Jun 4, 2025 · 1 hr 2 min · 13 segments
Bob McCowan, CIO at Regeneron, joins host Maryfran Johnson for this CIO Leadership Live interview. They discuss fostering high-performance tech teams, the benefits of a science-driven company culture…
Bob McCowanGuestMary Fran JohnsonHostWell, a few months ago, I think it was right at your 10th anniversary point, you posted a very lovely note on LinkedIn where you were basically thanking your team for being part of the amazing evolution and the hard work they've all done.
It's very nice reading, and I would recommend that people go and check out your profile to take a look at it.
It's an unusual thing for CIOs to do, to kind of organize their thoughts that way and say, and here's 10 wonderful things that I've been grateful for.
But especially the one that caught my eye was number nine, which was about elevating the use of data.
For example, we focused on capturing and connecting research data at individual experiment levels.
And this of course included the Regeneron data platform, data literacy training, and implementing the data catalog.
Now I know that all those four out of the last five years that you have come home with a CIO 100 Innovation Award, it has all been for various data plans and programs and stuff.
And if you ask some of our scientists, what's the biggest challenge? Often it's them trying to find the right information necessary to pick anything from researching an area of interest that they want to get up to speed on or find data of some of the experimentation or results they got from prior work from 5, 10, 15 years past.
We use these models to try and figure out how do you go and identify that, codify it in such a way that you can search for it.
Face value, it sounds straightforward because you hear about these large language models being designed for that.
And not only that, the data has to be, in some cases, structured in a way that they can be searched to make sure that you get the right results.
And so we put a lot of effort into working with our colleagues to understand what it was they were trying to achieve.
What was the internal information they wanted to get? What were the external sources they wanted to get? How did they identify where it came from so that they could actually reference it and understand and get credit where credit was due for where that information came from?
But solving it for this group is really a stepping stone to solving it for many other groups across the organization.
Well, a few months ago, I think it was right at your 10th anniversary point, you posted a very lovely note on LinkedIn where you were basically thanking your team for being part of the amazing evolution and the hard work they've all done.
It's very nice reading, and I would recommend that people go and check out your profile to take a look at it.
It's an unusual thing for CIOs to do, to kind of organize their thoughts that way and say, and here's 10 wonderful things that I've been grateful for.
But especially the one that caught my eye was number nine, which was about elevating the use of data.
For example, we focused on capturing and connecting research data at individual experiment levels.
And this of course included the Regeneron data platform, data literacy training, and implementing the data catalog.
Now I know that all those four out of the last five years that you have come home with a CIO 100 Innovation Award, it has all been for various data plans and programs and stuff.
And if you ask some of our scientists, what's the biggest challenge? Often it's them trying to find the right information necessary to pick anything from researching an area of interest that they want to get up to speed on or find data of some of the experimentation or results they got from prior work from 5, 10, 15 years past.
We use these models to try and figure out how do you go and identify that, codify it in such a way that you can search for it.
Face value, it sounds straightforward because you hear about these large language models being designed for that.
And not only that, the data has to be, in some cases, structured in a way that they can be searched to make sure that you get the right results.
And so we put a lot of effort into working with our colleagues to understand what it was they were trying to achieve.
What was the internal information they wanted to get? What were the external sources they wanted to get? How did they identify where it came from so that they could actually reference it and understand and get credit where credit was due for where that information came from?
But solving it for this group is really a stepping stone to solving it for many other groups across the organization.
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