Managing data continues to grow as a necessity for modern organizations. There are seemingly infinite opportunities for organic growth, reduction of costs, and creation of new products and services. It has become apparent that none of these opportunities can happen smoothly without data governance. The cost of exponential data growth and privacy / security concerns are becoming burdensome. Organizations will encounter unexpected consequences in new sources of risk. The solution to these challenges is also data governance; ensuring balance between risk and opportunity. Data Governance, Second Edition, is for any executive, manager or data professional who needs to understand or implement a data governance program. It is required to ensure consistent, accurate and reliable data across their organization. This book offers an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable. This valuable resource provides comprehensive guidance to beginning professionals, managers or analysts looking to improve their processes, and advanced students in Data Management and related courses. With the provided framework and case studies all professionals in the data governance field will gain key insights into launching successful and money-saving data governance program. - Incorporates industry changes, lessons learned and new approaches - Explores various ways in which data analysts and managers can ensure consistent, accurate and reliable data across their organizations - Includes new case studies which detail real-world situations - Explores all of the capabilities an organization must adopt to become data driven - Provides guidance on various approaches to data governance, to determine whether an organization should be low profile, central controlled, agile, or traditional - Provides guidance on using technology and separating vendor hype from sincere delivery of necessary capabilities - Offers readers insights into how their organizations can improve the value of their data, through data quality, data strategy and data literacy - Provides up to 75% brand-new content compared to the first edition

This Handbook intends to inform Data Providers and researchers on how to provide privacy-protected access to, handle, and analyze administrative data, and to link them with existing resources, such as a database of data use agreements (DUA) and templates. Available publicly, the Handbook will provide guidance on data access requirements and procedures, data privacy, data security, property rights, regulations for public data use, data architecture, data use and storage, cost structure and recovery, ethics and privacy-protection, making data accessible for research, and dissemination for restricted access use. The knowledge base will serve as a resource for all researchers looking to work with administrative data and for Data Providers looking to make such data available.

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.

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, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

When doing a DR drill, have you found the run book to be out of sync with the current configuration? How many man-hours can be recovered through effective configuration management? Do you need a new release, version or patch level of the software? Is the service usage appropriate to capacity? Defining, designing, creating, and implementing a process to solve a challenge or meet an objective is the most valuable role… In EVERY group, company, organization and department. Unless you are talking a one-time, single-use project, there should be a process. Whether that process is managed and implemented by humans, AI, or a combination of the two, it needs to be designed by someone with a complex enough perspective to ask the right questions. Someone capable of asking the right questions and step back and say, 'What are we really trying to accomplish here? And is there a different way to look at it?' This Self-Assessment empowers people to do just that - whether their title is entrepreneur, manager, consultant, (Vice-)President, CxO etc... - they are the people who rule the future. They are the person who asks the right questions to make Software Change and Configuration Management investments work better. This Software Change and Configuration Management All-Inclusive Self-Assessment enables You to be that person. All the tools you need to an in-depth Software Change and Configuration Management Self-Assessment. Featuring 965 new and updated case-based questions, organized into seven core areas of process design, this Self-Assessment will help you identify areas in which Software Change and Configuration Management improvements can be made. In using the questions you will be better able to: - diagnose Software Change and Configuration Management projects, initiatives, organizations, businesses and processes using accepted diagnostic standards and practices - implement evidence-based best practice strategies aligned with overall goals - integrate recent advances in Software Change and Configuration Management and process design strategies into practice according to best practice guidelines Using a Self-Assessment tool known as the Software Change and Configuration Management Scorecard, you will develop a clear picture of which Software Change and Configuration Management areas need attention. Your purchase includes access details to the Software Change and Configuration Management self-assessment dashboard download which gives you your dynamically prioritized projects-ready tool and shows your organization exactly what to do next. You will receive the following contents with New and Updated specific criteria: - The latest quick edition of the book in PDF - The latest complete edition of the book in PDF, which criteria correspond to the criteria in... - The Self-Assessment Excel Dashboard - Example pre-filled Self-Assessment Excel Dashboard to get familiar with results generation - In-depth and specific Software Change and Configuration Management Checklists - Project management checklists and templates to assist with implementation INCLUDES LIFETIME SELF ASSESSMENT UPDATES Every self assessment comes with Lifetime Updates and Lifetime Free Updated Books. Lifetime Updates is an industry-first feature which allows you to receive verified self assessment updates, ensuring you always have the most accurate information at your fingertips.

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.

A Complete Data Analytics Guide for Learners and Professionals. KEY FEATURES ● Learn Big Data, Hadoop Architecture, HBase, Hive and NoSQL Database. ● Dive into Machine Learning, its tools, and applications. ● Coverage of applications of Big Data, Data Analysis, and Business Intelligence. DESCRIPTION These days critical problem solving related to data and data sciences is in demand. Professionals who can solve real data science problems using data science tools are in demand. The book “Data Analytics: Principles, Tools, and Practices” can be considered a handbook or a guide for professionals who want to start their journey in the field of data science. The journey starts with the introduction of DBMS, RDBMS, NoSQL, and DocumentDB. The book introduces the essentials of data science and the modern ecosystem, including the important steps such as data ingestion, data munging, and visualization. The book covers the different types of analysis, different Hadoop ecosystem tools like Apache Spark, Apache Hive, R, MapReduce, and NoSQL Database. It also includes the different machine learning techniques that are useful for data analytics and how to visualize data with different graphs and charts. The book discusses useful tools and approaches for data analytics, supported by concrete code examples. After reading this book, you will be motivated to explore real data analytics and make use of the acquired knowledge on databases, BI/DW, data visualization, Big Data tools, and statistical science. WHAT YOU WILL LEARN ● Familiarize yourself with Apache Spark, Apache Hive, R, MapReduce, and NoSQL Database. ● Learn to manage data warehousing with real time transaction processing. ● Explore various machine learning techniques that apply to data analytics. ● Learn how to visualize data using a variety of graphs and charts using real-world examples from the industry. ● Acquaint yourself with Big Data tools and statistical techniques for machine learning. WHO THIS BOOK IS FOR IT graduates, data engineers and entry-level professionals who have a basic understanding of the tools and techniques but want to learn more about how they fit into a broader context are encouraged to read this book. TABLE OF CONTENTS 1. Database Management System 2. Online Transaction Processing and Data Warehouse 3. Business Intelligence and its deeper dynamics 4. Introduction to Data Visualization 5. Advanced Data Visualization 6. Introduction to Big Data and Hadoop 7. Application of Big Data Real Use Cases 8. Application of Big Data 9. Introduction to Machine Learning 10. Advanced Concepts to Machine Learning 11. Application of Machine Learning