Analytics E-Book

Phil Simon

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Serie: SAS Institute Inc
• Sprache: Englisch
• Veröffentlichungsjahr: 2017

Seitenzahl: 346

Beschreibung

For years, organizations have struggled to make sense out of their data. IT projects designed to provide employees with dashboards, KPIs, and business-intelligence tools often take a year or more to reach the finish line...if they get there at all. This has always been a problem. Today, though, it's downright unacceptable. The world changes faster than ever. Speed has never been more important. By adhering to antiquated methods, firms lose the ability to see nascent trends--and act upon them until it's too late. But what if the process of turning raw data into meaningful insights didn't have to be so painful, time-consuming, and frustrating? What if there were a better way to do Analytics? Fortunately, you're in luck... Analytics: The Agile Way is the eighth book from award-winning author and Arizona State University professor Phil Simon. Analytics: The Agile Way demonstrates how progressive organizations such as Google, Nextdoor, and others approach Analytics in a fundamentally different way. They are applying the same Agile techniques that software developers have employed for years. They have replaced large batches in favor of smaller ones...and their results will astonish you. Through a series of case studies and examples, Analytics: The Agile Way demonstrates the benefits of this new Analytics mind-set: superior access to information, quicker insights, and the ability to spot trends far ahead of your competitors.

Leseprobe

Praise for Analytics:The Agile Way

“These days, every company knows they need to embrace analytics—but all too often, they’re doing it wrong. Read Phil Simon’s new book to understand the path forward, and leverage the power of data in a faster, cheaper, better way.”

—Dorie Clark, author of Stand Out and adjunct professor, DukeUniversity Fuqua School of Business

“Most companies, especially the larger ones, are realizing that their data availability and data abilities will make or break them. Huge investments are being made into creating actionable analytics—that next level of data insight. Yet many continue to pursue analytics in a Waterfall, phase-gate approach that makes those insights late to the party. Analytics: The Agile Way contains the spot-on guidance that everyone involved needs to know about analytics, Agile methods, and their intersection.”

—William McKnight, President, McKnight Consulting Group

“Quite obviously, the value of analytical insights that make it into practice is far more important than the depth of the analytics. Increasing that value may be the key challenge in the analytics space. Phil Simon’s latest book provides some clues, adopting highly successful Agile methods to the cause.”

—Tom Redman, Ph.D., “the Data Doc,” and author ofGetting in Front on Data: Who Does What

“The real focus of technology today is around new answers to previously unsolvable problems: analytics. Unfortunately, old methods of developing analytics, algorithms, etc., are just too slow, risky, and costly. Businesses need a fast, cheap way to prove (and then improve) the worth of analytics. Phil explores the Agile approach to analytics and then backs it up with a number of case studies—many of which he helped developed. If you are unclear on how your firm should approach analytics, this book serves as the starting point.”

—Brian Sommer, IT industry analyst and President,TechVentive, Inc.

“Even for those who have been working with data for many years (like me), this is an eye-opening book.”

—Michael Schrenk, competitive intelligence specialist andauthor of Webbots, Spiders, and Screen Scrapers

“Phil Simon nails it. For more than a quarter century, high-impact business value from our efforts in analytics and business intelligence has been a hit-or-miss proposition. Today we have an entirely new generation of data management and analytics, and the approaches described in this book can help organizations pivot toward the best techniques—and an entirely new philosophy—to achieve the most from our new technology.”

—Alan Simon, senior lecturer, Arizona State University

Wiley & SAS Business Series

The Wiley & SAS Business Series presents books that help senior-level managers with their critical management decisions.

Titles in the Wiley & SAS Business Series include:

Analytics: The Agile Way

by Phil Simon

Analytics in a Big Data World: The Essential Guide to Data Science and its Applications by

Bart Baesens

A Practical Guide to Analytics for Governments: Using Big Data for Good

by Marie Lowman

Bank Fraud: Using Technology to Combat Losses

by Revathi Subramanian

Big Data Analytics: Turning Big Data into Big Money

by Frank Ohlhorst

Big Data, Big Innovation: Enabling Competitive Differentiation through Business Analytics

by Evan Stubbs

Business Analytics for Customer Intelligence

by Gert Laursen

Business Intelligence Applied: Implementing an Effective Information and Communications Technology Infrastructure

by Michael Gendron

Business Intelligence and the Cloud: Strategic Implementation Guide

by Michael S. Gendron

Business Transformation: A Roadmap for Maximizing Organizational Insights

by Aiman Zeid

Connecting Organizational Silos: Taking Knowledge Flow Management to the Next Level with Social Media

by Frank Leistner

Data-Driven Healthcare: How Analytics and BI Are Transforming the Industry

by Laura Madsen

Delivering Business Analytics: Practical Guidelines for Best Practice

by Evan Stubbs

Demand-Driven Forecasting: A Structured Approach to Forecasting, Second Edition

by Charles Chase

Demand-Driven Inventory Optimization and Replenishment: Creating a More Efficient Supply Chain

by Robert A. Davis

Developing Human Capital: Using Analytics to Plan and Optimize Your Learning and Development Investments

by Gene Pease, Barbara Beresford, and Lew Walker

The Executive’s Guide to Enterprise Social Media Strategy: How Social Networks Are Radically Transforming Your Business

by David Thomas and Mike Barlow

Economic and Business Forecasting: Analyzing and Interpreting Econometric Results

by John Silvia, Azhar Iqbal, Kaylyn Swankoski, Sarah Watt, and Sam Bullard

Economic Modeling in the Post Great Recession Era: Incomplete Data, Imperfect Markets

by John Silvia, Azhar Iqbal, and Sarah Watt House

Foreign Currency Financial Reporting from Euros to Yen to Yuan: A Guide to Fundamental Concepts and Practical Applications

by Robert Rowan

Harness Oil and Gas Big Data with Analytics: Optimize Exploration and Production with Data-Driven Models

by Keith Holdaway

Health Analytics: Gaining the Insights to Transform Health Care

by Jason Burke

Heuristics in Analytics: A Practical Perspective of What Influences Our Analytical World

by Carlos Andre Reis Pinheiro and Fiona McNeill

Human Capital Analytics: How to Harness the Potential of Your Organization’s Greatest Asset

by Gene Pease, Boyce Byerly, and Jac Fitz-enz

Implement, Improve and Expand Your Statewide Longitudinal Data System: Creating a Culture of Data in Education

by Jamie McQuiggan and Armistead Sapp

Intelligent Credit Scoring: Building and Implementing Better Credit Risk Scorecards, Second Edition

by Naeem Siddiqi

Killer Analytics: Top 20 Metrics Missing from Your Balance Sheet

by Mark Brown

Machine Learning for Marketers: Hold the Math

by Jim Sterne

On-Camera Coach: Tools and Techniques for Business Professionals in a Video-Driven World

by Karin Reed

Predictive Analytics for Human Resources

by Jac Fitz-enz and John Mattox II

Predictive Business Analytics: Forward-Looking Capabilities to Improve Business Performance

by Lawrence Maisel and Gary Cokins

Retail Analytics: The Secret Weapon

by Emmett Cox

Social Network Analysis in Telecommunications

by Carlos Andre Reis Pinheiro

Statistical Thinking: Improving Business Performance, Second Edition

by Roger W. Hoerl and Ronald D. Snee

Strategies in Biomedical Data Science: Driving Force for Innovation

by Jay Etchings

Style and Statistics: The Art of Retail Analytics

by Brittany Bullard

Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with Advanced Analytics

by Bill Franks

Too Big to Ignore: The Business Case for Big Data

by Phil Simon

The Analytic Hospitality Executive

by Kelly A. McGuire

The Value of Business Analytics: Identifying the Path to Profitability

by Evan Stubbs

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

by Phil Simon

Using Big Data Analytics: Turning Big Data into Big Money

by Jared Dean

Win with Advanced Business Analytics: Creating Business Value from Your Data

by Jean Paul Isson and Jesse Harriott

For more information on any of the above titles, please visit www.wiley.com.

Other Books byPhil Simon

Message Not Received: Why Business Communication Is Broken and How to Fix It

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

Too Big to Ignore: The Business Case for Big Data

The Age of the Platform: How Amazon, Apple, Facebook, and Google Have Redefined Business

The New Small: How a New Breed of Small Businesses Is Harnessing the Power of Emerging Technologies

The Next Wave of Technologies: Opportunities in Chaos

Why New Systems Fail: An Insider’s Guide to Successful IT Projects

Analytics

The Agile Way

Phil Simon

Cover image: Phil Simon/Wiley

Cover design: Wiley

Copyright © 2017 by John Wiley & Sons, Inc. All rights reserved.

Published by John Wiley & Sons, Inc., Hoboken, New Jersey.

Published simultaneously in Canada.

No part of this publication may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning, or otherwise, except as permitted under Section 107 or 108 of the 1976 United States Copyright Act, without either the prior written permission of the Publisher, or authorization through payment of the appropriate per-copy fee to the Copyright Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, [phone], fax [phone], or on the Web at www.copyright.com. Requests to the Publisher for permission should be addressed to the Permissions Department, John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, [phone], fax [phone], or online at www.wiley.com/go/permissions.

Limit of Liability/Disclaimer of Warranty: While the publisher and author have used their best efforts in preparing this book, they make no representations or warranties with respect to the accuracy or completeness of the contents of this book and specifically disclaim any implied warranties of merchantability or fitness for a particular purpose. No warranty may be created or extended by sales representatives or written sales materials. The advice and strategies contained herein may not be suitable for your situation. You should consult with a professional where appropriate. Neither the publisher nor author shall be liable for any loss of profit or any other commercial damages, including but not limited to special, incidental, consequential, or other damages.

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Wiley publishes in a variety of print and electronic formats and by print-on-demand. Some material included with standard print versions of this book may not be included in e-books or in print-on-demand. If this book refers to media such as a CD or DVD that is not included in the version you purchased, you may download this material at http://booksupport.wiley.com. For more information about Wiley products, visit www.wiley.com.

Library of Congress Cataloging-in-Publication Data

Names: Simon, Phil, author.

Title: Analytics : the agile way / Phil Simon.

Description: Hoboken, New Jersey : John Wiley & Sons, 2017. | Series: Wiley &

SAS business series | Includes bibliographical references and index. |

Identifiers: LCCN 2017020140 (print) | LCCN 2017026344 (ebook) |

ISBN 978-1-119-42420-8 (pdf) | ISBN 978-1-119-42419-2 (epub) |

ISBN 978-1-119-42347-8 (cloth) | ISBN 978-1-119-42421-5 (obook)

Subjects: LCSH: Business intelligence—Data processing. | Decision making.

Classification: LCC HD38.7 (ebook) | LCC HD38.7 .S535 2017 (print) | DDC

658.4/033—dc23

LC record available at https://lccn.loc.gov/2017020140

                   The readiness is all.

—Hamlet

Contents

Preface: The Power of Dynamic Data

Introduction It Didn’t Used to Be This Way

A Little History Lesson

Analytics and the Need for Speed

Book Scope, Approach, and Style

Intended Audience

Plan of Attack

Next

Notes

Part One Background and Trends

Chapter 1 Signs of the Times Why Data and Analytics Are Dominating Our World

The Moneyball Effect

Digitization and the Great Unbundling

Amazon Web Services and Cloud Computing

Not Your Father’s Data Storage

Moore’s Law

The Smartphone Revolution

The Democratization of Data

The Primacy of Privacy

The Internet of Things

The Rise of the Data-Savvy Employee

The Burgeoning Importance of Data Analytics

Data-Related Challenges

Companies Left Behind

The Growth of Analytics Programs

Next

Notes

Chapter 2 The Fundamentals of Contemporary Data A Primer on What It Is, Why It Matters, and How to Get It

Types of Data

Getting the Data

Data in Motion

Next

Notes

Chapter 3 The Fundamentals of Analytics Peeling Back the Onion

Defining Analytics

Types of Analytics

Streaming Data Revisited

A Final Word on Analytics

Next

Notes

Part Two Agile Methods and Analytics

Chapter 4 A Better Way to Work The Benefits and Core Values of Agile Development

The Case against Traditional Analytics Projects

Proving the Superiority of Agile Methods

The Case for Guidelines over Rules

Next

Notes

Chapter 5 Introducing Scrum Looking at One of Today’s Most Popular Agile Methods

A Very Brief History

Scrum Teams

User Stories

Backlogs

Sprints and Meetings

Releases

Estimation Techniques

Other Scrum Artifacts, Tools, and Concepts

Next

Notes

Chapter 6 A Framework for Agile Analytics A Simple Model for Gathering Insights

Perform Business Discovery

Perform Data Discovery

Prepare the Data

Model the Data

Score and Deploy

Evaluate and Improve

Next

Notes

Part Three Analytics in Action

Chapter 7 University Tutoring Center An In-Depth Case Study on Agile Analytics

The UTC and Project Background

Project Goals and Kickoff

Iteration One

Iteration Two

Iteration Three

Iteration Four

Results

Lessons

Next

Notes

Chapter 8 People Analytics at Google/Alphabet Not Your Father’s HR Department

The Value of Business Experiments

PiLab’s Adventures in Analytics

A Better Approach to Hiring

Staffing

The Value of Perks

Results and Lessons

Next

Notes

Chapter 9 The Anti-Google Beneke Pharmaceuticals

Project Background

Business and Data Discovery

The Friction Begins

Astonishing Results

Developing Options

The Grand Finale

Results and Lessons

Next

Notes

Chapter 10 Ice Station Zebra Medical How Agile Methods Solved a Messy Health-Care Data Problem

Paying Nurses

Enter the Consultant

User Stories

Agile: The Better Way

Results

Lessons

Next

Notes

Chapter 11 Racial Profiling at Nextdoor Using Data to Build a Better App and Combat a PR Disaster

Unintended but Familiar Consequences

Evaluating the Problem

Results and Lessons

Next

Notes

Part Four Making the Most Out of Agile Analytics

Chapter 12 The Benefits of Agile Analytics The Upsides of Small Batches

Life at IAC

Life at RDC

Comparing the Two

Next

Notes

Chapter 13 No Free Lunch The Impediments to—and Limitations of—Agile Analytics

People Issues

Data Issues

The Limitations of Agile Analytics

Next

Notes

Chapter 14 The Importance of Designing for Data Lessons from the Upstarts

The Genes of Music

The Tension between Data and Design

Next

Notes

Part Five Conclusions and Next Steps

Chapter 15 What Now? A Look Forward

A Tale of Two Retailers

The Blurry Futures of Data, Analytics, and Related Issues

Final Thoughts and Next Steps

Notes

Afterword

Acknowledgments

Selected Bibliography

Books

Articles and Essays

About the Author

Index

EULA

List of Tables

Introduction

Table I.1

Chapter 2

Table 2.1

Table 2.2

Chapter 3

Table 3.1

Table 3.2

Table 3.3

Chapter 5

Table 5.1

Table 5.2

Table 5.3

Table 5.4

Chapter 6

Table 6.1

Table 6.2

Table 6.3

Chapter 7

Table 7.1

Table 7.2

Table 7.3

Chapter 10

Table 10.1

Chapter 12

Table 12.1

List of Illustrations

Preface

Figure P.1

Foursquare Interest over Time, March 1, 2009, to March 29, 2017

Figure P.2

Chipotle Share of Restaurant Foot Traffic (Week over Week)

Chapter 1

Figure 1.1

Data-Storage Costs over Time

Figure 1.2

The World’s Most Valuable Companies by Market Cap as of July 29, 2016, at 10:50 a.m. ET

Chapter 2

Figure 2.1

Tweet about Data Scientists

Figure 2.2

Write and Rant Facebook Post

Figure 2.3

Initial Results of Cover Poll for

Analytics: The Agile Way

Cover Vote

Figure 2.4

Data Roundtable

Front Page as of December 16, 2016

Figure 2.5

Results of Web Scraping via import.io

Figure 2.6

Page Views by Author on

Data Roundtable

Figure 2.7

Simple CSV Example

Figure 2.8

Simple JSON Example

Figure 2.9

Simple XML Example

Chapter 3

Figure 3.1

Google Trends: Analytics versus Key Performance Indicators (March 7, 2007, to March 7, 2017)

Chapter 4

Figure 4.1

Agile Methods: Before and After

Chapter 5

Figure 5.1

Simple Visual of Scrum Team Makeup

Figure 5.2

Relationship between Product and Sprint Backlogs

Figure 5.3

Schedule for a One-Week Sprint

Figure 5.4

Simple Representation of Grass at Author’s Former and Current Home

Figure 5.5

Two Very Different Buildings

Figure 5.6

Two Very Similar Buildings

Figure 5.7

Fibonacci Sequence

Figure 5.8

Team Estimation Game, Round 1

Figure 5.9

Team Estimation Game, Round 2

Figure 5.10

Generic Burn-Down Chart

Figure 5.11

Sample Kanban Board for Analytics Project

Chapter 6

Figure 6.1

A Simple Six-Step Framework for Agile Analytics

Figure 6.2

Player Extra Value

Chapter 7

Figure 7.1

Heat Map for UTC Accounting Tutees

Figure 7.2

Heat Map for UTC Math Tutees

Figure 7.3

Heat Map for UTC Accounting Tutors

Figure 7.4

Heat Map for UTC Accounting STRs

Figure 7.5

Tutee Ethnicities and Courses Requested

Figure 7.6

Hispanic Tutees’ Popular Subjects and Visit Hours

Figure 7.7

Representation of New Tutor Schedule

Figure 7.8

Accounting STR, Thursday: Old versus New

Chapter 8

Figure 8.1

Tweet from @SusanWojcicki

Chapter 9

Figure 9.1

Average MBA Years of Tenure with Beneke: Rutgers versus Ivy League Schools

Figure 9.2

Percentage of MBAs Promoted within Three Years at Beneke: Rutgers versus Ivy League Schools

Chapter 10

Figure 10.1

Front End for ISZM Microsoft Access Application, Version 1.0

Figure 10.2

Front End for ISZM Microsoft Access Application, Version 1.1

Figure 10.3

Front End for ISZM Microsoft Access Application, Version 1.2

Chapter 11

Figure 11.1

Screenshot from Racially Charged User

Figure 11.2

Screenshot from Original Nextdoor App

Figure 11.3

Screenshot from Nextdoor’s Redesigned App

Figure 11.4

Screenshot from Nextdoor’s Redesigned App

Figure 11.5

Screenshot from Nextdoor’s Redesigned App

Chapter 14

Figure 14.1

Example of a Pandora Recommendation: “The King of Sunset Town” by Marillion

Guide

Cover

Table of Contents

Preface

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PrefaceThe Power of Dynamic Data

The most valuable commodity I know of is information.

—Michael Douglas as Gordon Gekko, Wall Street

On August 7, 2015, the mood at Chipotle headquarters in Denver, Colorado, was jovial. The stock (NYSE: CMG) of the chain of “fast casual” Mexican restaurants had just reached an all-time high of $749.12. Sure, the company faced its fair share of challenges (including an alarmingly high number of lawsuits), but today was a day to celebrate.

Fast-forward six months. As so often is the case these days, things had changed very quickly.

A series of food-borne illnesses came to light at the end of 2015—and not just a few mild stomachaches caused by a batch of bad salsa. The true culprit: E. coli. As the Centers for Disease Control and Prevention (CDC) announced on December 2, 2015, “52 people from nine states have been sickened, 20 have been hospitalized, and there are no deaths.”1

By April 16, 2016, Chipotle’s stock was in free fall, dropping 40 percent from its high to $444. Things continued to spiral downward for the chain. The stock hit $370 on December 9 of that year. In August 2016, nearly 10,000 employees sued the company for unpaid wages. In September, a 16-year-old girl won a $7.65 million lawsuit against the company for sexual harassment. One of the victim’s attorneys described the situation as “a brothel that just served food.”2 Damning words to be sure.

Sensing opportunity, activist investor Bill Ackman started gobbling up Chipotle equities. His hedge fund, Pershing Square Capital Management (PSCM), purchased large quantities of options trades, “normal” stock buys, and equity swaps. Rumor had it that Ackman wasn’t just looking to make a buck; he wanted seats on the Chipotle board and a significant say in the company’s long-term and daily management. And PSCM wasn’t the only hedge fund betting long on CMG in 2016. Plenty of others were taking notice.3

Ackman is an interesting cat and a mixed blessing to the Chipotles of the world.* Over the years, he has earned a reputation as a thorn in the side of many distraught companies and their boards of directors. Still, Chipotle executives knew that his hedge fund was keeping their portfolios healthy. No doubt that CMG would have fallen further if PSCM and other funds weren’t buying so aggressively.

Why were hedge funds buying Chipotle’s shares on the cheap in 2016? You don’t need to be Warren Buffett to see what was happening. The heads of these funds believed in the long-term value of the stock. Chipotle would eventually recover, they reasoned, so why not make a few bucks? In a way, Ackman and his ilk are no different from Homer Simpson. The patriarch of the iconic cartoon family once summarized his remarkably facile investment philosophy in the following seven words, “Buy low. Sell high. That’s my motto.”†

This begs the natural question: On what basis do these folks make their multibillion-dollar bets?

At a high level, sharks such as Ackman operate via a combination of instinct and analysis. With regard to the latter, hedge funds have always coveted highly quantitative employees—aka quants. As Scott Patterson writes in The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It, their complex and proprietary models factor in dozens or even hundreds of variables in attempting to predict stock prices and place large wagers.

New and unexpected data sources could be worth a fortune.

FOURSQUARE’S RISE, FALL, AND DATA-DRIVEN SECOND ACT

Although Facebook beat it by five years, Foursquare still arrived relatively early at the social-media party. Launched in March 2009 as a “local search-and-discovery service mobile app,” it didn’t take long for the company to approach unicorn status. Cofounder and CEO Dennis Crowley became a bona fide rock star. Millions of people used the app to check in to restaurants and bars. Of course, none of this would have been possible as late as 2006. By 2009, though, the smartphone revolution was in full swing. Foursquare could piggyback on the ubiquity of iPhones and Droids.

Crowley and Foursquare allowed anyone to download and use the app for free. Millions of people did. Oodles of active users, however, do not a business model make. There’s a world of difference between a user and a customer.

At some point, like all enterprises, Foursquare needed to make money. By growing its user base, Foursquare hoped to expand its customer base: local businesses that could create highly targeted ads that its millions of users would see and, it was hoped, act on.

Foursquare was about to take location-based advertising into the smartphone age. No longer would a city pub owner or restaurateur need to pay someone to interrupt passersby on the street and hand out cards that advertised two-for-one drink specials. Via Foursquare, eateries could reach potential customers in a way never before possible.

At least that was the theory.

Foursquare’s promise has always exceeded its financial results. For all of its users and hype, the company has never reported earning a profit.4 At different points in 2012, both Marissa Mayer’s acquisition-happy Yahoo! and Facebook reportedly flirted with acquiring Foursquare. In the end, though, the parties never consummated a deal.5 Yahoo! remained a mess, and Facebook didn’t really need Foursquare. Its network was enormous, and it wasn’t as if the idea of a check-in had never occurred to Mark Zuckerberg. (In August 2010, the social network launched Places, a feature “not unlike” Foursquare.6 ) Determined to remain relevant, Crowley and his troops soldiered on.

Version 2.0: Two Apps Are Better Than One

On May 15, 2014, Foursquare launched a spin-off app called Swarm. The new app allowed users to broadcast their locations to their friends on social networks such as Facebook and Twitter. The main Foursquare app would still exist, but with a new focus. It would attempt to wean market share from Yelp. Writing for The Verge, Ben Popper and Ellis Hamburger explained the two apps’ different purposes:

Swarm will be a social heat map, helping users find friends nearby and check in to share their location. The new Foursquare will ditch the check-in and focus solely on exploration and discovery, finally positioning itself as a true Yelp-killer in the battle to provide great local search.7

Splitting Swarm from the Foursquare app has not turned out to be a panacea. Over the past few years, many industry analysts have doubted its long-term financial solvency. Foursquare has lost its status as an it company. In Figure P.1, Google Trends shows just how far the company has fallen.

Figure P.1 Foursquare Interest over Time, March 1, 2009, to March 29, 2017

Source:Google Trends.

Something had to give.

On January 14, 2016, COO Jeff Glueck replaced Crowley as CEO. On the same day that that long-rumored change of leadership took place, Foursquare bit the bullet and announced a new investor lifeline at a fraction of its prior valuation. Yes, the company and its employees had to endure the ignominy of the dreaded down round. As Mike Isaac wrote for the New York Times:

Foursquare said it had raised $45 million in a new round of venture funding, as it tries to bolster its location data-based advertising and developer businesses. The financing pegs Foursquare’s valuation at roughly half of the approximately $650 million that it was valued at in its last round in 2013, according to three people with knowledge of the deal’s terms, who spoke on the condition of anonymity.8 [Emphasis mine.]

Despite Foursquare’s well-documented struggles, the app still sports a reported 50 million monthly active users.9 As Bloomberg TV’s Cory Johnson is fond of saying, “That ain’t nothin’.” Was it possible that Foursquare’s next and ultimately best business model was staring its management in the face?

Version 3.0: A Data-Induced “Pivot”

On April 12, 2016, Glueck penned a fascinating post on Medium10 that qualified as bragging or at least posturing. The Foursquare CEO revealed how his company collated user check-in data and other variables to accurately predict Chipotle’s first-quarter sales. (The number dropped nearly 30 percent compared to the fourth quarter of 2015.)

As anyone who has studied retail knows, foot traffic isn’t a terribly innovative concept these days. Brick-and-mortar retailers have known for many decades that it can serve as a valuable proxy for sales and revenue. All else being equal, there’s a direct relationship between the former and the latter. Still, Glueck’s lengthy data- and graph-laden article illustrated the power of “digital” foot traffic. Figure P.2 shows one of the post’s charts.

Figure P.2 Chipotle Share of Restaurant Foot Traffic (Week over Week)

Source:Foursquare Medium feed.

Glueck’s post does not formally ask the following questions, but it certainly implies them:

What if Foursquare could harness this type of data en masse and tie it to detailed user demographic information? (Foursquare allows its users to log in via Facebook, arguably the world’s richest data trove.)

Would companies with physical presences be willing to pony up for this kind of information? (Better question: Why

wouldn’t

they be willing to pay handsomely for it?)

What about opportunistic hedge-fund managers looking to outsmart the market? What about the Bill Ackmans of the world?

Could Chipotle use location-based information to offer different deals and coupons to its growing number of ex-customers? Could Foursquare help Chipotle rescue customers? Could it be a means to an end?

Would any chain restaurants be willing to pay Foursquare

not

to release this type of damning information? (Admittedly, this might qualify as

blackmail

or at least as

unethical

.)

Glueck wasn’t just speculating about what his company could theoretically offer. As it turns out, Foursquare no longer just sells in-app ads to local bars and restaurants; an unknown but evidently increasing revenue stream for the company involves “renting” its data to interested parties. Foursquare’s new Place Insights service purportedly offers:

Insights from the world’s largest opt-in foot traffic panel

Overnight analysis of a global, cross-category dataset

Translation of real-world behavior into business health, trend detection, and consumer insights

*

Put differently, Glueck is attempting to redefine Foursquare as a data-licensing company. Just look at the company’s website copy in early 2010:

Foursquare on your phone gives you & your friends new ways of exploring your city. Earn points & unlock badges for discovering new things.†

The focus is clearly on the consumer/user. At the time, many companies employed gamification strategies. Now contrast that one with the company’s business-first message today:

Foursquare is a technology company that uses location intelligence to build meaningful consumer experiences and business solutions.*

The differences between Foursquare’s early message and its contemporary one could not be more stark. Nothing against Joe Sixpack or Melissa Millennial, but they probably don’t understand what location intelligence and meaningful consumer experiences even mean. I doubt that they would sign up for them. It’s a moot point, though, because Foursquare isn’t chasing the Joes and Melissas anymore. They don’t pay the bills, at least directly.

Given Foursquare’s history, its decision to rebrand is no coincidence, nor is its new message isolated to its website. Foursquare now consistently refers to itself as a “location intelligence company.” Just view its official Twitter† and Medium‡ feeds. Its blog posts, while certainly informative, are meant to plant a very specific seed in the heads of prospective customers—that is, firms that would benefit from using Foursquare’s data. For instance, in a post on Medium dated August 4, 2016, Foursquare claimed that it knew precisely how many women (justifiably) avoided Trump properties during the 2016 presidential election.§ Other posts boast previously successful predictions of Apple iPhone 6 sales and the impact of the decision by McDonald’s to sell breakfast all day long.

To use Eric Reis’s now-hackneyed term, if Foursquare successfully pivots, it would be neither the first in history nor the craziest. YouTube began as “Tune In Hook Up,” a dating site redolent of HotorNot.** Instagram used to be Burbn, a location-based gaming and social networking app nearly identical to Foursquare.11 Before finding its footing with photos, Flickr focused on gaming.

Chapter 3 will return to Foursquare in the context of streaming data and application program interfaces. For now, suffice it to say that the current, data-oriented incarnation of Foursquare seems closer than ever to finally capitalizing on its promise.

CHAPTER REVIEW AND DISCUSSION QUESTIONS

Foot traffic has always mattered. What’s different about it today?Why would hedge funds and even individual investors be interested in data related to “digital” foot traffic? Can you think of any other uses for this data?Why has Foursquare struggled to meet its financial goals? What is it trying to do to finally meet them? Do you think that the company will succeed?

NEXT

Do stories, ideas, questions, and issues such as these interest you? Do you wish that you could use data and analytics in this way? If so, then keep reading. You have found the right book.

NOTES

*

Watch an interview with him on

Charlie Rose

at

http://bit.ly/2mTzWKv

.

†

From “Burns Verkaufen der Kraftwerk,” one of my very favorite episodes of

The Simpson

.

*

See

https://enterprise.foursquare.com/insights

.

†

See

http://bit.ly/2oImTsS

.

*

See

https://foursquare.com/about

.

†

See

https://twitter.com/@foursquare

.

‡

See

https://medium.com/@foursquare

.

§

See

http://bit.ly/2lOtPaE

.

**

See

http://mashable.com/2011/02/19/youtube-facts

.

1

“CDC Update: Chipotle-Linked

E. Coli

Outbreak Case Count Now at 52,”

Food Safety News

, December 4, 2015,

http://bit.ly/2na5c8C

.

2

Virginia Chamlee, “Teen Chipotle Worker Wins $7.65M in Sexual Harassment Suit,”

Eater

, September 29, 2016,

http://bit.ly/2mMEstY

.

3

John Maxfield, “Hedge Funds Gobbled Up Chipotle’s Stock Last Quarter,”

The Motley Fool

, February 25, 2017,

http://bit.ly/2nq54h7

.

4

Matthew Lynley, “How Foursquare Hopes to Hit Profitability,”

Tech Crunch

, May 9, 2016,

http://tcrn.ch/2naP2f1

.

5

Alyson Shontell, “Remember, Dennis Crowley Could Have Sold Foursquare for $120 Million,”

Business Insider

, January 11, 2013,

http://read.bi/2mTtgMn

.

6

Ryan Singel, “Facebook Launches ‘Check-In’ Service to Connect People in Real Space,”

Wired

, August 18, 2010,

http://bit.ly/2mvAraS

.

7

Ben Popper and Ellis Hamburger, “Meet Swarm: Foursquare’s Ambitious Plan to Split Its App in Two,”

The Verge

, May 1, 2014,

http://bit.ly/1hh0Dvd

.

8

Mike Isaac, “Foursquare Raises $45 Million, Cutting Its Valuation Nearly in Half,”

New York Times

, January 14, 2016,

http://nyti.ms/2md5S9c

.

9

Ken Yeung, “Foursquare Users Have Checked In over 10 Billion Times,”

VentureBeat

, September 13, 2016,

http://bit.ly/2mP7Wbx

.

10

Jeff Glueck, “Foursquare Predicts Chipotle’s Q1 Sales Down Nearly 30%; Foot Traffic Reveals the Start of a Mixed Recovery,”

Medium

, April 12, 2016,

http://bit .ly/2ngij51

.

11

Megan Garber, “Instagram Was First Called ‘Burbn,’”

The Atlantic

, July 2, 2014,

http://theatln.tc/2ohL9BI

.

IntroductionIt Didn’t Used to Be This Way

The value of an idea lies in the using of it.

—Thomas Edison

So how did Foursquare predict Chipotle’s sales for the first quarter of 2016 with such scary accuracy?

Permit me four answers to this question.

Here’s the really short one: data.

Here’s the second, just-plain-short one: Foursquare collected accurate and real-time data on Chipotle check-ins over time. Equipped with this information, the company’s data scientists built a model. That’s it.

Now, I don’t mean to oversimplify or to diminish Foursquare, its employees, or what it was able to do here. As explained in the preface, Foursquare merely answered a question by using the technology and data available to it with a considerable tip of the hat to:

The hardware of third-party smartphone manufacturers such as Apple, Samsung, and others.

Powerful software such as iOS and Android.

Related tools in the form of software development kits and application program interfaces.

The massive investments of Verizon, AT&T, and others to build their carrier networks.

Government research and infrastructure projects.

*

That is, Foursquare built something very impressive, but not entirely unprecedented—at least in today’s environment—and not without considerable assistance. Jeff Bezos of Amazon has made the same point: Yes, he worked very hard, but his company did not need to build a national transportation system. It merely took advantage of the existing one.†

The third and longer answer is: I’m not exactly sure. Like all but a few people, I can’t tell you precisely how the company worked its magic. For all sorts of valid reasons, Foursquare doesn’t make its code base and user data freely available to the general public.‡ It’s not an open-source project à la Atom, Github’s “hackable text editor.” I don’t know Foursquare’s technical specifications, nor have I studied the ins and outs of its application program interface.§ Finally, if I asked anyone in the know at Foursquare to fill me in, I wouldn’t get very far. Why tell me? Ex-employees in the know have most likely signed nondisclosure agreements, anyway.

The fourth and final answer is concurrently both more ambiguous and more definitive. Without fear of accurate contradiction, I can tell you that Foursquare derived these insights by not following the path of so many of its predecessors.

A LITTLE HISTORY LESSON

Many companies have historically attempted to glean insights very methodically. Large and midsized firms in particular would slowly build and integrate their enterprise systems, data warehouses, data lakes, and data marts. As for small businesses, their owners typically had neither the time nor the expertise to cobble together data from a bunch of disparate sources. Even if they did, most couldn’t afford the six- or even seven-figure price tags of software vendors’ best-of-breed solutions.

For instance, consider a typical project plan for a new business intelligence (BI) application in 2002. It typically involved the steps and approximate time frames listed in Table I.1

Table I.1 Project Plan for Launch of Generic BI Application

Phase

Description

Start Date

End Date

1

Evaluate proposals from software vendors, check references, and perform general due diligence.

2/1/02

5/31/02

2

Select winning bid. Negotiate terms and sign contract.

6/1/02

7/31/02

3

Extract data from legacy systems, clean up errors, and deduplicate records.

8/1/02

8/31/02

4

Implement and customize software, typically with help of expensive consultants.

9/1/02

10/31/02

5

Train users on new application.

11/1/02

2/28/03

6

Load purified data into BI application and address errors.

3/1/03

3/31/03

7

Launch application and squash bugs.

4/1/03

4/30/03

8

Engage vendor in on-site or remote application support.

5/1/03

6/30/03

Source: Phil Simon.

Think about it: More than a year would pass from the project’s formal kickoff until employees actually used the application in a production environment—and that’s if everything went according to plan. As I wrote in Why New Systems Fail, more than half of the time that doesn’t happen. On the contrary, these types of projects routinely exceed their budgets (often by ghastly amounts), take longer than expected, don’t deliver required or expected functionality, or experience some combination of all of these.

There are terms for these types of traditional, rigid, and ultimately unsuccessful information technology (IT) projects: phase-gate or Waterfall. In a nutshell, a new phase or stage cannot begin until the team has completed the prior one. You don’t need to be a certified project manager professional to see the limitations of this approach, outlined in Table I.1

For instance, what if the people gathering the business requirements miss a few key ones? This is a massive problem. In 2014, the Project Management Institute (PMI) released its Pulse of the Profession report. PMI found that “37 percent of all organizations reported inaccurate requirements as the primary reason for project failure.” Less than half of organizations possess the resources to properly manage their requirements. Astonishingly, only one-third of organizations’ leaders consider them to be critical.1

Beyond that, other questions abound:

What if a user fails to disclose an essential data source?

What if a key employee leaves the company?

What if there’s a bug in the software?

What if step 3 (data conversion, cleansing, and deduplicating) takes longer than expected? What if this isn’t nearly as simple as the software vendor and/or consulting firm intimated?

The answer to each of these questions is the same: The project won’t hit its date without some type of concession. Typically, these take the form of increased resources, reduced functionality, or new (read: postponed) deadlines.