New Billionaire: Dean Stoecker’s 22-Year Journey & The Software That Makes Almost Anyone A Data Savant


Sun Tzu meets software in mid-August at downtown Denver’s Crawford Hotel. The floors are terrazzo. The chandeliers are accented with gold. And Dean Stoecker, the CEO of data-science firm Alteryx, has summoned his executives for the annual strategy session he calls Bing Fa, after the Mandarin title of The Art of War. “Sun Tzu was all about how you conserve resources,” says Stoecker, 62. “How do you win a war without going into battle?”alteryx

Stoecker knows something about conserving resources. He cofounded Alteryx in 1997, when the data-science industry scarcely existed, and spent a decade growing the firm to a measly $10 million in annual revenue. “We had to wait for the market to catch up,” he says. As he waited, he kept the business lean, hiring slowly and forgoing outside investment until 2011. Then, as “big data” began eating the world, he raised $163 million before taking Alteryx public in 2017. The stock is up nearly 900% since, and Stoecker is worth an estimated $1.2 billion.

“People ask me, ‘Did you ever think it would get this big?’” he says. “And I say, ‘Yeah, I just never thought it would take this long.’ ”

Alteryx makes data science easy. Its simple, click-and-drop design lets anyone, from recent grads to emeritus chairmen, turn raw numbers into charts and graphics. It goes far beyond Excel. Plug in some numbers, select the desired operation—say data cleansing or linear regression—and presto.

There are applications in every industry. Coca-Cola uses Alteryx to help restaurants predict how much soda to order. Airlines use it to hedge the price of jet fuel. Banks use it to model derivatives. Data analysis “is the one skill that every human being has to have if they’re going to survive in this next generation,” says Stoecker. “More so than balancing a checkbook.”

Alteryx’s numbers support that forecast. The company, based in Irvine, California, generated $28 million in profit on $254 million in revenue in 2018, and Stoecker expects to hit $1 billion in annual sales by 2022.

Stoecker grew up the son of a tinkerer. His father built liquid nitrogen tanks for NASA before quitting his job to sell “pre-cut” vacation homes in Colorado. He made them himself. “It was literally just him nine months of the year, and he would cut wood for 50 buildings,” Stoecker recalls. As a teenager he joined his father, and by the time he arrived at the University of Colorado Boulder to study economics, he was able to pay his own way.

After graduating in 1979, Stoecker earned his M.B.A. from Pepperdine, then took a sales job in 1990 at Donnelley Marketing Information Services, a data company in Connecticut. There he met Libby Duane Adams, who worked in the firm’s Stamford office. Seven years later, the pair founded a data company of their own, which they cumbersomely named Spatial Re-Engineering Consultants. (A third cofounder, Ned Harding, joined around the same time; Stoecker, who came up with the idea, took the lion’s share of the equity.)

SRC’s first customer, a junk mail company in Orange County, paid $125,000 to better target its coupons. “We were building big-data analytic cloud solutions back in 1998,” says Stoecker, when many businesses were barely online and terms like “cloud computing” were years away.

SRC was profitable from the outset. “We didn’t spend ahead of revenue. We didn’t hire ahead of revenue,” says Adams, sitting in a remodeled 1962 Volkswagen bus at Alteryx headquarters, theoretically a symbol of the company’s journey. “We never calculated burn rates. That was a big topic in the whole dot-com era. We were not running the business like a dot-com.”

In 2006, as part of a pivot away from one-off consulting gigs, SRC released software to let customers do the number-crunching themselves. They named the software Alteryx, a nerdy joke for changing two variables simultaneously: “Alter Y, X.” Stoecker made Alteryx the company name, too, in 2010.

The market was still small. To grow revenue, “we just kept raising the price of our platform,” Stoecker says. In the beginning, Alteryx sold its subscription-based software for $7,500 per user; by 2013 it was charging $55,000. The next year, as Stoecker felt demand growing, he slashed prices to $4,000. Volume made up for the lower rate. Today Alteryx has 5,300 customers. “We immediately went from averaging eight, nine or ten [new clients] a quarter to north of 250,” he says.

Although data mining and data analytics is a long-established field, encompassing a slew of startups as well as giants like Oracle and IBM, “we see almost no direct competition,” Stoecker insists.

“It’s a pretty wide-open field,” says Marshall Senk, a senior research analyst at Compass Point Research & Trading. “The choice is you buy a suite from Alteryx or you go buy 15 different products and try to figure out how to get them to work together.”

Inside Alteryx’s offices, Stoecker pauses in front of a time line depicting his first 22 years in business. “The good stuff hasn’t even occurred yet,” he says. “I’m going to need a way bigger wall.”


I’ve been a reporter at Forbes since 2016. Before that, I spent a year on the road—driving for Uber in Cleveland, volcano climbing in Guatemala, cattle farming in Uruguay, and lots of stuff in between. I graduated from Tufts University with a dual degree in international relations and Arabic. Feel free to reach out at with any story ideas or tips, or follow me on Twitter @Noah_Kirsch.


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What is The Difference Between Data Analysis and Data Science?


Following the current technological transformations within the economy, there has been an emergence of enormous career options, wherein, Data Science is the hottest. According to the Glassdoor, Data Science arose as the highest paid area. On the other hand, there is a significant field which has been gazing attention since years, i.e., Data Analysis. Both the Data Science and Data Analysis is often confused by the individuals. However, the terms are incredibly different in accordance with their job roles and the contribution they do to the businesses. But, are these the only factors which make these two distinct from each other? Well, to know more we need to take a look below:

Data Analysis Data Science:

Data Analysis is referred as the process of accumulating the data and then analyzing it to persuade the decision making for the business. The analysis is undertaken with a business goal and impact the strategies. Whereas, Data Science is a much broader concept where a set of tools and techniques are implied upon to extract the insights from the data. It involves several aspects of mathematics, statistics, scientific methods, etc. to drive the essential analysis of data


The individuals misinterpret Data Analysis with Data Science, but the methodologies for both are diverse. The skill set for the two are distinct as well. The fundamental skills required for Data Analysis are Data Visualisation, HIVE, and PIG, Communication Skills, Mathematics, In-Depth understanding of R and python and Statistics. On the other hand, the Data Science embed the skills like – Machine Learning, Analytical Skills, Database Coding, SAS/R, understanding of Bayesian Networks and Hive


Though the areas – Data Analysis and Data Science, are often confused about being similar, but the methodology is different for both. The methods used in the two are diverse. The essential techniques used in Data Analysis are – Data Mining, Regression, Network Analysis, Simulation, Time Series Analysis, Genetic Algorithms and so on. While, the Data Science involves – Split Testing, categorizing the issues, cluster analysis and so on


Just like the areas are different, so are their goals. The Data analysis is basically about answering the questions generated, for the betterment of the businesses. While the Data Science is concerned with shaping the questions followed by answering The Data science, as illustrated above, is a more profound concept

The era of the Artificial Intelligence and Machine Learning is shaping economy in a much more comprehensive aspect. The organizations are moving towards data-driven decision-making process. The data is becoming imperative in functioning and are not limited to the Information Technology organizations. It is soon taking over the industries like – Sports, Medicine, Hospitality, etc.

Such technological advancements have led to a rise in the job opportunities in the area of Data Science and Analysis. The merely significant facet which needs to be taken into consideration is the understanding of the difference between the two. The Big Data is the future which is expected to lay a considerable impact on the operations of both industries and routine life.



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