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At first glance the link between baseball and associations is tenuous at best. But everyone loves a good sports metaphor and as it turns out, association professionals can learn from baseball, or specifically, from Oakland As manager, Billy Beane, the hero of the real-life Moneyball story.

I recommend you read our white paper The Moneyball Effect, but I wanted to share this sneak peek into how associations like yours can use existing data in different ways to gain new insights into their membership.

For a while now we’ve been hearing a lot about big data. Even the name, “BIG DATA” can be intimidating, especially if you don’t have the funds, or indeed the data, to go big. But don’t worry, it’s not the size of your data, or your budget that matters, it’s how you analyze and use it that’s valuable to the future of your organization.

You may already be familiar with the story from the book and movie of how the A’s Manager Billy Beane turned baseball on its head when he started to use statistics in a new way to achieve success. If you substitute “data” for “stats” we are proposing you take a new approach by looking at your member data in different ways to better understand what’s important. Perhaps it will inspire you to discover what you didn’t know you didn’t know, plan changes and reap the benefits. Everyone these days, from the largest corporation to the smallest association, benefits from becoming a data-driven organization.

The Moneyball Effect highlights six lessons for defining and achieving an actionable data plan at your association. To get you started, here are three of them.

1. Define the Win

Baseball has an advantage over associations in that success is clearly defined: winning games. To quote Jonah Hill’s character from the movie, who advised Beane:

“Your goal shouldn’t be to buy players. Your goal should be to buy wins. In order to buy wins, you need to buy runs.”

“Winning” may not be so easily defined in the association world. Perhaps the simplest definition would be financial: growth in net revenue. That’s an easy result to measure, but the factors that go into it are numerous and varied. Let’s say you’re able to gather comprehensive data on member engagement. Even if you can discern a noticeable trend showing increased engagement among a segment of your membership, it will be hard to make the case that that trend was a direct effect of revenue increase over the same time period.

If you’re unable to develop actionable insight from the data, don’t force it. There’s no shame in picking goals or “wins” that are more like subsets or contributing factors to the overall success of the organization. It’s more likely that analysis of member engagement data is going to connect more directly to member retention, for example.

2. Experiments Over Models

Beane never claimed to have discovered the design for the perfect baseball team. He simply used insights (drawn from extensive data analysis) to design experiments within his specific context, and he learned from those experiments.

As you dig deeper into your member data, remember that while it would be great to show your board that you have discovered an approach to guarantee member growth and net revenue growth, it’s not realistic. If you try to develop a big, perfect model, you’ll end up with analysis paralysis, and nothing will change.

Instead, focus on experiments. Don’t insist that the data prove what you think they will prove. Just pick a number of data points and dig into the analysis. Find a mix of some traditionally valued data and some that are below the radar. Do some analyses and see what comes up.

The results of your experiments may not be conclusive, and that’s fine. Use it as an opportunity to create new, more refined experiments. Learn, and try again.

3. Step Up to the Plate

The moves that Beane made were not popular with much of the rest of his organization at first. This made his ideas difficult to implement. But getting results from your data-based experiments requires trying new things.

So, what did Beane do? He removed the barriers to the action he sought. And while many did not understand his moves, they enabled him to see his experiment through to action and results. It is not enough to analyze data and wonder if a new approach would work. You have to step up to the plate and try new things. And if your experiments go against tradition, be ready for resistance.

This doesn’t mean that you’ll have to cancel your annual conference to make room for some social media experiments. But you may have to give people permission to try new things. You might have to do some extra outreach to the department that doesn’t understand or agree with the experiment you want to try. You might need to pull some volunteer leaders aside to talk about why you are conducting these experiments and what value they will provide over the long term.

Remember, your organization has a culture, and cultures are inherently stable. The “way we do things around here” developed because those ways were successful, and even if situations dictate the need for change, the culture might need some convincing.

How is your association analyzing and using data in new ways? Please share in the comments below your findings, challenges, wins, etc.