Tech Transformation Podcast: Data Science and Transformation Lessons for (and from) Retailers
Do you know who a sideliner is, and how they can advance your transformation strategy?
In this episode of Tech Transformation, we’re talking with Nicole Nelson, most recently SVP of decision science, data science, and applied machine learning at Best Buy, a longtime Target exec, about lessons for successful transformations. We dig into the ins and outs of getting teams to work more closely together, as well as run through a punch list of concrete suggestions.
PLUS: Nelson is the keynote speaker of Analytics Unite, being held live in Chicago June 21-23. Get a sneak preview of her keynote!
Listen to learn:
- How technology and analytics transformation are different yet one in the same
- How technology and data science teams can work together for successful transformations
- What a sideliner is, how to know one when you see one, and how they can advance your mission
- Why having shared goals is so important (yet often so challenging)
- How to identify your blind spots and help your team identify their own – and then how to move past them
- A sneak preview of Nelsons’ Analytics Unite keynote
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Excerpts
On the similarities between technology and analytics transformations: “Because these transformations tend to involve very technical people, we emphasize the ‘what’ on the transformation. We think about exactly the process and the task, and we under-emphasize the human and the ‘how.’ I think both [technology and analytics] transformations sort of share that bias.”
On the value of data science: “On the technology side, I think data science is probably underused as an enabler. Data science can really help those transformations see where it is being effective. They can help find efficiencies with machine learning that actually sort of free up resources, and let things shift in a way that isn't painful. Data science can help us see the things — hotspots or whatnot — that we don't know.”
On driving shared goals: “When I was at Target, the technology team was trying to put together a case for an enterprise data warehouse, which is an expensive thing. They just weren't getting it over the line. It was sitting there. I came back [from maternity leave] and was kind of given the job of to figure out how to get support for this thing.
“So what we did is we actually expanded the view of it. We brought in the business teams. On the data science initiatives we wanted them to do, we brought in the data science teams. We built a shared business case between technology, data science, and business.
“We ended up going to the executive committee, and for the very first time, they gave support. We even did it in a very interesting way, where we said, ‘We're not going to give you the normal five-year NPV. We're going to give you a range, because we're going to do 10 things — seven are going to work out three of them are, we don't know which ones they are. But we'll come back to you yearly, and we'll tell you about which ones are working and which ones that weren't.
“I bring that up in with shared goals, because it was this great experience about these teams actually sitting down and truly coming up with a set of goals that everybody was tied to.”
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