All in Thought Leadership

When you embark on a Tableau dashboarding project, you are creating a new product. When Apple releases the newest iPhone, they aren’t putting out a rough draft. They’ve done extensive user and product testing to make sure its the best product possible. You can (and should) use the same design sprint methodology on your own projects to ensure success.

My grandparents have a beach house in Island County and every 4th of July there is a big parade and community get together. One of the events is the “Penny Hunt”. The adults scatter a bunch of coins (of varying denominations) in the sand for the kids to search for. As kids, my brother and I got fed up with blindly digging in the sand so we convinced our dad to get us a cheap metal detector. I remember pulling in $40 the first summer we put it to use. Not bad for a couple of kids.

Let me be completely honest with you. I am writing this post in the aftermath of the 2019 Rose Bowl where the University of Washington lost to Ohio State so my views and opinions are most certainly biased. It’s not a surprise to me that the Huskies lost. I think Ohio State was the better team. Rather, it was a decision in the 4th quarter by the Huskies that surprised me…

Every dashboard you create is designed to do more than just communicate numbers. It’s a tool with a purpose. That purpose might be to increase revenue, decrease costs, or add value to your product. When creating dashboards, I like to keep that purpose forefront in my mind.

A powerful addition you can apply to your dashboards right now is to lead with questions. When you change the title of your dashboard into a question, you turn your audience from passive viewers to active viewers. They aren’t just reviewing numbers, they are now trying to answer a question.

It’s easy to be more complex than necessary. It’s hard to be simple and succinct. As Mark Twain said, “I didn’t have time to write a short letter, so I wrote a long one instead.” The same is true in the world of data analysis.

Most people just want to know: “based on what’s happened (data), what should I do next?”

Imagine trying to drive a Maserati at 180 miles per hour in traffic while keeping your entire focus on the rear view mirror. Impossible, right? Most data-driven reports are like this; aesthetically pleasing and historically-focused.

They are expensive and beautiful for a little while but almost always result in accidents.

How does OneNumber focus you on the windshield instead of the rear-view mirror? We study those historical results to inform what actions can improve future behavior.

American Airlines has a KPI called “D0”.  It means no flight can depart late, no matter what or there are severe consequences.  

“Many things go into whether or not a flight arrives on time. American’s management argues that:

“What they can most control is whether the flight departs on time. And if it does, that’s going to be the single biggest driver of on time arrivals. So American Airlines management is singularly focused on what they refer to as “D0” — departing exactly the minute that a flight is scheduled to depart (the government considers a flight to be ‘on time’ when it arrives within 15 minutes of schedule).”

Why do some bartenders get bigger tips than others?  This was one of the first problems OneNumber sought to solve.

We interviewed bartenders, bar owners, managers and patrons.  We asked them “what are the 3-5 things a bartender does that earns them greater tips?”  We got over forty different answers from tattoos to revealing clothing.

We ran the responses through our algorithms and settled on just four things that a bartender can do *right now* to improve their tips.  What do you think they are?