All Articles by Date

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

By Brian T. O'Neill | September 25, 2020

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Empty soccer pitch

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

By Brian T. O'Neill | September 14, 2020

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 | August 24, 2020

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 Designing for AI (UX, UI)

Inside view of empty airliner by JC Gellidon

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

By Brian T. O'Neill | July 21, 2020

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.

Crumpled paper

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

By Brian T. O'Neill | June 23, 2020

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?

Experiencing Data Podcast - Race/Gender Breakdown June 2020

My Too-White Data Podcast Looks ~Like This: 🧑‍🦲🧑‍🦲🧑‍🦲🧑‍🦲🧑‍🦲🧑‍🦲🧑‍🦲🧑‍🦲👩‍🦰👨🏾

By Brian T. O'Neill | June 12, 2020

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.

Covid-19 Healthcare Coalition Decision Support Dashboard

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

By Brian T. O'Neill | May 29, 2020

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.

1 pint of ice cream in an otherwise empty freezer that is iced over

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

By Brian T. O'Neill | May 21, 2020

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?

Clorox, Lysol, and cleaning bottles on a table.

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

By Brian T. O'Neill | May 12, 2020

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.

Cat looking frustrated at laptop

UX Designers & Data Scientists: United in Job Misery?

By Brian T. O'Neill | April 29, 2020

My conversations and research suggest that individual contributor UX designers and data scientists share one thing in common: it’s often a challenge to “get to do the job I was hired for.” People, process, and political roadblocks at every turn—so what can you do about it if this is you? And if you’re managing these people, what are you doing to ensure these people don’t leave?

Mother and child daughter hiking on coast together on a ledge with a gap the daughter must cross.

Empathy & Human-Centered Design in a World with the Coronavirus

By Brian T. O'Neill | March 23, 2020

It takes more than math and technical skills to develop simple, useful, and usable decision support solutions. It starts with problem clarity, customer empathy and…

Analytics dashboard on laptop screen on desk

Technically Right, Effectively Wrong: Why 85% Data Science Projects Fail

By Brian T. O'Neill | March 2, 2020

It takes more than math and technical skills to develop simple, useful, and usable decision support solutions. It starts with problem clarity, customer empathy and…

Sinking boat in the ocean in Cythera, Greece

10 human reasons your data product or solution may fail

By Brian T. O'Neill | February 5, 2020

It takes more than math and technical skills to develop simple, useful, and usable decision support solutions. It starts with problem clarity, customer empathy and…

Giant building full of green windows of the same size with 3 tiny workers hanging and cleaning them

Should a vision for a new AI or data product be inspired by existing data…or not?

By Brian T. O'Neill | January 21, 2020

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?

Airplane lost in forest

Metrics toilet? Or indispensable data product?

By Brian T. O'Neill | January 6, 2020

You got the data. Your model rocks. Your analytics are impressive. But, do customers see indispensable decision support….or a metrics toilet?

frustrated worker at computer with hands over his eyes

How to tell when poor UI/UX in a ML/AI application, analytics solution, or data product is impacting customer value

By Brian T. O'Neill | December 11, 2019

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 to necessarily come out in the words you may expect. Just as most designers (in … Read more How to tell when poor UI/UX in a ML/AI application, analytics solution, or data product is impacting customer value

Roaring fire

How to become unemployed as a data leader

By Brian T. O'Neill | November 13, 2019

One trend in analytics and data science I am hearing from the top thought leaders in this space is that (finally!) companies are realizing that the biggest opportunities for data that lay before us is in the pursuit of new products or enhancing existing ones, as apposed to solely focusing on incremental operational improvements. Gartner’s … Read more How to become unemployed as a data leader

Man jumping off cliff into crater

Ethics used to be a hassle. Now it’s not: Introducing Ethicize™, the fully AI-driven cloud-based ethics solution!

By Brian T. O'Neill | October 14, 2019

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. Buy Now License our latest AI automation platform now, and get a free “Ethics Power … Read more Ethics used to be a hassle. Now it’s not: Introducing Ethicize™, the fully AI-driven cloud-based ethics solution!

Women holding binoculars

10 Sample Questions to Ask Users of Data Science Solutions to Solicit Needs and Get Problem Clarity

By Brian T. O'Neill | September 2, 2019

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 cover the “how.” Human-centered design for data products and data science solutions doesn’t happen without … Read more 10 Sample Questions to Ask Users of Data Science Solutions to Solicit Needs and Get Problem Clarity

Rows of colorful soaps

Think clean data is the blocker to your AI/Data Science initiative? Try people.

By Brian T. O'Neill | July 24, 2019

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 at data projects, but if you don’t focus on the people involved, you can easily … Read more Think clean data is the blocker to your AI/Data Science initiative? Try people.