Making Data Sing

By Steve Yeskulsky

With the advent of software in parks and recreation in the late 1980s and subsequent proliferation, it is the ancillary byproduct that now garners much promise moving forward. While big data—and all the ways they can be extracted from software systems—are the focal point of most developers, how we interpret, analyze, and ultimately express the data lands at our feet as storytellers. That final piece—the expression of the data—can either make the analysis sing like a bird or croak like a frog.

The majority of data collected by parks and recreation departments falls under the category of quantitative, measurable points, such as:

  • Revenue generated
  • Enrollments logged
  • Memberships sold
  • Facilities/fields reserved

What Does It All Mean?
This type of data is used by municipalities to tell their story and/or secure funding. If you go to any city council meeting during budget season or a monthly parks and recreation council meeting, quantitative data abound with little to no support or color. But here is the question: Does this type of data by itself tell a story? Do public and city officials understand these data sets enough to form a fair and accurate opinion? This question never occurred to me until I presented my department’s budget to city council several years ago. As I worked through my PowerPoint, the 11-member council sat motionless and quiet. Thinking I had completely won them over with my facts and figures regarding the number of residents who were in various programs the previous year and how much revenue was generated, I opened the floor for questions. Standing confidently, I took the first question, which was simply put, “What does all this mean?” Even after an additional explanation on the background of the data and what they meant historically, as well as how the data tied into the current needs, the council still showed little signs of grasping their relevancy.

How is this possible, I thought? Was the council just naïve to basic parks and recreation information, or was the problem the information I gave the members or how I presented it? Did something go awry with the old metrics that more revenue/participants amounted to a successful program/facility? Clearly, something needed to change, and after just scratching the surface, I realized I needed to do a better job of telling my city’s story using that data.

Cracking The Interpretation Code
My first mistakes were my unawareness of what drives the council, as well as what the members collectively and individually value most. It was like trying to read Machiavelli’s The Prince to my kids. While the words might make relative sense, and the sentiment is there, my interpretation of what they want to hear just before bed is way off. Knowing the motivating factors that resonate through an audience is invaluable when crafting a story.

My other major fault was not fully exploring all the dimensions, observations, and outliers within the data. Nothing expresses this concept better than participation numbers. This simple quantitative data point is used time and time again to show attendance levels and involvement in a program or agency. Important, yes, but I equate it to pulling a large diamond from the earth; it is of great value on its own, but only after I work with it does its true value shine. So, for example, the standard presentation of participation numbers goes like this:

“We had 1,500 campers in our last summer program; in the previous year, we had 1,400.” Here comes the in-depth analysis part: “The patterns over the past two years show a net increase of 100 campers, giving us a 9-percent increase. What does this chart and data tell us? It tells us the number of participants in the program and that participation levels are up from the previous year.” Beyond that, the reader is left to his or her own devices to understand anything else about the health of the program or the 1,500 participants.

Breaking Down The Data
First, using our software program, let’s pull out a little more information about those participants. By creating two Excel files—one of all known participants (minus the most recent registration period) within that age group, and another of only the most recent registration period—we can then filter and merge the information, giving us the total number of campers who had their first experience with our organization through this class. This piece of data could be used to understand how effective a marketing campaign was for a particular class or camp. Speaking of marketing, this list would be fantastic in getting some perspective on customers and introducing them to other offerings. It also could be used, alongside other metrics, as an in-depth look into the health of a program. What better way to judge a program’s viability than to visually show how many participants are new and how many participants return year after year? 

Second, using the list of all the participants in the most recent summer program, create a new list of all participants from the rest of the programming during the year. This will show the total number of campers who participated in another camp or class during the same year. This data point is another useful metric tool in understanding not only a program’s health, but more globally a department’s health. It also reveals your customers’ tendencies and activity levels.

Third, create a list of all campers who participated in the previous year’s summer program and a separate list of participants from this year’s programming. By filtering and merging these files, we have the total number of campers who returned the following year and participated in another program. This piece of data speaks to the health of a program and the health of the department. With so many recreational providers available, customers are not going to return to subpar programs.

Is the extra work and analysis worth the trouble? I think the results speak for themselves. Where previously the department may have been limited to sending out only email blasts, it can now use the data to target specific segments of the program and track the results of these efforts. Contrasting the previous quasi-analysis with the latter makes the former appear almost archaic.

Making The Data Resonate
Even the most elaborate and beautiful visualization aid cannot bring dull data to life. Info graphics abound, as well as numerous software programs and companies that will help spiff up a presentation. I recently had a chance to interview Cole Nussbaumer Knaflic, author of Storytelling with Data, who suggests that by starting with some basic design principles, you can gain much by using visual aids “By combining color selectively to draw attention to the important parts of your data and using text to provide context and make the data accessible—you can shift from simply showing data, to telling a story with it. Your audience will know both where to look and what message to take away.”

Now that you have acquired a wealth of insightful data and an elaborate info graphic soaring above the room, it is time to hook the audience. Knaflic notes,” I can tell you we should keep a park open because X number of people visited it over the course of the year. Or, I can tell you a moving story about the boy who was able to take that very special camping trip with his dad because the park wasn't closed, and use that to shift conversation into the importance of the park system. You may not remember what X was. But you'll remember the story.”

Clearly telling your story using data goes beyond great info graphics. It begins with knowing an audience and adjusting the storytelling accordingly. Also, in this age of big data, the old metrics of what makes a program successful are being tested and rewritten daily. With so much data at your fingertips, why not explore the programs and participants further by using the data you’ve already collected?

Steve Yeskulsky works in the parks and recreation field in Santa Monica, Calif. He can be reached at