The DAMA Dictionary of Data Management


Author: Susan Earley
Publisher: Technics Publications Llc
ISBN: 9781935504122
Category: Computers
Page: 254
View: 1286
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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). This glossary is a physical book – it also comes in electronic format as a CD-ROM (see ISBN 9781935504115). 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

The DAMA Guide to the Data Management Body of Knowledge

(DAMA-DMBOK Guide)
Author: Susan Earley
Publisher: Technics Publications Llc
ISBN: 9781935504023
Category: Computers
Page: 406
View: 4035
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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 Modeling Made Simple with ER/Studio Data Architect

Adapting to Agile Data Modeling in a Big Data World
Author: Steve Hoberman
Publisher: Technics Publications
ISBN: 1634620941
Category: Computers
Page: 342
View: 9528
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Build a working knowledge of data modeling concepts and best practices, along with how to apply these principles with ER/Studio. This second edition includes numerous updates and new sections including an overview of ER/Studio’s support for agile development, as well as a description of some of ER/Studio’s newer features for NoSQL, such as MongoDB’s containment structure.

Data Modeling for MongoDB

Building Well-Designed and Supportable MongoDB Databases
Author: Steve Hoberman
Publisher: Technics Publications
ISBN: 1634620410
Category: Computers
Page: 226
View: 6903
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Congratulations! You completed the MongoDB application within the given tight timeframe and there is a party to celebrate your application’s release into production. Although people are congratulating you at the celebration, you are feeling some uneasiness inside. To complete the project on time required making a lot of assumptions about the data, such as what terms meant and how calculations are derived. In addition, the poor documentation about the application will be of limited use to the support team, and not investigating all of the inherent rules in the data may eventually lead to poorly-performing structures in the not-so-distant future. Now, what if you had a time machine and could go back and read this book. You would learn that even NoSQL databases like MongoDB require some level of data modeling. Data modeling is the process of learning about the data, and regardless of technology, this process must be performed for a successful application. You would learn the value of conceptual, logical, and physical data modeling and how each stage increases our knowledge of the data and reduces assumptions and poor design decisions. Read this book to learn how to do data modeling for MongoDB applications, and accomplish these five objectives: Understand how data modeling contributes to the process of learning about the data, and is, therefore, a required technique, even when the resulting database is not relational. That is, NoSQL does not mean NoDataModeling! Know how NoSQL databases differ from traditional relational databases, and where MongoDB fits. Explore each MongoDB object and comprehend how each compares to their data modeling and traditional relational database counterparts, and learn the basics of adding, querying, updating, and deleting data in MongoDB. Practice a streamlined, template-driven approach to performing conceptual, logical, and physical data modeling. Recognize that data modeling does not always have to lead to traditional data models! Distinguish top-down from bottom-up development approaches and complete a top-down case study which ties all of the modeling techniques together. This book is written for anyone who is working with, or will be working with MongoDB, including business analysts, data modelers, database administrators, developers, project managers, and data scientists. There are three sections: In Section I, Getting Started, we will reveal the power of data modeling and the tight connections to data models that exist when designing any type of database (Chapter 1), compare NoSQL with traditional relational databases and where MongoDB fits (Chapter 2), explore each MongoDB object and comprehend how each compares to their data modeling and traditional relational database counterparts (Chapter 3), and explain the basics of adding, querying, updating, and deleting data in MongoDB (Chapter 4). In Section II, Levels of Granularity, we cover Conceptual Data Modeling (Chapter 5), Logical Data Modeling (Chapter 6), and Physical Data Modeling (Chapter 7). Notice the “ing” at the end of each of these chapters. We focus on the process of building each of these models, which is where we gain essential business knowledge. In Section III, Case Study, we will explain both top down and bottom up development approaches and go through a top down case study where we start with business requirements and end with the MongoDB database. This case study will tie together all of the techniques in the previous seven chapters. Nike Senior Data Architect Ryan Smith wrote the foreword. Key points are included at the end of each chapter as a way to reinforce concepts. In addition, this book is loaded with hands-on exercises, along with their answers provided in Appendix A. Appendix B contains all of the book’s references and Appendix C contains a glossary of the terms used throughout the text.

The Data and Analytics Playbook

Proven Methods for Governed Data and Analytic Quality
Author: Lowell Fryman,Gregory Lampshire,Dan Meers
Publisher: Morgan Kaufmann
ISBN: 0128025476
Category: Computers
Page: 292
View: 2756
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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 Model Patterns: A Metadata Map


Author: David C. Hay
Publisher: Elsevier
ISBN: 9780080477039
Category: Computers
Page: 432
View: 6422
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Data Model Patterns: A Metadata Map not only presents a conceptual model of a metadata repository but also demonstrates a true enterprise data model of the information technology industry itself. It provides a step-by-step description of the model and is organized so that different readers can benefit from different parts. It offers a view of the world being addressed by all the techniques, methods, and tools of the information processing industry (for example, object-oriented design, CASE, business process re-engineering, etc.) and presents several concepts that need to be addressed by such tools. This book is pertinent, with companies and government agencies realizing that the data they use represent a significant corporate resource recognize the need to integrate data that has traditionally only been available from disparate sources. An important component of this integration is management of the "metadata" that describe, catalogue, and provide access to the various forms of underlying business data. The "metadata repository" is essential to keep track of the various physical components of these systems and their semantics. The book is ideal for data management professionals, data modeling and design professionals, and data warehouse and database repository designers. A comprehensive work based on the Zachman Framework for information architecture—encompassing the Business Owner's, Architect's, and Designer's views, for all columns (data, activities, locations, people, timing, and motivation) Provides a step-by-step description of model and is organized so that different readers can benefit from different parts Provides a view of the world being addressed by all the techniques, methods and tools of the information processing industry (for example, object-oriented design, CASE, business process re-engineering, etc.) Presents many concepts that are not currently being addressed by such tools — and should be

Building and Managing the Meta Data Repository

A Full Lifecycle Guide
Author: David Marco
Publisher: Wiley
ISBN: 9780471355236
Category: Computers
Page: 416
View: 7313
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"This is the first book to tackle the subject of meta data in data warehousing, and the results are spectacular . . . David Marco has written about the subject in a way that is approachable, practical, and immediately useful. Building and Managing the Meta Data Repository: A Full Lifecycle Guide is an excellent resource for any IT professional." -Steve Murchie Group Product Manager, Microsoft Corporation Meta data repositories can provide your company with tremendous value if they are used properly and if you understand what they can, and can't, do. Written by David Marco, the industry's leading authority on meta data and well-known columnist for DM Review, this book offers all the guidance you'll need for developing, deploying, and managing a meta data repository to gain a competitive advantage. After illustrating the fundamental concepts, Marco shows you how to use meta data to increase your company's revenue and decrease expenses. You'll find a comprehensive look at the major trends affecting the meta data industry, as well as steps on how to build a repository that is flexible enough to adapt to future changes. This vendor-neutral guide alsoincludes complete coverage of meta data sources, standards, and architecture, and it explores the full gamut of practical implementation issues.Taking you step-by-step through the process of implementing a meta data repository, Marco shows you how to: - Evaluate meta data tools Build the meta data project plan - Design a custom meta data architecture - Staff a repository team - Implement data quality through meta data - Create a physical meta data model - Evaluate meta data delivery requirements The CD-ROM includes: - A sample implementation project plan - A function and feature checklist of meta data tool requirements - Several physical meta datamodels to support specific business functions Visit our Web site at www.wiley.com/compbooks/ Visit the companion Web site at www.wiley.com/compbooks/marco

Architecture and Patterns for IT Service Management, Resource Planning, and Governance: Making Shoes for the Cobbler's Children


Author: Charles T. Betz
Publisher: Elsevier
ISBN: 008048834X
Category: Computers
Page: 424
View: 6970
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Architecture and Patterns for IT Service Management, Resource Planning, and Governance: Making Shoes for the Cobbler's Children provides an independent examination of developments in Enterprise Resource Planning for Information. Major companies, research firms, and vendors are offering Enterprise Resource Planning for Information Technology, which they label as ERP for IT, IT Resource Planning and related terms. This book presents on-the-ground coverage of enabling IT governance in architectural detail, which can be used to define a strategy for immediate execution. It fills the gap between high-level guidance on IT governance and detailed discussions about specific vendor technologies. It provides a unique value chain approach to integrating the COBIT, ITIL, and CMM frameworks into a coherent, unified whole. It presents a field-tested, detailed conceptual information model with definitions and usage scenarios, mapped to both process and system architectures. This book is recommended for practitioners and managers engaged in IT support in large companies, particularly those who are information architects, enterprise architects, senior software engineers, program/project managers, and IT managers/directors.

Data Modeling for the Business

A Handbook for Aligning the Business with IT using High-Level Data Models
Author: Steve Hoberman,Donna Burbank,Chris Bradley
Publisher: Technics Publications
ISBN: 1634620437
Category: Computers
Page: 288
View: 5737
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Did you ever try getting Businesspeople and IT to agree on the project scope for a new application? Or try getting Marketing and Sales to agree on the target audience? Or try bringing new team members up to speed on the hundreds of tables in your data warehouse — without them dozing off? Whether you are a businessperson or an IT professional, you can be the hero in each of these and hundreds of other scenarios by building a High-Level Data Model. The High-Level Data Model is a simplified view of our complex environment. It can be a powerful communication tool of the key concepts within our application development projects, business intelligence and master data management programs, and all enterprise and industry initiatives. Learn about the High-Level Data Model and master the techniques for building one, including a comprehensive ten-step approach and hands-on exercises to help you practice topics on your own. In this book, we review data modeling basics and explain why the core concepts stored in a high-level data model can have significant business impact on an organization. We explain the technical notation used for a data model and walk through some simple examples of building a high-level data model. We also describe how data models relate to other key initiatives you may have heard of or may be implementing in your organization. This book contains best practices for implementing a high-level data model, along with some easy-to-use templates and guidelines for a step-by-step approach. Each step will be illustrated using many examples based on actual projects we have worked on. Names have been changed to protect the innocent, but the pain points and lessons have been preserved. One example spans an entire chapter and will allow you to practice building a high-level data model from beginning to end, and then compare your results to ours. Building a high-level data model following the ten step approach you’ll read about is a great way to ensure you will retain the new skills you learn in this book. As is the case in many disciplines, using the right tool for the right job is critical to the overall success of your high-level data model implementation. To help you in your tool selection process, there are several chapters dedicated to discussing what to look for in a high-level data modeling tool and a framework for choosing a data modeling tool, in general. This book concludes with a real-world case study that shows how an international energy company successfully used a high-level data model to streamline their information management practices and increase communication throughout the organization—between both businesspeople and IT. Data modeling is one of the under-exploited, and potentially very valuable, business capabilities that are often hidden away in an organization’s Information Technology department. Data Modeling for the Business highlights both the resulting damage to business value, and the opportunities to make things better. As an easy-to follow and comprehensive guide on the ‘why’ and ‘how’ of data modeling, it also reminds us that a successful strategy for exploiting IT depends at least as much on the information as the technology. Chris Potts, Corporate IT Strategist and Author of fruITion: Creating the Ultimate Corporate Strategy for Information Technology One of the most critical systems issues is aligning business with IT and fulfilling business needs using data models. The authors of Data Modeling for the Business do a masterful job at simply and clearly describing the art of using data models to communicate with business representatives and meet business needs. The book provides many valuable tools, analogies, and step-by-step methods for effective data modeling and is an important contribution in bridging the much needed connection between data modeling and realizing business requirements. Len Silverston, author of The Data Model Resource Book series

Cultural Variations and Business Performance: Contemporary Globalism

Contemporary Globalism
Author: Christiansen, Bryan
Publisher: IGI Global
ISBN: 1466603070
Category: Business & Economics
Page: 418
View: 8852
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"This book offers the latest research in the field of Business Performance Management in the global economic environment of present conditions while looking at business as a whole entity instead of only at the divisional level"--Provided by publisher.

Extreme Scoping

An Agile Approach to Enterprise Data Warehousing and Business Intelligence
Author: Larissa T. Moss
Publisher: Technics Publications
ISBN: 1634620240
Category: Computers
Page: 306
View: 3836
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Do your business intelligence (BI) projects take too long to deliver? Is the value of the deliverables less than satisfactory? Do these projects propagate poor data management practices? If you screamed “yes” to any of these questions, read this book to master a proven approach to building your enterprise data warehouse and BI initiatives. Extreme Scoping, based on the Business Intelligence Roadmap, will show you how to build analytics applications rapidly yet not sacrifice data management and enterprise architecture. In addition, all of the roles required to deliver all seven steps of this agile methodology are explained along with many real-world examples. From Wayne Eckerson’s Foreword I’ve read many books about data warehousing and business intelligence (BI). This book by Larissa Moss is one of the best. I should not be surprised. Larissa has spent years refining the craft of designing, building, and delivering BI applications. Over the years, she has developed a keen insight about what works and doesn’t work in BI. This book brings to light the wealth of that development experience. Best of all, this is not some dry text that laboriously steps readers through a technical methodology. Larissa expresses her ideas in a clear, concise, and persuasive manner. I highlighted so many beautifully written and insightful paragraphs in her manuscript that it became comical. I desperately wanted the final, published book rather than the manuscript so I could dog-ear it to death and place it front-and-center in my office bookshelf! From David Well’s Foreword Extreme Scoping is rich with advice and guidance for virtually every aspect of BI projects from planning and requirements to deployment and from back-end data management to front-end information and analytics services. Larissa is both a pragmatist and an independent thinker. Those qualities come through in the style of this book. Extreme Scoping is a well-written book that is easy to absorb. It is not full of surprises. It is filled with a lot of common sense and lessons learned through experience.

Data Governance

How to Design, Deploy, and Sustain an Effective Data Governance Program
Author: John Ladley
Publisher: Newnes
ISBN: 0124158293
Category: Business & Economics
Page: 236
View: 9413
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This book is for any manager or team leader that has the green light to implement a data governance program What you will find in this book is an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable.

The Enterprise Data Model


Author: Andy Graham
Publisher: Koios Associates Limited
ISBN: 9780956582911
Category: Computers
Page: 160
View: 7411
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Wouldn't it be great to understand all the data in your organisation? Just imagine being able to define, agree and manage information concepts that impact on business strategy? Then image that these information concepts can be linked to the physical database attributes that ultimately are used to create them. That's what this book is about. It focuses on the data model as the foundation for achieving this understanding. This book provides a framework for the enterprise data model, the business reasons behind it and the differences between conceptual, logical and physical data models. The question of how, and why, to use a data model artifact as part of the data governance toolkit for the whole enterprise is also addressed. This publication is not an in-depth manual on how to model data for a new database system or your next design project. It instead focuses at a level above these implementation projects and addresses the issues that organisations typical struggling with such as: * How do we provide a framework within which we can manage our data assets? * How do we develop applications that adhere to a set of data standards; without creating a nightmare of administration and governance that is both unwieldy and unusable? * How can we get business value from our enterprise data? Chapter headings are: * Chapter 1 - Introduction * Chapter 2 - Information and Data * Chapter 3 - Pillars of Value * Chapter 4 - An Overview of Data Modelling * Chapter 5 - Data Architecture * Chapter 6 - The Enterprise Data Model * Chapter 7 - Build the Model one Project at a Time * Chapter 8 - Master Data * Chapter 9 - Data Governance * Chapter 10 - The Enterprise Data Framework This 2nd edition revises the original text to add extra details around key areas such as the enterprise data model framework and the pillars of value. It also improves the quality of the original text.

Voices for Change

Participatory Monitoring and Evaluation in China
Author: Ronnie Vernooy,Sun Qiu,Jianchu Xu,International Development Research Centre (Canada)
Publisher: IDRC
ISBN: 9780889369948
Category: Business & Economics
Page: 174
View: 3243
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Voices for Change: Participatory Monitoring and Evaluation in China

Making Enterprise Information Management (EIM) Work for Business

A Guide to Understanding Information as an Asset
Author: John Ladley
Publisher: Morgan Kaufmann
ISBN: 0123756960
Category: Computers
Page: 552
View: 5792
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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 Mining in Grid Computing Environments


Author: Werner Dubitzky
Publisher: John Wiley & Sons
ISBN: 0470699892
Category: Medical
Page: 288
View: 314
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Based around eleven international real life case studies and including contributions from leading experts in the field this groundbreaking book explores the need for the grid-enabling of data mining applications and provides a comprehensive study of the technology, techniques and management skills necessary to create them. This book provides a simultaneous design blueprint, user guide, and research agenda for current and future developments and will appeal to a broad audience; from developers and users of data mining and grid technology, to advanced undergraduate and postgraduate students interested in this field.

Data Model Scorecard

Applying the Industry Standard on Data Model Quality
Author: Steve Hoberman
Publisher: Technics Publications
ISBN: 1634620844
Category: Computers
Page: 202
View: 4314
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Data models are the main medium used to communicate data requirements from business to IT, and within IT from analysts, modelers, and architects, to database designers and developers. Therefore it’s essential to get the data model right. But how do you determine right? That’s where the Data Model Scorecard® comes in. The Data Model Scorecard is a data model quality scoring tool containing ten categories aimed at improving the quality of your organization’s data models. Many of my consulting assignments are dedicated to applying the Data Model Scorecard to my client’s data models – I will show you how to apply the Scorecard in this book. This book, written for people who build, use, or review data models, contains the Data Model Scorecard template and an explanation along with many examples of each of the ten Scorecard categories. There are three sections: In Section I, Data Modeling and the Need for Validation, receive a short data modeling primer in Chapter 1, understand why it is important to get the data model right in Chapter 2, and learn about the Data Model Scorecard in Chapter 3. In Section II, Data Model Scorecard Categories, we will explain each of the ten categories of the Data Model Scorecard. There are ten chapters in this section, each chapter dedicated to a specific Scorecard category: · Chapter 4: Correctness · Chapter 5: Completeness · Chapter 6: Scheme · Chapter 7: Structure · Chapter 8: Abstraction · Chapter 9: Standards · Chapter 10: Readability · Chapter 11: Definitions · Chapter 12: Consistency · Chapter 13: Data In Section III, Validating Data Models, we will prepare for the model review (Chapter 14), cover tips to help during the model review (Chapter 15), and then review a data model based upon an actual project (Chapter 16).

The Information Systems Academic Discipline in Australia


Author: Guy G. Gable,Shirley Gregor,Roger Clarke,Gail Ridley,Robert Smyth
Publisher: ANU E Press
ISBN: 1921313943
Category: Computers
Page: 346
View: 540
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This book represents the second phase of a multi-method, multi-study of the 'Information Systems Academic Discipline in Australia'. Drawing on Whitley's Theory of Scientific Change, the study analysed the degree of 'professionalisation' of the Information Systems Discipline, the overarching research question being 'To what extent is Information Systems a distinct and mature discipline in Australia?' The book chapters are structured around three main sections: a) the context of the study; b) the state case studies; and c) Australia-wide evidence and analysis. The book is crafted to be accessible to IS and non-IS types both within and outside of Australia. It represents a 'check point'; a snapshot at a point in time. As the first in a hoped for series of such snap-shots, it includes a brief history of IS in Australia, bringing us up to the time of this report. The editorial team comprises Guy Gable, architect and leader; Bob Smyth, project manager; Shirley Gregor, sponsor, host and co-theoretician; Roger Clarke, discipline memory; and Gail Ridley, theoretician. In phase two, the editors undertook to examine each component study, with a view to arriving at an Australia-wide perspective.

Corporate Data Quality

Prerequisite for Successful Business Models
Author: Boris Otto,Hubert Österle
Publisher: epubli
ISBN: 3737575932
Category: Business & Economics
Page: N.A
View: 6204
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Data is the foundation of the digital economy. Industry 4.0 and digital services are producing so far unknown quantities of data and make new business models possible. Under these circumstances, data quality has become the critical factor for success. This book presents a holistic approach for data quality management and presents ten case studies about this issue. It is intended for practitioners dealing with data quality management and data governance as well as for scientists. The book was written at the Competence Center Corporate Data Quality (CC CDQ) in close cooperation between researchers from the University of St. Gallen and Fraunhofer IML as well as many representatives from more than 20 major corporations. Chapter 1 introduces the role of data in the digitization of business and society and describes the most important business drivers for data quality. It presents the Framework for Corporate Data Quality Management and introduces essential terms and concepts. Chapter 2 presents practical, successful examples of the management of the quality of master data based on ten cases studies that were conducted by the CC CDQ. The case studies cover every aspect of the Framework for Corporate Data Quality Management. Chapter 3 describes selected tools for master data quality management. The three tools have been distinguished through their broad applicability (method for DQM strategy development and DQM maturity assessment) and their high level of innovation (Corporate Data League). Chapter 4 summarizes the essential factors for the successful management of the master data quality and provides a checklist of immediate measures that should be addressed immediately after the start of a data quality management project. This guarantees a quick start into the topic and provides initial recommendations for actions to be taken by project and line managers. Please also check out the book's homepage at http://www.cdq-book.org/

Cross-Border Resource Management


Author: Rongxing Guo
Publisher: Elsevier
ISBN: 0444640053
Category: Business & Economics
Page: 472
View: 4052
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Cross-Border Resource Management, Third Edition covers theoretical and analytical issues relating to cross-border resource management. This book holistically explores issues when two entities share a border, such as sovereign countries, dependent states and others, where each seeks to maximize their political and economic interests regardless of impacts on the environment. This new edition has been completely revised to reflect current issues, with new cases from North America and Europe and discussions and issues regarding air and space. Users will find a single resource that explores the many facets of managing and utilizing natural resources when they extend across defined borders. Presents a thoroughly updated edition with new cases and coverage on cross-border management Contains new content on geopolitical issues, environmental impacts of armed conflicts, dividing and managing shared natural resources, exploitation, competition and depletion of border resources Includes new cases from North America and Europe and discussions and issues regarding air and space