Why Hundreds of Reports Usually Means Nobody Uses Any of Them

Quick answer

When a data team produces hundreds of reports, the usual result is that nobody uses them. People can't find what they need, nobody maintains them, and there's no clear link to the decisions people actually make. The fix is simple to say: ask the people the reports are for what they want to see, model the data around the business so a few reports cover a lot, and help people answer day-to-day questions themselves.

Key takeaways

• More reports doesn't mean more value. Past a point, it means people stop looking.
• The most common mistake we see: data teams never ask their users what they want to see.
• Skilled data teams without business context build good things that don't fit the business.
• The best data teams aren't the bottleneck. They help people answer operational questions on their own.
• Using the BI tool your team already has usually beats switching to a better one nobody adopts.

"They never asked me what I wanted to see"

I was talking to a doctor who owns a clinic under a multi-location brand. The brand has its own data team, and that team builds reports and insights for every clinic in the network.

I asked him how useful it was. His answer: there are hundreds of reports, and he can never find anything. He's running a clinic, so time is the one thing he doesn't have. Even when he did sit down and dig through them, a few seemed sort of useful, but none of them showed what he actually wanted to see.

And when I asked if anyone had ever asked him what he wanted? No. Not him, not the other doctors. That's the whole problem in one conversation. If nobody uses the reports, the work behind them is wasted, no matter how good it is.

Centralized data is not the same as useful data

We see this a lot. A company invests in a data team, the data gets centralized, and reports pile up. Then you look closer and nothing is being used, because nobody has time to maintain hundreds of reports and none of them connect clearly to how the business runs.

What we do instead is model the data around the business first. When the model is focused, reports are easier to build, and when the logic changes, you update the model and the dashboards follow. You go back to one or two places, not a hundred reports. Nobody wants to spend their week updating reports. This is the same systems problem behind why your team keeps asking you for the same numbers.

Skilled teams without business context build the wrong things

Something I've noticed in teams I've worked with and managed: once a company decides to take data seriously, it starts hiring. The people are skilled and good at what they do, and the team keeps growing. But if they don't get enough business context, they end up building things that are well made and don't make sense for the business.

That's not a talent problem. It's a context problem. The fix is putting data people in regular contact with the people who use the data, and making "what are you trying to decide?" the first question on every request.

The best data teams aren't the bottleneck

Data teams are great when they ask users what they want to see, and when they don't become the bottleneck. Board decks, external reporting, anything going to the CEO: you want someone skilled building that. But for day-to-day operational questions, the teams that do best are the ones that help people answer things themselves.

Not everyone will want to self-serve, and not everyone has the skills. That's fine. If you aim for it anyway, you end up with a much more data-driven culture than a queue of requests ever builds. How to get your team to actually use Metabase covers the rollout side of this.

Use the tools your team already has

We have favorite tools, like everyone. But we work with whatever our clients already use, because change management is hard. A client we're working with right now has a pretty old BI tool. We've worked a lot with newer tools that make self-serve easier, and we'd be happy to evaluate a switch together if they asked. But moving everyone to a new tool means retraining, migrating, and convincing people, all for something they already know how to open. Adoption matters more than our preferences.

How to tell if this is you

• You have more reports than people who open them.
• Nobody can say who last used a given dashboard, or why it exists.
• People ask the data team for numbers that already live in a report somewhere.
• The data team has never sat down with the people the reports are built for.

This is what we build

We start by talking to the people who'll use the data, then model it around how the business works, so a small number of reports covers what people actually need and stays easy to maintain. Then we help your team answer their own day-to-day questions, in the tools they already use. See how it works.