Nobody Wants Your ML/AI or Analytics!

Are you struggling to produce clear business or organizational value with your ML and analytics? Is user adoption of your dashboards, models, and apps lower than you'd hoped—despite giving the stakeholder what they asked for?

I believe that a "product orientation" is critical for any data and technology team seeking to turn data products into high value, usable, and useful solutions. What does that mean?

Get started with some of my resources below on data product management, data product UX design, and more.

New to Data Products? Start here:

My Definition of a Data Product (Start Here!)

All of my work is framed around this definition, so start here before reading anything else. Definitions about reusable containers, assets, data mesh, and the like are wrong in my opinion. Data products are more about how we work than the output. It's rooted in the idea that the job of a data product leader is to produce benefits not outputs.

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This is home base for all of my thinking on data product UX design and product management. A portion of my weekly insights also get published on this website. Subscribers also get access to 1-page summaries of my podcast, early access to my newest offerings, and more.

15 Ways to Increase Adoption of Data Products

Available in both written and audio formats. One of my most popular resources for data product leaders struggling to increase the user adoption and business value of their ML and analytics solutions.

The Top (5) Reasons Enterprise ML/AI/Analytics SAAS Leaders Come to Me for UI/UX Design Help

Got that itch that maybe your product's data and tech isn't the reason customers aren't buying, demos aren't closing, churn is increasing, and the people who already paid aren't using the service? Here are the top (5) reasons founders, CxOs, and product leaders reach out to me for help—and the (20) key symptoms I see surface in the product itself.

Data Product Management Maturity Model

Do you lead a large enterprise data team? In 2024, I started to think about how orgs with large data teams might transition to a product-driven approach to building ML and analytics solutions—and what that maturity might look like. View the latest thinking

Data Product UX Design Resources

CED UX Framework for Advanced Analytics & ML Apps

A 3-part UX design framework for designing advanced decision support applications and tools specifically designed for data product leaders.

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.

Podcast, Free Videos, Article Search and More:

🎧 Podcast

Free Webinars

Videos/Conference Talks

See my YouTube Channel and Speaking page.

🔍 Articles

I recommend searching my archive via Google or take your chance with WordPress' search below!

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I only post select articles to the website. To get my latest thinking weekly in your inbox, plus summaries of my bi-weekly podcast, subscribe for free here.

Recent Articles by Brian

User Adoption: The Wrong Way to Measure the Value of Data Products

By Brian T. O'Neill

The sooner you stop believing this, the better off you will be-if you really want to increase the organizational, economic and human value of your data products. Stop measuring your product’s user adoption by counting things like viewing sessions, time … Read more

Data Product Management Maturity Model (v. 0.5)

By Brian T. O'Neill

I’ve developed a 4-level maturity model for data product management in enterprises, ranging from unaware order-takers to mature, cross-functional teams delivering strategic, high-value data products. Though imperfect, this emerging model aims to provide insights for assessing and advancing organizational maturity in this space.

The (5) Top Reasons AI/ML and Analytics SAAS Product Leaders Come to Me For UI/UX Design Help

By Brian T. O'Neill

Plus, a list of (20) symptoms that your enterprise data product, ML app, or SAAS analytics solution has a design problem.

What is a data product?

By Brian T. O'Neill

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.

15 Ways to Increase User Adoption of Data Products (Without Handcuffs, Threats and Mandates) 

By Brian T. O'Neill

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.

What was wrong with this founder’s SAAS Analytics UI?

By Brian T. O'Neill

“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.

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