Should maximum simplicity dictate success? We all love usability these days right? “User experience is important.” Of course it is! But, it doesn’t mean that every user you show the design to is going to, or should immediately be able to, understand fully what you’re showing them. Why? Most valuable things in life take a … Read moreDashboard Design: Is Maximum Simplicity the Goal?
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
Ok, you probably know this one, but let’s dig in a little farther. I recently started to explore using the TORBrowser when surfing on public wi-fi for more security (later finding out that using a VPN, and not TOR, is what will enable safer surfing). However, in the process of downloading and trying the TORBrowser … Read moreThe Easiest Way to Simplify Your Product or Solution’s Design
Today’s insight was originally inspired by a newsletter I read from Stephen Anderson on designing for comprehension, and I felt like this could be expanded on for analytics practitioners and people working on data products. One of the recurring themes I hear from my clients is around the topic of general engagement (or lack thereof) … Read more(8) invisible design problems that are business problems
When I work on products that primarily exist to display analytics information, I find most of them fall into roughly four different levels of design maturity: The best analytics-driven products give actionable recommendations or predictions written in prose telling a user what to do based on data. They are careful about the quantity and design of the … Read moreWhat internal analytics practitioners can learn from analytics “products” (like SAAS)
Not to be the bearer of bad news, but I recently found out just how many analytics, IOT, big data, and BI projects fail. And the numbers are staggering. Here’s a list of articles and primary sources. What’s interesting to me about many of these is the common issue around “technology solutions in search of … Read moreFailure rates for analytics, BI, and big data projects = 85% – yikes!
I ran into a an article about the Chief Data & Analytics Officer, Fall conference that summarized some of the key takeaways at the previous year’s conference. One paragraph in the article stuck out to me: … The Great Dilemma – Product vs Project vs Capability Analytics Approaches Although not one of these approaches will provide a … Read moreMy reactions to the Chief Data Officer, Fall 2017 conference summary
(As shown to customers in your UI) I’m talking to a prospective SAAS client right now, and they’re trying to expose some analytics on their customers’ data so that the customers can derive ROI from the SAAS on their own. The intent is that the data can also be useful to the SAAS sales team, … Read moreGetting confidence in the value of your data
If you are starting out on a new feature design, or analytics effort, can you clearly state what the value will be in quantifiable terms at the end of the sprint? Are you building an “exploratory” UI, or one that is supposed to drive home conclusions for the customer? When clients come to me about … Read moreTips to help focus your analytics design/engineering efforts on results
Post-truth. The 2016 word of the year. Yikes for some of us. This got me thinking about UX around data, analytics, and information, and what it means when we present conclusions or advice based on quantitative data. Are those “facts”? If your product generates actionable information for customers, then during your design phase, your team … Read more“Post-truth,” data analytics, and omissions–are these design considerations?