A month ago, data technology firm Cloudera finalized a $5.2 billion merger with rival Hortonworks.
The two companies, now operating under the Cloudera name, originally focused on the free open source Hadoop data crunching software, which Yahoo and Facebook used to better manage and store their data. Since then, the two companies have pushed into newer kinds of open-source data processing technologies as well as more buzzy machine-learning.
But despite their roots in a hot space, Cloudera and Hortonworks are still unprofitable. Making money with a business that is based on free open-source software is challenging, as many companies with a similar strategy have found out.
Even with the recent merger, Cloudera and Hortonworks will still compete with rivals including well-funded data technology startups Snowflake and Databricks, along with huge incumbents such as Amazon Web Services and Microsoft, both of which are debuting their own data analytics and machine-learning technology.
In an interview with Fortune, Cloudera CEO Tom Reilly talks about the Hortonworks merger, IBM buying open-source enterprise company Red Hat, and why he prefers discussing machine learning instead of artificial intelligence. The following has been edited for length and clarity:
Fortune: What are some other examples of rival companies merging?
Reilly: Two players in the semiconductor space, Magma and Synopsis, came together [that deal closed in Feb. 2012]. The reason I know that is that early on I said I’ve got to learn from other people’s mistakes. I met the CEO of Magma and asked him what happened—what didn’t do well, what did well.
One of the things I want us to focus on is speed, and speed relative to any other merger in the past. What I’ve learned is delayed decisions and certainty is bad.
Also, don’t keep two of anything. So we already have one leadership team, one product road map, one customer support organization, one sales force, and one engineering organization.
Did you have to do layoffs?
In any merger there are going to be duplicates. To get down to one of everything, there are going to be people that are going to lose their positions.
Fortunately, because we were both high-growth companies, a lot of our synergies were in future hires because we were hiring many of the duplicate things. While we do have layoffs, we’ve had a small number relative to other mergers because we’re such a high-growth company.
How do you create a culture that unifies people who were once rivals?
First off, you read about culture clash when you bring two companies together. I actually have a different view.
It’s not bringing two cultures together, because our cultures turned out to be very similar. It’s about bringing people together who have loyalties—loyalties to their boss and loyalties to the people on their team.
In a merger, you have to break loyalties, and that is the hard thing to do. I had to pick a new leadership team. I love my old leadership team, but I had to put in some new leaders in place. When we step back and break those kind of loyalties and focus on how to build a new business that’s the best of both, it turns out that the cultures are very similar.
Hortonworks used to be known for consulting whereas Cloudera was centered more on selling software. What’s the focus of the new entity?
When you compete, you intentionally create differences. Many times those are artificial differences. So when we brought the businesses together, holy smoke, these businesses look a lot more similar.
So we [Cloudera] license software, and Hortonworks basically licensed support to software. Put aside that little nuance—roughly 82% of their business were revenues from that license support. Eighty-two percent of our business was recurring revenues from licensed software, and we gave support for a lot of software as well.
So it turns out that our businesses are nearly identical. Our customer renewal rates are nearly identical. Our target market customers competed head-to-head. The real way to think of us is in our core overlapping businesses. They would say they’re more open [catering to the open source community], we would say we’re more enterprise grade. And yet they have enterprise-grade customers, and we are open. We have 93% of our code in open source.
So what defines your company going forward as you have open source technology but also a paid software business?
So it is our intent to be a 100% open source company. Now you could say we’re adopting that philosophy from Hortonworks, but we think it’s very important. Nearly all the innovation that’s happening in data management and analytics, from gathering data to doing machine learning is in open source.
Why do you think IBM’s acquisition of Red Hat was a good deal?
For IBM to compete in a cloud world, they could either say, “Well, we’ll build out our own public cloud infrastructure and try to build data centers all over the world,” which would be very difficult. Or they could acquire Red Hat and be that abstracted software layer that enables the multi-cloud [ability to manage both internal data centers and multiple public cloud infrastructures]. I think that is brilliant.
Is $34 billion a fair price?
Well, I think it’s a lot cheaper than trying to build 20 data centers around the world in every time zone, with all of the backup, infrastructure, and cooling.
I think we can all say that IBM wasn’t the front-runner in public cloud computing, right? I think this is a brilliant way for them to basically leverage all the public cloud that’s out there and already built.
Our value proposition is that companies could use those tools, but we want to help enterprises get access to them and bring them into their environment.
So lets say you want to do facial recognition. You could send an image to Google and get some results back. In the same token, if you’re an insurance company and you want to come up with new insurance products, there’s no black-box AI system akin to Google’s AI tools out there to do that.
But you don’t see those cloud AI tools replacing a company’s internal development?
Right. What we want to do is enable our customers to build products or services that distinguish them from their competitors. We are here to teach them how to fish—to give them the tools to make them more efficient. This is contrary to the idea that we’ve built the algorithms for you. Our job is to help them build that A.I. factory—to automate that process that’s building these new insights.
Is A.I. over hyped right now?
So our general manager of machine learning was just saying that in Saudi Arabia the first AI robot has become a citizen or some crazy thing. There’s a bit of a circus atmosphere to it, which I think just confuses the world. We tend to talk about machine learning today, because it’s much more pragmatic. We have use cases—we know how to deliver them and they already deliver business value.
A.I. is more associated with a lot of these theoretical things in the future. We could say, ‘Hey, we’re doing A.I., but pragmatically we’re just doing a lot of really impactful machine learning.”