The first big data contest in the US: the 1890 Census

The first big data contest in the US: the 1890 Census

(Each week, I write a post about OKRs. You can see the series of posts here; if you have a question about OKRs you’d like to see me cover down the road, drop me a line.)

(This essay is a reprint of an essay based on my keynote talk at Techstars FounderCon 2016. I’m reprinting it today to coincide with the release of the podcast episode The History of Surveys, in which I share some of Herman Hollerith’s story with host Liam Geraghty.)

The United States Census of 1880 came at a pivotal time in U.S. history. It was the last time the Census Office could identify anything resembling a U.S. frontier; for the first time, just half of the country worked in agriculture. The census itself had grown so large that the government was collecting more data than it could tabulate — it took a full eight years and thousands of people to produce the twenty-three volumes containing their analysis. At that rate, it was entirely likely that the 1900 Census would start before the 1890 Census results would be completed.

Herman Hollerith. Source: United States Library of Congress‘s Prints and Photographs division. Bell, C. M. (Charles Milton), ca. 1849-1893, photographer. Public Domain.

Enter Herman Hollerith. A teen graduate of the Columbia School of Mines, Hollerith’s first job out of college was in the Census Office in 1880. He’d seen first-hand the challenges they’d faced, experienced the frustration of knowing the information was contained in the voluminous records gathered by the census takers, unable to extract it. After just two years, he left to take a Mechanical Engineering professorship at MIT. He was 22.

Hollerith had been experimenting with punch cards as a storage medium; by 1888 he’d expanded on the idea by creating a corresponding machine to electrically tabulate the stored data. The idea of using punch cards to store information was not new — the Jacquard Loom, invented 80 years prior, used punch cards to store complex designs for textiles.

But the counting of the stored data? That was new. And Hollerith thought it might be an advantage.

In 1888, the Census Office — years late and over-budget with the 1880 census — decided to hold a contest to solve the country’s first “big data” problem. They needed more (and better) information about the growth of the country and they needed the information faster. The contest had two components: 1) data capture and 2) data tabulation.

Three people entered the contest. The third place submission captured the data in 145 hours. Hollerith’s machine captured the data in half the time. The third place submission tabulated the data in just over 55 hours. Hollerith’s machine needed just 5.5 hours, a 10X improvement. Hollerith had his first customer.

Hollerith Machine. Source: Computer History Museum.

Hollerith’s machines were exactly what the Census Office had hoped for: the population count was completed in months, not years. The entire census — including analysis, demographic and economic data — finished in less time, contained 40% more information than the 1880 Census, and saved the U.S. government $5M. Flush with his success in the U.S., Hollerith founded the Tabulating Machine Company, and went on to capture and tabulate data for governments in Russia, Austria, France, Norway, Cuba, Canada, and the Philippines.

There was just one problem: the Hollerith Machine produced data so quickly that many refused to accept the results initially. “Useless machines!” declared the Boston Herald. Local U.S. politicians — who wanted more federal money — refused to accept the lower-than-expected population counts. The New York Herald complained: “Slip shod [sic] work has ruined the Census.”


Far from ruining the Census, the Tabulating Machine Company was re-hired for the 1900 Census, and grew from those origins into other data-intensive industries. Later customers included the railroads, insurance companies, steel manufacturers, and the US Post Office. After a decade of steady (but slow) growth, The Tabulating Machine Company was one of four companies that merged to form a new company that went by the awkward name “Computing Tabulating Recording Company”, popularly referred to by its acronym, CTR. The Tabulating Machine Company was valued at $2.3M ($55M in today’s dollars); one of the other company’s CEOs led the combined entity and Hollerith eventually scaled back.

Three years later, CTR hired a new GM, a star salesman from National Cash Register who’d been fired along with 29 former NCR employees. Less than a year later, that GM became the CEO of CTR.

By 1924, that former NCR salesman shed the clunky acronym and renamed the company.

The salesman’s name? Thomas Watson.

The new name? International Business Machines.

Hollerith’s punch cards — and the methods to analyze the data they contained — ended up in use for nearly a century, and formed the foundation of the entire computing industry.

By any objective measure, Hollerith’s success — culminating in the CTR merger that made Hollerith a millionaire — was enviable. But once you know that his company became the foundation on which IBM was built — you can’t help but wonder if Hollerith’s tale is, in its own way, a cautionary tale. More than just a good idea, it’s not enough to have a good product — you need to have a good company too. It took Thomas Watson’s disciplined leadership over decades at the helm of IBM for CTR to become the IBM we know today.


At GV, I have the privilege of working with hundreds of entrepreneurs who are working hard to turn their own great ideas into great companies. The more I learned about Hollerith’s path, the more I was reminded of a Chris Sacca tweet from earlier this year, in response to Gino Zahnd, founder and CEO at GV portfolio company Cozy. Gino asked Chris how much of his own success was due to luck:

How much of IBM’s success was luck? Would Hollerith have had the Jacquard Loom punch cards in mind when he went to the Census Office if he hadn’t lived with his silk-weaving brother-in-law? What if Hollerith hadn’t taken the job at the Census Office? What if NCR’s CEO had groomed his top salesman to succeed him instead of firing him?


To build a successful startup today is to be lucky. But would it be accidental? Founders struggle to keep their teams focused, to avoid distractions, to scale their teams as they tackle bigger and bigger challenges. It turns out that a framework that’s been in place at Google since it was less than a year old is one way founders today can set their companies up for long-term success: OKRs.

A few years ago, I did a workshop for our portfolio about exactly that: how Google sets goals. The framework, called “Objectives and Key Results”, or OKRs, was brought to Google by Kleiner Perkins Partner John Doerr, who saw OKRs in action while working for Andy Grove at Intel. I’ve now had the chance to see OKRs in use at hundreds of startups, and have seen OKRs help founders chart their course as they grow.

OKRs give you a way to set ambitious goals, get your teams aligned, and hold yourselves accountable. Want to build a great company? Adopting a light-weight process like OKRs introduces discipline to your company, turns your work into data that can be managed, and helps everyone in the company think like a founder. If you’re new to OKRs, here are a few tips to get started:

  • Identify a few ambitious priorities. The fewer, the better. If the team can incrementally improve to achieve the goal, it’s not ambitious enough.Larry Page has been known to talk about getting the team “uncomfortably excited” about where the company is headed; certainty about the team’s ability to achieve the goal is one sign that you’re not thinking big enough. Once the CEO and leadership agree on what the company’s priorities are, you free the rest of the company up to say no to good ideas that are nevertheless not what everyone is focused on. Saying no this quarter doesn’t mean saying no forever — it just gives you an objective way to avoid constant distractions. Make sure each priority has a metric — a number that reflects successfully accomplishing that goal.

“If you want to achieve excellence, you can get there today. As of this second, quit doing less-than-excellent work.” Thomas J. Watson, Sr.

  • Get all teams unified around the company’s priorities, working together. This sounds simple, but it’s staggering how often individual teams work in isolation, completely unaware of cross-team dependencies. Surfacing these dependencies — encouraging open communication about what teams are doing and what they are not doing — is a key benefit of adopting OKRs at companies of all sizes. When you know (ahead of time!) that something you depend on is not a shared goal of another team, you can plan accordingly — either you convince them to change their goals, or you change yours.

“Knowledge creates enthusiasm.” Thomas J. Watson, Sr.

  • Score your progress. Each quarter, review how you did. Look at the metrics associated with each priority at the company level and at the team level. Give each OKR a score. Be honest, and be transparent with the entire company. While it’s valuable for the CEO to stand in front of the company each quarter to highlight the company’s successes, it’s just as rewarding for the team to hear the founder admit where she (or her teams) came up short, without retribution: it’s just data that will make the next quarter’s goals more informed.

“If you want to increase your success rate, double your failure rate.” Thomas J. Watson, Sr.

For teams looking to implement OKRs, here are a few common mistakes to avoid:

  • OKRs are not the same as employee performance reviews. If someone’s bonus is dependent on getting good scores, they’ll set very reasonable, achievable goals. Otherwise, the more ambitious the goals, the smaller their bonus. Align incentives for your employees by ensuring that you reward ambition and impact instead of incremental progress.OKRs can be an input to the performance review, but cannot be the performance review.
  • OKRs will not work if you use them as a team-wide todo list. Crossing items off a list is inevitably a relief — but teams can very quickly mistake completion for progress. How do you know that the thing you just did was the right thing to do? Hold yourself and your team accountable by focusing on the impact you intend to achieve, and then evaluate whether you actually achieved it.
  • OKRs work best when you don’t prescribe how a team will achieve an outcome. If you tell them exactly what to do, you’ll never get more than that. If you instead focus on what outcome you want — and if it’s ambitious enough, the path to get there isn’t at all clear — the team has no choice but to get creative and try non-obvious paths to the desired outcome.

It’s not uncommon to hear would-be entrepreneurs say something along these lines:

Herman Hollerith had a great idea, and even built a decent business as he anticipated the world’s growing need for information processing. Where Hollerith faltered was his belief that all he needed was the good idea. He believed his machines were good enough to sell themselves. He insisted on personally reviewing contracts even as the company grew, and failed to find a business model that could sustain that growth. It took the combination of Hollerith’s invention and Watson’s disciplined leadership to build a truly great company.

As founders, you are faced with numerous challenges that can get in the way. Hire the right people. Find the right customers. Partner with the right investors. Repeat. All will look to you for guidance as you grow. While there’s no simple recipe for consistently addressing the challenges you will face, OKRs give you a light`weight way of approaching those challenges so you spend less time assessing them, and more time solving them.

Companies that implement OKRs are luckier and more successful. In other words, not an accident.

Good luck!


Want to learn more? Here are a few resources I consulted when putting this essay together:

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