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Author: David Loshin
Publisher: Elsevier
ISBN: 0080920349
Category : Computers
Languages : en
Pages : 423
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Book Description
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.
Author: David Loshin
Publisher: Elsevier
ISBN: 0080920349
Category : Computers
Languages : en
Pages : 423
Get Book Here
Book Description
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.
Author: Marek Chmel
Publisher: Packt Publishing Ltd
ISBN: 1789950333
Category : Computers
Languages : en
Pages : 522
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Book Description
Use Microsoft SQL Server 2019 to implement, administer, and secure a robust database solution that is disaster-proof and highly available Key FeaturesExplore new features of SQL Server 2019 to set up, administer, and maintain your database solution successfullyDevelop a dynamic SQL Server environment and streamline big data pipelinesDiscover best practices for fixing performance issues, database access management, replication, and securityBook Description SQL Server is one of the most popular relational database management systems developed by Microsoft. This second edition of the SQL Server Administrator's Guide will not only teach you how to administer an enterprise database, but also help you become proficient at managing and keeping the database available, secure, and stable. You’ll start by learning how to set up your SQL Server and configure new and existing environments for optimal use. The book then takes you through designing aspects and delves into performance tuning by showing you how to use indexes effectively. You’ll understand certain choices that need to be made about backups, implement security policy, and discover how to keep your environment healthy. Tools available for monitoring and managing a SQL Server database, including automating health reviews, performance checks, and much more, will also be discussed in detail. As you advance, the book covers essential topics such as migration, upgrading, and consolidation, along with the techniques that will help you when things go wrong. Once you’ve got to grips with integration with Azure and streamlining big data pipelines, you’ll learn best practices from industry experts for maintaining a highly reliable database solution. Whether you are an administrator or are looking to get started with database administration, this SQL Server book will help you develop the skills you need to successfully create, design, and deploy database solutions. What you will learnDiscover SQL Server 2019’s new features and how to implement themFix performance issues by optimizing queries and making use of indexesDesign and use an optimal database management strategyCombine SQL Server 2019 with Azure and manage your solution using various automation techniquesImplement efficient backup and recovery techniques in line with security policiesGet to grips with migrating, upgrading, and consolidating with SQL ServerSet up an AlwaysOn-enabled stable and fast SQL Server 2019 environmentUnderstand how to work with Big Data on SQL Server environmentsWho this book is for This book is for database administrators, database developers, and anyone who wants to administer large and multiple databases single-handedly using Microsoft's SQL Server 2019. Basic awareness of database concepts and experience with previous SQL Server versions is required.
Author: Dimitrios Sargiotis
Publisher: Springer Nature
ISBN: 3031672682
Category :
Languages : en
Pages : 553
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Book Description
Author: Tim King
Publisher: BCS, The Chartered Institute for IT
ISBN: 9781780174594
Category :
Languages : en
Pages : 150
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Book Description
This book explains data quality management in practical terms, focusing on three key areas - the nature of data in enterprises, the purpose and scope of data quality management, and implementing a data quality management system, in line with ISO 8000-61. Examples of good practice in data quality management are also included.
Author: Agency for Healthcare Research and Quality/AHRQ
Publisher: Government Printing Office
ISBN: 1587634333
Category : Medical
Languages : en
Pages : 385
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Book Description
This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.
Author: Debra Zahay
Publisher: Business Expert Press
ISBN: 1951527933
Category : Business & Economics
Languages : en
Pages : 188
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Book Description
This book is for managers and would-be managers who need to upgrade their knowledge of digital marketing. Told from the perspective of marketing strategy, it puts digital marketing in the context of firm strategy selection. The first step in digital marketing is to understand your company and your brand. The next step is to put content and keywords on your web and mobile sites so that they can be found in search. Then use the delivery platforms of digital content, e-mail, social, and mobile, to deploy that content to the customer. The final topics in the book focus on the importance of data management and privacy as well as the emerging roles of analytics, artificial intelligence and marketing automation. Without quality data, no digital marketing program can be successful. This data can then be used in data analytics applications for predictive modeling. After reading this book, the reader will have a good idea of where to start on the path to an integrated digital marketing management strategy. Each chapter concludes with a list of action steps or “what to do next” to get started on implementing a digital marketing strategy as well as review questions and key terminology.
Author: Danette McGilvray
Publisher: Academic Press
ISBN: 0128180161
Category : Computers
Languages : en
Pages : 378
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Book Description
Executing Data Quality Projects, Second Edition presents a structured yet flexible approach for creating, improving, sustaining and managing the quality of data and information within any organization. Studies show that data quality problems are costing businesses billions of dollars each year, with poor data linked to waste and inefficiency, damaged credibility among customers and suppliers, and an organizational inability to make sound decisions. Help is here! This book describes a proven Ten Step approach that combines a conceptual framework for understanding information quality with techniques, tools, and instructions for practically putting the approach to work – with the end result of high-quality trusted data and information, so critical to today's data-dependent organizations. The Ten Steps approach applies to all types of data and all types of organizations – for-profit in any industry, non-profit, government, education, healthcare, science, research, and medicine. This book includes numerous templates, detailed examples, and practical advice for executing every step. At the same time, readers are advised on how to select relevant steps and apply them in different ways to best address the many situations they will face. The layout allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, best practices, and warnings. The experience of actual clients and users of the Ten Steps provide real examples of outputs for the steps plus highlighted, sidebar case studies called Ten Steps in Action. This book uses projects as the vehicle for data quality work and the word broadly to include: 1) focused data quality improvement projects, such as improving data used in supply chain management, 2) data quality activities in other projects such as building new applications and migrating data from legacy systems, integrating data because of mergers and acquisitions, or untangling data due to organizational breakups, and 3) ad hoc use of data quality steps, techniques, or activities in the course of daily work. The Ten Steps approach can also be used to enrich an organization's standard SDLC (whether sequential or Agile) and it complements general improvement methodologies such as six sigma or lean. No two data quality projects are the same but the flexible nature of the Ten Steps means the methodology can be applied to all. The new Second Edition highlights topics such as artificial intelligence and machine learning, Internet of Things, security and privacy, analytics, legal and regulatory requirements, data science, big data, data lakes, and cloud computing, among others, to show their dependence on data and information and why data quality is more relevant and critical now than ever before. - Includes concrete instructions, numerous templates, and practical advice for executing every step of The Ten Steps approach - Contains real examples from around the world, gleaned from the author's consulting practice and from those who implemented based on her training courses and the earlier edition of the book - Allows for quick reference with an easy-to-use format highlighting key concepts and definitions, important checkpoints, communication activities, and best practices - A companion Web site includes links to numerous data quality resources, including many of the templates featured in the text, quick summaries of key ideas from the Ten Steps methodology, and other tools and information that are available online
Author: Boris Otto
Publisher:
ISBN: 9783737575928
Category :
Languages : en
Pages :
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Book Description
Author: Dusan Petkovic
Publisher: McGraw Hill Professional
ISBN: 1260458881
Category : Computers
Languages : en
Pages : 865
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Book Description
Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Get Up to Speed on Microsoft® SQL Server® 2019 Quickly and Easily Start working with Microsoft SQL Server 2019 in no time with help from this thoroughly revised, practical resource. Filled with real-world examples and hands-on exercises, Microsoft SQL Server 2019: A Beginner’s Guide, Seventh Edition starts by explaining fundamental relational database system concepts. From there, you’ll learn how to write Transact-SQL statements, execute simple and complex database queries, handle system administration and security, and use powerful analysis and reporting tools. New topics such as SQL and JSON support, graph databases, and support for machine learning with R and Python are also covered in this step-by-step tutorial. • Install, configure, and customize Microsoft SQL Server 2019 • Create and modify database objects with Transact-SQL statements • Write stored procedures and user-defined functions • Handle backup and recovery, and automate administrative tasks • Tune your database system for optimal availability and reliability • Secure your system using authentication, encryption, and authorization • Work with SQL Server Analysis Services, Reporting Services, and other BI tools • Gain knowledge of relational storage, presentation, and retrieval of data stored in the JSON format • Manage graphs using SQL Server Graph Databases • Learn about machine learning support for R and Python
Author: Dama International
Publisher:
ISBN: 9781634622349
Category : Database management
Languages : en
Pages : 628
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Book Description
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.
