How This VP Data Science Turned Ambiguity into Opportunity by Encouraging Bold Risks

Um, let's see. I mean, I'm thinking of a few scenarios that were, you know, just fairly large stretch projects and, um, ambiguous is that along the lines what you're considering? OK. Um, Yeah, so there's um one individual that had Um, you know, been a leader in the team, and there was a new, a new space that we were, you know, wanted to advance the organization, um, but, you know, we. We're just starting to, to resource it, just starting to explore it, and I think the company was just starting to, um, You know, to think in that way as well, in order to be, um, this is around our overall Microsoft cloud initiative and so the, the organization had developed a lot of um Infrastructure, um, business context, Mindshare around expertise around Azure, um, but there's a, a huge opportunity to connect the Microsoft clouds and, um, you know, just in the past year and now our, our external messaging is starting to align with that, um, but it's still, you know, something that's growing across the company in terms of aligning ownership in that way. So that was, um. A, a risk, as opposed to go from an area that this individual was, you know, an had developed a lot of expertise and relationships and to kind of move into a new space, um, that had a lot of unknowns.

Um, that specific individual, I would say that's one of that individual's strengths, um, to take those ambiguous projects and frame them and, um, Think about, you know, what we need, what is the, the path that we need to take to get there, um. Also happens to have a skill set and a passion around data platform, which I think Starting any new space that's, you know, a key fundamental that needs to be sorted out in order to, you know, even start doing the, the counting and accounting and then more sophisticated methods on top and so I think that combination of, um, framing the ambiguous space, developing a simple. Actionable plan forward, um, and having the, the tool belt and passion around the foundation, foundational components, and I felt like was a, a good combination.

Um, I think in terms of putting it ultimately into action, um, a lot of the The work I mentioned is, is in partnership with the, the business stakeholder teams because we've essentially developed this. Central COE of data science and so, um, given that the company was still starting to think in that direction, um, not many other, you know, a lot of the teams have their ownerships aligned around individual products and so we're focused on the connection between them, um. You know, there would be challenges in terms of finding good partners who are also thinking about their business goals in that way. Tell me more about that. Yeah, so we're, we're essentially looking at what is the opportunity of users using, um, You know, Power BI

OK, yeah,

yeah, center of excellence, um, yeah, so we're, we're looking at, you know, the intersection of using one cloud product with another, and so. Um, given that, you know, many orgs would be designed around one product or the other, you know, finding someone who felt their responsibility or their objectives and key results were around the intersection of the two, Wasn't as common, I suppose.

Yeah, I, you know, I can think about a specific conversation that we had where it was And I felt like the coaching approach was effective there. Um, there were some periods as we were looking at How to measure it where um how to tackle this, or, you know, I felt like the individual was You know, bringing some interesting ideas and you know, it's early and exploratory, but, you know, we're potentially less actionable and so I think, you know, we had a, a conversation where I was able to kind of ask questions in that direction around, you know, what this would ultimately drive, how we would measure the, the impact and um that helped kind of spark some collaboration opportunities and um. Very Varied approaches and language, um. In terms of how we, how we look at the space.

Yeah, so, um, one of the, the projects was looking at a RFV, a recency, frequency and value approach, and so it seemed. Um, On the one hand, interesting to see what that um You know, what outputs, different combinations of the products would yield, um, but It, it wasn't immediately clear like what would be the action that would drive, or, you know, it could potentially be a way to Measure where we stood today in terms of the intersection of these products, um, But again, I don't think I didn't feel like that was a large enough, um. And output to be able to state as a result of this work together and so, um, it, you know, the, the conversation led to sparking new partnerships with our, um, our marketing team about ways that we could use some of those scores to, um, prioritize our, our marketing campaigns, um, and it, and then measure their effectiveness, um. And then ultimately just, you know, different approaches that we could use, for example, looking at Um, you know, beyond that, looking at The, the retention rates of the different cloud products used together which drove some more actionable um discussions around, you know, licensing in a particular product where customers were essentially hitting a cliff after the first month as they would try to actually um do more interesting work, um, that wasn't available in their current license so that was. That was, um, you know, one of the inhibitors of, of, uh, customer success, and so, yeah, led to, you know, a few different. Types of variations like that. OK.