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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.

      Chapter 1

      Mad 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.”8 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

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