Written by experts at the UK Data Archive, with over thirty years of experience in working with and teaching people to work with data, this book is the globally-reaching guide for any postgraduate student or researcher looking to build ...
Author: Louise Corti
Category: Social Science
Written by experts at the UK Data Archive, with over thirty years of experience in working with and teaching people to work with data, this book is the globally-reaching guide for any postgraduate student or researcher looking to build their data management skills. Focused on both primary and secondary data and packed with checklists and templates, it contains everything readers need to know for managing all types data before, during, and after the research process. Building on foundational data management techniques, it offers practical advice and insight into the unique skills needed to work with newer forms of data, like social media and big data. It also demonstrates how to: - Identify quality data that is credible, ethically-sound, and available for use - Choose and collect data suitable for particular research questions and project scopes - Work with personal, communal, administrative, and other sensitive and public data - Make the most of metadata - Visualise and share data using innovative platforms like blogs, infographics, and podcasts.
This book provides over 2,000 Exam Prep questions and answers to accompany the text Managing with Data Items include highly probable exam items: Dimensional modeling, Linux, XQuery, organization, industry, Ford, structure, strategy, Primary ...
The book is also aimed at (Bachelor’s/ Master’s) students with an interest in data management. The book is industry-agnostic and should be applicable in different industries such as government, finance, telecommunications etc.
Author: Bas van Gils
Publisher: Van Haren
The overall objective of this book is to show that data management is an exciting and valuable capability that is worth time and effort. More specifically it aims to achieve the following goals: 1. To give a “gentle” introduction to the field of DM by explaining and illustrating its core concepts, based on a mix of theory, practical frameworks such as TOGAF, ArchiMate, and DMBOK, as well as results from real-world assignments. 2. To offer guidance on how to build an effective DM capability in an organization.This is illustrated by various use cases, linked to the previously mentioned theoretical exploration as well as the stories of practitioners in the field. The primary target groups are: busy professionals who “are actively involved with managing data”. The book is also aimed at (Bachelor’s/ Master’s) students with an interest in data management. The book is industry-agnostic and should be applicable in different industries such as government, finance, telecommunications etc. Typical roles for which this book is intended: data governance office/ council, data owners, data stewards, people involved with data governance (data governance board), enterprise architects, data architects, process managers, business analysts and IT analysts. The book is divided into three main parts: theory, practice, and closing remarks. Furthermore, the chapters are as short and to the point as possible and also make a clear distinction between the main text and the examples. If the reader is already familiar with the topic of a chapter, he/she can easily skip it and move on to the next.
This volume provides a framework to guide information professionals in academic libraries, presses, and data centers through the process of managing research data from the planning stages through the life of a grant project and beyond.
Author: Joyce M. Ray
Publisher: Purdue University Press
Category: BUSINESS & ECONOMICS
It has become increasingly accepted that important digital data must be retained and shared in order to preserve and promote knowledge, advance research in and across all disciplines of scholarly endeavor, and maximize the return on investment of public funds. To meet this challenge, colleges and universities are adding data services to existing infrastructures by drawing on the expertise of information professionals who are already involved in the acquisition, management and preservation of data in their daily jobs. Data services include planning and implementing good data management practices, thereby increasing researchers' ability to compete for grant funding and ensuring that data collections with continuing value are preserved for reuse. This volume provides a framework to guide information professionals in academic libraries, presses, and data centers through the process of managing research data from the planning stages through the life of a grant project and beyond. It illustrates principles of good practice with use-case examples and illuminates promising data service models through case studies of innovative, successful projects and collaborations. Contributors include: James L. Mullins, Purdue University; MacKenzie Smith, University of California at Davis; Sherry Lake, University of Virginia; John Kunze, University of California; Bernard Reilly, Center for Research Libraries; Jacob Carlson, Purdue University; Melissa Levine, University of Michigan; Jenn Riley, University of North Carolina at Chapel Hill; Jan Brase, German National Library of Science and Technology; Seamus Ross, University of Toronto; Sarah Shreeves, University of Illinois at Urbana-Champaign; Jared Lyle, University of Michigan; Michele Kimpton, DuraSpace; Brian Schottlaender, University of California San Diego; Suzie Allard, University of Tennessee; Angus Whyte, Digital Curation Centre; Scott Brandt, Purdue University; Brian Westra, University of Oregon; Geneva Henry, Rice University; Gail Steinhart, Cornell University; and Cliff Lynch, Coalition for Networked Information. Charleston Insights in Library, Information, and Archival Sciences is a new series produced as a collaboration between the organizers of the Charleston Library Conference and Purdue University Press. Volumes in the series focus on important topics in library and information science, presenting the issues in a relatively jargon-free way that is accessible to all types of information professionals.
In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way.
Author: Kirill Dubovikov
Publisher: Packt Publishing Ltd
Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key Features Learn the basics of data science and explore its possibilities and limitations Manage data science projects and assemble teams effectively even in the most challenging situations Understand management principles and approaches for data science projects to streamline the innovation process Book Description Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learn Understand the underlying problems of building a strong data science pipeline Explore the different tools for building and deploying data science solutions Hire, grow, and sustain a data science team Manage data science projects through all stages, from prototype to production Learn how to use ModelOps to improve your data science pipelines Get up to speed with the model testing techniques used in both development and production stages Who this book is for This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book.
A comprehensive guide to everything scientists need to know about data management, this book is essential for researchers who need to learn how to organize, document and take care of their own data.
Author: Kristin Briney
Publisher: Pelagic Publishing Ltd
A comprehensive guide to everything scientists need to know about data management, this book is essential for researchers who need to learn how to organize, document and take care of their own data. Researchers in all disciplines are faced with the challenge of managing the growing amounts of digital data that are the foundation of their research. Kristin Briney offers practical advice and clearly explains policies and principles, in an accessible and in-depth text that will allow researchers to understand and achieve the goal of better research data management. Data Management for Researchers includes sections on: * The data problem – an introduction to the growing importance and challenges of using digital data in research. Covers both the inherent problems with managing digital information, as well as how the research landscape is changing to give more value to research datasets and code. * The data lifecycle – a framework for data’s place within the research process and how data’s role is changing. Greater emphasis on data sharing and data reuse will not only change the way we conduct research but also how we manage research data. * Planning for data management – covers the many aspects of data management and how to put them together in a data management plan. This section also includes sample data management plans. * Documenting your data – an often overlooked part of the data management process, but one that is critical to good management; data without documentation are frequently unusable. * Organizing your data – explains how to keep your data in order using organizational systems and file naming conventions. This section also covers using a database to organize and analyze content. * Improving data analysis – covers managing information through the analysis process. This section starts by comparing the management of raw and analyzed data and then describes ways to make analysis easier, such as spreadsheet best practices. It also examines practices for research code, including version control systems. * Managing secure and private data – many researchers are dealing with data that require extra security. This section outlines what data falls into this category and some of the policies that apply, before addressing the best practices for keeping data secure. * Short-term storage – deals with the practical matters of storage and backup and covers the many options available. This section also goes through the best practices to insure that data are not lost. * Preserving and archiving your data – digital data can have a long life if properly cared for. This section covers managing data in the long term including choosing good file formats and media, as well as determining who will manage the data after the end of the project. * Sharing/publishing your data – addresses how to make data sharing across research groups easier, as well as how and why to publicly share data. This section covers intellectual property and licenses for datasets, before ending with the altmetrics that measure the impact of publicly shared data. * Reusing data – as more data are shared, it becomes possible to use outside data in your research. This chapter discusses strategies for finding datasets and lays out how to cite data once you have found it. This book is designed for active scientific researchers but it is useful for anyone who wants to get more from their data: academics, educators, professionals or anyone who teaches data management, sharing and preservation. "An excellent practical treatise on the art and practice of data management, this book is essential to any researcher, regardless of subject or discipline." —Robert Buntrock, Chemical Information Bulletin
The book successfully combines theory with practical application, featuring case studies, examples and a ‘critical incidents’ feature that make these topics engaging and relevant for students of business and management.
Author: Simone Gressel
Category: Business & Economics
Accessible and concise, this exciting new textbook examines data analytics from a managerial and organizational perspective and looks at how they can help managers become more effective decision-makers. The book successfully combines theory with practical application, featuring case studies, examples and a ‘critical incidents’ feature that make these topics engaging and relevant for students of business and management. The book features chapters on cutting-edge topics, including: • Big data • Analytics • Managing emerging technologies and decision-making • Managing the ethics, security, privacy and legal aspects of data-driven decision-making The book is accompanied by an Instructor’s Manual, PowerPoint slides and access to journal articles. Suitable for management students studying business analytics and decision-making at undergraduate, postgraduate and MBA levels.
Yet you know there's something missing that could make your job even easier. That "something" is efficient and effective data management. Managing Data with Excel is the only book on the market that focuses on just that.
Author: Conrad George Carlberg
Publisher: Que Publishing
You have learned the methods to the madness of Excel. Formulas and functions are friends instead of foes. Yet you know there's something missing that could make your job even easier. That "something" is efficient and effective data management. Managing Data with Excel is the only book on the market that focuses on just that. Learn how to efficiently move data, automate data storage and import data into worksheets and pivot tables. Case studies are included in each chapter to illustrate real-world applications of these functions. Invest your time in learning this now so that you can stop wasting your time figuring out how to work around problems.
The practical, hands-on guidance in this book includes: Part 1: The importance of information management and analytics to business, and how data warehouses are used Part 2: The technologies and data that advance an organization, and extend ...
Author: William McKnight
Information Management: Gaining a Competitive Advantage with Data is about making smart decisions to make the most of company information. Expert author William McKnight develops the value proposition for information in the enterprise and succinctly outlines the numerous forms of data storage. Information Management will enlighten you, challenge your preconceived notions, and help activate information in the enterprise. Get the big picture on managing data so that your team can make smart decisions by understanding how everything from workload allocation to data stores fits together. The practical, hands-on guidance in this book includes: Part 1: The importance of information management and analytics to business, and how data warehouses are used Part 2: The technologies and data that advance an organization, and extend data warehouses and related functionality Part 3: Big Data and NoSQL, and how technologies like Hadoop enable management of new forms of data Part 4: Pulls it all together, while addressing topics of agile development, modern business intelligence, and organizational change management Read the book cover-to-cover, or keep it within reach for a quick and useful resource. Either way, this book will enable you to master all of the possibilities for data or the broadest view across the enterprise. Balances business and technology, with non-product-specific technical detail Shows how to leverage data to deliver ROI for a business Engaging and approachable, with practical advice on the pros and cons of each domain, so that you learn how information fits together into a complete architecture Provides a path for the data warehouse professional into the new normal of heterogeneity, including NoSQL solutions
The book is a must-read for data scientists, data engineers and corporate leaders who are implementing big data platforms in their organizations.
Author: Peter Ghavami
Publisher: Walter de Gruyter GmbH & Co KG
Category: Business & Economics
Data analytics is core to business and decision making. The rapid increase in data volume, velocity and variety offers both opportunities and challenges. While open source solutions to store big data, like Hadoop, offer platforms for exploring value and insight from big data, they were not originally developed with data security and governance in mind. Big Data Management discusses numerous policies, strategies and recipes for managing big data. It addresses data security, privacy, controls and life cycle management offering modern principles and open source architectures for successful governance of big data. The author has collected best practices from the world’s leading organizations that have successfully implemented big data platforms. The topics discussed cover the entire data management life cycle, data quality, data stewardship, regulatory considerations, data council, architectural and operational models are presented for successful management of big data. The book is a must-read for data scientists, data engineers and corporate leaders who are implementing big data platforms in their organizations.
This book was written for those who want to understand the lifeblood of their company, who want to be able to make better decisions, and who know that managing information is the best way to protect and grow a business.
Author: Theresa Kushner
Publisher: Racom Communication
Category: Business & Economics
As a businessperson, you probably have more data available than ever before. For many, more data just fuels the fire of their love/hate relationship with it. You love it when it gives you good information about how to compete more effectively. But, you hate it when you can't get sales figures in time. This book was written for those who want to understand the lifeblood of their company, who want to be able to make better decisions, and who know that managing information is the best way to protect and grow a business. This book is designed to help you think about the use of data and its impact on your daily operation. It provides practical guidance to balance the use of hard facts and professional instincts; how to identify and focus on the numbers that matter; how to meet risk and security standards; and how to grow a data culture and make it work for you.
"This is a great book! I have to admit I wasn't enthusiastic about the idea of a book with such a narrow topic initially, but, frankly, it's the first professional book I've read page to page in one sitting in a long time.
Author: Malcolm Chisholm
Publisher: Morgan Kaufmann
"This is a great book! I have to admit I wasn't enthusiastic about the idea of a book with such a narrow topic initially, but, frankly, it's the first professional book I've read page to page in one sitting in a long time. It should be of interest to DBAs, data architects and modelers, programmers who have to write database programs, and yes, even managers. This book is a winner." - Karen Watterson, Editor SQL Server Professional "Malcolm Chisholm has produced a very readable book. It is well-written and with excellent examples. It will, I am sure, become the Reference Book on Reference Data." - Clive Finkelstein, "Father" of Information Engineering, Managing Director, Information Engineering Services Pty Ltd Reference data plays a key role in your business databases and must be free from defects of any kind. So why is it so hard to find information on this critical topic? Recognizing the dangers of taking reference data for granted, Managing Reference Data in Enterprise Databases gives you precisely what you've been seeking: A complete guide to the implementation and management of reference data of all kinds. This book begins with a thorough definition of reference data, then proceeds with a detailed examination of all reference data issues, fully describing uses, common difficulties, and practical solutions. Whether you're a database manager, architect, administrator, programmer, or analyst, be sure to keep this easy-to-use reference close at hand. Features Solves special challenges associated with maintaining reference data. Addresses a wide range of reference data issues, including acronyms, redundancy, mapping, life cycles, multiple languages, and querying. Describes how reference data interacts with other system components, what problems can arise, and how to mitigate these problems. Offers examples of standard reference data types and matrices for evaluating management methods. Provides a number of standard reference data tables and more specialized material to help you deal with reference data, via a companion Web site
This book explains in comprehensive and user-friendly detail how to manage, make sense of, explore and share data, giving scientists at all levels the skills they need to maximize the usefulness of their data.
Author: Mark Gardener
Publisher: Pelagic Publishing Ltd
Microsoft Excel is a powerful tool that can transform the way you use data. This book explains in comprehensive and user-friendly detail how to manage, make sense of, explore and share data, giving scientists at all levels the skills they need to maximize the usefulness of their data. Readers will learn how to use Excel to: * Build a dataset – how to handle variables and notes, rearrangements and edits to data. * Check datasets – dealing with typographic errors, data validation and numerical errors. * Make sense of data – including datasets for regression and correlation; summarizing data with averages and variability; and visualizing data with graphs, pivot charts and sparklines. * Explore regression data – finding, highlighting and visualizing correlations. * Explore time-related data – using pivot tables, sparklines and line plots. * Explore association data – creating and visualizing contingency tables. * Explore differences – pivot tables and data visualizations including box-whisker plots. * Share data – methods for exporting and sharing your datasets, summaries and graphs. Alongside the text, Have a Go exercises, Tips and Notes give readers practical experience and highlight important points, and helpful self-assessment exercises and summary tables can be found at the end of each chapter. Supplementary material can also be downloaded on the companion website. Managing Data Using Excel is an essential book for all scientists and students who use data and are seeking to manage data more effectively. It is aimed at scientists at all levels but it is especially useful for university-level research, from undergraduates to postdoctoral researchers.
Managing data from remeasured plots : an evaluation of existing systems . Res .
Pap . INT - 451 . Ogden , UT : U . S . Department of Agriculture , Forest Service ,
Intermountain Research Station . 26 p . Proper management of the valuable data
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 ...
Author: Tim King
Publisher: BCS, The Chartered Institute for IT
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.
Effective capital and financial risk management now requires that information flows no longer be handled independently in business unit silos; they must be unified for use in front, middle and back offices and to aid compliance with ...
Author: Raj Nathan
Publisher: Easton Studio Press, LLC
Category: Business & Economics
Effective capital and financial risk management now requires that information flows no longer be handled independently in business unit silos; they must be unified for use in front, middle and back offices and to aid compliance with internal controls and regulatory demands. Drawing on their individual and corporate experience at the leading edge of IT developments, three of Sybase's senior IT executives offer a lucid analysis of IT needs in the rapidly changing capital markets. They show how innovative IT capabilities and architectures can strengthen risk management and establish a foundation for new and sustained capital markets growth.
When creating completeness statements about a data source, one might make
the assumption that the data source, regardless of time, is always complete for
the parts of data captured by the statements. Indeed, this is true under the
Author: F. Darari
Publisher: IOS Press
The increasing amount of structured data available on the Web is laying the foundations for a global-scale knowledge base. But the ever increasing amount of Semantic Web data gives rise to the question – how complete is that data? Though data on the Semantic Web is generally incomplete, some may indeed be complete. In this book, the author deals with how to manage and consume completeness information about Semantic Web data. In particular, the book explores how completeness information can guarantee the completeness of query answering. Optimization techniques for completeness reasoning and the conducting of experimental evaluations are provided to show the feasibility of the approaches, as well as a technique for checking the soundness of queries with negation via reduction to query completeness checking. Other topics covered include completeness information with timestamps, and two demonstrators – CORNER and COOL-WD – are provided to show how a completeness framework can be realized. Finally, the book investigates an automated method to generate completeness statements from text on the Web. The book will be of interest to anyone whose work involves dealing with Web-data completeness.
The heart of the book lies in the collaboration efforts of eight distinct bioinformatics teams that describe their own unique approaches to data integration and interoperability.
Author: Zoé Lacroix
Publisher: Academic Press
The heart of the book lies in the collaboration efforts of eight distinct bioinformatics teams that describe their own unique approaches to data integration and interoperability. Each system receives its own chapter where the lead contributors provide precious insight into the specific problems being addressed by the system, why the particular architecture was chosen, and details on the system's strengths and weaknesses. In closing, the editors provide important criteria for evaluating these systems that bioinformatics professionals will find valuable. * Provides a clear overview of the state-of-the-art in data integration and interoperability in genomics, highlighting a variety of systems and giving insight into the strengths and weaknesses of their different approaches.-
A companion volume to the authors' earlier book Getting Started with Evaluation, this guide illustrates how to use the data to support value, collection use, benchmarking, and other best practices.
Author: Peter Hernon
Publisher: ALA Editions
Category: Business & Economics
Most library managers have not taken a statistics course. This text is designed to help remove anxiety as they view data in addressing the value of the library to its community. Data are linked to accountability, which affects financial return on investment and documents the library's role and value to society.--
This combined learning approach connects key concepts throughout the text to the important, practical tools to get started in database management.
Author: Wilfried Lemahieu
Publisher: Cambridge University Press
This comprehensive textbook teaches the fundamentals of database design, modeling, systems, data storage, and the evolving world of data warehousing, governance and more. Written by experienced educators and experts in big data, analytics, data quality, and data integration, it provides an up-to-date approach to database management. This full-color, illustrated text has a balanced theory-practice focus, covering essential topics, from established database technologies to recent trends, like Big Data, NoSQL, and more. Fundamental concepts are supported by real-world examples, query and code walkthroughs, and figures, making it perfect for introductory courses for advanced undergraduates and graduate students in information systems or computer science. These examples are further supported by an online playground with multiple learning environments, including MySQL; MongoDB; Neo4j Cypher; and tree structure visualization. This combined learning approach connects key concepts throughout the text to the important, practical tools to get started in database management.