Squirrel talk with Juice Analytics

This month we talked with Michel Guillet, Product Manager from Juice Analytics, a company that works to help companies display and communicate their data in an exciting and easy-to-understand way.

Kylie: What is Juice Analytics, and how did it start?

Michel: Juice was founded 10 years ago by two brothers, Zach and Chris Gemignani, to help organizations communicate their data to non-analytical people. So, for example, one of the industries we work in is healthcare. Hospital staff don’t sit around looking at or analyzing data. However, data if shared correctly can make a big difference.

Another example is with advertisers or marketers to help them communicate campaign performance as well as package their optimization recommendations to the brands they’re working with. So in short, Juice Analytics helps companies communicate data to external audiences in a way that makes sense and drives improvements.

Kylie: Where did the name Juice Analytics come from?

Michel: We wanted a name that was evocative and struck very positive chords—when you think of juice, you think of vitamins, energy, electrical currents, and all of that good stuff. That’s what we hope to represent and do for our customers’ data communication.

Kylie: Some people hear the word “data” and instantly think it’s too complicated or boring. What gets you excited about working with data and with Juice? What got you into data initially?

Michel: One of things that all of us at Juice share in common is that at some point in our careers, we were responsible for working with data, gathering that data, and analyzing it. A common frustration for all of us, though, was that no one ever valued the work that we put into the data; it wasn’t used, or was left on the cutting room floor as they say. You work really hard collecting the data, and then at the “last mile” the question remains, “so what?” What do you with data? What decisions do you make?

That happened to me personally too many times to count—staying up late many evenings trying to get my queries or slides just right. I knew the data, but couldn’t get people to do anything about it or with it. So that frustration motivates all of us to solve that pain for others. We’re always thinking, “What can we do to help our customers help their customers?”

Kylie: I see that you guys are a fan of lean startup principles. How have you applied that at Juice Analytics?

Michel: As a small company/startup, Lean offers us a context to prioritize tasks and features. What does a user really need rather than what we want to do. We spend more time simplifying features, functionality, and new releases than designing or deploying new capabilities.

Kylie: Right now, “big data” is a pretty big buzzword. What does that mean to Juice?

Michel: We don’t do big data; we do small data. We come in post-analysis to help the individual user discover the small, important pieces of data that they need to make decisions, and to change their business. We’re focused on the value of small, understandable data that you can use to make decisions.

Kylie: I saw in your blog a discussion about how fantasy football has brought data fluency to the masses. Do you have an area that you loved bringing data fluency to through working at Juice?

Michael: We do a lot within healthcare. It’s hard to put this into words exactly, but as an example, we put together a web application of six charts, and during usability testing a hospital employee said, “You had me at the second one. Just these two charts alone are way better than what I have today.” It was really cool to see that impact, that just two charts gave them more information than they’d ever had before. It’s cool that we can have that impact in the healthcare industry and with hospitals.

Kylie: What’s the most unusual area you helped to visualize data, or the most interesting companies you’ve worked with?

Michel: We did something kind of playful with visualizing data about salaries of Hollywood actors. We found out some key points, like how valuable syndication is, and that it’s only after seven years that actors make good money. Visualization helped us reveal that. It wasn’t for a client, just something we put together, but it ended up sparking a lot of discussion.

We’ve also done work with companies related to immigration reform and unemployment—getting to look at where the jobs are located and how that impacts unemployment. When you get to look at the data yourself it’s pretty interesting. For example, the state of Georgia lost 7,000 cashier jobs between 2007-2008 which is just amazing.

We also helped a big healthcare organization build a data application to visualize the history of diseases. It won a company-wide innovation award.

Kylie: What’s the craziest thing you discovered through data?

Michel: I think one of the tops is that Miranda Cosgrove, the star of the tween show iCarly a few years back, made more per episode than most people do in a year.

Kylie: How much per episode?

Michel: $180,000 per episode! You can check out the link to see the video we created around it and some more information.

Kylie: Wowza! Any other final thoughts about data analysis?

Michel: I was just thinking how there are a lot of similarities between data and food. We’re always thinking about how we can make data enticing and consumable in the same way a chef thinks about sharing a meal. Data presentation matters in the same way that a chef presents the entire meal. It has to be memorable and an experience.

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