If you’re running an internal enterprise data science or analytics team, and you can’t get the time of your stakeholders and business partners “to help you help them,” there’s probably a reason – or two – or three. These are some of the cold hard facts that maybe they won’t tell you–but maybe they are … Read more
The work of enterprise data science and analytics teams is often experienced in software—whether it be via custom apps, dashboards, or BI tools. As such, data teams are software teams—but many of them do not build solutions the way the best software product teams do. What can data leaders learn—and steal—from software teams who put … Read more
If there’s one thing I see a lot of in my work, it’s dashboards. I don’t talk about dashboards a ton because a dashboard alone is neither a data product nor an experience. It is an output and artifact that is part of a user or stakeholder’s overall experience, typically in our case, in some decision making context. … Read more
Below are ten 2022 predictions for data product leaders and organizations trying to leverage ML and analytics in their software, tools, apps, and services. From the lens of a consulting product designer. Yea, you head that one right. If that PhD in physics, statistics, math or engineering in you is already making you cringe, you may depart this … Read more
Everybody loves a Top 10 list at the end of the year! How about one with pretty lousy data to back it up? 😉 Analytics on analytics here ladies and gentlemen! Since I know this audience will probably be asking what the data is behind the ranking, it’s pretty simple: it’s the number of downloads … Read more
A link list of articles and resources on building data products, and particularly the mind shift involved in approaching data products as just that: products, not projects. Is something missing here? Shoot me an email and let me know. From Data to Product (Newsletter, Eric Weber) Run Your Data Team as a Product Team (YouTube) feat. … Read more
Is there a faster way than MVPs to create data products that actually get used, are simple, and are trustworthy?
Satisfying internal vs. external customers is not the same. What do data science, analytics, and engineering leaders need to know about the messy world of birthing a new commercial data-driven product?
As a designer, I used to say “user-centered”—a lot. It’s terminology we now hear from non-designers now, people like many of you. That’s a good thing. But, I want you and your teams to think bigger. For me, that “user-centered” descriptor is missing something about design, and particularly so if you are working on ML/AI systems. … Read more
If you’re struggling to solve human problems with data, the mindset of your analytics org may be the problem.