Are you a leader in charge of creating innovative ML and analytics solutions within a very large enterprise organization? Getting the “makers” of the solutions talking to real end-users can be extremely difficult. Here’s how to navigate the gatekeepers and bureaucracy so that the data products you spend so much time and money building actually are useful, usable, and valuable.
I’m not putting out a long list of 2021 predictions, but I have a couple that I will mention to you that are on my radar. First, AI/Data Product Management Seems to be Picking Up There seem to be more jobs appearing in product management in the AI/ML space, in particular. I am not sure why we don’t … Read more
Today, I’m sharing my impressions of one of Spotify’s analytics touchpoints—a monthly email I receive with a boatload of design choices I mostly hope you will not copy, especially if you’re working in an enterprise capacity. Most of you by now probably know I have another career as a professional musician, and that includes having three recordings I … Read more
This is an ongoing list of links to articles, slide decks, toolkits, and other resources around designing AI user experiences. I will keep this updated. 7 Steps to AI Products – Allie K. Miller (slide deck) UX in the Age of AI: Where Does Design Fit In? – Carol Smith (slide deck) AIMeets.design PDF tool kit (Nadia Piet) … Read more
Customers want simple, well-designed decision support tools and UX’s that are actionable. Businesses want to see value from data and adoption of data-driven decision making. However, the UX that is afforded to is often simply a byproduct of the analytics team’s engineering, or, at best, “data viz” efforts—and it’s not working. A decade later, success rates for data projects remain unchanged, despite vendor/BI tooling improvements. What are BI/analytics teams still missing? Design.