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The visual organization : data visualization, big data, and the quest for better decisions /
"The era of Big Data as arrived, and most organizations are woefully unprepared. Slowly, many are discovering that stalwarts like Excel spreadsheets, KPIs, standard reports, and even traditional business intelligence tools aren't sufficient. These old standbys can't begin to handle to...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
Wiley,
[2014]
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Simon, Phil. | |
| 245 | 1 | 4 | |a The visual organization : |b data visualization, big data, and the quest for better decisions / |c Phil Simon. |
| 264 | 1 | |a Hoboken, New Jersey : |b Wiley, |c [2014] | |
| 264 | 4 | |c ©2014 | |
| 300 | |a 1 online resource (xxviii, 202 pages) | ||
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| 520 | |a "The era of Big Data as arrived, and most organizations are woefully unprepared. Slowly, many are discovering that stalwarts like Excel spreadsheets, KPIs, standard reports, and even traditional business intelligence tools aren't sufficient. These old standbys can't begin to handle today's increasing streams, volumes, and types of data. Amidst all of the chaos, though, a new type of organization is emerging. In The Visual Organization, award-winning author and technology expert Phil Simon looks at how an increasingly number of organizations are embracing new dataviz tools and, more important, a new mind-set based upon data discovery and exploration. Simon adroitly shows how Amazon, Apple, Facebook, Google, Twitter, and other tech heavyweights use powerful data visualization tools to garner fascinating insights into their businesses. But make no mistake: these companies are hardly alone. Organizations of all types, industries, sizes are representing their data in new and amazing ways. As a result, they are asking better questions and making better business decisions. Rife with real-world examples and case studies, The Visual Organization is a full-color tour-de-force"-- |c Provided by publisher | ||
| 504 | |a Includes bibliographical references and index. | ||
| 588 | 0 | |a Print version record and CIP data provided by publisher. | |
| 505 | 0 | |a The Visual Organization; Contents; List of Figures and Tables; Preface: A Tale of Two IPOs; Acknowledgments; How to Help This Book; Part One Book Overview and Background; Introduction; Adventures in Twitter Data Discovery; Contemporary Dataviz 101; Primary Objective; Benefits; More Important Than Ever; Revenge of the Laggards: The Current State of Dataviz; Book Overview; Defining the Visual Organization; Central Thesis of This Book; Cui Bono?; Methodology: Story Matters Here; The Quest for Knowledge and Case Studies; Differentiation: A Note on Other Dataviz Texts; Plan of Attack; Next; Notes. | |
| 505 | 8 | |a Chapter 1 The Ascent of the Visual OrganizationThe Rise of Big Data; Open Data; The Burgeoning Data Ecosystem; The New Web: Visual, Semantic, and API-Driven; The Arrival of the Visual Web; Linked Data and a More Semantic Web; The Relative Ease of Accessing Data; Greater Efficiency via Clouds and Data Centers; Better Data Tools; Greater Organizational Transparency; The Copycat Economy: Monkey See, Monkey Do; Data Journalism and the Nate Silver Effect; Digital Man; The Arrival of the Visual Citizen; Mobility; The Visual Employee: A More Tech- and Data-Savvy Workforce. | |
| 505 | 8 | |a Navigating Our Data-Driven WorldNext; Notes; Chapter 2 Transforming Data into Insights: The Tools; Dataviz: Part of an Intelligent and Holistic Strategy; The Tyranny of Terminology: Dataviz, BI, Reporting, Analytics, and KPIs; Do Visual Organizations Eschew All Tried-and-True Reporting Tools?; Drawing Some Distinctions; The Dataviz Fab Five; Applications from Large Enterprise Software Vendors; LESVs: The Case For; LESVs: The Case Against; Best-of-Breed Applications; Cost; Ease of Use and Employee Training; Integration and the Big Data World; Popular Open-Source Tools; D3.js; R; Others. | |
| 505 | 8 | |a Design FirmsStart-Ups, Web Services, and Additional Resources; The Final Word: One Size Doesn't Fit All; Next; Notes; Part Two Introducing the Visual Organization; Chapter 3 The Quintessential Visual Organization; Netflix 1.0: Upsetting the Applecart; Netflix 2.0: Self-Cannibalization; Dataviz: Part of a Holistic Big Data Strategy; Dataviz: Imbued in the Netflix Culture; Customer Insights; Better Technical and Network Diagnostics; Embracing the Community; Lessons; Next; Notes; Chapter 4 Dataviz in the DNA; The Beginnings; UX Is Paramount; The Plumbing; Embracing Free and Open-Source Tools. | |
| 505 | 8 | |a Extensive Use of APIsLessons; Next; Notes; Chapter 5 Transparency in Texas; Background; Early Dataviz Efforts; Embracing Traditional BI; Data Discovery; Better Visibility into Student Life; Expansion: Spreading Dataviz Throughout the System; Results; Lessons; Next; Notes; Part Three Getting Started: Becoming a Visual Organization; Chapter 6 The Four-Level Visual Organization Framework; Big Disclaimers; A Simple Model; Limits and Clarifications; Progression; Is Progression Always Linear?; Can a Small Organization Best Position Itself to Reach Levels 3 and 4? If So, How? | |
| 505 | 8 | |a Can an Organization Start at Level 3 or 4 and Build from the Top Down? | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Information technology |x Management. | |
| 650 | 0 | |a Information visualization. | |
| 650 | 0 | |a Big data. | |
| 650 | 0 | |a Business |x Data processing. | |
| 650 | 6 | |a Technologie de l'information |x Gestion. | |
| 650 | 6 | |a Visualisation de l'information. | |
| 650 | 6 | |a Données volumineuses. | |
| 650 | 6 | |a Gestion |x Informatique. | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Industrial Management. |2 bisacsh | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Management. |2 bisacsh | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Management Science. |2 bisacsh | |
| 650 | 7 | |a BUSINESS & ECONOMICS |x Organizational Behavior. |2 bisacsh | |
| 650 | 7 | |a Betriebswirtschaft. |2 bisacsh | |
| 650 | 7 | |a Big data |2 fast | |
| 650 | 7 | |a Business |x Data processing |2 fast | |
| 650 | 7 | |a Information technology |x Management |2 fast | |
| 650 | 7 | |a Information visualization |2 fast | |
| 650 | 7 | |a Informationstechnik |2 gnd | |
| 650 | 7 | |a Visualisierung |2 gnd | |
| 650 | 7 | |a Big Data |2 gnd | |
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