Written by leading industry experts, the Data Management Handbook is a comprehensive, single-volume guide to the most innovative ideas on how to plan, develop, and run a powerful data management function - as well as handle day-to-day operations. The book provides practical, hands-on guidance on the strategic, tactical, and technical aspects of dat

Written by leading industry experts, the Data Management Handbook is a comprehensive, single-volume guide to the most innovative ideas on how to plan, develop, and run a powerful data management function - as well as handle day-to-day operations. The book provides practical, hands-on guidance on the strategic, tactical, and technical aspects of data management, offering an inside look at how leading companies in various industries meet the challenges of moving to a data-sharing environment.

Packed with dozens of no-nonsense chapters written by leading professionals, Handbook of Data Management, 1999 Edition shows your students how to design, build, and maintain high-performance, high-availability databases in multiple environments. Handbook of Data Management, 1999 Edition is the most comprehensive, single-volume guide of its kind. The book provides the latest, most innovative solutions for planning, developing, and running a powerful data management function. Here students will find exhaustive coverage of the range of data repositories (from legacy indexed files to object data bases and data warehouses) as well as details on everything from strategic planning to maximizing database performance. Completely revised and updated to reflect latebreaking technologies, Handbook of Data Management, 1999 Edition includes extensive case studies and straightforward descriptions showing students how to: implement Web-enabled data warehouses build multimedia databases master data mining use enterprise database modeling stay up-to-date with data conversion and migration maximize OLAP architectures and tools Handbook of Data Management, 1999 Edition also provides ongoing coverage of the latest tools and techniques regarding: organization for quality information systems data definition database design and management object and hybrid databases and more Each contributor to Handbook of Data Management, 1999 Edition is an expert with first-hand experience in database and data management. These contributors provide a depth and breadth of coverage you and your students simply won't find anywhere else. Prepare your students for "real-world" business computing. Start them off with Handbook of Data Management, 1999 Edition.

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

The management of clinical data, from its collection during a trial to its extraction for analysis, has become a critical element in the steps to prepare a regulatory submission and to obtain approval to market a treatment. Groundbreaking on its initial publication nearly fourteen years ago, and evolving with the field in each iteration since then,

Handbook of Database Security: Applications and Trends provides an up-to-date overview of data security models, techniques, and architectures in a variety of data management applications and settings. In addition to providing an overview of data security in different application settings, this book includes an outline for future research directions within the field. The book is designed for industry practitioners and researchers, and is also suitable for advanced-level students in computer science.

This second edition of Data Management Using Stata focuses on tasks that bridge the gap between raw data and statistical analysis. It has been updated throughout to reflect new data management features that have been added over the last 10 years. Such features include the ability to read and write a wide variety of file formats, the ability to write highly customized Excel files, the ability to have multiple Stata datasets open at once, and the ability to store and manipulate string variables stored as Unicode. Further, this new edition includes a new chapter illustrating how to write Stata programs for solving data management tasks. As in the original edition, the chapters are organized by data management areas: reading and writing datasets, cleaning data, labeling datasets, creating variables, combining datasets, processing observations across subgroups, changing the shape of datasets, and programming for data management. Within each chapter, each section is a self-contained lesson illustrating a particular data management task (for instance, creating date variables or automating error checking) via examples. This modular design allows you to quickly identify and implement the most common data management tasks without having to read background information first. In addition to the "nuts and bolts" examples, author Michael Mitchell alerts users to common pitfalls (and how to avoid them) and provides strategic data management advice. This book can be used as a quick reference for solving problems as they arise or can be read as a means for learning comprehensive data management skills. New users will appreciate this book as a valuable way to learn data management, while experienced users will find this information to be handy and time saving--there is a good chance that even the experienced user will learn some new tricks.