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New DataRobot CEO sees bright AI future for the vendor

New CEO Dan Wright discusses how DataRobot can stay competitive in a crowded AI marketplace, new markets for the vendor, and how DataRobot has tackled the pandemic.

Machine learning vendor DataRobot is evolving.

The Boston-based technology company, which specializes in automated machine learning (autoML), has grown quickly since its founding in 2012.

Following a recent major funding round, DataRobot is valued at more than $2.7 billion, and the vendor appears poised to file an IPO soon.

Despite a round of layoffs early last year, DataRobot saw an increase in business in 2020, as enterprises accelerated their digital transformation plans amid the COVID-19 pandemic.

In March, DataRobot appointed Dan Wright CEO. In a Q&A, Wright, who started at DataRobot in 2020 as president and COO after several years at AppDynamics, talks about the future of DataRobot and his plans for the technology company.

You started working at DataRobot really at the beginning of the pandemic. What's that been like?

Dan WrightDan Wright

Dan Wright: I'm not going to lie; it was a little bit of a strange onboarding experience. I joined right before the pandemic hit, and then when it hit, we took a little bit of a pause and just said, 'What does this actually mean for our business? What can we expect? How can we help our customers?'

Fortunately for us, it turned out to be a tailwind. We were able to help customers, who really woke up to the fact that they had, in many cases, hundreds or even thousands of models that they were relying on for process automation and decision-making that were inaccurate. And worse still, they didn't know how inaccurate their models were, because they weren't monitoring and managing their models on an ongoing basis.

With our MLOps solution, we were able to address that and help them move more towards continuous learning, learning where you're constantly updating the models as data is changing. You get alerts if there's any sort of model drift. Anytime the model is going to be inaccurate due to changes in data, you can update the models based on the new data.

Fortunately for us, [the pandemic] turned out to be a tailwind.
Dan WrightCEO, DataRobot

Some interesting use cases include, for example, complex demand forecasting with retailers and manufacturers. With retailers, that's things like predicting how much of a certain product down to the SKU [stock-keeping unit] level you need at a particular store, at a particular time and a particular day. That's very complex to do at scale, and we work with many of the top retailers in the world to do that.

How do you expect the rollout of vaccinations, and the subsequent 'return to normal,' to affect business?

I think this falls in the category of it's like turning the lights on. Once you see the light, you're never going to go back to the dark, right?

The wheels are in motion where people realize it is a big opportunity, but it's also a big threat when it comes to AI if you don't keep pace and adopt it. That's true with any change in technology, but it's especially true with AI, given the feedback loop and the fact that the algorithms are continually improving; they're continually getting better. So, you need to be able to keep pace, which requires moving very, very fast.

I don't see ever going back. I gave the example with MLOps -- once you get a sense of continuous learning, your ability to constantly get accurate predictions and constantly make better and more accurate decision, why would you ever go back?

I think it will continue to accelerate, as people are realizing that there is so much value there.

Just as more enterprises are using AI, more technology vendors are selling AI products. What is DataRobot doing to stay competitive in the AI market?

As you mentioned, it is a crowded market. You've got a few different classes of competitors -- you have some of the legacy vendors that have been doing this for 30 years, but everything was designed for a pre-cloud era and focused on just manual processes with coding with very little transparency, visibility, trust and explainability built into those systems.

Then you have the cloud vendors, and what we're seeing is that we're very complementary to the cloud vendors. Our customers want the ability to optimize their architecture, to have the best security, trust and explainability in the system, but also to be able to move across clouds and through hybrid cloud architectures.

Then there are point solutions. We've been very aggressive in building an end-to-end platform that goes all the way from raw data to value. So, with that, we're able to really set ourselves up for a very long runway.

A few months ago, DataRobot announced it had raised a total of $320 million in a venture funding round. How might DataRobot use that money?

What you can expect from us is that we're going to continue to use that capital to invest aggressively in our growth, investing in R&D and continuing to build out our enterprise AI platform. We are going to continue to invest in sales and marketing and expanding aggressively across the globe and in every vertical.

There's also the partnership aspect of it. We're very excited about our partnerships with Snowflake, Salesforce and HPE. That involves some deeper product integration, for example, with Snowflake. We recently announced an enhanced product integration where customers can do feature engineering in Snowflake natively. I think there is definitely more of that to come.

DataRobot teased an IPO with that last major funding round. What can we expect with that?

I'll say that we certainly expect to go public in time. At the same time, we always talk about creating an iconic company, a company that changes the world over the long term in a positive way. One that really benefits our customers, as well as our employees.

I view the IPO as something that can accelerate that path to the longer-term goal of creating an iconic company. We don't have a definitive timeline.

What are markets is DataRobot targeting right now, and what markets does DataRobot hope to move into?

Over the last 12 months, we have invested more aggressively in AMEA [Asia, Middle East and Africa]. We have built out our presence in, for example, Dubai, Saudi Arabia -- in emerging markets and have brought in some great talent there, as well.

We've invested in [Asia Pacific Countries] and, also, related to that, we've significantly expanded our presence in Australia and New Zealand.

Over the past year, we've seen a big push for tech companies to bring more diversity into their workforce and their products. What is DataRobot doing there?

We're very focused on that. We are doing several things, from first focusing on the people that we do have -- we have development programs that are targeted on helping our talent, including our diverse talent to continue to progress in their careers and realize their dreams.

We're also doing work to look at other diverse sources of talent, and where can we add more diversity. For me, personally, I'm continuing to look at opportunities to add more diversity at every level of the company. It's something that's a priority for me and a priority for the company.

Next Steps

DataRobot integrates AI modeling tools with Snowflake

DataRobot acquires Algorithmia to further MLOps goal

DataRobot AI Cloud gives users multi cloud functionality

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