Somewhere right now, an attraction leader is writing a job description. It says something like:

“We’re seeking a curious, data driven professional to help us better understand our audiences. The ideal candidate will be comfortable with both quantitative and qualitative analysis, translating complex data into actionable insights for stakeholders across the organisation.”

Sounds great. Progressive, even. A signal that this institution takes data seriously. But here’s what that job description actually means, translated into the reality of a you average commercial attraction or cultural institution with little or no existing data infrastructure:

“We have Google Analytics, a ticketing system we half understand, an email platform with a few segments somebody set up in 2019, and a spreadsheet that Tracy updates monthly. The board said we needed a data warehouse two years ago and we did a proof of concept in Snowflake. We need someone to make sense of all of it. Good luck.”

This isn’t a data analyst role. It’s a rescue mission.

The first six months

Your new analyst arrives, bright and motivated. They have ideas. They want to build visitor personas, model membership propensity, analyse exhibition impact, connect the dots between marketing spend and ticket yield. They’ve been sold the story of the fun they’ll have. 

Here’s what they’ll actually do:

Month 1 – 2: Ask for access to systems. Learn that the ticketing data lives in one format, the CRM in another, and the email platform doesn’t talk to either. Discover that nobody’s quite sure what the Google Analytics property is tracking since the GA4 migration. Request a meeting with IT. Get scheduled for six weeks from now, after the CRM upgrade.

Month 3 – 4: Start pulling CSVs. Build a spreadsheet to reconcile ticketing data with finance reports. Notice the numbers don’t match. Spend two weeks figuring out that refunds are handled differently in each system. Begin building a “management dashboard” that they promise will be ready soon.

Month 5 – 6: The COO asks, “So, what are we learning about our visitors?” The analyst shows a dashboard, labelled work in progress. It has attendance by month and some demographic breakdowns from a survey that ran last year. COO nods politely. Both of them feel a quiet sense of disappointment but neither says it out loud.

This isn’t a failure of talent. It’s a failure of sequencing.

Meanwhile, in a parallel universe

In another version of this story, that same executive didn’t hire an analyst first. They connected their data to a platform. And three weeks later, sitting at their desk at 9am on a Tuesday with a cup of coffee, they typed:

“What did our weekend family visitors look like compared to last year?” And got an answer. Not a ticket in a queue. Not a “let me pull that for you by Friday.” An answer, with the data behind it, in the time it took to drink the coffee.

Then they asked a follow up: “What’s the average spend per visitor for members vs. non-members on weekends?” Another answer. Then: “How does that compare to school holidays?” Another. In twenty minutes, they’d explored a line of thinking that would have taken their (hypothetical) analyst two weeks to research, pull, clean, analyse, and present. Not because the analyst isn’t smart, but because the process of manually stitching data together is slow, and the process of asking a question of a connected platform is fast.

Here’s the thing that changes everything about this hire: the COO didn’t need to know SQL. They didn’t need to know which database the ticketing data lived in, or how to derive a visitation metric between that and their member scans from the CRM. They just needed to know what they wanted to understand.

The valuable skill wasn’t technical. It was the question.

The job you advertised vs. the job they’re doing

There’s a brutal mismatch at the heart of the traditional version of this story and it plays out at visitor attractions everywhere.

You hired someone to think. To find the story in the data. To sit in the room when you’re planning next season’s exhibition marketing and say, “Here’s what we know about who came last time, what they spent and what brought them through the door.”

Instead, you’ve got someone plumbing. Wrangling exports. Fixing data quality issues. Maintaining dashboards that nobody quite trusts. They’re not an insight analyst, they’re a one person IT department with a fancier title.

And increasingly, the plumbing work: the connecting, cleaning, querying and visualising, is exactly what modern platforms do. Not partially. Not as a rough draft that still needs a human to finish. Properly. The kind of data question that once justified a full time hire can now be answered conversationally, by anyone on the team, in minutes.

Which raises an uncomfortable question: if the technical work is automated, what’s left? The answer is: everything that actually matters.

A different kind of hire

When the infrastructure does the heavy lifting, the human skill that matters shifts dramatically. You stop needing someone who can write the best query and start needing someone who can ask the best question. Those are very different people.

That ‘best question person’ might be a former educator who understands how to better target a more diverse or wider catchment of school groups. An ex front of house manager who regularly walks the halls and always has an eye on visitors. A once membership coordinator who instinctively know where to probe when wondering why renewals dropped. These people don’t think in SQL. They think in visitors.

Give them a platform where they can ask their questions in plain language and they’re more dangerous than any analyst with a spreadsheet because they start from domain knowledge, not data structure. They know what to ask because they’re close to the experience. The platform handles the how. They take action. 

This doesn’t mean technical analytical skills are obsolete. Far from it. But it does mean the default hire, the generalist analyst brought in to “make sense of our data”, might not be the smartest use of a limited headcount at a mid size institution.

Maybe your next hire is more a qualitative researcher who spends time with visitors, not just their transaction records. Maybe it’s a strategist who sits at the intersect of experience, marketing, operations and programming and uses data (that the platform surfaces) to make the case for change. Maybe it’s an operational “data champion” whose job isn’t to produce analysis but to help every department develop the habit of asking. 

You won’t know which of these you need until the data is flowing and you can see where the real gaps are. And the gaps might surprise you. They’re rarely “we need someone who can query a database.” They’re almost always “we need someone who can act on what the data is telling us.”

The cycle nobody talks about

Here’s how the traditional version plays out over 18 to 24 months:

1. The hire feels like progress. Leadership is excited. “We’ve got a data person now.”
2. Reality sets in. The analyst spends most of their time on foundational work that’s invisible to leadership. Lots of meetings. 
3. Expectations drift apart. The COO wanted strategic insight. The analyst is drowning in data hygiene. Neither is wrong, but neither is getting what they need.
4. Frustration builds quietly. The analyst starts updating their LinkedIn. The executive starts wondering if they “hired the wrong person.”
5. The analyst leaves. And when they leave, they take everything with them. The dashboards break. The institutional knowledge evaporates. The spreadsheet that only they understood? Nobody can find it. 

You’re back to zero. Except now you’ve spent two years of salary and you still don’t have a data foundation. At Dexibit, that’s often when we get a call from a soon to be customer. 

A platform doesn’t get frustrated. Doesn’t leave. Doesn’t take the dashboards with it. And it doesn’t sit between your team and their data, it gives them direct access to it. The insight isn’t locked in one person’s head. It’s available to anyone with a question.

Let’s be clear about what this argument isn’t. It’s not that you don’t need smart, analytical, curious people at your institution. You absolutely do. Visitor attractions are sitting on a goldmine of audience data – most are barely scratching the surface of what it could tell them. But the role of those people has fundamentally changed and job descriptions haven’t caught up.

The old model was: hire someone technical to access the data, then wait for them to translate it for everyone else. One person with the keys. Everyone else waits in line. The new model is: give everyone access to the data, then hire someone brilliant to help the organisation ask better questions and act on what they find. That’s a higher value role, upstream. It’s more strategic, more creative and frankly more interesting for the person in the seat.

The order matters. Infrastructure before headcount. Foundation before hire. Not because the technology is more important than the person, but because the person is too important to waste on work the technology should be doing.

If you genuinely value audience insight (and if you’re reading this, the fact that you’re considering this hire says you do) then give your future analyst the one thing they’ll never ask for in the interview but will absolutely need to succeed: a running start.