Ad-Hoc Reporting: The Silent Killer of Data Velocity

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How ad-hoc requests become a problem as you grow

In the early days, an ad-hoc request was a sign of getting things done. The Head of Product could send a Slack message to an engineer and get a list of users in an hour. This was the human glue holding things together: fast, simple, and it worked.

But you're a fintech scale-up now. Transaction volume is ten times what it was, you have regulators to consider, and the questions from the commercial team are more complex. The 'quick pull' that used to take ten minutes now involves joining a few tables and hoping for the best. What used to be helpful glue is now causing friction.

You have likely moved to the cloud and hired good engineers, but if the underlying process is messy, you're just making the same mess, only faster. Every answered Slack query is a small piece of unmanaged, undocumented work. It's a report without a clear definition, an answer without a clear history, and it's slowing your team down.

Why your data team is stuck answering tickets

The most obvious sign of this is a Jira board full of tickets. The underlying cause, though, is usually a lack of a clear data structure. Your data team has become a reactive helpdesk because the business has no other reliable way to get information. People might not trust the dashboards, or they simply don't know where else to look.

This is a common trap for scaling companies. You've invested in Snowflake, dbt, and Looker, but you're still working like a five-person startup. The problem isn't the dashboard tool. It's that the business logic is hidden away in hundreds of different SQL scripts instead of being managed in a central Semantic Layer. This means your engineers often have to start from scratch for similar requests, which can lead to burnout and, worse, different answers to the same question. That sort of thing really undermines trust in the data.

I've seen this pattern at a few fast-growing fintech firms. With one team, we set up a 'Self-Serve Hub' which cut down the number of 'Can you just pull this number?' messages by half in about three months. We didn't just clear the backlog; we stopped it from building up again.

Moving from reactive tickets to a self-serve model

You can't really hire your way out of this problem. The only long-term solution is to change the system itself. This means a deliberate shift designed to Reduce Data Tickets by making many of them unnecessary.

Build the core, not the one-offs: First, you need to manage the immediate problem. We'd typically look at all the recurring requests to find the core business questions. Then, we'd build a small set of reliable, senior-level dashboards. This isn't about building more dashboards, it's about building the right ones and retiring the others.

Create a 'Self-Serve Hub': This is just a central place for your internal data, perhaps in Notion or Confluence. It holds the core dashboards, a data dictionary with clear metric definitions, and some simple 'how-to' guides. It becomes the first place people in the business go before they think about raising a ticket.

Develop champions in other teams: You can usually find a 'data-curious' person in marketing, operations, and product. We deliver hands-on workshops with them, using their own data to solve their own problems. They become the first point of contact for their teams, handling the simpler questions that used to clog up the data team's queue.

This whole approach is the basis of a sensible Fintech Data Strategy. It's about treating your internal data reporting like a product, not a helpdesk.

Handling the cultural change

To be clear, this is as much a people challenge as a technical one. Your commercial teams are probably used to getting answers on demand. When you start pointing them to a self-serve hub, you can expect some pushback. They might say it's slower.

This is where the Head of Data needs support from senior leadership to see the change through. The explanation is straightforward: "We're going to move a bit slower for this quarter so we can move much faster for the next few years." You are not taking their data away; you are giving them a more reliable and scalable way to get it. This is the quiet, essential work that helps a chaotic scale-up become a more professional, data-led organisation.

By making this shift, you get your engineers out of the reactive ticket trap. They can get back to building the solid, scalable data platform you hired them for. And you can move from being a manager of a ticket queue to the data leader the business actually needs.