Nobody Wants ML, AI, and Analytics.
They want a UX that provides indispensable decision support, useful insights, and actionable intelligence.
My free resources below will help you learn how to use human-centered design to create innovative user experiences that turn technical outputs into valuable customer outcomes.
Are customers not getting the value out of your data product, analytics SAAS, or decision support application?
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.
Want to learn how to design engaging data products your customers and stakeholders will use and value?
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
How should we be innovating in AI? Work backwards from in-shape data? Or start with a vision for how we’ll create value for the customer or organization?
You got the data. Your model rocks. Your analytics are impressive. But, do customers see indispensable decision support….or a metrics toilet?
How to tell when poor UI/UX in a ML/AI application, analytics solution, or data product is impacting customer value
Today I want to tell you why your ugly, clunky, hard-to-use data/AI product or analytics solution should scare you. But first, you, your boss, your customer, your stakeholder—somebody—has to pass that judgement on it. They probably have, but don’t expect it … Read more
Ethics used to be a hassle. Now it’s not: Introducing Ethicize™, the fully AI-driven cloud-based ethics solution!
As Seen in the Book: 97 Things About Ethics Everyone in Data Science Should Know: Collective Wisdom from the Experts 1st Edition The essay below was eventually published as the #2 essay in this 2020 O’Reilly publication by Bill Franks. … Read more
This is a two-part article focused on “what” to ask users of data science solutions and data products, and how to ask/conduct these types of research sessions. In part one, we will look at the “what,” and part two will … Read more
A few years ago when I started DFA, I wrote this article that aggregates many of the studies on failure rates for big data, analytics, and now AI projects. It serves as a reminder that you can keep throwing money … Read more
Technology-driven projects that do not center around human needs continue to fail at a high rate. Here are the continued numbers to back it up.
There’s a lot of buzz about analytics translators these days. In general, I find the name to be a really poor choice for what effectively is a product management role applied to internal data science or analytics services. But, I … Read more