Winning with Data E-Book

Tomasz Tunguz

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 263

Veröffentlichungsjahr: 2016

Beschreibung

Crest the data wave with a deep cultural shift 

Winning with Data  explores the cultural changes big data brings to business, and shows you how to adapt your organization to leverage data to maximum effect. Authors Tomasz Tunguz and Frank Bien draw on extensive background in big data, business intelligence, and business strategy to provide a blueprint for companies looking to move head-on into the data wave. Instrumentation is discussed in detail, but the core of the change is in the culture—this book provides sound guidance on building the type of organizational culture that creates and leverages data daily, in every aspect of the business. Real-world examples illustrate these important concepts at work: you'll learn how data helped Warby-Parker disrupt a $13 billion monopolized market, how ThredUp uses data to process more than 20 thousand items of clothing every day, how Venmo leverages data to build better products, how HubSpot empowers their salespeople to be more productive, and more. From decision making and strategy to shipping and sales, this book shows you how data makes better business.

Big data has taken on buzzword status, but there is little real guidance for companies seeking everyday business data solutions. This book takes a deeper look at big data in business, and shows you how to shift internal culture ahead of the curve.

• Understand the changes a data culture brings to companies
• Instrument your company for maximum benefit
• Utilize data to optimize every aspect of your business
• Improve decision making and transform business strategy

Big data is becoming the number-one topic in business, yet no one is asking the right questions. Leveraging the full power of data requires more than good IT—organization-wide buy-in is essential for long-term success. Winning with Data is the expert guide to making data work for your business, and your needs.

Leseprobe

Table of Contents

Title Page

Copyright

Introduction

Chapter 1: Mad Men to Math Men: The Power of the Data-Driven Culture

Operationalizing Data: Uber's Competitive Weapon

The Era of Instant Data: You Better Get Yourself Together

Data Supply Chains: Buckling Under the Load

Management by Opinion: The Illusion of Knowledge

Our Vantage Points

Chapter 2: Four Problems with Data Today: Breadlines, Obscurity, Fragmentation, and Brawls

Data Breadlines for the Data-Poor

Data Obscurity: The Failure of the Card Catalog

Rogue Databases and Analysts: The Data Fragmentation Problem

Data Brawls: When Miscommunication Devolves into Arguments

Chapter 3: Business Intelligence: How We Got Here

Business Intelligence Is Born: The First Query

Databases for the Masses: Oracle Commercializes Codd's Invention

Legacy BI: A Three-Layer Cake

Google's Answer to Huge Data: Vanilla Boxes

600 Petabytes per Day: HiPal at Facebook

Extreme Data Collection: The New Normal

Looker: Weaving the Data Fabric

Chapter 4: Achieving Data Enlightenment: Gathering Data in the Morning and Changing Your Business's Operations in the Afternoon

Not Just Another Person with an Opinion

Aligning Sales Teams in Real Time

Scaling Sales Teams with Data

Determining Customer Satisfaction at Every Point in the Buyer Journey

The Rosetta Stone: Developing a Shared Data Language

The One Equation That Defines the Business

Brutal Intellectual Honesty: Speaking Data to Power

Putting Pride in Its Place: How Data Transforms Cultures

Chapter 5: Five Steps to Creating a Data-Driven Company—From Recruiting to Regression, It All Starts with Curiosity: Changing the Culture

It All Starts with Curiosity

Why You Should Stop Listening to Your Boss

How to Recruit Curious People

Chapter 6: From Hacks to Harmony: The Typical Progression of Data-Driven Companies

Step 1: Ask Your Friend, the Engineer

Step 2: Bastardize an Existing Solution

Step 3: Access Raw Data

The Crux of the Problem

Bring Your Own BI: The Five Letters That Will Change the Data World

The Power of a Unified Data-Modeling Layer

The Final Step: A Data Fabric

Chapter 7: Data Literacy and Empowerment: The Core Responsibilities of the Data Team

The Illusion of Validity: How to Avoid Data Biases

Correlation versus Causation

How Facebook and Zendesk Engender Data Literacy

Walking the Data Gemba: Training by Walking Around

Chapter 8: Deeper Analyses: Asking the Right Questions

When Data Confounds Our Intuition: How to Handle Ambiguity

Data Is Useless Unless You Can Act On It

Defining New Opportunities by Creating New Metrics That Matter

The Fastest Growing Media Site of All Time

How to Run a Data-Backed Experiment: Step by Step

Chapter 9: Changing the Way We Operate

Change Begins with a Story

Deliver Data with Panache: Structuring Presentations to Inspire

Chapter 10: Putting It All Together

Acknowledgments

Appendix Revenue Metrics

Business Revenue Metrics

Engagement Metrics

Distribution Metrics

Index

End User License Agreement

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Guide

Coverf

Table of Contents

Begin Reading

List of Illustrations

Chapter 4: Achieving Data Enlightenment: Gathering Data in the Morning and Changing Your Business's Operations in the Afternoon

Figure 4.1 Looker's Sales Productivity Report

Chapter 5: Five Steps to Creating a Data-Driven Company—From Recruiting to Regression, It All Starts with Curiosity: Changing the Culture

Figure 5.1 The Performance/Culture 2 × 2 Matrix

Chapter 6: From Hacks to Harmony: The Typical Progression of Data-Driven Companies

Figure 6.1 The Traditional On-Premise Business Intelligence Stack

Figure 6.2 The Bring Your Own Business Intelligence (BYOBI) Stack

Chapter 8: Deeper Analyses: Asking the Right Questions

Figure 8.1 Gartner's Data Sophistication Journey

Figure 8.2 Formula for Sample Size Required to Achieve Statistical Significance

Figure 8.3 Z-Score Table

Figure 8.4 Sample Size Calculation for 80% Confidence

Chapter 9: Changing the Way We Operate

Figure 9.1 Overall Scoring Production (Total goals and assists versus games played since 2010 World Cup)

Figure 9.2 Sample Fund Return Strategies for a $50M and $500M Venture Fund

Appendix Revenue Metrics

Figure A.1 Account Value by Cohort at 5 Percent Churn

Figure A.2 Account Value by Cohort at 5 Percent Negative Churn

List of Tables

WINNINGWITHDATA

TRANSFORM YOUR CULTURE,EMPOWER YOUR PEOPLE,ANDSHAPE THE FUTURE

TOMASZ TUNGUZ AND FRANK BIEN

This book is printed on acid-free paper.

Copyright © 2016 by Tomasz Tunguz and Frank Bien. All rights reserved

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

Published simultaneously in Canada

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Introduction

Silicon Valley owes its existence to a Frenchman living in Boston. Born in France in 1899, Georges Doriot graduated from the University of Paris in 1920 and matriculated at the Harvard Business School in 1921. Four years after graduation, he became the assistant dean and associate professor of industrial management at Harvard.1 Five years later, he would be promoted to full professor, in large part due to his beloved manufacturing course that graduated more than 7,000 students during his tenure through 1966. The year-long course tested the general management skills of second-year MBA students, and the final reports of students often exceeded 600 pages.2 In Creative Capital, Doriot biographer Spencer E. Ante summarized his interviews of former Doriot students:

“His lectures were so memorable and controversial—he once lectured students on how to pick a wife—that many former students who have forgotten most of what they learned at business school still remember Doriot vividly.”3

A sinewy 5 feet 10 inches tall, with incisive blue eyes, a thin mustache, and a penchant for fine tobacco to stuff his iconic pipe, Doriot was highly decorated by the U.S. military. In 1940, he became a U.S. citizen to assume a military post created for him by a former student, Major General Edmund Gregory. Appointed lieutenant colonel and chief of the Military Planning Division, Doriot managed all the procurement for the U.S. Army, from trucks to uniforms to rations.

In the jungles of Southeast Asia, indigenous forces easily tracked American infantryman by their footprints. Unlike the barefooted natives, Americans left boot outlines as they marched through mud. So, Doriot contracted an anthropologist to develop molds of the feet of the locals and manufactured boots with these imprints on the soles. “If you ran down a muddy road you'd swear that was not an American, it was a native,” remembered Lieutenant Colonel William H. McLean.4

In addition to these tactical advances, Doriot and his team resolved large-scale logistical problems that supplied the Allied Forces with the ammunition, nourishment, and equipment to fuel their success. Doriot was ultimately promoted to brigadier general, received the Distinguished Service Medal (the highest U.S. military metal given to a noncombatant), rose to the rank of commander of the British Empire, and was awarded the French Legion of Honor.

After the war concluded, Doriot continued to change the world. In 1959, he and three of his students from Harvard Business School founded INSEAD (Institut European d'Administration des Affairs), the preeminent business school outside the United States.

In addition, he is widely regarded as the father of venture capital. His firm, American Research and Development (ARD), led the first institutional venture capital investment of $70,000 in Digital Equipment Corporation (DEC), maker of minicomputers, in 1957. Eleven years later, DEC went public and netted more than $355 million to ARD, for a 5,000-times return and an internal rate of return (IRR) of more than 100 percent annually. Among other notable investments, Georges Doriot financed the first company of future 41st U.S. president George H. W. Bush.5

American Research and Development's success launched the venture capital industry. A cottage industry through the late 1990s, venture capital exploded in size and impact during the dot-com era.

In the 1980s, venture capital firms in total raised roughly $10 billion per year. During the height of the dot-com era, that figure catapulted to more than $100 billion adjusted for inflation. Since then, in the course of a typical year, venture capitalists raise more than $25 billion to invest into technology, biotechnology, and other kinds of startups.

And the innovation fueled by this capital has transformed the world. FedEx, Google, Intel, Apple, Tesla, Genentech, Bed Bath and Beyond, Whole Foods, Starbucks, Uber, AirBnB: Is there an industry venture-backed startups have not yet disrupted? According to a recent study completed by Stanford researchers Ilya Strebulaev and Will Gornall, 43 percent of U.S. publicly traded companies founded after 1974 have been venture backed, accounting for 63 percent of the total U.S. stock exchange market capitalization. Further, 38 percent of American workers are employed by venture-backed businesses, including 82 percent of research and development employees.6

But, to hear my senior partners tell the story of the heyday of venture capital in the 1990s is to envision a completely different industry than the one we operate in today. One old-time venture capitalist recounted the ways of the bygone days: The 10 or so key members of various firms would eat lunch together on a weekly basis. Like trading baseball cards, they would swap information on the companies they'd seen and decide to invest with each other or not. The capital requirements of these startups outstripped these early funds, so they partnered to ensure the business would have enough runway to achieve success.

Of course, these syndicates competed. But even then, it was friendly. Whoever won the right to lead the series A, the first institutional round, would invite the firm that lost the opportunity to invest in the next one. However, this quid pro quo environment evaporated when the sums of money flooding the industry treated stiffer and stiffer competition from new and existing venture capital firms.

The secular increase in competition has continued over the last 20 years as the scale of technology companies has skyrocketed. Google is now worth nearly $500 billion. Facebook is worth $250 billion. And we venture capitalists chase the next one. The competition drives firms and partners within those firms to develop competitive advantages, and in our business that means information asymmetries, and that means data and relationships. The firm that finds the next breakout company first will often win the right to invest in that business.

There are many different means for venture capital firms to establish that information asymmetry. Some of them develop unique relationships with key angel investors, individuals who invest in very early-stage companies, with just two founders and a dream. Other firms rely on strong relationships with universities and professors who refer standout students to investors. Yet others specialize, focusing on financial services technologies or consumer subscription businesses. At Redpoint, we have tried to develop an information asymmetry using data. That initiative started almost a decade ago.

I started at Redpoint, a venture capital firm headquartered on storied Sand Hill Road in Menlo Park, in 2008. During my first week, I remember receiving a thick envelope in the mail from the National Venture Capital Association (NVCA). The envelope contained the NVCA's directory, a thick tome listing all the different venture capitalists across the country. They numbered more than 5,000. Looking out of my office over the Santa Cruz Mountains, I despaired; how would I ever differentiate myself in such a competitive industry? “What would Doriot do?,” I wondered.

I was very fortunate to work closely with three of the six Redpoint founders, Geoff Yang, Tim Haley, and Jeff Brody, three preeminent venture capitalists who financed billion-dollar businesses like Netflix, Juniper Networks, and HomeAway from their earliest days, and advised those businesses as they transformed huge industries. Over the next few years, they mentored me extensively, and boy did I need it.

As I started to attend board meetings with these senior partners, I began to realize how little I actually knew about startup management. Sure, I could help them with their Google advertising strategies. But founders would ask questions like “How much should I pay a VP of sales?” or “What is a reasonable cost per click on Google?” or “How fast will the business have to grow to be able to raise the next round of capital?” I was at a complete loss to answer these questions. I hoped no one in the room noted my silence.

But I knew, from my days at Google, this data must exist somewhere. So, each time a founder asked me a question about his business, be it revenue per employee benchmarks or marketing efficiencies compared to publicly traded companies, I searched for data.

Once, I found a data set containing startup IPO data dating back to the very earliest days of venture capital that Jay Ritter, a professor at the University of Florida, collected. Startups were surprisingly willing to share their internal data in surveys—anonymously, of course. So, I surveyed them. Friends working at investment banks showed me how to access the data reported by publicly traded companies.

Armed with those data sets and others, I began to answer the questions posed by founders, using the basic statistics ideas I studied in college. The data proved useful to a few of the CEOs I knew, and they asked me if they could share the data. Of course, I agreed. And one of them in particular suggested publishing the results on a blog.

I bought the tomtunguz.com domain, selected a simple blogging layout, and began to write. I jumped when 15 people read my first post. Fifteen daily readers grew to 100. One sunny summer day, I watched as my Google Analytics account reported 1,000 people had visited tomtunguz.com. In disbelief, I called my wife. All those hours spent on nights and weekends writing were finally showing some promise. That night we celebrated with some champagne.

Over the spumante, my wife asked which topics garnered the most interest. I didn't know the answer. So, I began to study the factors that attracted readers: title length, the number of subheadings, the presence of images, voice and tone, time of day to publish, and many others. I learned quite a bit.

I have 48 seconds with a reader. No pretty images, no witty title, no amount of social media validation from influencers will entice the reader to linger. Tweets sent at 8:54 to 8:59 a.m. Pacific Time generate 25 percent more views than those sent a few minutes after 9 a.m. But e-mail subscribers prefer to read content around 10 a.m., a nice midmorning break. Would e-mail readers like to read posts after lunch?, I wondered. A two-week experiment showed they most certainly did not! Open rates fell in half.

As I had done before, I published most of my findings and readers contributed experimental ideas. Over time, this iterative effort grew readership to more than 100,000 readers per month and more than 200,000 social media followers.

But what did all this content marketing ultimately create for Redpoint? A bit of a brand boost, perhaps. Could I justify investing five hours each week to this effort, especially in an industry where the most sought-after startups can raise capital in just a day or two?

At about the same time, I read Aaron Ross's book Predictable Revenue, which describes Salesforce's processes and tools for growing from zero to more than $6 billion in revenue. The former director of corporate sales, Aaron described Salesforce's process of finding potential customers, educating them through sales efforts, and cajoling them through the sales funnel into a satisfied, paying customer. The heart of this software process was, naturally, Salesforce's software, which catalogued the journey of all the potential buyers.

Predictable Revenue inspired me to create a sales funnel from my blog. Read by many startup founders, the blog generated leads—startups in which Redpoint might want to invest. If I could consistently and quickly identify those readers, I might be able to grow Redpoint's network of great entrepreneurs and pinpoint the next great business idea. I decided to call it Scour.

Here's how the system works. I write a blog post. That post is distributed on the web page and through e-mail, social media channels, and some other websites. This content marketing engages a broad network of people. Some of those readers elect to fortify their relationship with the content by electing to receive blog posts by e-mail.

Scour captures their e-mail address in a database. Using that e-mail address, Scour determines who the reader is by looking across the Internet: Where do they work, do they belong to a startup that could be a good fit for Redpoint, whom do we know in common, are they influential in a particular sphere like open-source software or consumer product design? This research process concludes by prioritizing a list of people to meet for us to build our network and find new startups.

Unlike the late 1990s, when the startup ecosystem encompassed perhaps 1,000 founders, today more than 4,000 technology businesses are financed each year. And, again in contrast to the previous era, today those 4,000 businesses leave digital footprints all over the Internet.

Two young computer science students might launch an experimental mobile application for iPhones. The app's success is recorded by Apple. The data is freely available for anyone to download and analyze.

As founders recruit a team, they open requisitions on job boards all over the Internet. One of the founders might decide to blog in order to build an audience of like-minded people who might eventually work for the business and also generate early demand for the product they are building. Twitter accounts, LinkedIn profiles, Facebook interactions, comments in public forums, job listings—with enough data, we have found it possible to identify very early stage startups with promise consistently.

Consequently, we have built data infrastructure to aggregate all these signals scattered across the Internet. We store them in a cloud database and continue to grow the size of that database in the hope that all this data will eventually help us find the next great business before anyone else. With this repository of information, we can experiment and explore investment hypotheses.

Some firms like First Round Capital publish their results on these kinds of trends.7 For example, in their 10-year analysis of their investments, they found female founders outperformed their male peers by 63 percent in terms of returns generated. And founding teams with an average age less than 25 at the time of investment generate 30 percent more returns to the firm than other demographics. But the average age of all founders within the portfolio is 35. Understanding these data points is key to debunking some of the biases that lurk within the Monday partner meetings.

With this kind of data, investors can consistently make better decisions and generate more compelling returns. Again, an information asymmetry manifested in better decision making.

From its modest beginnings with American Research and Development, the venture capital industry has grown in size and sophistication. From marketing to deal sourcing and selection, data has infused every key process of a venture capital firm. And it was that data that led the Redpoint team to Looker.

In 2012, I met Frank Bien and Lloyd Tabb, the CEO and CTO of a Santa Cruz startup, Looker. Jamie Davidson, a friend and colleague from Google, and now a partner at Redpoint, had been using Looker technology at his startup HotelTonight. Another Redpoint portfolio company, Thredup, had been using Looker to manage the operations of more than 100 employees. And they raved about it.

When Lloyd demoed Looker's technology, I fell out of my chair. I knew he had built something unique, a product that would solve the data access problem that plagued nearly every business.

The race to win the opportunity to invest in Looker was on. Over the next week, we gathered as much information on the company as possible. We called existing customers, prospective customers, former coworkers, and industry experts. They all concurred: “Looker is special.”

July 8, 2013, was a Monday, a partner meeting Monday. I remember sending Frank and Lloyd access to our database a few hours before the 1:30 p.m. pitch. The database contained all the information we had aggregated on mobile startups. Lloyd told me later he modeled the data in the car, typing in the copilot seat, while Frank negotiated the conifer-curbed curves of Highway 17 from Santa Cruz to Menlo Park.

During the pitch, Lloyd showed us our data in a completely new way—the way a modern startup explores data, the way businesses create lasting information asymmetries data, the way companies win with data.

That was the beginning of our partnership.

1

 McQuiston, J. T., “Molder of U.S. Businessmen.”

New York Times

, June 3, 1987. Retrieved from

www.nytimes.com/1987/06/03/obituaries/george-f-doriot-dies-at-87-molder-of-us-businessmen.html

.

2

 Christina Pazzanese, “The Talented Georges Doriot,”

Harvard Gazette

, February 24, 2015. Retrieved from

http://news.harvard.edu/gazette/story/2015/02/the-talented-georges-doriot/

.

3

 S. E. Ante,

Creative Capital: Georges Doriot and the Birth of Venture Capital

(Boston, MA: Harvard Business Press, 2008), 3.

4

 S. E. Ante,

Creative Capital: Georges Doriot and the Birth of Venture Capital

(Boston, MA: Harvard Business Press, 2008), 88.

5

 S. Karabell, “INSEAD at 50: The Defining Years,” October 21, 2009. Retrieved from

http://knowledge.insead.edu/entrepreneurship-innovation/insead-at-50-the-defining-years-1356

.

6

 Will Gornall and Ilya A. Strebulaev, “The Economic Impact of Venture Capital: Evidence from Public Companies,” November 1, 2015, Stanford University Graduate School of Business Research Paper No. 15-55.

7

 “First Round 10 Year Project,” January 2016. Retrieved from

http://10years.firstround.com/

.

Chapter 1Mad Men to Math Men:The Power of the Data-Driven Culture

If we have data, let's look at data. If all we have are opinions, let's go with mine.

—Jim Barksdale, CEO of Netscape

As the television series Mad Men depicted, the Madison Avenue executives of the 1960s swirled scotch and smoked cigars from their Eames chairs, stoking their creative powers and developing the memorable advertising campaigns of the era. But very little of that reality remains today.

Modern marketing bears more resemblance to high-frequency stock trading than to Mad Men. Marketers sit in front of computers to buy and sell impressions on online advertising exchanges in a matter of milliseconds. Outputs of algorithms determine, in real time, precisely on which web page or mobile app to place an ad, precisely which variation of the ad to serve based on what the software knows about the user, and precisely how much to pay for it based on the probability the viewer will convert to a paid customer.

The paradigm shift from Mad Men to Math Men hasn't happened exclusively on Madison Avenue. This new era of marketing heralds analogous transformations in sales, human resources, and product management. No matter the role, no matter the sector, data is transforming it.

Modern sales teams employ predictive scoring technologies that crawl the web to aggregate data about potential customers and calculate the likelihood a customer will close. Each morning, sales account executives log into their customer relationship management software to a list of leads prioritized by likelihood to close. These are the new leads. The Glengarry leads.

Recruiters use data to identify the best candidates to pursue based on online profiles, blogs, social media accounts, and open-source software contributions. Product managers record the actions of users by the millisecond to understand exactly which customer journeys optimize revenue and where in the product customers exhibit confusion or drop off. Data courses through these teams by the gigabyte and supplies the essential foundation for decision making throughout the organization.

As novelist William Gibson said, “The future is already here—it's just not very evenly distributed.”1 A small number of companies have restructured themselves, their hiring practices, their internal processes, their data systems, and their cultures to seize the opportunity provided by data. And they are winning because of it. They exemplify the future. Inevitably, these techniques will diffuse through industry until everyone remaining employs them.

With this book, we'll illuminate how forward-thinking businesses already operate in the future, and outline how we have seen others evolve their businesses, their technology, and their cultures to win with data.

Operationalizing Data: Uber's Competitive Weapon

Who among us does not say that data is the lifeblood of their company? The largest hoteling company [AirBnB] owns no hotel rooms. The largest taxi company [Uber] owns no taxis.

—Ash Ashutosh, CEO of Actifio

At their core, the best data-driven companies operationalize data. Instead of regarding data as a retrospective report card of a team's performance, data informs the actions of each employee every morning and every evening. From harnessing customer survey responses to evaluating loan applications, these Math Men and Women are transforming every industry and every function.

As Ash Ashutosh said, the biggest transportation and lodging companies own no infrastructure. Instead, they manage data better than anyone else. Just four years after Uber was founded, its San Francisco revenues totaled more than three times all the revenues of all the taxi cab companies in the city. Two years later, the Yellow Cab Cooperative, which has operated the largest fleet of taxis in San Francisco for decades, filed for bankruptcy.

Among many innovations, Uber brought data to the taxi industry. Using historical data, Uber advises drivers to be in certain hotspots during certain times of day to maximize their revenue because customers tell them with the push of a button where to be. Uber matches the closest driver with the customer to minimize wait time and maximize driver utilization and earnings.

In contrast, disconnected Yellow Cab drivers listen to a coffee-fueled, fast-talking dispatcher relaying telephone call requests by radio. Individual drivers claim passenger pickups by responding over the CB, even if they are the furthest cab from the customer. “How long until the taxi arrives?”

Dispatchers can handle only one request at a time, serially. In rush hour, potential passengers redial after hearing a busy tone. Let too much time elapse coming from the other side of town and your passenger has already jumped into an Uber. For the Yellow Cab driver, the gas, time, and effort are all wasted because of an information asymmetry. In comparison to Uber, Yellow Cab drivers are driving blind to the demand of the city, and Yellow Cab customers are blind to the supply of taxi cabs.

Uber changes its pricing as a function of demand, telling drivers when it makes sense to start and stop working. Surge pricing, though controversial, establishes a true market for taxi services. Yellow Cab drivers don't know the best hours to work and prices are fixed regardless of demand.

Data improves more than the marketplace efficiency. Uber employs drivers based on their customer satisfaction data provided by consumers. Drivers who score below a 4.4 on a 5.0 scale risk “deactivation”—inability to access Uber's passenger base. Meanwhile, the Yellow Cab company maintains an average Yelp review of less than 1.5 stars out of 5.

The data teams that optimize Uber driver locations, maximize revenue for drivers, and drive customer satisfaction operate on a different plane from the management of the Yellow Cab company. Blind, Yellow Cab drivers are completely outgunned in the competitive transportation market. They don't have what it takes to compete: data.