Data Science is a Team Sport… Combining Differentiated Data with Powerful AI
Companies that want to maximize their business strategies should look at data science as a team sport. In the latest episode of our Data Dialogues podcast, I interviewed Sri Ambati, founder and CEO of H2O.ai. We discussed the tools his company is creating to help democratize data. Here is a portion of that interview.
Note: This transcript is edited for brevity.
I love the democratization of AI, which we talked about and the tools and the platforms you’re building that support that. And we’re seeing that data science is a team sport now. You’re helping to bring more people into the fold that may not be traditional data scientists, but giving them the tools that enable them to do more. Can you explain what you’re trying to accomplish?
Data is actually at the heart of all machine learning models. So you fuel a lot of innovation in AI with data. Data has gravity as well. And most of the time, our customers’ data is sitting in their data centers or in their cloud. But they need to have alternative datasets to truly spice up their signal in their analysis. And customers love bringing in new data sets that have a different angle on the problem they’re trying to solve.
And the team sport part of data is really important. Adoption of AI is limited by culture, right? Most often our giant cultures are able to resonate deeply to new found insights in their data, and react to them, and use them. They need to be very, very fast learning organizations. And domain expertise shouldn’t be too far away from data science. Neither should the data engineering capabilities be too far away from the science. And the business applicability, which I call the business pain for it, should drive analysis. And I think that ability to go from strategy to data, and data to insight again, and back and forth – I think the ability to go back and monetize your data assets becomes really important. And that’s really a team sport.
The best teams we’ve seen across our customer base are working very closely with their business teams and design teams to get that work that responds to a scientific finding in the data. Then, they apply them to their businesses. This kind of ability to bring those multi-faceted teams together is super important. And I think we found an example of that with both Equifax and our common customers.
Yes, we’re all trying to work towards that common goal of getting more out of our data. And I like to look at it like this, with H2O and tools like that, we’re working on that development front end. Developing the insights that you want. But I also see the phases that happen after development. That seamless deployment to production to execute on that and get the monetization of those insights. And then that third phase is monitoring that. H2O is obviously a capability that allows that to happen and gives you all those amazing insights and way more predictability in a higher performing way.
Machine learning and AI are associated with black box models or large models. And proxy variables can sneak into those models relatively easily. That’s where explaining these models and preventing accidental bias, and attacking these models with ways to test and stabilize your methods are very appropriate techniques. And now of course, we have a great tool chain for it. But a tool chain is as good as the person using it. Companies and organizations are able to build good, safe guardrails in deploying AI at scale and start building a strong core competency in this phase.
A key KPI for organizations will be how fast they learn. How fast they can deploy data pipelines. It’s not just the size of your data in your data centers. It’s how quickly they can create rich monetization for the assets. But also in general, trying to make decision-making cheaper, faster, easier. When we say democratization, we’re really calling for faster, cheaper and easier, so that you can do more experiments and not fear failure. Because failure is no longer an option. It’s a must-have. It’s a feature, not a bug.
For more of this interview, listen to the full recording or check out some of our other episodes.
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