In this episode, I’m exploring the mindset shift data professionals need to make when moving into analytics and AI data product management. From how to ask the right questions to designing for meaningful adoption, I share four key ways to think more like a product manager, and less like a deliverables machine, so your data products earn applause instead of a shoulder shrug.
Highlights/ Skip to:
- Why shift to analytics and AI data product management (00:34)
- From accuracy to impact and redefining success with AI and analytical data products (01:59)
- Key Idea 1: Moving from question asker (analyst) to problem seeker (product) (04:31)
- Key Idea 2: Designing change management into solutions; planning for adoption starts in the design phase (12:52)
- Key Idea 3: Creating tools so useful people can’t imagine working without them. (26:23)
- Key Idea 4: Solving for unarticulated needs vs. active needs (34:24)
Quotes from Today’s Episode
“Too many analytics teams are rewarded for accuracy instead of impact. Analysts give answers, and product people ask questions.The shift from analytics to product thinking isn’t about tools or frameworks, it’s about curiosity.It’s moving from ‘here’s what the data says’ to ‘what problem are we actually trying to solve, and for whom?’That’s where the real leverage is, in asking better questions, not just delivering faster answers.”
“We often mistake usage for success.Adoption only matters if it’s meaningful adoption. A dashboard getting opened a hundred times doesn’t mean it’s valuable... it might just mean people can’t find what they need.Real success is when your users say, ‘I can’t imagine doing my job without this.’That’s the level of usefulness we should be designing for.”
“The most valuable insights aren’t always the ones people ask for.
Solving active problems is good, it’s necessary. But the big unlock happens when you start surfacing and solving latent problems, the ones people don’t think to ask for.Those are the moments when users say, ‘Oh wow, that changes everything.’That’s how data teams evolve from service providers to strategic partners.”
“Here’s a simple but powerful shift for data teams: know who your real customer is.
Most data teams think their customer is the stakeholder who requested the work…
But the real customer is the end user whose life or decision should get better because of it.
When you start designing for that person, not just the requester, everything changes: your priorities, your design, even what you choose to measure.”
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