Free Insights on Turning ML, AI, and Analytics Into Indispensable Decision Support Applications and Products

Nobody wants another technically right, effectively wrong solution. As a software leader, your analytics/ML/AI technology is only good if you're delivering a positive outcome, not just an output.Β 

My resources below will help you learn how to begin applying human-centered design to produce indispensable data-driven software.

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 courseβ€”Designing Human-Centered Data Productsβ€”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

Re: Your “Home Depot” Approach to AI/ML

By Brian T. O'Neill

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Better data visualization won’t convince me when to play ⚽️ again

By Brian T. O'Neill

Presenting data and evidence isn’t the same thing as providing indispensable decision support, especially when your insights are experienced in a software application with no Powerpoint deck, narrator, or intimate storytelling.

Designing for AI (UX, UI)

By Brian T. O'Neill

This is an ongoing list of links to articles, slide decks, toolkits, and other resources around designing AI user experiences. Β I will keep this updated. 7 Steps to AI Products – Allie K. MillerΒ (slide deck) UX in the Age of … Read more Designing for AI (UX, UI)

(8) reasons why data visualization training for your BI team may not increase analytics adoption

By Brian T. O'Neill

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” effortsβ€”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.

Humans – The Weak Link in your ML / AI Strategy?

By Brian T. O'Neill

In many cases, machine learning needs to be deployed to augment human decision making, not automate it. What are you doing to account for this dependency on the success of your data product?

My Too-White Data Podcast Looks ~Like This: πŸ§‘β€πŸ¦²πŸ§‘β€πŸ¦²πŸ§‘β€πŸ¦²πŸ§‘β€πŸ¦²πŸ§‘β€πŸ¦²πŸ§‘β€πŸ¦²πŸ§‘β€πŸ¦²πŸ§‘β€πŸ¦²πŸ‘©β€πŸ¦°πŸ‘¨πŸΎ

By Brian T. O'Neill

Self-reflecting on #BLM, the makeup of my podcast guests to date, racism, and the responsibilities of consultants with platforms and audiences in fighting injustice.

A UI Design Audit of MITRE’s Covid-19 Decision Support Dashboard

By Brian T. O'Neill

I performed a rapid UI/UX and data visualization audit on the MITRE Covid-19 Healthcare Coalition Decision Support Dashboard. Watch it here, and see my recommended design changes the team should make.

$1M spent on a predictive model/data science w/ $0 value and no user engagement?

By Brian T. O'Neill

How to avoid spending 6-8 months on a technically right, effectively wrong model. Are you successful with ML if nobody uses your solution, model, or application?

(6) Ways to Sanitize Your Data Product, Dashboard Visuals or Analytics due to Covid-19

By Brian T. O'Neill

Covid-19 presents a major disruption to our lives and businesses. However, sanitizing your hands isn’t the only thing you data leaders need to be considering. Your data product, dashboards, or UI may also need to be cleaned up. No hard-to-find Clorox wipes needed; just some good design thinking centered around your customers.