Best of 2019: Betting on Data Eating the World

[July 23, 2109]

Data is eating the world. All businesses, non-profits, and governments around the world are now in full digital transformation mode, figuring out what data can do to the quality of their decisions and the effectiveness of their actions. In the process, they tap into IT resources and landscape that have changed dramatically over the last decade, offering unprecedented choice, flexibility, and speed, facilitating the management of data eating work.

Launched in 2012, DraftKings is a prime example of a new breed of data-driven, perpetually-learning companies. One of the few players in the market for fantasy sports, it has faced “unique challenges that haven’t been solved by other businesses yet,” says Greg Karamitis, Senior Vice President of Fantasy Sports. To solve these challenges, “we have to lean on our analytical expertise and our ability to absorb and utilize vast amounts of data to drive our business decisions.”

Founded by serial entrepreneur Ash Ashutosh 10 years ago, Actifio is a prime example of the new breed of IT vendors transforming the IT landscape from a processor-centric to data-centric paradigm, from a primary emphasis on the speed of computing to a new focus on the speed of accessing data. “It used to be that only the backup people cared about data,” says Ashutosh. “Then it was the CIO, and later, the Chief Data Officer or CDO. Now, every CEO is a data-driven CEO. If you are not data-driven, most likely you are not the CEO for long.”

Data “as a strategic asset” was the vision driving Ashutosh and Actifio in 2009, bringing to the enterprise the same attitude towards data that has made the fortunes of consumer-oriented, digital native companies such as Amazon. “We wanted to facilitate getting to the data as fast as possible, to make it available to anybody, anywhere,” recalls Ashutosh. Since only backup people cared about enterprise data 10 years ago, they were the customers Actifio initially targeted.

The value proposition for these customers centered on reduced cost, as Actifio helped them maintain only one copy of any piece of data, available for multiple uses, instead of maintaining numerous copies, each one for a specific application or data management activity. Actifio achieved this magic trick (e.g., reducing 50 terabytes of data to only 2 terabytes) by capitalizing on another trend shaking the tech world 10 years ago, virtualization. Replacing analog data with digital data gave rise to data-driven companies and replacing the physical with the logical—virtualization—created a new IT landscape.

It took a while for enterprises to adapt to the new IT realities. But around 2015 the cloud flood gates opened because of the business pressures to do everything faster and faster, especially the development of new (online) applications, and the fact that more and more enterprise roles and activities required at the very least some creation, management, manipulation, analysis, and consumption of data. These changes manifested themselves in Actifio’s business. In its most recent quarter (ended April 30, 2019), “60% of our customers used Actifio to accelerate application development, up from close to 0% in 2016,” reports Ashutosh, “and over 30% of our workloads today are in cloud platforms.”

The new attitude towards data as a strategic asset and the widespread availability of cloud computing have opened up new uses for Actifio’s offerings such as compliance with data regulations and near real-time security checks on the data. But possibly the most important recent development is the increased use by data scientists for machine learning and artificial intelligence-related tasks.

A very significant chunk of data scientists’ time (and their most popular complaint) is the time they spend on data preparation. And a significant chunk of the time spent on data preparation is simply waiting for the data. In the past, they had to wait between eight to forty days for the IT department to deliver the data to them. Now, says Ashutosh, “they have an automated, on-demand process,” providing them data from the relevant pool of applications, in the format they require. Bringing up the new term “MLOps” (as in machine learning operations),  Ashutosh defines it as “allowing people to make decisions faster by not having data as a bottleneck.” The end result? “The more you give people access to data in self-service way, the more they find new and smarter ways of using it.”

As 40% of Actifio’s sales come from $1 million-plus deals, these new uses of data are not “tiny departmental stuff,” says Ashutosh. “Large enterprises are beginning to use data as a strategic asset, as a service, on premises or in multi-cloud environments.”

Large enterprises today learn from, compete with, and often invest in or acquire the likes of DraftKings, a startup that runs on data. Growing the business for DraftKings means making their contests bigger and bigger. “But if we make our contests too big and we don’t get enough users to fill 90% of the seats, we start to lose money really fast,” says DraftKings’ Karamitis. Balancing user satisfaction and engagement with the company’s business performance requires accurate demand predictions for each of the thousands of contests which DraftKings runs daily in a constantly changing sports environment.

“We need to absorb tons of data points to figure this out on a daily basis,” explains Karamitis. But these data points are not only based on DraftKings accumulated experience with past contests. Befitting a business living online, another important data source is social networks—“our users are giving us enormous amount of data in terms of what they are twitting to us, what they engage with and what they don’t, allowing us to understand better which ways we want to shift as a company and which way we want to build a product,” says Karamitis.

There is yet another source of the data that is driving decisions at DrafKings, possibly the most important one: The data DraftKings creates by constantly experimenting. “We create data points by running structured tests,” says Karamitis. They cannot run A/B tests, he explains, because running smaller size contests will not produce the same effect of larger size contests and because their users tend to communicate a lot among themselves and compare notes about their experiences. “We are willing to take the risk testing our underlying beliefs around user behavior,” says Karamitis, by changing the top prize or changing the marketing treatment, for example.

In the past, domain expertise was the key to a company’s success. Today, it is data expertise, and the skill of applying it instantly to new opportunities as they arise. “Our analytical expertise allows us to learn really fast, learn in the traditional meaning of learning from experience and figuring out what matters to our users and also learn in the more modern sense of building machine learning algorithms around the key principles our users care about,” says Karamitis. Learning from data has become a core competency that can be applied to new markets, a competency that should serve DraftKings well as it pursues the new business opportunity of legalized sports betting.

DraftKings—and the new breed of data-driven companies—are data science labs, creating data and acquiring new insights with continuous experimentation. Like good scientists, they test their hypotheses through carefully structured experiments, challenging their own assumptions about customers and markets. Karamitis recalls the start of the 2017 NFL season when DraftKings offered a new contest site: ”We had a very specific expectation as to who it’s going to appeal to and how big it will be. We were totally wrong, 100% wrong.” But because the new product was developed through experimentation, the data led to what is now a “super valuable product, so different from what we initially offered.”

Ashutosh predicts that over the next few years we will see a bifurcation of the economy into two segments, one focused on producing physical assets and the other comprised of data-driven companies. Like DraftKings, these companies, whether startups or established enterprises, will view data as a strategic asset and its analysis as a core competency and a key competitive differentiator.

And like DraftKings, these companies will increasingly resemble scientific labs, continuously learning through experimentation and creating new data points. Data growth will drive business growth as data continues eating the world.

Originally published on Forbes.com