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.
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 moreWhy your ugly, clunky, hard-to-use data product or solution should scare you
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
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–what I sometimes call—”Capital D Design”—has the power to make your data sing, delight customers/users, bring new/better ROI to your organization, provide inspiration to teams, reduce complexity, reduce engineering cost, save time for users, and expose new value in your existing service. However, the big gains usually don’t come from focusing on the surface level alone. Better data visualization cannot fix every data product and analytics problem.