Often, to do something worthwhile, getting started is the hardest part. Take going to the gym or writing this blog post. I feel great once I’m done, but I can come up with a million excuses not to even begin.
And so it is with data visualization, at least historically. Analysis paralysis rears its ugly head and you’ve thought of too many ways to depict your data to even begin to put pen to paper, or cursor to application, as the case may be. Conversely, you’re at your computer with a blank slate staring back at you and no earthly idea how you’ll begin to fill it. Sometimes, you have something created but the hoops you’d have to go through to change it creates a barrier to – you guessed it – starting.
A handful of years ago, I joined a relatively mature dashboarding project for TeraThink. It was humming along, with hundreds of metrics and data collection happening with a predicted cadence. Until of course, someone needed a new metric.
We’d scurry around and define the requirements. Maybe we’d throw in a wireframe mockup. Then we’d sit with the end user, only to find out we missed a key point or they needed to change direction. We’d go back to the drawing board and start over. Eventually something was coded into existence on our dashboard. It was incredibly time consuming because you needed to “get it right” before coding started.
Each time we had to revamp, we dealt with increased “everything” (costs, schedule lags, and frustration). It got old and, in turn, interest in the data and dashboard began to wane. The group was losing momentum, which was rapidly being replaced with frustration.
Game Changers: Purpose-built Data Viz Tools
Enter data visualization software. This new(ish) breed of software facilitates rapid, iterative deployment of dashboards. In my experience, it breathed new life into our project. We didn’t have to worry about having the perfect idea from the get-go. We just need to start and allow the visualization to evolve. Using the data visualization tool Tableau, our data analysts (notice I didn’t say coders or developers) would sit down with our users and rapidly prototype a metric or dashboard on the fly. It became collaborative. Most of the time it met their needs. Sometimes it didn’t, but knowing that up-front allowed us to explore and innovate without the risk.
Music to a project manager’s ears.
The planning and development time decreased while the number of visualizations produced drastically increased. And we got it right. These things we created were valuable, and therefore they got used. We helped users see something that they couldn’t otherwise articulate. It was a lot easier to point to something on the screen and say, “Yeah, that’s it! Just change X a little bit,” than to play telephone between an end user, analyst, and developer on a concept.
In fact, the ability of our users to interact with the data and visualizations, even after publication, led to more questions. This led, ultimately, to more designs, insights, and answers. We could tweak things as information needs evolved. That flexibility kept our visuals relevant.
Now one (arguable) drawback to the speed is that the tool can’t be everything to everybody, but it has best practices baked in. So I’m here to tell you that if our user wanted a fuel cell chart and we gave them a bullet graph, the world did not end. We all adapted and were better for it.
Understanding Data Spurs Innovation
Overall, using Tableau encouraged innovation. We were empowered to try something new since the risks were low; drafts could be developed in a matter of hours, not weeks. We used best practices for information display to introduce new designs that conveyed information more effectively. Sometimes our most innovative designs were adopted, other times they weren’t. However, they were always seen and given fair consideration.
In the end, Tableau became part of our culture, one more powerful tool in our toolbox. Sure, we used it for metrics on the dashboard, but we also used it for fun stuff, like to track and predict shortages for the water cooler (now if we could just get it to change the empty jug). We used it to graphically depict the conversion process of old metrics to new. And we used it to track and predict data source submission on an ongoing basis. We were drinking our own Kool-Aid and reaping the benefits.
This has changed the game. We have moved our customer away from the hamster wheel of data prep and dashboard development towards a culture of performance. When a new data call happened, we were ready with the data and visuals to support it. We changed our mindset from pixel perfect reporting to collaborating on dashboards that helped our users gain insights. We focused on using our dashboards, rather than creating them, and performance improved.
Start Your Journey
Without a doubt, modern data visualization tools have a lot of benefits. Perhaps the most important is the ability to start – and later explore – without fear of failure.