This post is from Brian’s weekly mailing list. As I write this, I’m heading off to London for another edition of O’Reilly’s Strata conf. If you are headed there, you can catch my talk and mini-workshop on Wed, May. With the schedule this week, in lieu of writing a longer insight piece, I thought I … Read moreA giant mess, peeps to follow, and an “anatomy of a decision”
A three-part UX framework for designing your ML / predictive / prescriptive analytics UI around trust, engagement, and indispensability. As you continue to design interfaces and experiences into your analytics tools that rely more and more on machine-based analysis and prediction, the challenge within the design starts to change. Whereas before, we might be dealing … Read moreC-E-D: A Design (UX) Framework for Integrating Advanced Analytics into Decision Support Software
Low engagement; it’s a common challenge for many of my clients and the people I talk to in the analytics world. In fact, it’s the subject of my talk this year at the IIA Symposium next week. One of the concepts with design I like clients to think about with analytics is around whether your … Read moreHow do you make data products and services engaging without AI and advanced analytics?
Here are (25) design faults that should trigger the check-engine light I really don’t know much about cars. Furthermore, with all the computers on them now, I probably never will. However, I do care when the “CEL” goes on. The CEL, or check-engine light, is that often cryptic, blood-pressure-raising notification that mostly just makes you … Read moreIs an engineering or data-driven culture driving your current data product or analytics initiative toward risk?
.If you’re concerned about low engagement with your enterprise data product, analytics service, or decision support tool, then you might be focusing on the wrong problem. What you need to do is design an engaging experience, instead of focusing on the quantity of engagement. Gartner just posted new numbers in early 2019; once again, 80% … Read moreWhy Low Engagement May Not be the Problem With Your Data Product or Analytics Service
AI and Machine Learning Are Not a Panacea for Underused Analytics Services Ears, Eyes and Empathy Guide the Best MVPs Since AI, predictive, and prescriptive analytics are big right now, there is a tendency for companies to “want” to use this technology and throw it into their marketing jargon as well. Boards and executives are … Read moreKeeping Analytics Solutions in Check with Customer Needs
Author’s Note: This article was originally published to my mailing list, hence the reference to previous emails and published podcast episodes. Before I jump into this week’s article on MVPs for custom data products, just wanted to address one listener’s response to the new podcast in case others had the same experience. Re: the audio … Read moreDesigning MVPs for Data Products and Decision Support Tools
I recently started playing percussion in a new Celtic ensemble in Boston called Ishna, and we were recently invited to be a guest artist with Symphony NH (New Hampshire). After our concerts concluded, the executive director invited Ishna to a dinner with some of the symphony staff and board members. This is pretty typical: board members … Read moreDoes your data product enable surgery, or healing?
This is recording of my presentation at the O’Reilly Strata Data Conference in New York City in 2018. Gartner says 85%+ of big data projects will fail, despite the fact your company may have invested millions on engineering implementation. Why are customers and employees not engaging with these products and services? Brian O’Neill explains why a … Read moreVideo Sample: 2018 O’Reilly Strata Conference (NYC)
Good design happens at the intersection of discovering real user needs/wants and business goals that are ACTIONABLE (by design and engineering). Yes, there’s a little magic/instinct that creeps into good design too, but you can get far without a lot of this magic. It’s really more about nailing the problem set, and having really clear … Read moreReasons your next sprint, product, or project might fail