Sometimes, I’m stumped when people ask me what I do for a living.  When I reply that I help organizations communicate better with and about data, or call myself a data translator, I see the gaze of the person I’m talking to fog over. You may be familiar with this look – it’s a common reaction many people have once data or numbers are introduced into a conversation.

A perfect example to demonstrate what being a data translator means came up this past weekend.  My partner and a bunch of his college buddies are huge UNC basketball fans, and on Saturday, the Tar Heels (UNC) played Duke – a fierce and famed rivalry.  The game was tight – much closer than anyone who has been following either team this season could have expected. In the post-game recap conversation, our friend shared this analysis of the probability of the Tar Heels winning the game out to the group. I sensed the immediate opportunity to translate this chart for the members of our group chat that do not have a master’s degree in statistics.

Thanks to Luke Benz (@recspecs730) for putting out this (and many other) visualizations about college hoops on his Twitter feed.

This chart demonstrates the hope, and ultimate defeat, that Tar Heel fans felt throughout the game. But if you’re unfamiliar with an output like this one, the clear heartbreak may not be immediately understood.

My job as a data translator and storyteller is to take charts like the one above (from the ncaahoopR package developed by Luke Benz), and transform them to be accessible and easily understood to the general population. Like a version of Google Translate dedicated to breaking down the work of data scientists.

I’d make a few changes to this chart to help interpret the game for my Tar Heel fan friends.

• Remove Duke from the equation – win probabilities for a two-team game are inverses, so the other team values are not essential and ultimately a bit confusing.
• Add in some annotations to call out key points in the game.
• Create a title that makes clear what the chart is about and the additional context that will be important for a Tar Heel fan to walk away with.

My version below contains the same data and information but transformed for an audience of college basketball enthusiasts.

We are living in an incredible time for data – never before have we had access to so many data sets (big and small) or visualization tools.  With this access comes the need to really put thought into the data we share – the why and the how – because we no longer need to put a ton of thought or work into creating visualizations.  The most impressive data analysis is useless without the ability to clearly communicate essential takeaways and offer up persuasive recommendations.

I challenge you to think about the data and dataviz you create and distribute through the lens of a translator.  Be particular and intentional about the data and visualizations you share. Determine their importance and how these data points help influence decision-makers and tell clear stories to your audience.  Consider the possibility that everyone who uses your data does not have your background, and instead, help them learn through your expertise via clear insights and uncluttered visualizations.

I’m excited to share more about creating strong data stories at ODSC East.  Please come check out my session “The Art (and Importance) of Data Storytelling” to learn more about strategic choices in visualization design and the influential power you can harness as a data translator.

Bio:

Diedre Downing is a Lead Data Storytelling Trainer at StoryIQ where she helps organizations improve their communication with and about data. An accidental math teacher, Diedre learned the power of demystifying numbers in New York City classrooms and the power of influencing decision-makers with data during her time running WeTeachNYC.org for the NYC Department of Education. Diedre is an Adjunct Lecturer at Hunter College in New York and has spoken at NCTM, iNACOL, and Learning Forward about adult learning methodology and best practices in professional learning.