Initial customers show up for unique reasons, and their interests aren’t representative of what other people want. The further the company gets from that group, the more it struggles to sell. (View Highlight)
Katie Bauer’s recent post about data teams being left on the sidelines is excellent—and depressingly evergreen. Two years ago, Erik Bernhardsson wrote a story about navigating the same problems of being misunderstood, left out, and asked to do the wrong work (View Highlight)
For as long as we’ve had analytics and BI teams, we’ve tried to create good processes for people to ask them questions. We’ve shared intake forms. We’ve built products. And yet, most data teams still can’t convince their business partners to regularly use them, and our most common ticket management system is still Slack DMs. We’ve put self-serve interfaces between us and everyone else, and declared it disaster—no, critical—no, a lie (View Highlight)
Products that have product-market fit are bought, not sold. And as people’s responses to posts like those from Katie and Erik show, most data teams still have to do an awful lot of selling. (View Highlight)
If we want to be in the room where it happens, we shouldn’t spend our time trying to sell an unnecessary service to a reluctant buyer; we should spend it figuring out what we can do that would make it necessary for us to be there. (View Highlight)
The corollary to both of these points is that we have to talk to our customers. We have to research them; understand them; put ourselves in their shoes and figure out why they do what they do. When they push us aside, we shouldn’t assume that we’re offering something valuable—strategic advice! Metrics and alignment! Experimentation and the scientific method!—and our job is to sell it; we should instead ask them why they don’t want it.
The answers may surprise us. Our advice might be bad. Metrics might not be that useful. We might create more disruption and doubt than we do agreement and alignment. If there’s a case to be made for hiring a data PM, this is it—to do discovery, and figure out what people actually want from a data team. (View Highlight)
