Executing Data Quality Projects

• Author : Danette McGilvray
• Publisher : Morgan Kaufmann
• Release : 24 May 2022

Introduces a systematic, effective approach to enhancing and creating data and information quality that integrates a conceptual framework with essential tools, techniques, and instructions, accompanied by helpful templates, real-world examples, and advice, as well as highlighted definitions, key concepts, checkpoints, warnings, communication activities, and best practices. Original. (Intermediate)

• Author : Danette McGilvray
• Publisher : Academic Press
• Release : 27 May 2021

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

• Author : Project Management Institute Project Management Institute
• Publisher : Project Management Institute
• Release : 01 August 2021

PMBOK&® Guide is the go-to resource for project management practitioners. The project management profession has significantly evolved due to emerging technology, new approaches and rapid market changes. Reflecting this evolution, The Standard for Project Management enumerates 12 principles of project management and the PMBOK&® Guide &– Seventh Edition is structured around eight project performance domains.This edition is designed to address practitioners' current and future needs and to help them be more proactive, innovative and nimble in enabling desired project outcomes.This

• Author : Alex Berson,Larry Dubov
• Publisher : McGraw Hill Professional
• Release : 06 December 2010

The latest techniques for building a customer-focused enterprise environment "The authors have appreciated that MDM is a complex multidimensional area, and have set out to cover each of these dimensions in sufficient detail to provide adequate practical guidance to anyone implementing MDM. While this necessarily makes the book rather long, it means that the authors achieve a comprehensive treatment of MDM that is lacking in previous works." -- Malcolm Chisholm, Ph.D., President, AskGet.com Consulting, Inc. Regain control of

• Author : Aurélien Géron
• Publisher : "O'Reilly Media, Inc."
• Release : 05 September 2019

Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems.

• Author : Jody Zall Kusek,Ray C. Rist
• Publisher : World Bank Publications
• Release : 15 June 2004

This Handbook provides a comprehensive ten-step model that will help guide development practitioners through the process of designing and building a results-based monitoring and evaluation system.

• Author : Henry R. Frey,Gerald F. Appell
• Publisher : Unknown Publisher
• Release : 24 May 1981

• Author : Martin Kleppmann
• Publisher : "O'Reilly Media, Inc."
• Release : 16 March 2017

Data is at the center of many challenges in system design today. Difficult issues need to be figured out, such as scalability, consistency, reliability, efficiency, and maintainability. In addition, we have an overwhelming variety of tools, including relational databases, NoSQL datastores, stream or batch processors, and message brokers. What are the right choices for your application? How do you make sense of all these buzzwords? In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape

• Author : Rupa Mahanti
• Publisher : Quality Press
• Release : 18 March 2019

“This is not the kind of book that you’ll read one time and be done with. So scan it quickly the first time through to get an idea of its breadth. Then dig in on one topic of special importance to your work. Finally, use it as a reference to guide your next steps, learn details, and broaden your perspective.” from the foreword by Thomas C. Redman, Ph.D., “the Data Doc” Good data is a source of myriad

• Author : Agency for Healthcare Research and Quality/AHRQ
• Publisher : Government Printing Office
• Release : 01 April 2014

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

• Author : Hadley Wickham,Garrett Grolemund
• Publisher : "O'Reilly Media, Inc."
• Release : 12 December 2016

Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring,

• Author : Institute of Medicine,Board on Health Sciences Policy,Committee on Strategies for Responsible Sharing of Clinical Trial Data
• Publisher : National Academies Press
• Release : 20 April 2015

Data sharing can accelerate new discoveries by avoiding duplicative trials, stimulating new ideas for research, and enabling the maximal scientific knowledge and benefits to be gained from the efforts of clinical trial participants and investigators. At the same time, sharing clinical trial data presents risks, burdens, and challenges. These include the need to protect the privacy and honor the consent of clinical trial participants; safeguard the legitimate economic interests of sponsors; and guard against invalid secondary analyses, which could undermine

• Author : Gerald W. McLaughlin,Association for Institutional Research,Richard D. Howard,Lore Balkan Cunningham,Elizabeth W. Blythe,Ernest Payne
• Publisher : Assn for Institutional Research
• Release : 30 June 2004

• Author : David Loshin
• Publisher : Elsevier
• Release : 22 November 2010

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

• Author : Niall Richard Murphy,Betsy Beyer,Chris Jones,Jennifer Petoff
• Publisher : "O'Reilly Media, Inc."
• Release : 23 March 2016

The overwhelming majority of a software system’s lifespan is spent in use, not in design or implementation. So, why does conventional wisdom insist that software engineers focus primarily on the design and development of large-scale computing systems? In this collection of essays and articles, key members of Google’s Site Reliability Team explain how and why their commitment to the entire lifecycle has enabled the company to successfully build, deploy, monitor, and maintain some of the largest software systems