Having recently completed a migration from an outdated, on-premise BI platform to a modern cloud stack, I documented the key technical and process steps. The goal was to preserve historical reporting logic while enabling new capabilities. This isn't just a "lift and shift"; it's a strategic rebuild.
**Phase 1: Audit & Map**
* **Inventory existing assets:** Catalog all reports, dashboards, and underlying data sources from the legacy tool. Tag each by business owner, usage frequency, and criticality.
* **Document the logic:** This is the most crucial step. For every chart and KPI, document the exact SQL or transformation logic, filters, and date ranges. We found numerous hidden business rules embedded in old report definitions.
* **Gap analysis:** Compare the capabilities of the old tool with Ideogram. Identify any visualizations or data source connections that require a workaround or a phased approach.
**Phase 2: Rebuild & Validate**
* **Establish a new semantic layer:** Recreate core metrics (e.g., `qualified_mql`, `cac_payback_period`) in the new environment's data model. This ensures consistency.
* **Reconstruct iteratively:** Prioritize high-impact reports. Rebuild each chart in Ideogram, using the documented logic. We used a parallel run period, comparing outputs between the old and new systems for a defined time window to validate accuracy.
* **Implement attribution:** This was our value-add. The legacy tool handled basic last-touch. We rebuilt the core funnel charts but connected them to a new multi-touch attribution model in our data pipeline, providing immediate uplift in insight quality.
**Phase 3: Decommission & Train**
* **Manage the cutover:** Communicate timelines clearly to stakeholders. We provided access to both tools for one month post-migration, with the legacy system set to "read-only."
* **Develop documentation:** Create simple guides for the new dashboards, highlighting new features like self-service filtering or drill-down paths that weren't previously available.
**Key Pitfall to Avoid:** Do not attempt to replicate inefficient data models. The migration is the perfect opportunity to fix underlying data issues. For example, we consolidated three separate legacy "customer" tables into a single clean dimension during this project.
The process took approximately 10 weeks for a suite of ~50 core marketing and sales dashboards. The result is not just a new charting tool, but a more reliable and actionable data foundation.
-- J
Data never lies, but it can be misleading
Great to see this structured approach. The emphasis on documenting logic is something I can't stress enough. So many migrations fail because teams assume the legacy report *is* the specification, when it's often just an artifact of old constraints.
I'd add one thing to your validation step: parallel runs. For those high-criticality reports, having the old and new system produce outputs side-by-side for a full business cycle (a month, a quarter) builds immense confidence and often uncovers edge cases your unit tests missed.
What was your method for prioritizing which reports to tackle first? We used a mix of stakeholder interviews and usage analytics, but I'm curious if you had a different metric.
Read the guidelines before posting
Parallel runs are absolutely critical, but they introduce a cost variable rarely discussed: the compute and storage duplication required to maintain two full reporting pipelines. For a cloud migration, you need to budget for that dual infrastructure, potentially for a full quarter, and account for the data transfer fees between the old on-prem system and the new cloud environment if you're syncing source data.
On prioritization, we used usage analytics as a base but weighted it heavily with a "cost to remediate" factor. A low-use report with extremely convoluted, undocumented logic embedded in the old tool's proprietary script became a lower priority because the migration effort was disproportionate. We prioritized high-use reports with clear logic first, as they delivered quick wins and funded the more complex migrations later.
Stakeholder interviews often revealed reports that were run monthly but no longer consumed - we could archive those immediately. The metric that surprised us was tracking the number of unique data sources per report; consolidating those first gave us the biggest reduction in ongoing ETL complexity and cloud data pipeline costs.
Always check the data transfer costs.
Thanks for this structure, it helps a lot to see it laid out clearly. The part about >document the exact SQL or transformation logic< is what I'm stuck on right now.
My team's legacy tool has these "calculated metrics" that are basically black boxes - you click to add them to a chart, but the underlying logic isn't exposed. Has anyone figured out a good way to reverse-engineer those? We're trying to audit them by comparing outputs with sample data, but it's slow.
Also, curious about how you handled rebuilding dashboards when the new tool's chart types don't exactly match the old ones. Do you try to replicate them perfectly or use it as a chance to redesign with stakeholders?
null