Lean & Scrappy

At Monumental Sports & Entertainment (MSE), I was tasked with building and running our Strategy & Research department. The team oversaw all consumer insights operations (surveys, focus groups, empirical research) across MSE’s properties (NHL’s Washington Capitals, NBA’s Washington Wizards, WNBA’s Washington Mystics, AFL’s Washington Valor, AFL’s Baltimore Brigade, eSports Team Liquid, and our venues Capital One Arena, EagleBank Arena, Kettler Capitals Iceplex). Within each of these properties we worked with each department (marketing, sales, corporate partnerships, etc.) to help them meet their insights needs with a blend of quantitative and qualitive approaches.

The problem our organization (and nearly all in sport) had was frequency of reporting, connectivity of reporting, pricing and valuation of assets, and data visualization structure to help those without data backgrounds feel empowered to act and build relevant data-backed strategies. In order to do this, most organizations would need to spend north of a hundred thousand dollars on systems, software, and personnel. However, sports organizations are notorious for having extremely limited budgets. Therefore, obtaining the necessary outside resources to assist with the extensive data connectivity, housing, cleanse, and creation of centrally accessible dashboards and visuals, would not be an option. Instead, I was faced with needing to create solutions for the following:

• Frequency of data refreshing and reporting

• Connecting and housing data together

• Creating the first comprehensive pricing and valuation predictions

• Telling a story and identifying opportunities through data visualization

• Implementing insights

Although it might sound simple, accomplishing these steps would take a great deal of effort, alignment, and collaboration. And that’s exactly what I set out to do. Here are some of the notable frugal and scrappy ways in which we made the task a success:

• There was a lot of data. It lived in multiple different programs or databases, some of which our organization had direct access to, some of which were controlled by third parties. Not everyone wanted to share the data they had. But over time and through great effort and alignment, we were slowly able to receive the data feeds we needed and started manually connecting disparate data sets together through common unique identifiers. We did this in part to avoid expensive automation systems.

• Some data sets were easier to work with than others. In a few instances, we had to work with internal tech teams to either change or build custom reporting capabilities to ensure the data was usable.

• Several of our external vendors couldn’t produce the data we required en masse, so custom API’s were developed with several vendors to allow better flow of data into our data repository.

• Once we felt we were in a good spot to collect and work with data, extensive time was spent with managers and directors from across the organization to better understand what they needed and the applications in which they might be able to use different data insights.

• Being short on budget and building complex data visualizations meant a lot of trial and error and self-teaching. I enlisted other industry experts I had relationships with to offer their consults on how to go about undertaking such a large and complex build.

• We took our data practices and insights a step further and started to tie in both internal empirical research (surveys, focus groups) and external empirical research (Nielsen Scarborough) to help in forecasting and pricing schemas.

• After we felt our data was clean and ready to use, we systematized pricing our assets and recording inventory levels in relation to partnership assets.

• Essentially, we created a Wiki for the corporate partnership division of our organization.

After several months, I was able to aggregate our data sources and create a series of visualizations that helped transform our data into actionable insights. As this was the first iteration of a major sports team piecing together this many data sources to tell a more full and better picture of team assets, I was asked to present our approach at team-wide attended meetings for both the NHL and NBA.