BI Migration Failure: Why You're Paying for Two BI Tools

Executive Summary

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Why a new BI tool often doesn't solve reporting issues

You've signed a large contract for a tool like Looker, Tableau, or Power BI. It was meant to bring clarity, speed, and self-service. But six months later, you're still paying for the old system, and the finance team is quietly exporting data to do their actual work in Excel.

I see this happen quite a lot with companies from Series B to D. They invest in a modern data stack, hire engineers, and buy the latest tools. But often, all they've achieved is to move their existing problems over to a new system. Automating a messy process just means you get the wrong answers more quickly. The idea that a new tool can fix a fundamental problem with your business logic is, in my experience, one of the costliest assumptions in data.

Your team isn't being difficult. They're doing something quite sensible. They stick with the old system because, for all its faults, they trust the numbers it produces. The new platform gives them different numbers, so they don't use it. This is a common sign of a failed BI adoption, and it's rarely about the quality of the new tool.

Why the numbers don't match: migrating dashboards instead of logic

In my experience, the reason the numbers don't match is nearly always the same. The migration project focused on recreating the old dashboards, not on defining the business logic clearly. The definitions for important metrics like 'Net Revenue', 'Active User', or 'Customer Churn' are often unclear, tucked away in various SQL scripts and unwritten rules.

When the new tool is introduced, this confusion is just carried over. You end up with two platforms giving different answers to the same question. When faced with that, people will naturally fall back on what they're used to. The constant requests for analysts to provide a CSV export is a symptom of broken data trust, not a sign that you need a better export button.

I recently looked at a Looker setup for a fintech scale-up in this exact spot. They were paying for Looker and their old BI tool. We found that about 60% of the new reports hadn't been looked at in months, yet the data team was still being asked for 'more data'. The teams were using the old system because they didn't trust the numbers in the new one. The new tool was effectively a ghost town, and the CFO was, in a sense, paying rent on an empty building.

A practical approach: build a reliable foundation first

You can't fix a lack of trust with training sessions. The solution is to fix the underlying structure. It means you have to stop building more reports and instead focus on building a reliable data foundation. It's about doing less, but doing it better.

Pause and review: The first step is to stop creating new reports for a while. Put all new development on hold. Then, do a proper audit of the new platform and remove any dashboards that haven't been used in the last 90 days. This helps to clear out the clutter.

Define the business logic: This is the difficult part. It's as much about getting people to agree as it is about data. You need to get the heads of Finance, Sales, and Operations together and agree on single, written definitions for your 5-10 most important business metrics. This logic then needs to be set in stone in a Semantic Layer, so it becomes the single source of truth for everyone.

Rebuild a small set of core reports: Once the logic is agreed upon, you can rebuild a small, carefully chosen set of management dashboards. These 10-15 reports then become the standard for all important meetings. Their accuracy has to be completely reliable.

This is really the heart of a workable CFO Data Strategy. It puts accuracy and trust ahead of having lots of dashboards.

Be prepared for some short-term disruption

Let's be honest, this process can be disruptive. It involves telling department heads that some of their familiar reports will be going away. It means pausing new analytics work for a short time to fix the foundations. You might feel like you're moving slower for a few weeks, but it's to help you move much faster for the next few years.

Your team is used to the old system. Trying to force them to switch before they see that the new one is better is unlikely to work. The aim is to make the new platform so clearly correct and reliable that people stop using the old one naturally. It becomes obsolete because people trust the new one more, not because of a top-down order.

By fixing the foundations, you rebuild trust. Once people trust the data, they'll start using the tool. And only then can you finally stop paying for two business intelligence tools.