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 data trust issues

Let's be direct. You were probably told that a new, modern BI tool would solve the constant debates about whose numbers were correct. Looker, with its promise of a governed, code-based semantic layer, likely seemed like the perfect answer to the slightly chaotic state of your old Tableau setup. The project was signed off, the budget was spent, and the dashboards were rebuilt.

Six months later, the situation is often quite different. You look at the Looker usage stats, and they aren't what you'd hoped for. The finance team still exports everything to Excel. The marketing team still asks for CSVs because they don't trust the attribution dashboard. And, more telling, you're getting quiet requests to keep the old Tableau server running for 'a few more months' for 'historical comparisons'.

This is a common sign that the migration hasn't gone as planned. It’s a situation I often see in companies that have hoped buying a new tool would be a substitute for sorting out their data strategy. You haven't solved the underlying data problem. You've just found a more expensive way to get the same confusing answers.

The problem is usually the logic, not the tool

The mistake is often in believing the tool was the problem. In my experience, it rarely is. Tableau, Looker, and Power BI are all perfectly capable tools. The real issue is that the underlying process for creating reliable insights hasn't been addressed.

You have likely moved to the cloud and hired smart engineers. But it seems this has just moved the existing problems to a new home. Automating a process that isn't sound just means you get confusing results more quickly.

What has usually happened is that the essential, foundational work was skipped. The difficult conversations about how key metrics are defined were never concluded. The messy, contradictory logic hidden in countless different SQL scripts and Excel files was simply copied into a new environment. A low BI adoption rate isn't a training issue, it's a direct reflection of a lack of data trust in the numbers themselves.

I once worked with a company paying over £150,000 a year for a new Looker instance. We found that 60% of their reports hadn't been viewed in six months, yet the data team was still getting requests to 'just pull the data from the old system'. They were paying twice for one source of confusion.

A way forward: fix the foundation, not the dashboards

The way out of this expensive situation isn't more training, more dashboards, or another tool. The solution is to subtract and consolidate. It's about doing the hard work that was avoided the first time.

Pause new dashboard requests: Put a temporary hold on all new requests. Your team is not a 'dashboard factory'. Building more untrusted reports only makes the problem worse. This frees up your team's time to fix the foundation.

Build the semantic layer: This is probably the most important step. The goal is to get the business to agree on core definitions. What is an 'active user'? How is 'net revenue' calculated? These definitions must be written down in one place, like LookML in Looker, not scattered across a dozen dbt models and a hundred different reports. This is political engineering disguised as data architecture.

Introduce light governance: This isn't about creating bureaucracy. It's about having 'decision insurance'. It means putting simple checks in place, like a CI/CD workflow, so that when a metric definition changes, it's reviewed, approved, and documented. A proper Data Governance framework protects the CEO from looking unprepared in a board meeting because the sales and finance numbers don't match.

Acknowledging the difficulty of this work

I won't pretend this process is easy. It requires telling senior people they can't have their preferred dashboards for a little while. It means getting department heads in a room and not leaving until you have an agreed definition for 'churn'. Your team may be used to being reactive, so you need to support them in becoming architects. They will meet resistance. You will have to move a bit slower for a few weeks to be able to move much faster for the next few years.

The outcome of getting the foundation right

Once the foundation is solid, the BI tool can finally do its job properly: provide a clear view of the business. The arguments over 'whose number is right' should fade away. The ad-hoc requests tend to diminish because people can finally trust the self-serve environment. You will, at last, be able to decommission that old Tableau server and stop paying two bills.

Only then was the migration worth it. You didn't just buy a new tool; you built a more reliable system for making decisions.