New SVP of Product Ecosystems and Partners at Databricks Stephen Orban said that he admires how technical the company is and the vision it has for democratizing data and artificial intelligence for all.
“Generative AI over the last couple of years is not just a CIO-level concern, that’s a CEO- and boardroom-level concern,” Orban told IT Brew. “Every company I’ve talked to in the last two years is trying to figure out what AI means for their business, and many of them are turning to Databricks to help them break down those silos and use the analytics and AI on top of data that’s unified across their teams.”
That’s part of the reason he joined the early-stage data intelligence platform after time at Google Cloud and Amazon Web Services. During his time at AWS, Orban launched AWS Data Exchange, which offers data from hundreds of providers.
He sat down with IT Brew to share why he made the decision to join Databricks, and what he’d like to do now.
The conversation has been edited for length and clarity.
You worked at AWS and Google Cloud before coming to Databricks. What made you decide to join the team?
I’m in admiration of both how technical Databricks is and how deeply technical all the way up to the top—the founders are still very much here running the company and I admire the vision that they have to democratize data and AI for all.
It’s very technical, and that was very exciting to me, just to get to something a little bit more early. AWS and Google are also very technical, but I wouldn’t say that they're in the early stage anymore. They’re both very mature businesses.
So, that was one, and then just the high growth. Customers seem to love Databricks and [its a] high-growth environment.
Lastly, I’m a big believer in writing down and living through cultural principles. As I got to learn from and work with Databricks a lot, and I got to learn about the culture principles here, they really resonated with me. The combination of customer obsession, bias for action, first principles, thinking all of that is really core to how I operate. The confluence of all those things are really exciting for me.
Something on a lot of people’s minds in both the public and private sector is the importance of breaking down data silos. Can we talk about how, in your position, you want to prioritize that piece of things? How it could potentially make for a streamlined process for IT professionals?
Step one is to get the data in. So, connect, whether it be with a partner ecosystem or with various capabilities that we have, and then we have partnerships with all those SaaS companies that we have to connect to, like a Salesforce to help get that data in. So, that’s one way we’re working to break down the data silos.
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The second is, how does that get governed? Once your data is all in one place and you want to start to give all your employees and your business analysts different capabilities that they can query across it, they still want to have permissions and governance on top of all that data. It’s not just because we have all of our data in one place. They want permissions and governance across that so you only get to see the things that you’re entitled to see.
Another way to break down those silos is all the business intelligence capabilities we’re offering inside our platform. Right now, we have notebooks that data scientists can use natively in Databricks to build their reports and analysis. But then again, we also have a lot of partnerships, and we have some business intelligence capabilities ourselves.
My aspiration is to make sure that we have any data set from any third party that a customer would want when they’re using Databricks.
I know you’re planning on increasing access to more third-party datasets at Databricks, and we know that cybersecurity is usually the utmost priority. How does cybersecurity fit into this goal, and what would it take to accomplish this?
Cybersecurity is a super interesting space. I’m actually pretty interested in going a lot deeper and seeing if there are ways for us to work with some of the cybersecurity vendors who have lots of threat intel or other valuable data that a lot of our customers might be interested in. I intend to start having a bunch of conversations with them around what we could be doing together to have the right datasets on Databricks to help our customers also manage their cybersecurity footprint. I think that’s a pretty big opportunity for us to look into.
I’m still learning the exact lift for our data prior to get material data sets up, however, I will say that many of them are using Databricks already and their data is already in the platform enabling Delta Share on that data, and they get to decide what data, how much of it, when, how often as part of setting up their Delta Share. They’re in full control of who they want to share, how much [of it], and when. We basically give the provider all the controls to be able to manage that on their own. Their customers can then start to use that data wherever they see fit when they go through Delta Sharing.