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?
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
I know to a lot of software teams, getting features/fixes/releases out the door feels like improvement. However, did you actually create or improve the value of your service? To to that, you have to understand what your users actually value, so you can align your efforts accordingly. Most of the time, these nuggets of useful … Read moreDesign KPIs – what improvement did you celebrate in your last analytics software release?
So a guy walks into a bar and starts talking about unsupervised learning… Ok, not quite. Well actually, where I live in Cambridge, MA, that’s not really so improbable 😉 I found this article on Medium interesting, written by a data scientist talking in part about why data science projects may not be working, and one of … Read moreFrom a data scientist on Medium: “It’s easy to not understand your customer’s needs.”
Readers of DFA know that I’m big on not immediately giving customers what they asked for, and instead asking the question “why” to learn what the real latent customer needs are. And for you internal analytics folks, remember your employees, vendors, etc. are your “customers” whether you think of them that way or not! Anyhow, … Read moreHow to solicit *real* needs from users via UX research interviews