Migrating BI platforms? Here's how to ensure it won't break: A guide to refactoring your BI logic safely

When teams migrate BI systems, the work that creates the most risk is rarely the dashboards themselves. It’s the logic that has accumulated around them over time.

By the time migration becomes a serious discussion, most BI environments reflect years of incremental decisions. Metrics exist in multiple variants. Filters behave differently depending on context. Calculations depend on assumptions that are no longer documented and are often understood only by the people who originally built them.

The difficulty is not that this logic is necessarily wrong. It’s that it lives in too many places to be examined as a system.

Why Most Migrations Preserve the Problem

Most BI migrations follow a predictable sequence.

Dashboards are recreated first so users can continue working. Existing logic is copied as closely as possible to minimize visible discrepancies. Validation focuses on whether outputs resemble those produced by the legacy system.

From a delivery perspective, this approach works. From a system perspective, it preserves the existing structure.

Once logic is running in production again, deeper cleanup becomes difficult to justify. Any change carries unclear risk. Refactoring is postponed because there is no longer a safe window to do it. The migration finishes, but the underlying complexity remains.

Refactoring Requires Making Existing Logic Explicit

Safe refactoring starts with visibility.

Before teams can make changes, they need to see:

• how changes to tables or data models in BI tools affect metrics and results
• how many variants of the same metric exist
• where joins and filters differ
• which definitions are actively referenced
• which ones no longer affect results

As long as logic remains embedded in dashboards and proprietary files, this kind of review is not possible. Decisions are based on partial information, and refactoring becomes speculative.

Externalizing logic into a form that can be inspected and compared is a prerequisite for doing this work responsibly.

Comparison Comes Before Rewrite

A common failure in migrations is attempting to “fix” logic immediately after extraction.

In practice, teams make more progress by comparing definitions before changing them. When multiple implementations of the same concept are laid out side by side, differences become clear. Some reflect intentional business rules. Others are the result of historical workarounds or incremental changes that were never consolidated.

By focusing on comparison first, teams can decide which differences matter before altering behavior. Refactoring then proceeds incrementally. Definitions are normalized, duplication is reduced, and outputs are validated against legacy results.

Structural changes come first. Behavioral changes are introduced explicitly. This sequencing is what keeps refactoring contained and predictable.

Separating Logic From Presentation Changes the Migration Surface

Once definitions are consolidated, they need a single place to live.

Instead of pushing logic back into dashboards, teams centralize it in a governed semantic model that becomes the reference layer for everything downstream.

Dashboards consume definitions rather than embedding them. Applications reuse the same logic rather than reimplementing rules. Changes are applied once and propagate consistently.

At this point, migration stops being about individual reports and starts being about managing analytics as a system.

Why Treating Analytics as Code Matters

Another shift occurs when logic is no longer stored in proprietary dashboard files.

When definitions are represented as text:

• changes can be reviewed
• differences are explicit
• history is preserved
• rollback is straightforward

This enables teams to refactor continuously instead of batching changes into high-risk efforts. The benefit is not developer convenience. It is operational safety. Teams can reason about impact before changes reach production.

Keeping Systems Live While Refactoring

Refactoring during migration only works if existing systems remain operational.

Legacy dashboards continue to run while refactored logic is validated in parallel. Results are compared directly. Differences are investigated intentionally, not discovered by users after deployment.

Some consumers migrate early. Others move later. There is no forced cutover. This parallel operation is what allows teams to address deeper issues without interrupting delivery.

Where Automation Actually Helps

In real BI environments, the largest time investment is not writing new logic. It is understanding how existing definitions differ across dashboards, models, and queries.

Once logic is extracted into a structured representation, much of this comparison work can be automated. Automated analysis can surface duplicate metrics, inconsistent filters, and unused dependencies across large BI estates.

Automation does not decide which definitions are correct. Its role is to reduce the amount of manual inspection required before refactoring can proceed safely.

The practical effect is time compression. Work that often stretches over months when done manually, auditing definitions, comparing variants, and validating outputs, can happen earlier and in parallel, while systems remain live.

When Logic Cannot Be Cleanly Extracted

Not every BI environment exposes logic in a structured, extractable form.

Some logic exists only in undocumented expressions. Some behavior only appears at the dashboard level. In other cases, legacy tools make it intentionally difficult to export definitions in a usable form.

Refactor-first migration accounts for this reality.

When logic cannot be fully extracted, teams switch to behavior-based reconstruction. Dashboards, screenshots, and known outputs are treated as specifications rather than artifacts to be copied. Definitions are rebuilt explicitly, validated against observed results, and reviewed before being centralized.

Missing structure does not block progress. It changes the input, but the refactoring workflow remains the same: make behavior explicit, compare it, validate it, and govern it centrally.

How Refactor-First Migration Is Implemented at GoodData

Refactor-first migration is only viable if extracted logic can be inspected, compared, and changed using standard engineering workflows.

At GoodData, logic extracted from existing BI tools is converted into human-readable definitions that engineers work with directly. Metrics, joins, and filters live as version-controlled files. Changes are reviewed as diffs, validated in parallel, and rolled out incrementally.

Machine-assisted analysis is used to compare definitions across large BI environments and surface differences that require review. The system does not infer intent or choose a “correct” definition. It eliminates the need to manually search through dashboards to understand what exists.

Because this work happens before dashboards are rebuilt, refactoring proceeds while legacy systems remain in use. Validation is continuous rather than deferred. This allows migration and cleanup to occur simultaneously without increasing risk.

In practice, much of this work is driven by AI-assisted analysis and code-based workflows, which allows teams to refactor and validate logic far faster than manual approaches without changing the underlying process.

What to Look for in a Migration POC

When evaluating a migration approach, dashboards are usually the least informative signal.

More meaningful questions include:

• how existing logic is extracted
• how differences between definitions are surfaced
• how validation is handled
• how long systems can run in parallel

Any approach that cannot refactor logic while keeping systems live will eventually force a tradeoff between speed and trust.

Conclusion: A Practical Path to Modernized BI

Modernizing BI does not require a freeze, a rebuild, or a leap of faith. It requires changing the order in which work is done.

Teams that extract, refactor, and govern logic as part of migration end up with systems that are easier to change, easier to reason about, and ready to be reused without repeating the same cleanup work later.

That is the difference between moving dashboards and modernizing BI.