Nobody Wants ML, AI, and Analytics.
Customers and users want a UX that provides indispensable decision support, useful insights, and actionable intelligence—their way.
Before business value can emerge from your data product, there must be adoption, trust, usabilty and utility.
This is the domain of human-centered design.
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Recent Articles by Brian
How Adopting a Product Mindset Can Improve the UX and ROI of Your Data Science and Analytics Work
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 … Read more
Why you need to stop saying yes when they ask, “can you build us a dashboard that shows this data?”
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 … Read more
My Top 10 Predictions for Data Product Leaders in 2022
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 … Read more
Top 10 Experiencing Data Podcast Episodes for 2021
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 … Read more
Data as Product: Links to Talks and Articles on Building Data Products
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 … Read more
MVPs: The Slow, Expensive Way to Build Data Products?
Is there a faster way than MVPs to create data products that actually get used, are simple, and are trustworthy?
10 challenges internal data leaders will face creating a revenue-generating data product
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?
Why software teams need to look beyond “user-centered” when referring to ML or AI-driven data products
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” … Read more
Heads vs. Hands
If you’re struggling to solve human problems with data, the mindset of your analytics org may be the problem.