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| data management maturity 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 maturity model: 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 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 maturity model: 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 model: 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 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 maturity 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 maturity 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 maturity 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 maturity 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 maturity model: Customer Data Integration Jill Dyché, Evan Levy, 2011-01-31 Customers are the heart of any business. But we can't succeed if we develop only one talk addressed to the 'average customer.' Instead we must know each customer and build our individual engagements with that knowledge. If Customer Relationship Management (CRM) is going to work, it calls for skills in Customer Data Integration (CDI). This is the best book that I have seen on the subject. Jill Dyché is to be complimented for her thoroughness in interviewing executives and presenting CDI. -Philip Kotler, S. C. Johnson Distinguished Professor of International Marketing Kellogg School of Management, Northwestern University In this world of killer competition, hanging on to existing customers is critical to survival. Jill Dyché's new book makes that job a lot easier than it has been. -Jack Trout, author, Differentiate or Die Jill and Evan have not only written the definitive work on Customer Data Integration, they've made the business case for it. This book offers sound advice to business people in search of innovative ways to bring data together about customers-their most important asset-while at the same time giving IT some practical tips for implementing CDI and MDM the right way. -Wayne Eckerson, The Data Warehousing Institute author of Performance Dashboards: Measuring, Monitoring, and Managing Your Business Whatever business you're in, you're ultimately in the customer business. No matter what your product, customers pay the bills. But the strategic importance of customer relationships hasn't brought companies much closer to a single, authoritative view of their customers. Written from both business and technicalperspectives, Customer Data Integration shows companies how to deliver an accurate, holistic, and long-term understanding of their customers through CDI. |
| data management maturity model: 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 model: 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 model: 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 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 maturity model: 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 model: 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 model: 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 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 maturity model: 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 model: CMMI Ralf Kneuper, 2009 CMMI is a well-known and standardized model for assessing and improving software and systems development processes. It can be used to guide process improvement across a project, a division, or an entire organization. CMMI was developed at the Carnegie Mellon Software Engineering Institute (SEI). The current version, 1.2, was published in 2006 and is being adopted worldwide. This book provides hands-on experience and will help the reader to gain an understanding of CMMI. It is an introduction to the model and its fundamental ideas. Through numerous examples, it helps the reader to get started with CMMI and to understand the interrelationship among model components (practices, goals, and process areas). The book covers the following topics: Model-based process improvement Overview of CMMI components History of CMMI and comparison to CMM Process areas of CMMI models Application, potential, and limitations of CMMI |
| data management maturity model: Design Patterns Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides, 1995 Software -- Software Engineering. |
| data management maturity model: 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 model: 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 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 maturity 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 maturity 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 maturity model: Information Management William McKnight, 2013-11-30 Information Management: Gaining a Competitive Advantage with Data is about making smart decisions to make the most of company information. Expert author William McKnight develops the value proposition for information in the enterprise and succinctly outlines the numerous forms of data storage. Information Management will enlighten you, challenge your preconceived notions, and help activate information in the enterprise. Get the big picture on managing data so that your team can make smart decisions by understanding how everything from workload allocation to data stores fits together. The practical, hands-on guidance in this book includes: - Part 1: The importance of information management and analytics to business, and how data warehouses are used - Part 2: The technologies and data that advance an organization, and extend data warehouses and related functionality - Part 3: Big Data and NoSQL, and how technologies like Hadoop enable management of new forms of data - Part 4: Pulls it all together, while addressing topics of agile development, modern business intelligence, and organizational change management Read the book cover-to-cover, or keep it within reach for a quick and useful resource. Either way, this book will enable you to master all of the possibilities for data or the broadest view across the enterprise. - Balances business and technology, with non-product-specific technical detail - Shows how to leverage data to deliver ROI for a business - Engaging and approachable, with practical advice on the pros and cons of each domain, so that you learn how information fits together into a complete architecture - Provides a path for the data warehouse professional into the new normal of heterogeneity, including NoSQL solutions |
| data management maturity model: 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 model: Systems, Software and Services Process Improvement Alastair Walker, Rory V. O'Connor, Richard Messnarz, 2019-09-09 This volume constitutes the refereed proceedings of the 26th European Conference on Systems, Software and Services Process Improvement, EuroSPI conference, held in Edinburgh, Scotland, in September 2019. The 18 revised full papers presented were carefully reviewed and selected from 28 submissions. They are organized in topical sections: Visionary Papers, SPI and Safety and Security, SPI and Assessments, SPI and Future Qualification & Team Performance, and SPI Manifesto and Culture. The selected workshop papers are also presented and organized in following topical sections: GamifySPI, Digitalisation of Industry, Infrastructure and E-Mobility. -Best Practices in Implementing Traceability. -Good and Bad Practices in Improvement. -Functional Safety and Cybersecurity. -Experiences with Agile and Lean. -Standards and Assessment Models. -Team Skills and Diversity Strategies. -Recent Innovations. |
| data management maturity model: Research Anthology on Privatizing and Securing Data Management Association, Information Resources, 2021-04-23 With the immense amount of data that is now available online, security concerns have been an issue from the start, and have grown as new technologies are increasingly integrated in data collection, storage, and transmission. Online cyber threats, cyber terrorism, hacking, and other cybercrimes have begun to take advantage of this information that can be easily accessed if not properly handled. New privacy and security measures have been developed to address this cause for concern and have become an essential area of research within the past few years and into the foreseeable future. The ways in which data is secured and privatized should be discussed in terms of the technologies being used, the methods and models for security that have been developed, and the ways in which risks can be detected, analyzed, and mitigated. The Research Anthology on Privatizing and Securing Data reveals the latest tools and technologies for privatizing and securing data across different technologies and industries. It takes a deeper dive into both risk detection and mitigation, including an analysis of cybercrimes and cyber threats, along with a sharper focus on the technologies and methods being actively implemented and utilized to secure data online. Highlighted topics include information governance and privacy, cybersecurity, data protection, challenges in big data, security threats, and more. This book is essential for data analysts, cybersecurity professionals, data scientists, security analysts, IT specialists, practitioners, researchers, academicians, and students interested in the latest trends and technologies for privatizing and securing data. |
| data management maturity 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 maturity model: Strategic Planning for Project Management Using a Project Management Maturity Model Harold Kerzner, 2002-03-14 It has often been said that 'to improve, one must be prepared to measure the improvement' and 'one must inspect what one expects.' The Kerzner Project Management Maturity Model has provided this tangible measure of maturity. The rest is up to a company to set the expectations and to inspect the results.--Bill Marshall, Nortel Global Project Process Standards (from the Foreword) Strategic planning for project management-a proven model for assessment and continuous improvement Harold Kerzner's landmark Project Management has long been the reference of choice for outstanding coverage of the basic principles and concepts of project management. Now, with the Project Management Maturity Model (PMMM) detailed in this new book, Kerzner has developed a unique, industry-validated tool for helping companies assess their progress in integrating project management throughout their organization. Strategic Planning for Project Management Using a Project Management Maturity Model begins by examining the principles of strategic planning and how they relate to project management. The second part of the book introduces the PMMM, detailing the five different levels of development for achieving maturity, along with benchmarking instruments for measuring an organization's progress along the maturity curve. These assessment tools can easily be customized to suit individual companies-a particularly valuable feature of the model. Offering vital guidance for making project management a strategic tool for competitive advantage, this book helps managers, engineers, project team members, business consultants, and others build a powerful foundation for company improvement and excellence. |
| data management maturity model: International Handbook of Internet Research Jeremy Hunsinger, Lisbeth Klastrup, Matthew Allen, 2010-06-17 Internet research spans many disciplines. From the computer or information s- ences, through engineering, and to social sciences, humanities and the arts, almost all of our disciplines have made contributions to internet research, whether in the effort to understand the effect of the internet on their area of study, or to investigate the social and political changes related to the internet, or to design and develop so- ware and hardware for the network. The possibility and extent of contributions of internet research vary across disciplines, as do the purposes, methods, and outcomes. Even the epistemological underpinnings differ widely. The internet, then, does not have a discipline of study for itself: It is a ?eld for research (Baym, 2005), an open environment that simultaneously supports many approaches and techniques not otherwise commensurable with each other. There are, of course, some inhibitions that limit explorations in this ?eld: research ethics, disciplinary conventions, local and national norms, customs, laws, borders, and so on. Yet these limits on the int- net as a ?eld for research have not prevented the rapid expansion and exploration of the internet. After nearly two decades of research and scholarship, the limits are a positive contribution, providing bases for discussion and interrogation of the contexts of our research, making internet research better for all. These ‘limits,’ challenges that constrain the theoretically limitless space for internet research, create boundaries that give de?nition to the ?eld and provide us with a particular topography that enables research and investigation. |
| data management maturity model: 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 model: Big Data MBA Bill Schmarzo, 2015-12-11 Integrate big data into business to drive competitive advantage and sustainable success Big Data MBA brings insight and expertise to leveraging big data in business so you can harness the power of analytics and gain a true business advantage. Based on a practical framework with supporting methodology and hands-on exercises, this book helps identify where and how big data can help you transform your business. You'll learn how to exploit new sources of customer, product, and operational data, coupled with advanced analytics and data science, to optimize key processes, uncover monetization opportunities, and create new sources of competitive differentiation. The discussion includes guidelines for operationalizing analytics, optimal organizational structure, and using analytic insights throughout your organization's user experience to customers and front-end employees alike. You'll learn to “think like a data scientist” as you build upon the decisions your business is trying to make, the hypotheses you need to test, and the predictions you need to produce. Business stakeholders no longer need to relinquish control of data and analytics to IT. In fact, they must champion the organization's data collection and analysis efforts. This book is a primer on the business approach to analytics, providing the practical understanding you need to convert data into opportunity. Understand where and how to leverage big data Integrate analytics into everyday operations Structure your organization to drive analytic insights Optimize processes, uncover opportunities, and stand out from the rest Help business stakeholders to “think like a data scientist” Understand appropriate business application of different analytic techniques If you want data to transform your business, you need to know how to put it to use. Big Data MBA shows you how to implement big data and analytics to make better decisions. |
| data management maturity model: 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 model: 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 model: Big Data Bill Schmarzo, 2013-09-23 Leverage big data to add value to your business Social media analytics, web-tracking, and other technologies help companies acquire and handle massive amounts of data to better understand their customers, products, competition, and markets. Armed with the insights from big data, companies can improve customer experience and products, add value, and increase return on investment. The tricky part for busy IT professionals and executives is how to get this done, and that's where this practical book comes in. Big Data: Understanding How Data Powers Big Business is a complete how-to guide to leveraging big data to drive business value. Full of practical techniques, real-world examples, and hands-on exercises, this book explores the technologies involved, as well as how to find areas of the organization that can take full advantage of big data. Shows how to decompose current business strategies in order to link big data initiatives to the organization’s value creation processes Explores different value creation processes and models Explains issues surrounding operationalizing big data, including organizational structures, education challenges, and new big data-related roles Provides methodology worksheets and exercises so readers can apply techniques Includes real-world examples from a variety of organizations leveraging big data Big Data: Understanding How Data Powers Big Business is written by one of Big Data's preeminent experts, William Schmarzo. Don't miss his invaluable insights and advice. |
| data management maturity 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 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 …
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 …
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 …
MDM Components and the Maturity Model - Knowledge …
By presenting the MDM component layers in terms of their maturity, enterprise architects can target a desired level of MDM maturity and develop a design and implementation roadmap that …
Data Management Maturity (DMM) Model Update - NDIA …
Data Management Maturity Model Partnership between the Enterprise Data Management Council (EDM Council) and the SEI to develop a model for data management.
DATA MANAGEMENT MATURITY ASSESSMENT REVIEW 2020
The maturity model by Data Crossroads includes a maturity assessment of the data value chain as well as core data management sub-capabilities: • A data value chain is a set of processes …
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 …
Data Management Maturity Models - ResearchGate
• Review existing data management maturity models to identify core set of characteristics of an effective data maturity model −DMBOK (Data Management Book of Knowledge) from DAMA...
Data Management Maturity Model Introduction
Dec 22, 2014 · • Organizations needed a comprehensive reference model to evaluate data management capabilities and measure improvements –benchmark and guidance • DMM …
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.
The Federal Government Data Maturity Model - New Editions
The following document details the six lanes of the Federal Gov ernment Data Maturity Model, including each of the five milestones within the lanes. The six lanes are: Analytics Capability, …
A Maturity Model for Data Governance, Data Quality …
MAMD is an ISO/IEC 33000-based framework for data governance, data management, and data quality management. MAMD consists of a Process Reference Model (PRM) and a Process …
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 …
MD3M: The Master Data Management Maturity Model
This research aims to assess the master data maturity of an organization. It is based on thorough literature study to derive the main concepts and best practices in master data maturity...
CMMI Model Quick Reference Guide - processgroup.com
CMMI Model: What Is It? The Capability Maturity Model Integration (CMMI) is a proven set of global best practices that drives business performance through building and benchmarking key …
MD3M: The master data management maturity model
To assess the maturity of the master data management of an enterprise, we propose the MDM maturity model. The MDM matu-rity model is a means of assessing the whole process of …
Chapter 7 Maturity Models for Data Governance - Springer
7.2.3 Data Management Maturity (DMM) Model . The SEI (Software Engineering Institute) published the DMM (Data Management Maturity) Model [24], which is analogous to the …
Data Management Maturity Assessment - Amazon Web Services
data maturity assessment is a precursor to the development of a data strategy. Some high level goals may include: Demonstrate a commitment within PSB to improving its data management …
The Road to Enterprise Data Governance: Applying the Data …
Data Maturity Model (DMM) details series of best practice recommendations and standard assessment criteria for evaluating data management capabilities. DMM results provide …
Maturity Model for Organizational Research Data …
By analyzing existing RDM service maturity models, we extract six key dimensions —awareness, data policy, budget, services, user needs, and IT infrastructure—and develop a structured …
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 …
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 …
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 …
MDM Components and the Maturity Model - Knowledge …
By presenting the MDM component layers in terms of their maturity, enterprise architects can target a desired level of MDM maturity and develop a design and implementation roadmap that …
Data Management Maturity (DMM) Model Update - NDIA …
Data Management Maturity Model Partnership between the Enterprise Data Management Council (EDM Council) and the SEI to develop a model for data management.
DATA MANAGEMENT MATURITY ASSESSMENT REVIEW …
The maturity model by Data Crossroads includes a maturity assessment of the data value chain as well as core data management sub-capabilities: • A data value chain is a set of processes …
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 …
Data Management Maturity Models - ResearchGate
• Review existing data management maturity models to identify core set of characteristics of an effective data maturity model −DMBOK (Data Management Book of Knowledge) from DAMA...
Data Management Maturity Model Introduction
Dec 22, 2014 · • Organizations needed a comprehensive reference model to evaluate data management capabilities and measure improvements –benchmark and guidance • DMM …
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.
The Federal Government Data Maturity Model - New Editions
The following document details the six lanes of the Federal Gov ernment Data Maturity Model, including each of the five milestones within the lanes. The six lanes are: Analytics Capability, …
A Maturity Model for Data Governance, Data Quality …
MAMD is an ISO/IEC 33000-based framework for data governance, data management, and data quality management. MAMD consists of a Process Reference Model (PRM) and a Process …
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 …
MD3M: The Master Data Management Maturity Model
This research aims to assess the master data maturity of an organization. It is based on thorough literature study to derive the main concepts and best practices in master data maturity...
CMMI Model Quick Reference Guide - processgroup.com
CMMI Model: What Is It? The Capability Maturity Model Integration (CMMI) is a proven set of global best practices that drives business performance through building and benchmarking key …
MD3M: The master data management maturity model
To assess the maturity of the master data management of an enterprise, we propose the MDM maturity model. The MDM matu-rity model is a means of assessing the whole process of …
Chapter 7 Maturity Models for Data Governance - Springer
7.2.3 Data Management Maturity (DMM) Model . The SEI (Software Engineering Institute) published the DMM (Data Management Maturity) Model [24], which is analogous to the …
Data Management Maturity Assessment - Amazon Web …
data maturity assessment is a precursor to the development of a data strategy. Some high level goals may include: Demonstrate a commitment within PSB to improving its data management …
The Road to Enterprise Data Governance: Applying the …
Data Maturity Model (DMM) details series of best practice recommendations and standard assessment criteria for evaluating data management capabilities. DMM results provide …
Maturity Model for Organizational Research Data …
By analyzing existing RDM service maturity models, we extract six key dimensions —awareness, data policy, budget, services, user needs, and IT infrastructure—and develop a structured …
