November 23-24


Brian O’Neill

Designer, Founder & Principal

Designing for Analytics, US


Brian T. O’Neill helps data product leaders use design-driven innovation to create indispensable ML and analytics solutions. For over 20 years, he has worked with companies including DellEMC, Tripadvisor, Fidelity, JP Morgan Chase, ETrade, and multiple startups. In addition to being the founder and principal of Designing for Analytics, Brian is also an international speaker, having given talks at premier technology conferences including Strata, the International Institute for Analytics Symposium, and Predictive Analytics World. Brian also hosts the podcast, Experiencing Data, where he reveals the strategies that product, data science, and analytics leaders are using to deliver human-centered data products. Brian also authored the DFA Self-Assessment Guide, teaches a seminar called Designing Human-Centered Data Science Solutions, and publishes a weekly Insights mailing list. In 2020, Brian began advising students in MIT’s Sandbox Innovation Fund and was published in O’Reilly Media’s 97 Things About Ethics in Data Science Everyone Should Know. When he’s not helping data leaders leverage the power of design and product thinking, Brian leads a second life as a professional jazz and classical percussionist who has performed at the Kennedy Center, Carnegie Hall, and the Montreal Jazz Festival. His own award-winning group, Mr. Ho’s Orchestrotica, has received critical international acclaim and is enjoyed by 12,000 monthly listeners on Spotify and in tiki bars across the world. Keep up with Brian via his Insights mailing list at


Why Customers Can’t or Won’t Use Your Technically-Right Data Products

It’s simple: your team’s AI/ML applications, dashboards, and other data products will be meaningless if the humans in the loop cannot or will not use them.

Yes, they may have asked your team for those ML models or dashboards.

Unfortunately, giving stakeholders what they asked for doesn’t always result in meaningful engagement with AI and analytics—and data products cannot produce value until the first hurdle is crossed: engagement.

Until users actually use, trust, and believe your ML and analytics solutions, they won’t produce value.

“Just give me the CSV/excel export.” How many times have you heard that—even after you thought your team gave them the exact ML model, dashboard, or application they asked for?

No customer wants a technically right, effectively wrong data product from your team, but this is what many data science and analytics teams are routinely producing—because the focus is on producing outputs instead of outcomes. The thing is, the technical outputs are often only about 30% of the solution; the other 70% of the work is what is incorrectly framed as “change management” or “operationalization”—and it all presumes that the real end-user needs have actually been surfaced upfront.

If you want to move your team from “cost center” to “innovation partner,” your team will need to adopt a mindset that is relentlessly customer-centered and measures its success based on delivering outcomes. However, this is a different game: it’s a human game where ML/AI and analytics are behind the scenes and customers’ pains, problems, jobs to be done, and tasks are at the forefront.

Enter human-centered design and data product management: the other skills that modern data science and analytics teams will need if they want to become indispensable technology partners to their business counterparts.

This talk is for data product leaders who have talented technical teams, but struggle to regularly deliver innovative, usable, useful data products that their customers find indispensable.

You’ve heard for 20 years how Gartner and other research studies continue to predict limited value creation from enterprise data science and analytics engagements, with 80% of projects on average failing to deliver value.

MIT Sloan/BCG’s 2020 AI research shows that companies who are designing human-centered ML/AI experiences that enable co-learning between technology and people are realizing significant financial benefits.

The best data product leaders aren’t repeating yesterday.

If your data science and analytics require human interaction before it can deliver any business value, you won’t want to miss this session with Brian T. O’Neill–the host of the Experiencing Data podcast and founder of Designing for Analytics.

Brian will discuss strategies that leaders should know about designing effective data products. He’ll discuss the mindshift change around outputs and outcomes, the role of data product management, how separating “operationalization” from core ML and analytics work leads to failures, and how human-centered design provides teams a step-by-step method for “doing innovation” that leads to better data products.

Session Keywords

🔑 Product
🔑 Design
🔑 Analytics
🔑 ML

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