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| data management maturity assessment: 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 maturity assessment: 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 maturity assessment: Non-Invasive Data Governance Robert S. Seiner, 2014-09-01 Data-governance programs focus on authority and accountability for the management of data as a valued organizational asset. Data Governance should not be about command-and-control, yet at times could become invasive or threatening to the work, people and culture of an organization. Non-Invasive Data Governance™ focuses on formalizing existing accountability for the management of data and improving formal communications, protection, and quality efforts through effective stewarding of data resources. Non-Invasive Data Governance will provide you with a complete set of tools to help you deliver a successful data governance program. Learn how: • Steward responsibilities can be identified and recognized, formalized, and engaged according to their existing responsibility rather than being assigned or handed to people as more work. • Governance of information can be applied to existing policies, standard operating procedures, practices, and methodologies, rather than being introduced or emphasized as new processes or methods. • Governance of information can support all data integration, risk management, business intelligence and master data management activities rather than imposing inconsistent rigor to these initiatives. • A practical and non-threatening approach can be applied to governing information and promoting stewardship of data as a cross-organization asset. • Best practices and key concepts of this non-threatening approach can be communicated effectively to leverage strengths and address opportunities to improve. |
| data management maturity assessment: 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 maturity assessment: Using the Project Management Maturity Model Harold Kerzner, 2011-11-29 Updated for today's businesses-a proven model FOR assessment and ongoing improvement Using the Project Management Maturity Model, Second Edition is the updated edition of Harold Kerzner's renowned book covering his Project Management Maturity Model (PMMM). In this hands-on book, Kerzner offers a unique, industry-validated tool for helping companies of all sizes assess and improve their progress in integrating project management into every part of their organizations. Conveniently organized into two sections, this Second Edition begins with an examination of strategic planning principles and the ways they relate to project management. In the second section, PMMM is introduced with in-depth coverage of the five different levels of development for achieving maturity. Easily adaptable benchmarking instruments for measuring an organization's progress along the maturity curve make this a practical guide for any type of company. Complete with an associated Web site packed with both teaching and learning tools, Using the Project Management Maturity Model, Second Edition helps managers, engineers, project team members, business consultants, and others build a powerful foundation for company improvement and excellence. |
| data management maturity assessment: 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 maturity assessment: 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 management maturity assessment: 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 maturity assessment: 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 maturity assessment: Interop John Palfrey, Urs Gasser, 2012-06-05 In Interop, technology experts John Palfrey and Urs Gasser explore the immense importance of interoperability -- the standardization and integration of technology -- and show how this simple principle will hold the key to our success in the coming decades and beyond. The practice of standardization has been facilitating innovation and economic growth for centuries. The standardization of the railroad gauge revolutionized the flow of commodities, the standardization of money revolutionized debt markets and simplified trade, and the standardization of credit networks has allowed for the purchase of goods using money deposited in a bank half a world away. These advancements did not eradicate the different systems they affected; instead, each system has been transformed so that it can interoperate with systems all over the world, while still preserving local diversity. As Palfrey and Gasser show, interoperability is a critical aspect of any successful system -- and now it is more important than ever. Today we are confronted with challenges that affect us on a global scale: the financial crisis, the quest for sustainable energy, and the need to reform health care systems and improve global disaster response systems. The successful flow of information across systems is crucial if we are to solve these problems, but we must also learn to manage the vast degree of interconnection inherent in each system involved. Interoperability offers a number of solutions to these global challenges, but Palfrey and Gasser also consider its potential negative effects, especially with respect to privacy, security, and co-dependence of states; indeed, interoperability has already sparked debates about document data formats, digital music, and how to create successful yet safe cloud computing. Interop demonstrates that, in order to get the most out of interoperability while minimizing its risks, we will need to fundamentally revisit our understanding of how it works, and how it can allow for improvements in each of its constituent parts. In Interop, Palfrey and Gasser argue that there needs to be a nuanced, stable theory of interoperability -- one that still generates efficiencies, but which also ensures a sustainable mode of interconnection. Pointing the way forward for the new information economy, Interop provides valuable insights into how technological integration and innovation can flourish in the twenty-first century. |
| data management maturity assessment: 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 maturity assessment: 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 management maturity assessment: Stakeholder Relationship Management Lynda Bourne, 2016-04-01 In any activity an organisation undertakes, whether strategic, operational or tactical, the activity can only be successful with the input, commitment and support of its people - stakeholders. Gaining and maintaining the support and commitment of stakeholders requires a continuous process of engaging the right stakeholders at the right time and understanding and managing their expectations. Unfortunately, most organisations have difficulty implementing such culture change, and need assistance and guidance to implement a consistent process for identification and management of stakeholders and their changing expectations. As a continuous improvement process, stakeholder management requires understanding and support from everyone in the organisation from the CEO to the short-term contractor. This requires the concepts and practices of effective stakeholder management to become embedded in the culture of the organisation: 'how we do things around here', this book provides the 'road map' to help organisations achieve these objectives. The text has two specific purposes. Firstly, it is an 'how-to' book providing the fundamental processes and practices for improving stakeholder management in endeavours such as projects, and program management offices (PMO), it also gives guidance on organisational survival during mergers and acquisitions, preparing for the tender bidding, and marketing campaigns. Secondly, Lynda Bourne's book is for organisations that have recognised the importance of stakeholder engagement to their success, it is a guidebook for assessing their current maturity regarding implementation of stakeholder relationship management with a series of guidelines and milestones for achieving the preferred level of maturity. |
| data management maturity assessment: SOA Maturity Model Pericles Antoniades, 2013-12-02 Companies have long sought to integrate existing Information Systems (IS) in order to support existing and potentially new business processes spread throughout their “territories” and possibly to collaborating organizations. A variety of designs can be used to this end, ranging from rigid point-to-point electronic data interchange (EDI) interactions to “Web auctions”. By updating older technologies, such as “Internet-enabling” EDI-based systems, companies can make their IT systems available to internal or external customers; but the resulting systems have not proven to be flexible enough to meet business demands. A more flexible, standardized architecture is required to better support the connection of various applications and the sharing of data. Service-Oriented Architecture (SOA) is one such architecture. It unifies (“orchestrates”) business processes by structuring large applications as an ad-hoc collection of smaller modules called “Services”. These applications can be used by different groups of people both inside and outside the company, and new applications built from a mix of services (located in a global repository) exhibit greater agility and uniformity. Thus, SOA is a design framework for realizing rapid and low-cost system development and improving total system quality. SOA uses the Web Services standards and technologies and is rapidly becoming a standard approach for enterprise information systems integration. SOA adoption by enterprises has been identified as one of the highest business priorities by a recent Gartner study (Gartner 2007) and enterprises increasingly recognize the requirement for an increased “Service-orientation” and relevant comprehensive frameworks, which will not only help them position themselves and evaluate their SOA initiatives, but also guide them in achieving higher levels of SOA maturity. This in turn, will help enterprises acquire (and retain) competitive advantage over other players in the market who are not (using SOA and thus they are not) so flexibly adjusting themselves to address new business requirements. This book proposes a new SOA Maturity Model (MM) using a Delphi-variant technique and this constitutes one of its distinguishing features because none of the relevant existing works utilized Delphi. Moreover, the fact that the proposed SOA MM supports inter-enterprise setups makes it even more distinct. The newly proposed SOA MM is then used to help the participating organizations position themselves in respect to SOA (current status), guide them to achieve higher levels of SOA maturity, and anticipate their SOA maturity in five years’ time. Furthermore, the “local” or “global” nature of the proposed SOA MM is investigated. This is checked firstly against selected expert panel participants and secondly against local business practitioners. |
| data management maturity assessment: Organizational Project Management Maturity Model (OPM3) Project Management Institute, 2008 A second edition provides tools for organizations to measure their maturity against a comprehensive set of best practices, providing updated coverage of current PMI standards, guidelines for promoting smoother transitions and strategies for eliminating redundancy. |
| data management maturity assessment: 2018 International Conference on Information Management and Technology (ICIMTech) IEEE Staff, 2018-09-03 The scope topics include, but are not limited to Analysis & Design of Information System Big Data and Data Mining Cloud & Grid Computing Creative and digital arts technology Decision Support System Digital Marketing Enterprise Resources Planning Financial and Accounting Information System Green Computing Green Information Technology Green Software Development Human Computer Interaction Information System Risk Management Information System Security IS IT Governance IT Compliance Knowledge Management Management Information System Manufacturing Information System Mobile Computing & Applications Network Security Services Information System Social Networking & Application Software Engineering Supply chain and logistics management technology Technopreneur Trading Information System Web Engineering |
| data management maturity assessment: 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 maturity assessment: CMMI for Acquisition Brian Gallagher, Mike Phillips, Karen Richter, Sandra Shrum, 2011-03-04 CMMI® for Acquisition (CMMI-ACQ) describes best practices for the successful acquisition of products and services. Providing a practical framework for improving acquisition processes, CMMI-ACQ addresses the growing trend in business and government for organizations to purchase or outsource required products and services as an alternative to in-house development or resource allocation. Changes in CMMI-ACQ Version 1.3 include improvements to high maturity process areas, improvements to the model architecture to simplify use of multiple models, and added guidance about using preferred suppliers. CMMI® for Acquisition, Second Edition, is the definitive reference for CMMI-ACQ Version 1.3. In addition to the entire revised CMMI-ACQ model, the book includes updated tips, hints, cross-references, and other author notes to help you understand, apply, and quickly find information about the content of the acquisition process areas. The book now includes more than a dozen contributed essays to help guide the adoption and use of CMMI-ACQ in industry and government. Whether you are new to CMMI models or are already familiar with one or more of them, you will find this book an essential resource for managing your acquisition processes and improving your overall performance. The book is divided into three parts. Part One introduces CMMI-ACQ in the broad context of CMMI models, including essential concepts and useful background. It then describes and shows the relationships among all the components of the CMMI-ACQ process areas, and explains paths to the adoption and use of the model for process improvement and benchmarking. Several original essays share insights and real experiences with CMMI-ACQ in both industry and government environments. Part Two first describes generic goals and generic practices, and then details the twenty-two CMMI-ACQ process areas, including specific goals, specific practices, and examples. These process areas are organized alphabetically and are tabbed by process area acronym to facilitate quick reference. Part Three provides several useful resources, including sources of further information about CMMI and CMMI-ACQ, acronym definitions, a glossary of terms, and an index. |
| data management maturity assessment: Big Data For Dummies Judith S. Hurwitz, Alan Nugent, Fern Halper, Marcia Kaufman, 2013-04-02 Find the right big data solution for your business or organization Big data management is one of the major challenges facing business, industry, and not-for-profit organizations. Data sets such as customer transactions for a mega-retailer, weather patterns monitored by meteorologists, or social network activity can quickly outpace the capacity of traditional data management tools. If you need to develop or manage big data solutions, you'll appreciate how these four experts define, explain, and guide you through this new and often confusing concept. You'll learn what it is, why it matters, and how to choose and implement solutions that work. Effectively managing big data is an issue of growing importance to businesses, not-for-profit organizations, government, and IT professionals Authors are experts in information management, big data, and a variety of solutions Explains big data in detail and discusses how to select and implement a solution, security concerns to consider, data storage and presentation issues, analytics, and much more Provides essential information in a no-nonsense, easy-to-understand style that is empowering Big Data For Dummies cuts through the confusion and helps you take charge of big data solutions for your organization. |
| data management maturity assessment: 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 maturity assessment: 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 maturity assessment: 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 maturity assessment: Business Process Maturity Amy Van Looy, 2014-01-27 Organisations face many challenges, which induce them to perform better, and thus to establish mature (or excellent) business processes. As they now face globalisation, higher competitiveness, demanding customers, growing IT possibilities, compliancy rules etc., business process maturity models (BPMMs) have been introduced to help organisations gradually assess and improve their business processes (e.g. CMMI or OMG-BPMM). In fact, there are now so many BPMMs to choose from that organisations risk selecting one that does not fit their needs or one of substandard quality. This book presents a study that distinguishes process management from process orientation so as to arrive at a common understanding. It also includes a classification study to identify the capability areas and maturity types of 69 existing BPMMs, in order to strengthen the basis of available BPMMs. Lastly it presents a selection study to identify criteria for choosing one BPMM from the broad selection, which produced a free online selection tool, BPMM Smart-Selector. |
| data management maturity assessment: 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 maturity assessment: 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 maturity assessment: Architecting Modern Data Platforms Jan Kunigk, Ian Buss, Paul Wilkinson, Lars George, 2018-12-05 There’s a lot of information about big data technologies, but splicing these technologies into an end-to-end enterprise data platform is a daunting task not widely covered. With this practical book, you’ll learn how to build big data infrastructure both on-premises and in the cloud and successfully architect a modern data platform. Ideal for enterprise architects, IT managers, application architects, and data engineers, this book shows you how to overcome the many challenges that emerge during Hadoop projects. You’ll explore the vast landscape of tools available in the Hadoop and big data realm in a thorough technical primer before diving into: Infrastructure: Look at all component layers in a modern data platform, from the server to the data center, to establish a solid foundation for data in your enterprise Platform: Understand aspects of deployment, operation, security, high availability, and disaster recovery, along with everything you need to know to integrate your platform with the rest of your enterprise IT Taking Hadoop to the cloud: Learn the important architectural aspects of running a big data platform in the cloud while maintaining enterprise security and high availability |
| data management maturity assessment: Implementing the Capability Maturity Model James R. Persse, 2001-08-27 Practical guidelines for an effective implementation of software development processes Designed to ensure effective software development processes, the Capability Maturity Model (CMM)--North America's leading standard for software development--requires companies to complete five steps, or levels, in the development process. But while it is widely adopted by Fortune 500 companies, many others get stuck at the initial planning stage. Focusing on Levels 2 and 3 of the CMM, this book helps readers to get over the hurdle of the two most problematic areas in this process--the project management and software development steps. It offers clear, step-by-step guidance on how to establish basic project management processes to track costs, schedules, and functionality; how to document, standardize, and integrate software processes; and how to improve software quality. |
| data management maturity assessment: Navigating the Labyrinth Laura Sebastian-Coleman, An Executive Guide to Data Management |
| data management maturity assessment: Making Enterprise Information Management (EIM) Work for Business John Ladley, 2010-07-03 Making Enterprise Information Management (EIM) Work for Business: A Guide to Understanding Information as an Asset provides a comprehensive discussion of EIM. It endeavors to explain information asset management and place it into a pragmatic, focused, and relevant light. The book is organized into two parts. Part 1 provides the material required to sell, understand, and validate the EIM program. It explains concepts such as treating Information, Data, and Content as true assets; information management maturity; and how EIM affects organizations. It also reviews the basic process that builds and maintains an EIM program, including two case studies that provide a birds-eye view of the products of the EIM program. Part 2 deals with the methods and artifacts necessary to maintain EIM and have the business manage information. Along with overviews of Information Asset concepts and the EIM process, it discusses how to initiate an EIM program and the necessary building blocks to manage the changes to managed data and content. - Organizes information modularly, so you can delve directly into the topics that you need to understand - Based in reality with practical case studies and a focus on getting the job done, even when confronted with tight budgets, resistant stakeholders, and security and compliance issues - Includes applicatory templates, examples, and advice for executing every step of an EIM program |
| data management maturity assessment: Digital Libraries for Open Knowledge Eva Méndez, Fabio Crestani, Cristina Ribeiro, Gabriel David, João Correia Lopes, 2018-09-04 This book constitutes the proceedings of the 22nd International Conference on Theory and Practice of Digital Libraries, TPDL 2018, held in Porto, Portugal, in September 2018. The 51 full papers, 17 short papers, and 13 poster and tutorial papers presented in this volume were carefully reviewed and selected from 81 submissions. The general theme of TPDL 2018 was Digital Libraries for Open Knowledge. The papers present a wide range of the following topics: Metadata, Entity Disambiguation, Data Management, Scholarly Communication, Digital Humanities, User Interaction, Resources, Information Extraction, Information Retrieval, Recommendation. |
| data management maturity assessment: Software Ecosystems Slinger Jansen, Michael A. Cusumano, Sjaak Brinkkemper, 2013-01-01 This book describes the state-of-the-art of software ecosystems. It constitutes a fundamental step towards an empirically based, nuanced understanding of the implications for management, governance, and control of software ecosystems. This is the first book of its kind dedicated to this emerging field and offers guidelines on how to analyze software ecosystems; methods for managing and growing; methods on transitioning from a closed software organization to an open one; and instruments for dealing with open source, licensing issues, product management and app stores. It is unique in bringing together industry experiences, academic views and tackling challenges such as the definition of fundamental concepts of software ecosystems, describing those forces that influence its development and lifecycles, and the provision of methods for the governance of software ecosystems. This book is an essential starting point for software industry researchers, product managers, and entrepreneurs. |
| data management maturity assessment: Data Governance Handbook Wendy S. Batchelder, 2024-05-31 Build an actionable, business value driven case for data governance to obtain executive support and implement with excellence Key Features Develop a solid foundation in data governance and increase your confidence in data solutions Align data governance solutions with measurable business results and apply practical knowledge from real-world projects Learn from a three-time chief data officer who has worked in leading Fortune 500 companies Purchase of the print or Kindle book includes a free PDF eBook Book Description2.5 quintillion bytes! This is the amount of data being generated every single day across the globe. As this number continues to grow, understanding and managing data becomes more complex. Data professionals know that it’s their responsibility to navigate this complexity and ensure effective governance, empowering businesses with the right data, at the right time, and with the right controls. If you are a data professional, this book will equip you with valuable guidance to conquer data governance complexities with ease. Written by a three-time chief data officer in global Fortune 500 companies, the Data Governance Handbook is an exhaustive guide to understanding data governance, its key components, and how to successfully position solutions in a way that translates into tangible business outcomes. By the end, you’ll be able to successfully pitch and gain support for your data governance program, demonstrating tangible outcomes that resonate with key stakeholders. What you will learn Comprehend data governance from ideation to delivery and beyond Position data governance to obtain executive buy-in Launch a governance program at scale with a measurable impact Understand real-world use cases to drive swift and effective action Obtain support for data governance-led digital transformation Launch your data governance program with confidence Who this book is for Chief data officers, data governance leaders, data stewards, and engineers who want to understand the business value of their work, and IT professionals seeking further understanding of data management, will find this book useful. You need a basic understanding of working with data, business needs, and how to meet those needs with data solutions. Prior coding experience or skills in selling data solutions to executives are not required. |
| data management maturity assessment: The Promise of Adolescence National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Division of Behavioral and Social Sciences and Education, Board on Children, Youth, and Families, Committee on the Neurobiological and Socio-behavioral Science of Adolescent Development and Its Applications, 2019-07-26 Adolescenceâ€beginning with the onset of puberty and ending in the mid-20sâ€is a critical period of development during which key areas of the brain mature and develop. These changes in brain structure, function, and connectivity mark adolescence as a period of opportunity to discover new vistas, to form relationships with peers and adults, and to explore one's developing identity. It is also a period of resilience that can ameliorate childhood setbacks and set the stage for a thriving trajectory over the life course. Because adolescents comprise nearly one-fourth of the entire U.S. population, the nation needs policies and practices that will better leverage these developmental opportunities to harness the promise of adolescenceâ€rather than focusing myopically on containing its risks. This report examines the neurobiological and socio-behavioral science of adolescent development and outlines how this knowledge can be applied, both to promote adolescent well-being, resilience, and development, and to rectify structural barriers and inequalities in opportunity, enabling all adolescents to flourish. |
| data management maturity assessment: Adolescence and Emerging Adulthood Jeffrey Jensen Arnett, 2013 Helps students understand how culture impacts development in adolescence and emerging adulthood. Grounded in a global cultural perspective (within and outside of the US), this text enriches the discussion with historical context and an interdisciplinary approach, including studies from fields such as anthropology and sociology, in addition to the compelling psychological research on adolescent development. This book also takes into account the period of emerging adulthood (ages 18-25), a term coined by the author, and an area of study for which Arnett is a leading expert. Arnett continues the fifth edition with new and updated studies, both U.S. and international. With Pearson's MyDevelopmentLab Video Series and Powerpoints embedded with video, students can experience a true cross-cultural experience. A better teaching and learning experience This program will provide a better teaching and learning experience-- for you and your students. Here's how: Personalize Learning - The new MyDevelopmentLab delivers proven results in helping students succeed, provides engaging experiences that personalize learning, and comes from a trusted partner with educational expertise and a deep commitment to helping students and instructors achieve their goals. Improve Critical Thinking - Students learn to think critically about the influence of culture on development with pedagogical features such as Culture Focus boxes and Historical Focus boxes. Engage Students - Arnett engages students with cross cultural research and examples throughout. MyVirtualTeen, an interactive simulation, allows students to apply the concepts they are learning to their own virtual teen. Explore Research - Research Focus provides students with a firm grasp of various research methods and helps them see the impact that methods can have on research findings. Support Instructors - This program provides instructors with unbeatable resources, including video embedded PowerPoints and the new MyDevelopmentLab that includes cross-cultural videos and MyVirtualTeen, an interactive simulation that allows you to raise a child from birth to age 18. An easy to use Instructor's Manual, a robust test bank, and an online test generator (MyTest) are also available. All of these materials may be packaged with the text upon request. Note: MyDevelopmentLab does not come automatically packaged with this text. To purchase MyDevelopmentLab, please visit: www.mydevelopmentlab.com or you can purchase a ValuePack of the text + MyDevelopmentlab (at no additional cost): ValuePack ISBN-10: 0205911854/ ValuePack ISBN-13: 9780205911851. Click here for a short walkthrough video on MyVirtualTeen! http://www.youtube.com/playlist'list=PL51B144F17A36FF25&feature=plcp |
| data management maturity assessment: Data Governance and Compliance Rupa Mahanti, 2021-04-27 This book sets the stage of the evolution of corporate governance, laws and regulations, other forms of governance, and the interaction between data governance and other corporate governance sub-disciplines. Given the continuously evolving and complex regulatory landscape and the growing number of laws and regulations, compliance is a widely discussed issue in the field of data. This book considers the cost of non-compliance bringing in examples from different industries of instances in which companies failed to comply with rules, regulations, and other legal obligations, and goes on to explain how data governance helps in avoiding such pitfalls. The first in a three-volume series on data governance, this book does not assume any prior or specialist knowledge in data governance and will be highly beneficial for IT, management and law students, academics, information management and business professionals, and researchers to enhance their knowledge and get guidance in managing their own data governance projects from a governance and compliance perspective. |
| data management maturity assessment: Data Strategy Sid Adelman, Larissa Terpeluk Moss, Majid Abai, 2005 Without a data strategy, the people within an organization have no guidelines for making decisions that are absolutely crucial to the success of the IT organization and to the entire organization. The absence of a strategy gives a blank check to those who want to pursue their own agendas, including those who want to try new database management systems, new technologies (often unproven), and new tools. This type of environment provides no hope for success. Data Strategy should result in the development of systems with less risk, higher quality systems, and reusability of assets. This is key to keeping cost and maintenance down, thus running lean and mean. Data Strategy provides a CIO with a rationale to counter arguments for immature technology and data strategies that are inconsistent with existing strategies. This book uses case studies and best practices to give the reader the tools they need to create the best strategy for the organization. |
| data management maturity assessment: IT Capability Maturity FrameworkTM (IT-CMFTM) Van Haren Publishing, Jim Kenneally, Marian Carcary, 2016 Business organizations, both public and private, are constantly challenged to innovate and generate real value. CIOs are uniquely well-positioned to seize this opportunity and adopt the role of business transformation partner, helping their organizations to grow and prosper with innovative, IT-enabled products, services and processes. To succeed in this, however, the IT function needs to manage an array of inter-related and inter-dependent disciplines focused on the generation of business value. In response to this need, the Innovation Value Institute, a cross-industry international consortium, developed the IT Capability Maturity Framework(TM) (IT-CMF(TM)). This second edition of the IT Capability Maturity Framework(TM) (IT-CMF(TM)) is a comprehensive suite of tried and tested practices, organizational assessment approaches, and improvement roadmaps covering key IT capabilities needed to optimize value and innovation in the IT function and the wider organization. It enables organizations to devise more robust strategies, make better-informed decisions, and perform more effectively, efficiently and consistently. IT-CMF is: - An integrated management toolkit covering 36 key capability management disciplines, with organizational maturity profiles, assessment methods, and improvement roadmaps for each. - A coherent set of concepts and principles, expressed in business language, that can be used to guide discussions on setting goals and evaluating performance. - A unifying (or umbrella) framework that complements other, domain-specific frameworks already in use in the organization, helping to resolve conflicts between them, and filling gaps in their coverage. - Industry/sector and vendor independent. IT-CMF can be used in any organizational context to guide performance improvement. - A rigorously developed approach, underpinned by the principles of Open Innovation and guided by the Design Science Research methodology, synthesizing leading academic research with industry practitioner expertise |
| data management maturity assessment: Collaborative Enterprise Architecture Stefan Bente, Uwe Bombosch, Shailendra Langade, 2012-08-29 Why collaborative enterprise architecture? -- What is enterprise architecture -- What enterprise architects do: core activities of EA -- EA frameworks -- EA maturity models -- Foundations of collaborative EA -- Towards pragmatism: lean and agile EA -- Inviting to participation: eam 2.0 -- The next steps: taking collaborative EA forward. |
| data management maturity assessment: 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 management maturity assessment: 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 Maturity Assessment - Amazon Web Services
To support PSBs in understanding their data management capability, this advice note will explain data maturity frameworks, outlining their purpose, their benefits and how to conduct an …
DATA MANAGEMENT MATURITY (DMM)SM - Capability …
What is the Data Management Maturity (DMM) SM Model? The Data Management Maturity (DMM) Model provides the best practices to help organizations build, improve, and measure their …
DATA MANAGEMENT MATURITY ASSESSMENT REVIEW …
Data Crossroads prepared a review of data management (DM) maturity assessment. This review is based on the results of the data management maturity scan performed by 233 participants on an …
Data Maturity Assessment Tool Guide - Department of Finance
Data Maturity Assessment Tool (the Tool) is designed to help agencies periodically assess their data maturity and track their progress over time, to support implementation of the Strategy and …
TDWI Analytics Maturity Model Assessment Guide
TDWI created the Analytics Maturity Model and Assessment in response to requests from organizations to understand how their analytics deployments compare to those of their peers in …
DMMM: Data Management Maturity Model - Research Euranova
In this paper, we present a new perspective on how to construct maturity models to assess companies’ maturity in terms of data management and advanced analytics with a focus on …
State Data-Maturity Assessment - Beeck Center
data-maturity assessment is a tool that empowers states to identify strengths and weak-nesses in harnessing data insights for informed policymaking and service delivery. By conducting this …
Sandhill Data Management Maturity Assessment
For organizations that seek to evaluate and improve their data management practices, CMMI® Institute has developed the Data Management Maturity (DMM)SM model to bridge the …
Data Maturity Framework - Scorecard Questionnaire
What is the problem you are trying to solve? What does success look like/how much does the needle need to move? What interventions do you have available to solve the problem? If this is …
Enterprise Data Management Current State Maturity Assessment
Enterprise Data Management – Current State Maturity Assessment 15 The main goal of EDM current state maturity assessment is to identify the gaps and push each component to an effective level.
GAP ANALYSIS – SCDOT DATA MANAGEMENT MATURITY …
• A gap assessment comparing the Agency’s current level of data governance maturity and the desired maturity for the Agency. • Determining the Agency’s desired maturity level for data …
Data Management Maturity Assessment - Amazon Web Services
These steps are derived from the activities outlined in the DAMA-DMBoK approach, which outline how best to carry out a data maturity assessment. It is important to bear-in-mind that a data …
Data Management Maturity Assessment - gehli.vcu.edu
data set using a combination of Microsoft Excel functions and formulas to segregate, analyze and compute the detailed payroll data to move employees disaster response payroll expenses.
Data Management Maturity Assessment Outcomes
Data quality management must be established, including clear ownership for issue remediations. Organisation requires mature data governance across all our data assets. Organisation needs an …
Data Maturity Assessment Tool Guide - Department of Finance
How does the assessment work? The data maturity assessment is structured to enable a systematic review of your agency’s data governance, systems, and processes. It is a self-assessment …
Data Management Maturity - DAMA Phoenix
Provides leadership and structure for implementing data management principles and best practices across the organization. Knowledge-based continuity for enterprise data and for persistent …
Measuring Data Management Practice Maturity: Community’s …
Increasing data management practice maturity levels can positively impact the coordination of data flow among organizations,individuals,and systems. Results from a self-assessment provide a …
Data Management Maturity Assessment - Amazon Web Services
These questions are designed to be generic and applicable to individuals at different grades within a PSB and with varying levels of knowledge and experience about Data Management. They are …
DATA MANAGEMENT MATURITY ASSESSMENT REVIEW 2019
Data Crossroads prepared a review of the data management (DM) maturity of medium-sized companies. This review is based on the results of the data management maturity survey
A Survey of Maturity Models in Data Management - Research …
data management maturity models where models can be compared and evaluated based on their approaches to identify and categorize the data management related functions.
Data Management Maturity Assessment - Amazon Web …
To support PSBs in understanding their data management capability, this advice note will explain data maturity frameworks, outlining their purpose, their benefits and how to conduct an …
DATA MANAGEMENT MATURITY (DMM)SM - Capability …
What is the Data Management Maturity (DMM) SM Model? The Data Management Maturity (DMM) Model provides the best practices to help organizations build, improve, and measure …
DATA MANAGEMENT MATURITY ASSESSMENT REVIEW …
Data Crossroads prepared a review of data management (DM) maturity assessment. This review is based on the results of the data management maturity scan performed by 233 participants …
Data Maturity Assessment Tool Guide - Department of Finance
Data Maturity Assessment Tool (the Tool) is designed to help agencies periodically assess their data maturity and track their progress over time, to support implementation of the Strategy and …
TDWI Analytics Maturity Model Assessment Guide
TDWI created the Analytics Maturity Model and Assessment in response to requests from organizations to understand how their analytics deployments compare to those of their peers …
DMMM: Data Management Maturity Model - Research …
In this paper, we present a new perspective on how to construct maturity models to assess companies’ maturity in terms of data management and advanced analytics with a focus on …
State Data-Maturity Assessment - Beeck Center
data-maturity assessment is a tool that empowers states to identify strengths and weak-nesses in harnessing data insights for informed policymaking and service delivery. By conducting this …
Sandhill Data Management Maturity Assessment
For organizations that seek to evaluate and improve their data management practices, CMMI® Institute has developed the Data Management Maturity (DMM)SM model to bridge the …
Data Maturity Framework - Scorecard Questionnaire
What is the problem you are trying to solve? What does success look like/how much does the needle need to move? What interventions do you have available to solve the problem? If this …
Enterprise Data Management Current State Maturity …
Enterprise Data Management – Current State Maturity Assessment 15 The main goal of EDM current state maturity assessment is to identify the gaps and push each component to an …
GAP ANALYSIS – SCDOT DATA MANAGEMENT MATURITY …
• A gap assessment comparing the Agency’s current level of data governance maturity and the desired maturity for the Agency. • Determining the Agency’s desired maturity level for data …
Data Management Maturity Assessment - Amazon Web …
These steps are derived from the activities outlined in the DAMA-DMBoK approach, which outline how best to carry out a data maturity assessment. It is important to bear-in-mind that a data …
Data Management Maturity Assessment - gehli.vcu.edu
data set using a combination of Microsoft Excel functions and formulas to segregate, analyze and compute the detailed payroll data to move employees disaster response payroll expenses.
Data Management Maturity Assessment Outcomes
Data quality management must be established, including clear ownership for issue remediations. Organisation requires mature data governance across all our data assets. Organisation needs …
Data Maturity Assessment Tool Guide - Department of Finance
How does the assessment work? The data maturity assessment is structured to enable a systematic review of your agency’s data governance, systems, and processes. It is a self …
Data Management Maturity - DAMA Phoenix
Provides leadership and structure for implementing data management principles and best practices across the organization. Knowledge-based continuity for enterprise data and for …
Measuring Data Management Practice Maturity: …
Increasing data management practice maturity levels can positively impact the coordination of data flow among organizations,individuals,and systems. Results from a self-assessment …
Data Management Maturity Assessment - Amazon Web …
These questions are designed to be generic and applicable to individuals at different grades within a PSB and with varying levels of knowledge and experience about Data Management. …
DATA MANAGEMENT MATURITY ASSESSMENT REVIEW 2019
Data Crossroads prepared a review of the data management (DM) maturity of medium-sized companies. This review is based on the results of the data management maturity survey
A Survey of Maturity Models in Data Management
data management maturity models where models can be compared and evaluated based on their approaches to identify and categorize the data management related functions.
