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| data management book of knowledge: 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 book of knowledge: The DAMA Guide to the Data Management Body of Knowledge Mark Mosley, 2010 Written by over 120 data management practitioners, this is the most impressive compilation of data management principals and best practices, ever assembled. It provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure. The equivalent of the PMBOK or the BABOK, the DAMA-DMBOK provides information on: Data Governance; Data Architecture Management; Data Development; Database Operations Management; Data Security Management; Reference & Master Data Management; Data Warehousing & Business Intelligence Management; Document & Content Management; Meta Data Management; Data Quality Management; Professional Development. As an authoritative introduction to data management, the goals of the DAMA-DMBOK Guide are: To build consensus for a generally applicable view of data management functions; To provide standard definitions for commonly used data management functions, deliverables, roles, and other terminology; To document guiding principles for data management; To present a vendor-neutral overview to commonly accepted good practices, widely adopted methods and techniques, and significant alternative approaches; To clarify the scope and boundaries of data management; To act as a reference which guides readers to additional resources for further understanding. |
| data management book of knowledge: Big Data Governance and Perspectives in Knowledge Management Strydom, Sheryl Kruger, Strydom, Moses, 2018-11-16 The world is witnessing the growth of a global movement facilitated by technology and social media. Fueled by information, this movement contains enormous potential to create more accountable, efficient, responsive, and effective governments and businesses, as well as spurring economic growth. Big Data Governance and Perspectives in Knowledge Management is a collection of innovative research on the methods and applications of applying robust processes around data, and aligning organizations and skillsets around those processes. Highlighting a range of topics including data analytics, prediction analysis, and software development, this book is ideally designed for academicians, researchers, information science professionals, software developers, computer engineers, graduate-level computer science students, policymakers, and managers seeking current research on the convergence of big data and information governance as two major trends in information management. |
| data management book of knowledge: Workforce Asset Management Book of Knowledge Lisa Disselkamp, 2013-03-20 The official study guide for the Workforce Management Technology Certification, containing core knowledge for time and labor management The worldwide standard for the time and labor management technology profession, Workforce Asset Management Book of Knowledge is the official guide to the Workforce Asset Management Certification. Establishing a common lexicon within the profession for talking about workforce management and systems, this essential guide is designed to establish a body of generally accepted and applicable practices and standards within the industry. Includes contributions from leaders in the field Covers everything from vendor and product selection, to implementation planning and execution, system design, testing and change control, financial analytics, fundamentals of scheduling people against workload and skill sets, and how to use these systems to manage labor costs and productivity Body of knowledge is focused on workers and technologies for every industry and every type of employer Designed around timekeeping and labor scheduling technologies With contributions from leaders in the field, this book expertly covers the knowledge, practices, regulations, and technologies within the domain of workforce management systems. It provides the body of knowledge for managing a workforce using time and attendance systems, labor scheduling, productivity, staffing budgets, workforce software applications, or data, compensation and benefits for payroll and human resources. |
| data management book of knowledge: Data Mining: Concepts and Techniques Jiawei Han, Micheline Kamber, Jian Pei, 2011-06-09 Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. - Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects - Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields - Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data |
| data management book of knowledge: 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 book of knowledge: Enterprise Knowledge Management David Loshin, 2001 This volume presents a methodology for defining, measuring and improving data quality. It lays out an economic framework for understanding the value of data quality, then outlines data quality rules and domain- and mapping-based approaches to consolidating enterprise knowledge. |
| data management book of knowledge: Encyclopedia of Knowledge Management, Second Edition Schwartz, David, 2010-07-31 Knowledge Management has evolved into one of the most important streams of management research, affecting organizations of all types at many different levels. The Encyclopedia of Knowledge Management, Second Edition provides a compendium of terms, definitions and explanations of concepts, processes and acronyms addressing the challenges of knowledge management. This two-volume collection covers all aspects of this critical discipline, which range from knowledge identification and representation, to the impact of Knowledge Management Systems on organizational culture, to the significant integration and cost issues being faced by Human Resources, MIS/IT, and production departments. |
| data management book of knowledge: Data Governance and Data Management Rupa Mahanti, 2021-09-08 This book delves into the concept of data as a critical enterprise asset needed for informed decision making, compliance, regulatory reporting and insights into trends, behaviors, performance and patterns. With good data being key to staying ahead in a competitive market, enterprises capture and store exponential volumes of data. Considering the business impact of data, there needs to be adequate management around it to derive the best value. Data governance is one of the core data management related functions. However, it is often overlooked, misunderstood or confused with other terminologies and data management functions. Given the pervasiveness of data and the importance of data, this book provides comprehensive understanding of the business drivers for data governance and benefits of data governance, the interactions of data governance function with other data management functions and various components and aspects of data governance that can be facilitated by technology and tools, the distinction between data management tools and data governance tools, the readiness checks to perform before exploring the market to purchase a data governance tool, the different aspects that must be considered when comparing and selecting the appropriate data governance technologies and tools from large number of options available in the marketplace and the different market players that provide tools for supporting data governance. This book combines the data and data governance knowledge that the author has gained over years of working in different industrial and research programs and projects associated with data, processes and technologies with unique perspectives gained through interviews with thought leaders and data experts. This book is highly beneficial for IT students, academicians, information management and business professionals and researchers to enhance their knowledge and get guidance on implementing data governance in their own data initiatives. |
| data management book of knowledge: INFORMS Analytics Body of Knowledge James J. Cochran, 2018-10-23 Standardizes the definition and framework of analytics #2 on Book Authority’s list of the Best New Analytics Books to Read in 2019 (January 2019) We all want to make a difference. We all want our work to enrich the world. As analytics professionals, we are fortunate - this is our time! We live in a world of pervasive data and ubiquitous, powerful computation. This convergence has inspired and accelerated the development of both analytic techniques and tools and this potential for analytics to have an impact has been a huge call to action for organizations, universities, and governments. This title from Institute for Operations Research and the Management Sciences (INFORMS) represents the perspectives of some of the most respected experts on analytics. Readers with various backgrounds in analytics – from novices to experienced professionals – will benefit from reading about and implementing the concepts and methods covered here. Peer reviewed chapters provide readers with in-depth insights and a better understanding of the dynamic field of analytics The INFORMS Analytics Body of Knowledge documents the core concepts and skills with which an analytics professional should be familiar; establishes a dynamic resource that will be used by practitioners to increase their understanding of analytics; and, presents instructors with a framework for developing academic courses and programs in analytics. |
| data management book of knowledge: MASTER DATA MANAGEMENT AND DATA GOVERNANCE, 2/E Alex Berson, Larry Dubov, 2010-12-06 The latest techniques for building a customer-focused enterprise environment The authors have appreciated that MDM is a complex multidimensional area, and have set out to cover each of these dimensions in sufficient detail to provide adequate practical guidance to anyone implementing MDM. While this necessarily makes the book rather long, it means that the authors achieve a comprehensive treatment of MDM that is lacking in previous works. -- Malcolm Chisholm, Ph.D., President, AskGet.com Consulting, Inc. Regain control of your master data and maintain a master-entity-centric enterprise data framework using the detailed information in this authoritative guide. Master Data Management and Data Governance, Second Edition provides up-to-date coverage of the most current architecture and technology views and system development and management methods. Discover how to construct an MDM business case and roadmap, build accurate models, deploy data hubs, and implement layered security policies. Legacy system integration, cross-industry challenges, and regulatory compliance are also covered in this comprehensive volume. Plan and implement enterprise-scale MDM and Data Governance solutions Develop master data model Identify, match, and link master records for various domains through entity resolution Improve efficiency and maximize integration using SOA and Web services Ensure compliance with local, state, federal, and international regulations Handle security using authentication, authorization, roles, entitlements, and encryption Defend against identity theft, data compromise, spyware attack, and worm infection Synchronize components and test data quality and system performance |
| data management book of knowledge: Data Management at Scale Piethein Strengholt, 2020-07-29 As data management and integration continue to evolve rapidly, storing all your data in one place, such as a data warehouse, is no longer scalable. In the very near future, data will need to be distributed and available for several technological solutions. With this practical book, you’ll learnhow to migrate your enterprise from a complex and tightly coupled data landscape to a more flexible architecture ready for the modern world of data consumption. Executives, data architects, analytics teams, and compliance and governance staff will learn how to build a modern scalable data landscape using the Scaled Architecture, which you can introduce incrementally without a large upfront investment. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including technological developments, regulatory requirements, and privacy concerns Go deep into the Scaled Architecture and learn how the pieces fit together Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata |
| data management book of knowledge: Data Quality Rupa Mahanti, 2019-03-18 This is not the kind of book that youll read one time and be done with. So scan it quickly the first time through to get an idea of its breadth. Then dig in on one topic of special importance to your work. Finally, use it as a reference to guide your next steps, learn details, and broaden your perspective. from the foreword by Thomas C. Redman, Ph.D., the Data Doc Good data is a source of myriad opportunities, while bad data is a tremendous burden. Companies that manage their data effectively are able to achieve a competitive advantage in the marketplace, while bad data, like cancer, can weaken and kill an organization. In this comprehensive book, Rupa Mahanti provides guidance on the different aspects of data quality with the aim to be able to improve data quality. Specifically, the book addresses: -Causes of bad data quality, bad data quality impacts, and importance of data quality to justify the case for data quality-Butterfly effect of data quality-A detailed description of data quality dimensions and their measurement-Data quality strategy approach-Six Sigma - DMAIC approach to data quality-Data quality management techniques-Data quality in relation to data initiatives like data migration, MDM, data governance, etc.-Data quality myths, challenges, and critical success factorsStudents, academicians, professionals, and researchers can all use the content in this book to further their knowledge and get guidance on their own specific projects. It balances technical details (for example, SQL statements, relational database components, data quality dimensions measurements) and higher-level qualitative discussions (cost of data quality, data quality strategy, data quality maturity, the case made for data quality, and so on) with case studies, illustrations, and real-world examples throughout. |
| data management book of knowledge: Navigating the Labyrinth Laura Sebastian-Coleman, An Executive Guide to Data Management |
| data management book of knowledge: Knowledge Management Strategies: A Handbook of Applied Technologies Lytras, Miltiadis D., Russ, Meir, Maier, Ronald, Naeve, Ambjrn, 2008-04-30 We recognize knowledge management as a socio-technical phenomenon where the basic social constructs such as person, team, and organization require support from information communication technology applications. In an era of business transition, the effective management of knowledge is proposed as a strategy that effectively utilizes organizational intangible assets. Knowledge Management Strategies: A Handbook of Applied Technologies provides practical guidelines for the implementation of knowledge management strategies through the discussion of specific technologies and taxonomies of knowledge management applications. A critical mass of some of the most sought-after research of our information technology and business world, this book proves an essential addition to every reference library collection. |
| data management book of knowledge: The Book of Alternative Data Alexander Denev, Saeed Amen, 2020-07-21 The first and only book to systematically address methodologies and processes of leveraging non-traditional information sources in the context of investing and risk management Harnessing non-traditional data sources to generate alpha, analyze markets, and forecast risk is a subject of intense interest for financial professionals. A growing number of regularly-held conferences on alternative data are being established, complemented by an upsurge in new papers on the subject. Alternative data is starting to be steadily incorporated by conventional institutional investors and risk managers throughout the financial world. Methodologies to analyze and extract value from alternative data, guidance on how to source data and integrate data flows within existing systems is currently not treated in literature. Filling this significant gap in knowledge, The Book of Alternative Data is the first and only book to offer a coherent, systematic treatment of the subject. This groundbreaking volume provides readers with a roadmap for navigating the complexities of an array of alternative data sources, and delivers the appropriate techniques to analyze them. The authors—leading experts in financial modeling, machine learning, and quantitative research and analytics—employ a step-by-step approach to guide readers through the dense jungle of generated data. A first-of-its kind treatment of alternative data types, sources, and methodologies, this innovative book: Provides an integrated modeling approach to extract value from multiple types of datasets Treats the processes needed to make alternative data signals operational Helps investors and risk managers rethink how they engage with alternative datasets Features practical use case studies in many different financial markets and real-world techniques Describes how to avoid potential pitfalls and missteps in starting the alternative data journey Explains how to integrate information from different datasets to maximize informational value The Book of Alternative Data is an indispensable resource for anyone wishing to analyze or monetize different non-traditional datasets, including Chief Investment Officers, Chief Risk Officers, risk professionals, investment professionals, traders, economists, and machine learning developers and users. |
| data management book of knowledge: Data Stewardship David Plotkin, 2013-09-16 Data stewards in business and IT are the backbone of a successful data governance implementation because they do the work to make a company's data trusted, dependable, and high quality. Data Stewardship explains everything you need to know to successfully implement the stewardship portion of data governance, including how to organize, train, and work with data stewards, get high-quality business definitions and other metadata, and perform the day-to-day tasks using a minimum of the steward's time and effort. David Plotkin has loaded this book with practical advice on stewardship so you can get right to work, have early successes, and measure and communicate those successes, gaining more support for this critical effort. - Provides clear and concise practical advice on implementing and running data stewardship, including guidelines on how to organize based on company structure, business functions, and data ownership - Shows how to gain support for your stewardship effort, maintain that support over the long-term, and measure the success of the data stewardship effort and report back to management - Includes detailed lists of responsibilities for each type of data steward and strategies to help the Data Governance Program Office work effectively with the data stewards |
| data management book of knowledge: Investing in Information Andy Bytheway, 2014-11-28 This book gathers together, in a new way, established and contemporary thinking about how to get the best out of information technology and information systems investments. Working managers who are beset by the complexities of information management in the age of Big Data and the Social Web, and students who are trying to make sense of information management in a chaotic world that is more and more driven by the Internet, will all benefit from this new treatment of a long-standing and problematic domain. Importantly, the book reveals and clarifies the dependencies that exist between the inner world of information technology and the outer world of people and organisations at work. The book differs from other books in its reflective approach. It avoids lengthy, descriptive, and prescriptive dogma. Rather, it provides tools for thinking about information management and it identifies strategic and tactical options at six levels: from the simple consideration of information technology and information systems, right through to issues of organisational performance and business strategy. At the heart of the matter are two critical and tightly connected issues: the ways that we conceive and manage an organisation’s processes, and the ways that we conceive and manage the information that an organisation needs to sustain those processes. The six-level framework that achieves this clarity is the “Information Management Body of Knowledge” (familiarly known as the “IMBOK”). This easy-to-understand and easy-to-remember framework has been found to be extremely useful in business, in government, in civil society and in education. Throughout the book, selected research papers are identified and summarised. There are also summary chapters from three different operational perspectives: performance and competency assessment using the IMBOK, undertaking research into related issues, and a review of parallel expert thinking. This book stands as a reference point and resource for all those who need to straddle the disparate worlds of “information technology” and “business”. It provides firm pedagogical foundations for courses dealing with business management in the information age, and it provides a sound reference framework for researchers who need to position research projects related to information technology and information systems in a wider context. For busy managers, who simply wish to identify, understand and successfully manage information technology-related opportunities, it provides an ideal arrangement of ideas and tools that will help them. |
| data management book of knowledge: Working Knowledge Thomas H. Davenport, Laurence Prusak, 2000-04-26 This influential book establishes the enduring vocabulary and concepts in the burgeoning field of knowledge management. It serves as the hands-on resource of choice for companies that recognize knowledge as the only sustainable source of competitive advantage going forward. Drawing from their work with more than thirty knowledge-rich firms, Davenport and Prusak--experienced consultants with a track record of success--examine how all types of companies can effectively understand, analyze, measure, and manage their intellectual assets, turning corporate wisdom into market value. They categorize knowledge work into four sequential activities--accessing, generating, embedding, and transferring--and look at the key skills, techniques, and processes of each. While they present a practical approach to cataloging and storing knowledge so that employees can easily leverage it throughout the firm, the authors caution readers on the limits of communications and information technology in managing intellectual capital. |
| data management book of knowledge: Enterprise Information Portals and Knowledge Management Joseph M. Firestone, 2003 Practical and comprehensive approach to enterprise portals and their relationship to knowledge management. |
| data management book of knowledge: Enterprise Master Data Management Allen Dreibelbis, Eberhard Hechler, Ivan Milman, Martin Oberhofer, Paul van Run, Dan Wolfson, 2008-06-05 The Only Complete Technical Primer for MDM Planners, Architects, and Implementers Companies moving toward flexible SOA architectures often face difficult information management and integration challenges. The master data they rely on is often stored and managed in ways that are redundant, inconsistent, inaccessible, non-standardized, and poorly governed. Using Master Data Management (MDM), organizations can regain control of their master data, improve corresponding business processes, and maximize its value in SOA environments. Enterprise Master Data Management provides an authoritative, vendor-independent MDM technical reference for practitioners: architects, technical analysts, consultants, solution designers, and senior IT decisionmakers. Written by the IBM ® data management innovators who are pioneering MDM, this book systematically introduces MDM’s key concepts and technical themes, explains its business case, and illuminates how it interrelates with and enables SOA. Drawing on their experience with cutting-edge projects, the authors introduce MDM patterns, blueprints, solutions, and best practices published nowhere else—everything you need to establish a consistent, manageable set of master data, and use it for competitive advantage. Coverage includes How MDM and SOA complement each other Using the MDM Reference Architecture to position and design MDM solutions within an enterprise Assessing the value and risks to master data and applying the right security controls Using PIM-MDM and CDI-MDM Solution Blueprints to address industry-specific information management challenges Explaining MDM patterns as enablers to accelerate consistent MDM deployments Incorporating MDM solutions into existing IT landscapes via MDM Integration Blueprints Leveraging master data as an enterprise asset—bringing people, processes, and technology together with MDM and data governance Best practices in MDM deployment, including data warehouse and SAP integration |
| data management book of knowledge: Measuring Data Quality for Ongoing Improvement Laura Sebastian-Coleman, 2012-12-31 The Data Quality Assessment Framework shows you how to measure and monitor data quality, ensuring quality over time. You'll start with general concepts of measurement and work your way through a detailed framework of more than three dozen measurement types related to five objective dimensions of quality: completeness, timeliness, consistency, validity, and integrity. Ongoing measurement, rather than one time activities will help your organization reach a new level of data quality. This plain-language approach to measuring data can be understood by both business and IT and provides practical guidance on how to apply the DQAF within any organization enabling you to prioritize measurements and effectively report on results. Strategies for using data measurement to govern and improve the quality of data and guidelines for applying the framework within a data asset are included. You'll come away able to prioritize which measurement types to implement, knowing where to place them in a data flow and how frequently to measure. Common conceptual models for defining and storing of data quality results for purposes of trend analysis are also included as well as generic business requirements for ongoing measuring and monitoring including calculations and comparisons that make the measurements meaningful and help understand trends and detect anomalies. - Demonstrates how to leverage a technology independent data quality measurement framework for your specific business priorities and data quality challenges - Enables discussions between business and IT with a non-technical vocabulary for data quality measurement - Describes how to measure data quality on an ongoing basis with generic measurement types that can be applied to any situation |
| data management book of knowledge: Key Issues in the New Knowledge Management Joseph M. Firestone, Mark W. McElroy, 2012-06-25 In 'Key Issues in the New Knowledge Management,' Firestone and McElroy, the architects of the New Knowledge Management (TNKM) provide an in-depth analysis of the most important issues in the field of Knowledge Management. The issues the book addresses are central in the field today: * The Knowledge Wars, or the issue of how you define knowledge determines how you manage it * The nature of knowledge processing * Information management or knowledge management? * Three views on the evolution of knowledge management * The role of knowledge claim evaluation in knowledge processing, or the difference between opinion, judgements, information, data, and real knowledge in knowledge management systems * Is culture a barrier in knowledge management? * The Open Enterprise and accelerated sustainable innovation * Portals * How should one evaluate KM software? * Intellectual Capital * Measuring the impact of KM initiatives on the organization and the bottom line * KM and terrorism |
| data management book of knowledge: 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 book of knowledge: Classification, Data Analysis, and Knowledge Organization Hans-Hermann Bock, Peter Ihm, 2012-12-06 In science, industry, public administration and documentation centers large amounts of data and information are collected which must be analyzed, ordered, visualized, classified and stored efficiently in order to be useful for practical applications. This volume contains 50 selected theoretical and applied papers presenting a wealth of new and innovative ideas, methods, models and systems which can be used for this purpose. It combines papers and strategies from two main streams of research in an interdisciplinary, dynamic and exciting way: On the one hand, mathematical and statistical methods are described which allow a quantitative analysis of data, provide strategies for classifying objects or making exploratory searches for interesting structures, and give ways to make comprehensive graphical displays of large arrays of data. On the other hand, papers related to information sciences, informatics and data bank systems provide powerful tools for representing, modelling, storing and retrieving facts, data and knowledge characterized by qualitative descriptors, semantic relations, or linguistic concepts. The integration of both fields and a special part on applied problems from biology, medicine, archeology, industry and administration assure that this volume will be informative and useful for theory and practice. |
| data management book of knowledge: The Complete Idiot's Guide to Knowledge Management Melissie Clemmons Rumizen, 2002 Discusses management models and concepts, strategies for sharing knowledge, and ways to implement the concept within a company. |
| data management book of knowledge: Knowledge Management: Awad, 2003 Knowledge Management is a subset of content taught in the Decision Support Systems course. Knowledge Management is about knowledge and how to capture it, transfer it, share it, and how to manage it. The authors take students through a process-oriented examination of the topic, striking a balance between the behavioral and technical aspects of knowledge management and use it. |
| data management book of knowledge: Successes and Failures of Knowledge Management Jay Liebowitz, 2016-06-17 Successes and Failures of Knowledge Management highlights examples from across multiple industries, demonstrating where the practice has been implemented well—and not so well—so others can learn from these cases during their knowledge management journey. Knowledge management deals with how best to leverage knowledge both internally and externally in organizations to improve decision-making and facilitate knowledge capture and sharing. It is a critical part of an organization's fabric, and can be used to increase innovation, improve organizational internal and external effectiveness, build the institutional memory, and enhance organizational agility. Starting by establishing KM processes, measures, and metrics, the book highlights ways to be successful in knowledge management institutionalization through learning from sample mistakes and successes. Whether an organization is already implementing KM or has been reluctant to do so, the ideas presented will stimulate the application of knowledge management as part of a human capital strategy in any organization. - Provides keen insights for knowledge management practitioners and educators - Conveys KM lessons learned through both successes and failures - Includes straightforward, jargon-free case studies and research developed by the leading KM researchers and practitioners across industries |
| data management book of knowledge: Data Mining and Knowledge Discovery Handbook Oded Maimon, Lior Rokach, 2006-05-28 Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management. |
| data management book of knowledge: DAMA-DMBOK Data Management Association, 2017 The second edition of DAMA International's Guide to the Data Management Body of Knowledge (DAMA-DMBOK2) updates and augments the highly successful DMBOK1. An accessible, authoritative reference book written by leading thinkers in the field and extensively reviewed by DAMA members, DMBOK2 brings together materials that comprehensively describe the challenges of data management and how to meet them by: 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, including the 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.--Publisher description |
| data management book of knowledge: Knowledge Management Case Book Thomas H. Davenport, Gilbert J. B. Probst, 2000-12-27 With a Foreword by Dr. Heinrich von Pierer President and CEO of Siemens AG While theoretical perspectives on knowledge management abound, there is clearly a lack of shared practical applications and experiences. This book provides a perspective on knowledge management at Siemens - an internationally recognised benchmark. Tom Davenport and Gilbert Probst bring together instructive case studies from different areas of this major transnational corporation that reflect the rich insights gained from years of experience in practising knowledge management. The Knowledge Management Case Book provides a comprehensive account of how organisational knowledge assets can be managed effectively. Specific emphasis is given to the development of generic lessons that can be learned from Siemens' experience. The book also offers a roadmap to building a 'mature knowledge enterprise', thereby enhancing our understanding of the steps that need to be taken in order to sustain competitive dominance in the knowledge economy. |
| data management book of knowledge: The Knowledge Graph CookBook Andreas Blumauer, Helmut Nagy, 2020 |
| data management book of knowledge: The DAMA Guide to the Data Management Body of Knowledge DAMA International, 2009-04 This is the definitive introduction to the field of data management. Use this guide to build consensus, introduce standard definitions, and identify guiding principles for data management gement functions, roles, and deliverables. DAMA-DMBOK references the DAMA Dictionary of Data Management. Under the umbrella and support of the non-profit association DAMA International, the DAMA International Foundation is a 501 c (6) not-for-profit entity, whose mission is to foster the advancement of the data management profession and community through education and research. By purchasing this indispensable piece of knowledge you will continue to support the data management community. |
| data management book of knowledge: The DAMA Guide to the Data Management Body of Knowledge Enterprise Server Version Dama International, 2009-04-01 Written by over 120 data management practitioners, the DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK) is the most impressive compilation of data management principals and best practices, ever assembled. It provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure. The equivalent of the PMBOK or the BABOK, the DAMA-DMBOK provides information on: Data Governance Data Architecture Management Data Development Database Operations Management Data Security Management Reference |
| data management book of knowledge: DAMA-DMBOK, Italian Version Dama International, 2020-04-03 Il Data Management Body of Knowledge (DAMA-DMBOK2) presenta una vista complessiva delle sfide, complessità e valore di un'efficace gestione dei dati. Le organizzazioni odierne riconoscono che la gestione dei dati è fondamentale per il loro successo. Riconoscono il valore dei loro dati e cercano di sfruttare tale valore. Con l'aumento della nostra capacità di creare e sfruttare i dati, aumenta anche la necessità di pratiche affidabili di gestione dei dati. La seconda edizione della Guida di DAMA International al Data Management Body of Knowledge (DAMA-DMBOK2) aggiorna e accresce il DMBOK1, che ha avuto grande successo. Libro di riferimento accessibile e autorevole scritto da pensatori leader del settore e ampiamente recensito dai membri DAMA, il DMBOK2 riunisce materiali che descrivono in modo esaustivo le sfide del data management e come affrontarle: Definendo una serie di principi guida per il data management e descrivendo come questi principi possono essere applicati all'interno delle aree funzionali del data management. Fornendo un framework funzionale per l'implementazione dell'enterprise data management, includendo pratiche ampiamente adottate, metodi e tecniche, funzioni, ruoli deliverable e metriche. Stabilendo un vocabolario comune per i concetti del data management e fornendo il fondamento delle best practice per i data management professional. |
| data management book of knowledge: The Semantic Web for Knowledge and Data Management Ma, Zongmin, Wang, Huaiqing, 2008-08-31 Provides a single record of technologies and practices of the Semantic approach to the management, organization, interpretation, retrieval, and use of Web-based data. |
| data management book of knowledge: The Data and Analytics Playbook Lowell Fryman, Gregory Lampshire, Dan Meers, 2016-08-12 The Data and Analytics Playbook: Proven Methods for Governed Data and Analytic Quality explores the way in which data continues to dominate budgets, along with the varying efforts made across a variety of business enablement projects, including applications, web and mobile computing, big data analytics, and traditional data integration. The book teaches readers how to use proven methods and accelerators to break through data obstacles to provide faster, higher quality delivery of mission critical programs. Drawing upon years of practical experience, and using numerous examples and an easy to understand playbook, Lowell Fryman, Gregory Lampshire, and Dan Meers discuss a simple, proven approach to the execution of multiple data oriented activities. In addition, they present a clear set of methods to provide reliable governance, controls, risk, and exposure management for enterprise data and the programs that rely upon it. In addition, they discuss a cost-effective approach to providing sustainable governance and quality outcomes that enhance project delivery, while also ensuring ongoing controls. Example activities, templates, outputs, resources, and roles are explored, along with different organizational models in common use today and the ways they can be mapped to leverage playbook data governance throughout the organization. - Provides a mature and proven playbook approach (methodology) to enabling data governance that supports agile implementation - Features specific examples of current industry challenges in enterprise risk management, including anti-money laundering and fraud prevention - Describes business benefit measures and funding approaches using exposure based cost models that augment risk models for cost avoidance analysis and accelerated delivery approaches using data integration sprints for application, integration, and information delivery success |
| data management book of knowledge: The Dama Guide to the Data Management Body of Knowledge (Japanese edition) Dama International, 2009-04-01 Written by over 120 data management practitioners, the DAMA Guide to the Data Management Body of Knowledge (DAMA-DMBOK) is the most impressive compilation of data management principals and best practices, ever assembled. It provides data management and IT professionals, executives, knowledge workers, educators, and researchers with a framework to manage their data and mature their information infrastructure. The equivalent of the PMBOK or the BABOK, the DAMA-DMBOK provides information on: • Data Governance • Data Architecture Management • Data Development • Database Operations Management • Data Security Management • Reference & Master Data Management • Data Warehousing & Business Intelligence Management • Document & Content Management • Meta Data Management • Data Quality Management • Professional Development As an authoritative introduction to data management, the goals of the DAMA-DMBOK Guide are: • To build consensus for a generally applicable view of data management functions.• To provide standard definitions for commonly used data management functions, deliverables, roles, and other terminology.• To document guiding principles for data management.• To present a vendor-neutral overview to commonly accepted good practices, widely adopted methods and techniques, and significant alternative approaches.• To clarify the scope and boundaries of data management.• To act as a reference which guides readers to additional resources for further understanding. The Editors are Mark Mosley, Editor - Development, Michael Brackett, Editor - Production, Susan Early, Assistant Editor, and Deborah Henderson, Project Sponsor. The Foreword is by John Zachman (DAMA I Lifetime Achievement Award recipient), the Preface is by John Schley (DAMA International President) and Deborah Henderson (DAMA Foundation President, DAMA International VP Education and Research), and the Afterword is by Michael Brackett (DAMA International Lifetime Achievement Award recipient). From the Foreword by John Zachman: The book is an exhaustive compilation of every possible subject and issue that warrants consideration in initiating and operating a Data Management responsibility in a modern Enterprise. It is impressive in its comprehensiveness. It not only identifies the goals and objectives of every Data Management issue and responsibility but it also suggests the natural organizational participants and end results that should be expected.The publication began as a non-trivial, sorely needed compilation of articles and substantive facts about the little understood subject of data management orchestrated by some folks from the DAMA Chicago Chapter. It was unique at the time as there was little substantive reference material on the subject. It has grown to become this pragmatic practitioner's handbook that deserves a place on every Data Management professional's bookshelf. There is a wealth of information for the novice data beginner, but it is also invaluable to the old timer as a check-list and validation of their understanding and responsibilities to ensure that nothing “falls through the cracks”! It is impressive in it breadth and completeness.The DAMA-DMBOK Guide deserves a place on every Data Management professional's bookshelf and for the General Manager, it will serve as a guide for setting expectations and assigning responsibilities for managing and practicing what has become the very most critical resource owned by an Enterprise as it (the Enterprise) progresses into the Information Age: DATA! |
| data management book of knowledge: Handbook of Data Quality Shazia Sadiq, 2013-08-13 The issue of data quality is as old as data itself. However, the proliferation of diverse, large-scale and often publically available data on the Web has increased the risk of poor data quality and misleading data interpretations. On the other hand, data is now exposed at a much more strategic level e.g. through business intelligence systems, increasing manifold the stakes involved for individuals, corporations as well as government agencies. There, the lack of knowledge about data accuracy, currency or completeness can have erroneous and even catastrophic results. With these changes, traditional approaches to data management in general, and data quality control specifically, are challenged. There is an evident need to incorporate data quality considerations into the whole data cycle, encompassing managerial/governance as well as technical aspects. Data quality experts from research and industry agree that a unified framework for data quality management should bring together organizational, architectural and computational approaches. Accordingly, Sadiq structured this handbook in four parts: Part I is on organizational solutions, i.e. the development of data quality objectives for the organization, and the development of strategies to establish roles, processes, policies, and standards required to manage and ensure data quality. Part II, on architectural solutions, covers the technology landscape required to deploy developed data quality management processes, standards and policies. Part III, on computational solutions, presents effective and efficient tools and techniques related to record linkage, lineage and provenance, data uncertainty, and advanced integrity constraints. Finally, Part IV is devoted to case studies of successful data quality initiatives that highlight the various aspects of data quality in action. The individual chapters present both an overview of the respective topic in terms of historical research and/or practice and state of the art, as well as specific techniques, methodologies and frameworks developed by the individual contributors. Researchers and students of computer science, information systems, or business management as well as data professionals and practitioners will benefit most from this handbook by not only focusing on the various sections relevant to their research area or particular practical work, but by also studying chapters that they may initially consider not to be directly relevant to them, as there they will learn about new perspectives and approaches. |
| data management book of knowledge: A Very Short, Fairly Interesting and Reasonably Cheap Book About Knowledge Management Joanne Roberts, 2015-06-18 Written in a lively, conversational style, Knowledge Management looks at the nature of knowledge, including its definition and measurement, before the main concepts and theoretical contributions to knowledge management are reviewed and challenged, providing fresh insights into the central debates. Conceived by Chris Grey as an antidote to conventional textbooks, each book in the ‘Very Short, Fairly Interesting and Reasonably Cheap’ series takes a core area of the curriculum and turns it on its head by providing a critical and sophisticated overview of the key issues and debates in an informal, conversational and often humorous way. Suitable for students of Business and Management courses at Undergraduate and Postgraduate level and anyone interested in the concept of knowledge management. |
DAMA-DMBOK2 and CDMP - DAMA Phoenix
The DAMA-DMBOK2 Guide is intended to be a definitive introduction to data management as it currently exists. management Knowledge Areas. terminology. To identify guiding principles for …
THE DATA MANAGEMENT COOKBOOK - Data Crossroads
• Data Management Body of Knowledge, 2nd edition (DAMA-DMBOK 2), by DAMA International 1 ; • Data Management Capability Assessment Model (DCAM), by EDM Council 2 ;
D v P u v } Ç } ( < v } Á o P - DAMA Denmark
• Data Management Body of Knowledge (DAMA-DMBOK Guide) is a collection of processes and best practices. • Contains generally accepted as best practices and references for each Data …
The Ultimate Guide to Data Management Certification
Are you thinking about getting certified in Data Management, but unsure what choice is right for you? Are you trying to train a team, or to adopt an Industry Standard framework like the DAMA …
Data Analysis & Knowledge Management Definitions and …
Having sound data management practices in place important for is an organization, but it is data analysis that allows an organization to maximize the utility of its data, turning it into...
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 …
DAMA-DMBOK Functional Framework - Governance …
Provide a cohesive structure for organizing the Data Management Body of Knowledge (DAMA-DMBOK Guide) document. • Define standard terms and definitions for data management …
CHAPTER 1 Introduction: Fundamentals of Data …
economical, secure, and effective is known as data management. Data management enables individuals, groups, and networked devices to optimize data utilization in order to make good …
CIT 304 DATA MANAGEMENT Course Team - nou.edu.ng
Introduce you to basic concepts pertaining to the data, information and knowledge management; Demonstrate the variety of common contexts in which data organization and management is …
DAMA-DMBOK2 - DAMA Denmark
The second edition of DAMA International’s Guide to the Data Management Body of Knowledge (DAMA-DMBOK2) updates and augments the highly successful DMBOK1. An accessible, …
Introduction to Data Management - SAGE Publications Inc
This module will show you some common data management planning tools that will allow you to start your project with a detailed plan that takes into consideration important issues like …
DAMA DMBOK Functonal Framework - I.T.Matters
Guiding the development and delivery of data management curriculum content for higher education. Suggesting areas of further research in the field of data management. Helping data …
THE DATA MANAGEMENT TOOLKIT - Data Crossroads
proved my know-how knowledge by participating in implementation of data management at a large multinational company. This book is a collection of my hands-on knowledge, experience …
1. Principles of Data Management 2020 - UMass
“Data Management is the process of providing the appropriate labeling, storage, and access for data at all stages of a research project. Here you can find best practices, resources, and …
Data and Knowledge Management - Springer
This chapter offers a theoretical overview on Data and Knowl-edge Management and thus provides a theoretic foundation for the following parts of this book. Moreover, if you implement …
DATA GOVERNANCE AND DATA MANAGEMENT WHITE …
What is “Data Management?” The Data Management Book of Knowledge (DMBOK) published by DAMA International, the professional organization for those in the data management …
Introduction to Data Management Part One
Learn what a data management plan (DMP) is and why it is important. Learn the parts of a good data management plan. Complete your own basic plan. Know where to go next for more …
THE ORANGE MODEL OF DATA MANAGEMENT - Data …
The key value proposition of data management is enabling the process of the transformation of data into meaningful information. The data management function delivers this value …
DATA AND KNOWLEDGE MANAGEMENT - INTRAC
a knowledge management system is to generate and share usable knowledge based on this data. Data and knowledge management systems are often supported through information …
Knowledge Graph Data Management: Models, Methods, …
In this paper, we comprehensively introduce the state-of-the-art research on knowledge graph data management, which consists of knowledge graph data models, query languages, storage …
DAMA-DMBOK2 and CDMP - DAMA Phoenix
The DAMA-DMBOK2 Guide is intended to be a definitive introduction to data management as it currently exists. management Knowledge Areas. terminology. To identify guiding principles for …
THE DATA MANAGEMENT COOKBOOK - Data Crossroads
• Data Management Body of Knowledge, 2nd edition (DAMA-DMBOK 2), by DAMA International 1 ; • Data Management Capability Assessment Model (DCAM), by EDM Council 2 ;
D v P u v } Ç } ( < v } Á o P - DAMA Denmark
• Data Management Body of Knowledge (DAMA-DMBOK Guide) is a collection of processes and best practices. • Contains generally accepted as best practices and references for each Data …
The Ultimate Guide to Data Management Certification
Are you thinking about getting certified in Data Management, but unsure what choice is right for you? Are you trying to train a team, or to adopt an Industry Standard framework like the DAMA …
Data Analysis & Knowledge Management Definitions and …
Having sound data management practices in place important for is an organization, but it is data analysis that allows an organization to maximize the utility of its data, turning it into...
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 …
DAMA-DMBOK Functional Framework - Governance …
Provide a cohesive structure for organizing the Data Management Body of Knowledge (DAMA-DMBOK Guide) document. • Define standard terms and definitions for data management …
CHAPTER 1 Introduction: Fundamentals of Data …
economical, secure, and effective is known as data management. Data management enables individuals, groups, and networked devices to optimize data utilization in order to make good …
CIT 304 DATA MANAGEMENT Course Team - nou.edu.ng
Introduce you to basic concepts pertaining to the data, information and knowledge management; Demonstrate the variety of common contexts in which data organization and management is …
DAMA-DMBOK2 - DAMA Denmark
The second edition of DAMA International’s Guide to the Data Management Body of Knowledge (DAMA-DMBOK2) updates and augments the highly successful DMBOK1. An accessible, …
Introduction to Data Management - SAGE Publications Inc
This module will show you some common data management planning tools that will allow you to start your project with a detailed plan that takes into consideration important issues like …
DAMA DMBOK Functonal Framework - I.T.Matters
Guiding the development and delivery of data management curriculum content for higher education. Suggesting areas of further research in the field of data management. Helping data …
THE DATA MANAGEMENT TOOLKIT - Data Crossroads
proved my know-how knowledge by participating in implementation of data management at a large multinational company. This book is a collection of my hands-on knowledge, experience …
1. Principles of Data Management 2020 - UMass
“Data Management is the process of providing the appropriate labeling, storage, and access for data at all stages of a research project. Here you can find best practices, resources, and …
Data and Knowledge Management - Springer
This chapter offers a theoretical overview on Data and Knowl-edge Management and thus provides a theoretic foundation for the following parts of this book. Moreover, if you implement …
DATA GOVERNANCE AND DATA MANAGEMENT WHITE …
What is “Data Management?” The Data Management Book of Knowledge (DMBOK) published by DAMA International, the professional organization for those in the data management …
Introduction to Data Management Part One
Learn what a data management plan (DMP) is and why it is important. Learn the parts of a good data management plan. Complete your own basic plan. Know where to go next for more …
THE ORANGE MODEL OF DATA MANAGEMENT - Data …
The key value proposition of data management is enabling the process of the transformation of data into meaningful information. The data management function delivers this value …
DATA AND KNOWLEDGE MANAGEMENT - INTRAC
a knowledge management system is to generate and share usable knowledge based on this data. Data and knowledge management systems are often supported through information …
Knowledge Graph Data Management: Models, Methods, …
In this paper, we comprehensively introduce the state-of-the-art research on knowledge graph data management, which consists of knowledge graph data models, query languages, storage …
