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Data Virtualization for Business Intelligence Systems

Data Virtualization for Business Intelligence Systems
  • Author : Rick van der Lans
  • Publisher :Unknown
  • Release Date :2012-07-25
  • Total pages :296
  • ISBN : 9780123978172
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Summary : Data virtualization can help you accomplish your goals with more flexibility and agility. Learn what it is and how and why it should be used with Data Virtualization for Business Intelligence Systems. In this book, expert author Rick van der Lans explains how data virtualization servers work, what techniques to use to optimize access to various data sources and how these products can be applied in different projects. You’ll learn the difference is between this new form of data integration and older forms, such as ETL and replication, and gain a clear understanding of how data virtualization really works. Data Virtualization for Business Intelligence Systems outlines the advantages and disadvantages of data virtualization and illustrates how data virtualization should be applied in data warehouse environments. You’ll come away with a comprehensive understanding of how data virtualization will make data warehouse environments more flexible and how it make developing operational BI applications easier. Van der Lans also describes the relationship between data virtualization and related topics, such as master data management, governance, and information management, so you come away with a big-picture understanding as well as all the practical know-how you need to virtualize your data. First independent book on data virtualization that explains in a product-independent way how data virtualization technology works. Illustrates concepts using examples developed with commercially available products. Shows you how to solve common data integration challenges such as data quality, system interference, and overall performance by following practical guidelines on using data virtualization. Apply data virtualization right away with three chapters full of practical implementation guidance. Understand the big picture of data virtualization and its relationship with data governance and information management.

Data Virtualization for Business Intelligence Systems

Data Virtualization for Business Intelligence Systems
  • Author : Rick F. van der Lans
  • Publisher :Unknown
  • Release Date :2012
  • Total pages :275
  • ISBN : 9780123944252
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Summary : Annotation In this book, Rick van der Lans explains how data virtualization servers work, what techniques to use to optimize access to various data sources and how these products can be applied in different projects.

Business Intelligence Guidebook

Business Intelligence Guidebook
  • Author : Rick Sherman
  • Publisher :Unknown
  • Release Date :2014-11-04
  • Total pages :550
  • ISBN : 9780124115286
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Summary : Between the high-level concepts of business intelligence and the nitty-gritty instructions for using vendors’ tools lies the essential, yet poorly-understood layer of architecture, design and process. Without this knowledge, Big Data is belittled – projects flounder, are late and go over budget. Business Intelligence Guidebook: From Data Integration to Analytics shines a bright light on an often neglected topic, arming you with the knowledge you need to design rock-solid business intelligence and data integration processes. Practicing consultant and adjunct BI professor Rick Sherman takes the guesswork out of creating systems that are cost-effective, reusable and essential for transforming raw data into valuable information for business decision-makers. After reading this book, you will be able to design the overall architecture for functioning business intelligence systems with the supporting data warehousing and data-integration applications. You will have the information you need to get a project launched, developed, managed and delivered on time and on budget – turning the deluge of data into actionable information that fuels business knowledge. Finally, you’ll give your career a boost by demonstrating an essential knowledge that puts corporate BI projects on a fast-track to success. Provides practical guidelines for building successful BI, DW and data integration solutions. Explains underlying BI, DW and data integration design, architecture and processes in clear, accessible language. Includes the complete project development lifecycle that can be applied at large enterprises as well as at small to medium-sized businesses Describes best practices and pragmatic approaches so readers can put them into action. Companion website includes templates and examples, further discussion of key topics, instructor materials, and references to trusted industry sources.

Accelerating Digital Transformation on Z Using Data Virtualization

Accelerating Digital Transformation on Z Using Data Virtualization
  • Author : Blanca Borden,Calvin Fudge,Jen Nelson,Jim Porell,IBM Redbooks
  • Publisher :Unknown
  • Release Date :2018-12-18
  • Total pages :24
  • ISBN : 9780738457291
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Summary : This IBM® RedpaperTM publication introduces a new data virtualization capability that enables IBM z/OS® data to be combined with other enterprise data sources in real-time, which allows applications to access any live enterprise data anytime and use the power and efficiencies of the IBM Z® platform. Modern businesses need actionable and timely insight from current data. They cannot afford the time that is necessary to copy and transform data. They also cannot afford to secure and protect each copy of personally identifiable information and corporate intellectual property. Data virtualization enables direct connections to be established between multiple data sources and the applications that process the data. Transformations can be applied, in line, to enable real-time access to data, which opens up many new ways to gain business insight with less IT infrastructure necessary to achieve those goals. Data virtualization can become the backbone for advanced analytics and modern applications. The IBM Data Virtualization Manager for z/OS (DVM) can be used as a stand-alone product or as a utility that is used by other products. Its goal is to enable access to live mainframe transaction data and make it usable by any application. This “ this what?” enables customers to use the strengths of mainframe processing with new agile applications. Additionally, its modern development environment and code-generating capabilities enable any developer to update, access, and combine mainframe data easily by using modern APIs and languages. If data is the foundation for building new insights, IBM DVM is a key tool for providing easy, cost-efficient access to that foundation.

Data Virtualization

Data Virtualization
  • Author : Judith R. Davis,Robert Eve
  • Publisher :Unknown
  • Release Date :2011-09-30
  • Total pages :201
  • ISBN : 0979930421
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Summary :

Managing Data in Motion

Managing Data in Motion
  • Author : April Reeve
  • Publisher :Unknown
  • Release Date :2013-02-26
  • Total pages :204
  • ISBN : 9780123977915
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Summary : Managing Data in Motion describes techniques that have been developed for significantly reducing the complexity of managing system interfaces and enabling scalable architectures. Author April Reeve brings over two decades of experience to present a vendor-neutral approach to moving data between computing environments and systems. Readers will learn the techniques, technologies, and best practices for managing the passage of data between computer systems and integrating disparate data together in an enterprise environment. The average enterprise's computing environment is comprised of hundreds to thousands computer systems that have been built, purchased, and acquired over time. The data from these various systems needs to be integrated for reporting and analysis, shared for business transaction processing, and converted from one format to another when old systems are replaced and new systems are acquired. The management of the "data in motion" in organizations is rapidly becoming one of the biggest concerns for business and IT management. Data warehousing and conversion, real-time data integration, and cloud and "big data" applications are just a few of the challenges facing organizations and businesses today. Managing Data in Motion tackles these and other topics in a style easily understood by business and IT managers as well as programmers and architects. Presents a vendor-neutral overview of the different technologies and techniques for moving data between computer systems including the emerging solutions for unstructured as well as structured data types Explains, in non-technical terms, the architecture and components required to perform data integration Describes how to reduce the complexity of managing system interfaces and enable a scalable data architecture that can handle the dimensions of "Big Data"

Data Warehousing in the Age of Big Data

Data Warehousing in the Age of Big Data
  • Author : Krish Krishnan
  • Publisher :Unknown
  • Release Date :2013-05-02
  • Total pages :370
  • ISBN : 9780124059207
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Summary : Data Warehousing in the Age of the Big Data will help you and your organization make the most of unstructured data with your existing data warehouse. As Big Data continues to revolutionize how we use data, it doesn't have to create more confusion. Expert author Krish Krishnan helps you make sense of how Big Data fits into the world of data warehousing in clear and concise detail. The book is presented in three distinct parts. Part 1 discusses Big Data, its technologies and use cases from early adopters. Part 2 addresses data warehousing, its shortcomings, and new architecture options, workloads, and integration techniques for Big Data and the data warehouse. Part 3 deals with data governance, data visualization, information life-cycle management, data scientists, and implementing a Big Data–ready data warehouse. Extensive appendixes include case studies from vendor implementations and a special segment on how we can build a healthcare information factory. Ultimately, this book will help you navigate through the complex layers of Big Data and data warehousing while providing you information on how to effectively think about using all these technologies and the architectures to design the next-generation data warehouse. Learn how to leverage Big Data by effectively integrating it into your data warehouse. Includes real-world examples and use cases that clearly demonstrate Hadoop, NoSQL, HBASE, Hive, and other Big Data technologies Understand how to optimize and tune your current data warehouse infrastructure and integrate newer infrastructure matching data processing workloads and requirements

Fundamentals of Business Intelligence

Fundamentals of Business Intelligence
  • Author : Wilfried Grossmann,Stefanie Rinderle-Ma
  • Publisher :Unknown
  • Release Date :2015-06-02
  • Total pages :348
  • ISBN : 9783662465318
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Summary : This book presents a comprehensive and systematic introduction to transforming process-oriented data into information about the underlying business process, which is essential for all kinds of decision-making. To that end, the authors develop step-by-step models and analytical tools for obtaining high-quality data structured in such a way that complex analytical tools can be applied. The main emphasis is on process mining and data mining techniques and the combination of these methods for process-oriented data. After a general introduction to the business intelligence (BI) process and its constituent tasks in chapter 1, chapter 2 discusses different approaches to modeling in BI applications. Chapter 3 is an overview and provides details of data provisioning, including a section on big data. Chapter 4 tackles data description, visualization, and reporting. Chapter 5 introduces data mining techniques for cross-sectional data. Different techniques for the analysis of temporal data are then detailed in Chapter 6. Subsequently, chapter 7 explains techniques for the analysis of process data, followed by the introduction of analysis techniques for multiple BI perspectives in chapter 8. The book closes with a summary and discussion in chapter 9. Throughout the book, (mostly open source) tools are recommended, described and applied; a more detailed survey on tools can be found in the appendix, and a detailed code for the solutions together with instructions on how to install the software used can be found on the accompanying website. Also, all concepts presented are illustrated and selected examples and exercises are provided. The book is suitable for graduate students in computer science, and the dedicated website with examples and solutions makes the book ideal as a textbook for a first course in business intelligence in computer science or business information systems. Additionally, practitioners and industrial developers who are interested in the concepts behind business intelligence will benefit from the clear explanations and many examples.

The Data Warehouse Toolkit

The Data Warehouse Toolkit
  • Author : Ralph Kimball,Margy Ross
  • Publisher :Unknown
  • Release Date :2011-08-08
  • Total pages :464
  • ISBN : 9781118082140
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Summary :

Data Architecture: A Primer for the Data Scientist

Data Architecture: A Primer for the Data Scientist
  • Author : W.H. Inmon,Daniel Linstedt,Mary Levins
  • Publisher :Unknown
  • Release Date :2019-04-30
  • Total pages :431
  • ISBN : 9780128169179
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Summary : Over the past 5 years, the concept of big data has matured, data science has grown exponentially, and data architecture has become a standard part of organizational decision-making. Throughout all this change, the basic principles that shape the architecture of data have remained the same. There remains a need for people to take a look at the "bigger picture" and to understand where their data fit into the grand scheme of things. Data Architecture: A Primer for the Data Scientist, Second Edition addresses the larger architectural picture of how big data fits within the existing information infrastructure or data warehousing systems. This is an essential topic not only for data scientists, analysts, and managers but also for researchers and engineers who increasingly need to deal with large and complex sets of data. Until data are gathered and can be placed into an existing framework or architecture, they cannot be used to their full potential. Drawing upon years of practical experience and using numerous examples and case studies from across various industries, the authors seek to explain this larger picture into which big data fits, giving data scientists the necessary context for how pieces of the puzzle should fit together. New case studies include expanded coverage of textual management and analytics New chapters on visualization and big data Discussion of new visualizations of the end-state architecture

Corporate Information Factory

Corporate Information Factory
  • Author : W. H. Inmon,Claudia Imhoff,Ryan Sousa
  • Publisher :Unknown
  • Release Date :2002-03-14
  • Total pages :400
  • ISBN : 9780471437505
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Summary : The "father of data warehousing" incorporates the latesttechnologies into his blueprint for integrated decision supportsystems Today's corporate IT and data warehouse managers are required tomake a small army of technologies work together to ensure fast andaccurate information for business managers. Bill Inmon created theCorporate Information Factory to solve the needs ofthese managers. Since the First Edition, the design of the factoryhas grown and changed dramatically. This Second Edition, revisedand expanded by 40% with five new chapters, incorporates thesechanges. This step-by-step guide will enable readers to connecttheir legacy systems with the data warehouse and deal with a hostof new and changing technologies, including Web access mechanisms,e-commerce systems, ERP (Enterprise Resource Planning) systems. Thebook also looks closely at exploration and data mining servers foranalyzing customer behavior and departmental data marts forfinance, sales, and marketing.

Business unIntelligence

Business unIntelligence
  • Author : Dr. Barry Devlin
  • Publisher :Unknown
  • Release Date :2013-10-01
  • Total pages :442
  • ISBN : 9781634620321
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Summary : Business intelligence (BI) used to be so simple—in theory anyway. Integrate and copy data from your transactional systems into a specialized relational database, apply BI reporting and query tools and add business users. Job done. No longer. Analytics, big data and an array of diverse technologies have changed everything. More importantly, business is insisting on ever more value, ever faster from information and from IT in general. An emerging biz-tech ecosystem demands that business and IT work together. Business unIntelligence reflects the new reality that in today’s socially complex and rapidly changing world, business decisions must be based on a combination of rational and intuitive thinking. Integrating cues from diverse information sources and tacit knowledge, decision makers create unique meaning to innovate heuristically at the speed of thought. This book provides a wealth of new models that business and IT can use together to design support systems for tomorrow’s successful organizations. Dr. Barry Devlin, one of the earliest proponents of data warehousing, goes back to basics to explore how the modern trinity of information, process and people must be reinvented and restructured to deliver the value, insight and innovation required by modern businesses. From here, he develops a series of novel architectural models that provide a new foundation for holistic information use across the entire business. From discovery to analysis and from decision making to action taking, he defines a fully integrated, closed-loop business environment. Covering every aspect of business analytics, big data, collaborative working and more, this book takes over where BI ends to deliver the definitive framework for information use in the coming years. As the person who defined the conceptual framework and physical architecture for data warehousing in the 1980s, Barry Devlin has been an astute observer of the movement he initiated ever since. Now, in Business unIntelligence, Devlin provides a sweeping view of the past, present, and future of business intelligence, while delivering new conceptual and physical models for how to turn information into insights and action. Reading Devlin’s prose and vision of BI are comparable to reading Carl Sagan’s view of the cosmos. The book is truly illuminating and inspiring. --Wayne Eckerson, President, BI Leader Consulting Author, “Secrets of Analytical Leaders: Insights from Information Insiders”

Building a Scalable Data Warehouse with Data Vault 2.0

Building a Scalable Data Warehouse with Data Vault 2.0
  • Author : Dan Linstedt,Michael Olschimke
  • Publisher :Unknown
  • Release Date :2015-09-15
  • Total pages :684
  • ISBN : 9780128026489
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Summary : The Data Vault was invented by Dan Linstedt at the U.S. Department of Defense, and the standard has been successfully applied to data warehousing projects at organizations of different sizes, from small to large-size corporations. Due to its simplified design, which is adapted from nature, the Data Vault 2.0 standard helps prevent typical data warehousing failures. "Building a Scalable Data Warehouse" covers everything one needs to know to create a scalable data warehouse end to end, including a presentation of the Data Vault modeling technique, which provides the foundations to create a technical data warehouse layer. The book discusses how to build the data warehouse incrementally using the agile Data Vault 2.0 methodology. In addition, readers will learn how to create the input layer (the stage layer) and the presentation layer (data mart) of the Data Vault 2.0 architecture including implementation best practices. Drawing upon years of practical experience and using numerous examples and an easy to understand framework, Dan Linstedt and Michael Olschimke discuss: How to load each layer using SQL Server Integration Services (SSIS), including automation of the Data Vault loading processes. Important data warehouse technologies and practices. Data Quality Services (DQS) and Master Data Services (MDS) in the context of the Data Vault architecture. Provides a complete introduction to data warehousing, applications, and the business context so readers can get-up and running fast Explains theoretical concepts and provides hands-on instruction on how to build and implement a data warehouse Demystifies data vault modeling with beginning, intermediate, and advanced techniques Discusses the advantages of the data vault approach over other techniques, also including the latest updates to Data Vault 2.0 and multiple improvements to Data Vault 1.0

SQL Server Big Data Clusters

SQL Server Big Data Clusters
  • Author : Benjamin Weissman,Enrico van de Laar
  • Publisher :Unknown
  • Release Date :2020-05-23
  • Total pages :260
  • ISBN : 9781484259856
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Summary : Use this guide to one of SQL Server 2019’s most impactful features—Big Data Clusters. You will learn about data virtualization and data lakes for this complete artificial intelligence (AI) and machine learning (ML) platform within the SQL Server database engine. You will know how to use Big Data Clusters to combine large volumes of streaming data for analysis along with data stored in a traditional database. For example, you can stream large volumes of data from Apache Spark in real time while executing Transact-SQL queries to bring in relevant additional data from your corporate, SQL Server database. Filled with clear examples and use cases, this book provides everything necessary to get started working with Big Data Clusters in SQL Server 2019. You will learn about the architectural foundations that are made up from Kubernetes, Spark, HDFS, and SQL Server on Linux. You then are shown how to configure and deploy Big Data Clusters in on-premises environments or in the cloud. Next, you are taught about querying. You will learn to write queries in Transact-SQL—taking advantage of skills you have honed for years—and with those queries you will be able to examine and analyze data from a wide variety of sources such as Apache Spark. Through the theoretical foundation provided in this book and easy-to-follow example scripts and notebooks, you will be ready to use and unveil the full potential of SQL Server 2019: combining different types of data spread across widely disparate sources into a single view that is useful for business intelligence and machine learning analysis. What You Will Learn Install, manage, and troubleshoot Big Data Clusters in cloud or on-premise environments Analyze large volumes of data directly from SQL Server and/or Apache Spark Manage data stored in HDFS from SQL Server as if it were relational data Implement advanced analytics solutions through machine learning and AI Expose different data sources as a single logical source using data virtualization Who This Book Is For Data engineers, data scientists, data architects, and database administrators who want to employ data virtualization and big data analytics in their environments

The Enterprise Big Data Lake

The Enterprise Big Data Lake
  • Author : Alex Gorelik
  • Publisher :Unknown
  • Release Date :2019-02-21
  • Total pages :224
  • ISBN : 9781491931509
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Summary : The data lake is a daring new approach for harnessing the power of big data technology and providing convenient self-service capabilities. But is it right for your company? This book is based on discussions with practitioners and executives from more than a hundred organizations, ranging from data-driven companies such as Google, LinkedIn, and Facebook, to governments and traditional corporate enterprises. You’ll learn what a data lake is, why enterprises need one, and how to build one successfully with the best practices in this book. Alex Gorelik, CTO and founder of Waterline Data, explains why old systems and processes can no longer support data needs in the enterprise. Then, in a collection of essays about data lake implementation, you’ll examine data lake initiatives, analytic projects, experiences, and best practices from data experts working in various industries. Get a succinct introduction to data warehousing, big data, and data science Learn various paths enterprises take to build a data lake Explore how to build a self-service model and best practices for providing analysts access to the data Use different methods for architecting your data lake Discover ways to implement a data lake from experts in different industries

Disruptive Analytics

Disruptive Analytics
  • Author : Thomas W. Dinsmore
  • Publisher :Unknown
  • Release Date :2016-08-27
  • Total pages :262
  • ISBN : 9781484213117
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Summary : Learn all you need to know about seven key innovations disrupting business analytics today. These innovations—the open source business model, cloud analytics, the Hadoop ecosystem, Spark and in-memory analytics, streaming analytics, Deep Learning, and self-service analytics—are radically changing how businesses use data for competitive advantage. Taken together, they are disrupting the business analytics value chain, creating new opportunities. Enterprises who seize the opportunity will thrive and prosper, while others struggle and decline: disrupt or be disrupted. Disruptive Business Analytics provides strategies to profit from disruption. It shows you how to organize for insight, build and provision an open source stack, how to practice lean data warehousing, and how to assimilate disruptive innovations into an organization. Through a short history of business analytics and a detailed survey of products and services, analytics authority Thomas W. Dinsmore provides a practical explanation of the most compelling innovations available today. What You'll Learn Discover how the open source business model works and how to make it work for you See how cloud computing completely changes the economics of analytics Harness the power of Hadoop and its ecosystem Find out why Apache Spark is everywhere Discover the potential of streaming and real-time analytics Learn what Deep Learning can do and why it matters See how self-service analytics can change the way organizations do business Who This Book Is For Corporate actors at all levels of responsibility for analytics: analysts, CIOs, CTOs, strategic decision makers, managers, systems architects, technical marketers, product developers, IT personnel, and consultants.

Artificial Intelligence in Theory and Practice IV

Artificial Intelligence in Theory and Practice IV
  • Author : Tharam Dillon
  • Publisher :Unknown
  • Release Date :2015
  • Total pages :229
  • ISBN : 3319252623
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Summary : This book constitutes the refereed proceedings of the 4th IFIP TC 12 International Conference on Artificial Intelligence, IFIP AI 2015, Held as Part of WCC 2015, in Daejeon, South Korea, in October 2015. The 13 full papers presented were carefully reviewed and selected from 36 submissions. The papers are organized in topical sections on artificial intelligence techniques in biomedicine, artificial intelligence for knowledge management, computational intelligence and algorithms, and intelligent decision support systems.

Business Intelligence For Dummies

Business Intelligence For Dummies
  • Author : Swain Scheps
  • Publisher :Unknown
  • Release Date :2011-02-04
  • Total pages :384
  • ISBN : 9781118051412
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Summary : You're intelligent, right? So you've already figured out that Business Intelligence can be pretty valuable in making the right decisions about your business. But you’ve heard at least a dozen definitions of what it is, and heard of at least that many BI tools. Where do you start? Business Intelligence For Dummies makes BI understandable! It takes you step by step through the technologies and the alphabet soup, so you can choose the right technology and implement a successful BI environment. You'll see how the applications and technologies work together to access, analyze, and present data that you can use to make better decisions about your products, customers, competitors, and more. You’ll find out how to: Understand the principles and practical elements of BI Determine what your business needs Compare different approaches to BI Build a solid BI architecture and roadmap Design, develop, and deploy your BI plan Relate BI to data warehousing, ERP, CRM, and e-commerce Analyze emerging trends and developing BI tools to see what else may be useful Whether you’re the business owner or the person charged with developing and implementing a BI strategy, checking out Business Intelligence For Dummies is a good business decision.

SQL for MySQL Developers

SQL for MySQL Developers
  • Author : Rick F. van der Lans
  • Publisher :Unknown
  • Release Date :2007-04-20
  • Total pages :1032
  • ISBN : 0132715899
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Summary : The Most Complete and Practical Guide to MySQL Version 5’s Powerful SQL Dialect MySQL version 5 offers a SQL dialect with immense power. In SQL for MySQL Developers, Rick F. van der Lans helps you master this version ofSQL and take advantage of its full potential. Using case study examplesand hands-on exercises, van der Lans illuminates every key concept,technique, and statement–including advanced features that make iteasier to create even the most complex statements and programs. Drawing on decades of experience as an SQL standards team member and enterprise consultant, he reveals exactly why MySQL’s dialect works as it does–and how to get the most out of it. You’ll gain powerful insight into everything from basic queries to stored procedures, transactions to data security. Whether you’re a programmer, Web developer, analyst, DBA, or database user, this book can take you from “apprentice” to true SQL expert. If you’ve used SQL in older versions of MySQL, you’ll become dramatically more effective–and if you’re migrating from other database platforms, you’ll gain practical mastery fast.

Decision Management: Concepts, Methodologies, Tools, and Applications

Decision Management: Concepts, Methodologies, Tools, and Applications
  • Author : Management Association, Information Resources
  • Publisher :Unknown
  • Release Date :2017-01-30
  • Total pages :2314
  • ISBN : 9781522518389
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Summary : The implementation of effective decision making protocols is crucial in any organizational environment in modern society. Emerging advancements in technology and analytics have optimized uses and applications of decision making systems. Decision Management: Concepts, Methodologies, Tools, and Applications is a compendium of the latest academic material on the control, support, usage, and strategies for implementing efficient decision making systems across a variety of industries and fields. Featuring comprehensive coverage on numerous perspectives, such as data visualization, pattern analysis, and predictive analytics, this multi-volume book is an essential reference source for researchers, academics, professionals, managers, students, and practitioners interested in the maintenance and optimization of decision management processes.

The Practitioner's Guide to Data Quality Improvement

The Practitioner's Guide to Data Quality Improvement
  • Author : David Loshin
  • Publisher :Unknown
  • Release Date :2010-11-22
  • Total pages :432
  • ISBN : 0080920349
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Summary : The Practitioner's Guide to Data Quality Improvement offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. It shares the fundamentals for understanding the impacts of poor data quality, and guides practitioners and managers alike in socializing, gaining sponsorship for, planning, and establishing a data quality program. It demonstrates how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. It includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning. This book is recommended for data management practitioners, including database analysts, information analysts, data administrators, data architects, enterprise architects, data warehouse engineers, and systems analysts, and their managers. Offers a comprehensive look at data quality for business and IT, encompassing people, process, and technology. Shows how to institute and run a data quality program, from first thoughts and justifications to maintenance and ongoing metrics. Includes an in-depth look at the use of data quality tools, including business case templates, and tools for analysis, reporting, and strategic planning.