9781118794388

The Visual Organization Data Visualization, Big Data, and the Quest for Better Decisions

by
  • ISBN13:

    9781118794388

  • ISBN10:

    1118794389

  • Edition: 1st
  • Format: Hardcover
  • Copyright: 3/31/2014
  • Publisher: Wiley
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Supplemental Materials

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Summary

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.

Table of Contents

List of Figures and Tables

Preface

Acknowledgments

Part I 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 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

Chapter 1: The Ascent of the Visual Organization

The Rise of Big Data

Open Data

The Burgeoning Data Ecosystem

The New Web: Semantic, Visual, 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

Navigating Our Data-Driven World

Next

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

Design Firms

Startups, Web Services, and Additional Resources

The Final Word: One Size Doesn’t Fit All

Next

Notes

Part II 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: Using Dataviz to Create a Compelling User Experience

The Plumbing

Embracing Open Source Tools

Extensive Use of APIs

Lessons

Next

Note

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 III 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?

Can an Organization Start at Level 3 or 4 and Build from the Top Down?

Is Intra-Level Progression Possible?

Are Intra- and Inter-Level Progression Inevitable?

Can Different Parts of the Organization Exist on Different Levels?

Should an Organization Struggling with Levels 1 and 2 Attempt to Move to Level 3 or 4?

Regression: Reversion to Lower Levels

Complements, Not Substitutes

Accumulated Advantage

The Limits of Lower Levels

Relativity and Sub-Levels

Should Every Organization Aspire to Level 4?

Next

Chapter 7: WWVOD?

Visualizing the Impact of a Reorg

Visualizing Employee Movement

Starting Down the Dataviz Path

Results and Lessons

Future

A Marketing Example

Next

Notes

Chapter 8: Building the Visual Organization

Data Tips and Best Practices

Data: The Primordial Soup

Walk Before You Run…At Least for Now

A Dataviz Is Often Just the Starting Point

Visualize Both Small and Big Data

Don’t Forget the Metadata

Look Outside of the Enterprise

The Beginnings: All Data Is Not Required

Visualize Good and Bad Data

Enable Drill-Down

Design Tips and Best Practices

Begin with the End in Mind (Sort of)

Subtract When Possible

UX: Participation and Experimentation Are Paramount

Encourage Interactivity

Use Motion and Animation Carefully

Use Relative—Not Absolute—Figures

Technology Tips and Best Practices

Where Possible, Consider Using APIs

Embrace New Tools

Know the Limitations of Dataviz Tools

Be Open

Management Tips and Best Practices

Encourage Self-Service, Exploration, and Data Democracy

Exhibit a Healthy Skepticism

Trust the Process, Not the Result

Avoid the Perils of Silos and Specialization

If Possible, Visualize

Seek Hybrids When Hiring

Think Direction First, Precision Later

Next

Notes

Chapter 9: The Inhibitors: Mistakes, Myths, and Challenges

Mistakes

Falling into the Traditional ROI Trap

Always—and Blindly—Trusting a Dataviz

Ignoring the Audience

Developing in a Cathedral

Set it and Forget it

Bad Dataviz

TMI

Using Tiny Graphics

Myths

Data Visualizations Guarantee Certainty and Success

Data Visualization Is Easy

Data Visualizations Are Projects

There Is One “Right” Visualization

Excel Is Sufficient

Challenges

The Quarterly Visualization Mentality

Data Defiance

Unlearning History: Overcoming the Disappointments of Prior Tools

Next

Notes

Part IV Conclusion and the Future of DataViz

Coda: We’re Just Getting Started

Four Critical Data-Centric Trends

Wearable Technology and the Quantified Self

Machine Learning and the Internet of Things

Multi-Dimensional Data

The Forthcoming Battle Over Data Portability and Ownership

Final Thoughts: Nothing Stops This Train

Notes

Afterword: My Life in Data

Appendix: Supplemental Dataviz Resources

Bibliography

About the Author

Index

Rewards Program

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