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.
Learn my C-E-D UX Framework for Designing Analytics & ML Apps
A 3-part UX design framework for designing advanced decision support applications and tools specifically designed for data product leaders.
Learn my PiCAA Framework for Envisioning AI Use Cases
A 5-part method to generate ML/AI use cases from a UX perspective.
Download my Self-Assessment Guide for Analytics Apps
My free self-assessment guide covers 9 key topics to help you make your service indispensable. Each day, for 9 days, you will also get an email lesson that goes deeper into the topic and provides recommendations on how to start taking action.
Free Course Sample from Designing Human-Centered Data Products
My self-guided video course—Designing Human-Centered Data Products—can help you learn the creative problem solving skills that data-driven software leaders need to produce useful, usable applications and solutions. Download the first module's video and written supplement, free.
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Recent Articles by Brian
Most definitions of data product focus on trying to firmly define the boundaries of the technology output. My definition focuses on the benefits to the user and the innate requirement of value to be present.
You probably rock at building enterprise ML and analytics applications, software products, and dashboards-but if buyers, stakeholders, and users just aren’t seeing the value, your problem isn’t your tech. Similarly, the solution is not in your code, pipeline, model, or GitHub. However, there are tactics and strategies from UX design, product management, psychology, marketing and other fields that you can apply so your technical work has impact, creates delight, and generates value.
“Can you take a look at my UI and provide feedback?” It’s a common question I get. How do we judge a UI/UX design of an SAAS analytics tool? By understanding what the customer is trying to do and what a meaningful outcome looks like. What are the signs we’re on track? Learn more in this story from my time advising MIT Sandbox Venture Fund founders.
10 reasons your customers/stakeholders don’t make time for your data science and analytics initiatives
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 … 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 … 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 … 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 … 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 … 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 … Read more