Has an art museum director ever lost their job because they didn't know enough about art history? Has a science center CEO been fired for insufficient understanding of physics?
I asked these questions recently on LinkedIn, inspired by @John H Falk's challenge to our assumptions about what makes an effective museum leader. The post sparked many thoughtful responses that point to a deeper tension in the museum field: When museum professionals hear "data-driven decisions", some instinctively recoil. For some, the phrase seems to conjure images of profit-focused metrics and attendance targets that prioritize quantity over quality.
This reaction reveals a fundamental misunderstanding of data: Just because data can and often is used to advance a growth-centric agenda, it doesn't have to be.
The Real Question
As museums look to become more "data-driven”, they may hire consultants, build dashboards, and train staff in data analysis. But before spending resources on data capabilities, there's a more fundamental question to answer, which both Falk and @Robert Janes have been asking in MaP this season: To what end?
When I first brought this question of becoming more data-driven to Falk earlier this year, he asked, "To what end?” In other words, what does a data-inclusive culture afford? What result will integrating data help achieve?
A generic response might be, “We want to use data so we can make better decisions.”
The question then becomes "Decisions about what?" or "Which decisions are most important?" Only once we have the answers to those questions will we know if the solution we're reaching for (better data or integrated data) is the right one.
For the Value Realization Collaborative, we chose the challenge of audience engagement and diversification as the vehicle — That is, the program does aim to address engagement and diversification, but it’s also true that the challenge is just an armature. Engagement and diversification is an anchor that keeps participants grounded while we help them adopt a more expansive view of what it means to collect and make sense of data. Participants will make progress on the challenge, but we could have also chosen other challenges for the program — measuring and communicating impact or financial sustainability or community outreach and partnerships … Any of these challenges can be addressed with data, but data is not an end in itself.
And, in truth, engagement and diversification is just another layer in the onion. After all, a museum can seek to increase engagement or diversify its audience for any number of reasons:
To demonstrate relevance to funders
To support specific community needs
To advance institutional sustainability
To address societal challenges
Without clarity about purpose, we risk collecting data that tells us how we're doing but not whether we're doing the right things.
This isn't about choosing between being "data-driven" or staying true to cultural mission. That’s a false choice. Evidence doesn't replace institutional purpose or necessarily put your museum on a path to becoming a business — it helps ensure the museum is actually fulfilling its role.
A More Purposeful Approach
If your museum is considering how to become more data-driven, here’s one way to begin:
Identify your critical challenge: What is the one thing everyone is (ideally) working to address? If you don't know, then that is the first and biggest question — not "how can we be more data-driven".
Understand current decision-making: Identify how decisions are being made today in relation to that critical challenge (don't assume you know!). What actually goes through people's minds as they make decisions at your museum? What have they felt as they've tried to address questions related to the challenge? Thoughts and feelings are what will enable or prevent people from using data. Focus on thoughts and feelings, not activities or opinions. In practice, this means you need to collect specific stories from staff — not what staff say they "usually" do or what they believe is "best practice". Remember: A single story is an anecdote, many stories collected in a systematic way are qualitative data that can be used to uncover opportunities for improvement. (You can learn how to do this.)
Find patterns and define metrics: Look across the stories you've collected to identify common themes in how decisions get made. How do people approach decisions today? When do they reach for data? When do they avoid it? These patterns will enable valuable discussion among your teams about how you can begin to address barriers or better leverage what’s working today.
As you work through these steps, you'll develop both the understanding and the skills needed to make data meaningful in your organization. In this case, the process is a cure in itself — making the medicine is at least as valuable as taking it.
If you’d like to explore this approach in greater depth, join us for the Value Realization Collaborative, where we'll work with John Falk to apply this evidence-based approach to addressing a critical challenge — audience engagement and diversification. Your staff will get hands-on experience listening deeply and applying Falk’s Value Realization Cycle, which will give you a repeatable process for tackling critical challenges in the future.