, data lineage remains an abstract/ unknown concept for many users. The implementation is complex and resource-consuming. Even if implemented, it is not used as expected. This book uncovers different aspects of data lineage for data management and business professionals. It provides the definition and metamodel of data lineage, demonstrates best practices in data lineage implementation, and discusses the key areas of data lineage usage. Several groups of professionals can use this book in different ways: Data management and business professionals can develop ideas about data lineage and its application areas. Professionals with a technical background may gain a better understanding of business needs and requirements for data lineage. Project management professionals can become familiar with the best practices of data lineage implementation. |
| data management capability assessment model: Research Data Management Joyce M. Ray, 2014 It has become increasingly accepted that important digital data must be retained and shared in order to preserve and promote knowledge, advance research in and across all disciplines of scholarly endeavor, and maximize the return on investment of public funds. To meet this challenge, colleges and universities are adding data services to existing infrastructures by drawing on the expertise of information professionals who are already involved in the acquisition, management and preservation of data in their daily jobs. Data services include planning and implementing good data management practices, thereby increasing researchers' ability to compete for grant funding and ensuring that data collections with continuing value are preserved for reuse. This volume provides a framework to guide information professionals in academic libraries, presses, and data centers through the process of managing research data from the planning stages through the life of a grant project and beyond. It illustrates principles of good practice with use-case examples and illuminates promising data service models through case studies of innovative, successful projects and collaborations. |
| data management capability assessment model: 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 management capability assessment model: Cloud Computing, Big Data & Emerging Topics Marcelo Naiouf, Enzo Rucci, Franco Chichizola, Laura De Giusti, 2023-09-11 This book constitutes the revised selected papers of the 11th International Conference on Cloud Computing, Big Data & Emerging Topics, JCC-BD&ET 2023, held in La Plata, Argentina*, in June 2023. The 14 full papers were carefully reviewed and selected from a total of 38 submissions. The papers are organized in topical sections on: Parallel and Distributed Computing, Big Data, Machine and Deep Learning, Smart Cities and E-Government, Visualization. |
| data management capability assessment model: Software Process Improvement and Capability Determination Paul M. Clarke, Rory V. O'Connor, Terry Rout, Alec Dorling, 2016-05-11 This book constitutes the refereed proceedings of the 16th International Conference on Software Process Improvement and Capability Determination, SPICE 2016, held in Dublin, Ireland, in June 2016. The 28 full papers presented together with 5 short papers were carefully reviewed and selected from 52 submissions. The papers are organized in the following topical sections: SPI in regulated and safety critical domains; gamification and education issues in SPI; SPI in agile and small settings; SPI and assessment; SPI and project management concerns; empirical research case studies of SPI; knowledge and human communications issues in SPI. |
| data management capability assessment model: Performance Dashboards Wayne W. Eckerson, 2005-10-27 Tips, techniques, and trends on how to use dashboard technology to optimize business performance Business performance management is a hot new management discipline that delivers tremendous value when supported by information technology. Through case studies and industry research, this book shows how leading companies are using performance dashboards to execute strategy, optimize business processes, and improve performance. Wayne W. Eckerson (Hingham, MA) is the Director of Research for The Data Warehousing Institute (TDWI), the leading association of business intelligence and data warehousing professionals worldwide that provide high-quality, in-depth education, training, and research. He is a columnist for SearchCIO.com, DM Review, Application Development Trends, the Business Intelligence Journal, and TDWI Case Studies & Solution. |
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 …
