Executive Summary
You've spent a good deal on a BI tool like PowerBI or Looker, but your team still exports data to Excel for most of their work. The new tool isn't being used.
Important decisions are being made with numbers from spreadsheets that aren't checked or governed. This creates real risk for audit and strategy, and it's far more costly than just the licence fees.
The issue isn't usually the team or the tool. It's a lack of trust in the data, which often points to problems in the underlying data setup. The answer is to fix that foundation, rather than just booking more training sessions.
Why more training isn't the answer
This is a pattern I see quite often in the scale-ups I work with. The leadership team has invested in a modern data stack. You have Snowflake, you have dbt, and you have a BI tool. And yet, the question that comes back most often is, "Could you just send me that in a spreadsheet?"
It’s tempting to blame the users for being resistant to change, or to think about switching tools. The most common reaction is to schedule more training.
To be clear, if your team is exporting to a CSV, it's usually not a training problem. It's a sign that they don't trust the system. What's happening is that they trust their own work in a spreadsheet more than the expensive data setup you've provided. More training won't fix a trust issue. You may have moved to the cloud and hired some very capable engineers, but if the underlying process is flawed, you've only found a way to get the wrong answers more quickly.
Common reasons for a lack of trust in the data
Exporting to Excel is a symptom of a deeper problem. It's a perfectly sensible response when the main system isn't giving people what they need. In my experience, the issue usually comes down to a few common problems.
Falling back on Excel isn't about preference. It's a practical way for your team to get the confidence they need to do their jobs when the official system lets them down.
How to build trust: do less, but better
The answer isn't another tool or more dashboards. It's often about doing less, but doing it better. It's about subtraction, not addition. I once looked at a Looker setup for a client where 60% of the dashboards hadn't been viewed in six months, yet the data team was still getting requests for 'more data'. The problem wasn't a shortage of data, it was a shortage of clarity.
The way to improve BI Adoption is to build a small, carefully chosen set of reports that are correct in every sense: mathematically, and in a way that all departments can agree on. The goal is to create a Single Source of Truth so reliable that exporting to Excel feels like the harder option.
This means doing the unglamorous but essential work that often gets skipped:
This is a people problem, not just a technical one
You should expect a bit of resistance. Taking away a department head's favourite (but misleading) metric can be a delicate conversation. Getting your Head of Sales and Head of Product to agree on a single definition of 'churn' is a job of negotiation, not just writing SQL. It usually means slowing down for a few weeks to get the foundations right, which allows you to move much faster for years to come. The goal is to make the official dashboards more reliable and easier to use than any spreadsheet.
Once you've done that, adoption tends to take care of itself. It becomes the path of least resistance. Your team will stop exporting to Excel, not because you've told them to, but because they simply don't need to anymore.