Dashboard Confessions: Being a Data Nonconformist

I have no formal background in data or statistics. The skills and experience I’ve gained have been a direct result of working in analytics, figuring it out as I’ve gone along and working hard to solve problems about which I’m passionate. Big data, machine learning, complex algorithms—these are all now common associations with the world of analytics. While I have colleagues who specialize in these technical areas of our work, my journey with data has been unconventional. I often describe my own work as “finding the story” within a dataset.

The Art of Data Science

Data and narrative may seem like an odd pairing. Typically, we think of data as a hard science, primarily a left-brained activity. We associate stories with the arts and creative right-brained activities, and the idea that these can coexist can be difficult to grasp. Although some areas of data analytics are certainly more science than art, we live in a world where data is so common that we’ve had to democratize the analytics process.

Gone are the days when a small group of coders are the only ones trusted to develop insights. In many companies today, everyone is expected to analyze data, requiring even more overlap and nontraditional approaches to data. Tools like Tableau and Alteryx have proven invaluable for turning ordinary people into analysts, but jumping into analytics without the typical background can still feel like a bit of a leap.

In my last job, I worked as an analyst for KIPP DC, a national network for public charter schools. I helped district leaders, principals and teachers understand and interpret data about our schools so that we could make them stronger. The idea of writing a formula in Excel made most of these people cringe. But I quickly discovered that the key to success in my role was not being able to write complex calculations or model polynomial regressions. Rather, it was something far simpler: understanding what insights people needed and telling a story with data that they could understand. In some regards, this required me to take a step back from the typical left-brained approach to analysis and assume a more organic and creative posture—it wasn’t just “What does the data say?” but it became “What is the data prompting us to do?”.

But I quickly discovered that the key to success in my role was not being able to write complex calculations or model polynomial regressions. Rather, it was something far simpler: understanding what insights people needed and telling a story with data that they could understand.

Thinking About Data in Terms of Design

In this same vein of blurring the lines between science and art, left brain and right brain, I’ve recently been learning about design thinking. In the simplest of terms, it’s just understanding what users need and how to meet those needs in an intuitive and enjoyable way. Although I didn’t realize it at the time, in retrospect, I’ve realized that my early approach to data analytics was through the lens of design thinking. I was constantly asking myself, “What are people going to do with this data?” and I tried to anticipate their next question and what other data they might need. I put myself in the shoes of the user and developed all my dashboards with them in mind. Many times, this took me outside the stereotypical box of an analyst and forced me to consider the data differently.

A couple months into my job at KIPP, I overheard a specific goal that district leaders had for our schools. Although we already had a dashboard with data related to this goal, I found myself running into dead ends and unanswered questions when I used it. So I added several new ways of breaking down the data. With each new graph I built, I asked myself, “What can someone do with this data?” and if that answer didn’t lead to practical action, I knew my work wasn’t done and found a way to make it better. This stands in direct contrast to how some people perceive data analysis: inputting data, yielding results and communicating them as is. However, I’ve learned that data analysis isn’t like this—especially at InterWorks. It’s much more about trying different things over and over, thinking about data in ways you never have before in order to glean meaningful insights and deliver better reports than ever before.

Above: Me with some KIPP students at the Washington Monument

Crafting a Data Narrative

When I presented the updated dashboard to school leaders, they were excited about the new possibilities it unlocked. My graphs led to insights that started conversations that produced better performing schools. But none of this was the result of something I had learned in a statistics class. Instead, it was simply the result of a mindset that anyone can develop: understanding your audience and designing solutions with their specific needs in mind.

Every organization has unique questions it needs its data to answer, and more often than not, getting to those answers isn’t a straight and narrow path. It’s one that necessitates adaptability, living in the grey rather than sticking to black or white, and it invites creativity. While my path into analytics wasn’t what the textbook may have prescribed, I’m thankful for the outsider’s perspective it lent me. It makes balancing art and science easier, it allows me the freedom to bend and not break, and it more closely resembles what big data really looks like today: a blend of beautiful design and actionable insights—a unifying of both sides of the brain.

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