The core disruption in a SaaS migration is loss of direct data access. In a traditional on-premises or single-tenant hosted environment, your reporting tools and integrations query the underlying transactional database directly. In a multi-tenant SaaS environment, direct access disappears. Reports must be rebuilt to source from a new data warehouse, compliant APIs, or purpose-built analytics layers.
The practical implications are far-reaching. Every report that queries the legacy database must be evaluated — and most will need to be rewritten. Integrations built on direct SQL connections or stored procedures will break. Custom tables, batch jobs, and workaround views that have accumulated over years will no longer function. Self-service users who know exactly where to find data in the old system will face a steep learning curve in the new one.
Compounding the technical challenge is the organizational one. Large migrations involve many teams, contractors, and stakeholders working in parallel. Without a shared, authoritative source of documentation, decisions get made inconsistently, work gets duplicated, and critical context lives in emails and individual memories rather than a place where the whole team can access it.
One of the most common and costly mistakes organizations make is treating data governance as something to address after the migration is complete. The reasoning is understandable: teams are stretched thin, the implementation is all-consuming, and data governance feels like a separate initiative that can wait. But this thinking creates a false choice.
A SaaS migration is the single best opportunity to build your data governance foundation. Your teams are already touching every report, integration, and data definition in the organization. The context needed to document those assets — who own them, what they calculate, where the data comes from — is fresh and accessible in ways it simply will not be once the project is over. Waiting means going back to trying to document decisions that no one fully remembers, about systems that are no longer in use. Almost no organization does this effectively.
Instead, integrate data governance work directly into the migration workflow. Document as you build. Capture functional requirements before rewriting a report, not after. The additional effort is modest compared to the return — and the knowledge base you create becomes a permanent asset for every future report, integration, and audit.
Before migrating anything, take stock of what you have. A systematic inventory of your existing reports, integrations, and data systems is the foundation for everything that follows. This assessment serves two purposes: it forces prioritization decisions (not everything needs to be migrated), and it captures functional requirements for the things that do.
Your legacy assessment should include:
A particularly important practice is using AI-assisted curation to analyze legacy code and queries. Many organizations have reports that have been running for a decade, written by staff who are no longer around, with no surviving documentation. Modern data catalog tools can ingest SQL code, stored procedures, or query packages and automatically generate functional descriptions, identify key data items, propose business glossary terms, and map data lineage. A human reviewer then refines and approves the output — transforming an opaque legacy artifact into a clear functional specification that can guide reconstruction in the new environment.
This approach dramatically reduces the time required to document legacy assets while surfacing tacit knowledge that might otherwise be lost entirely in the migration.
Not every report from your legacy system deserves a place in the new one. A SaaS migration is an opportunity to clean house. Inventory your reports and assess each one: Is this actively used? By whom? Does it serve a current business need, or has it simply never been turned off? Migrating reports no one uses wastes effort and clutters the new environment.
Once you have your prioritized list, assign ownership and status tracking to each item. Managing a report migration across a large team without a shared tracking system leads to duplication of effort, conflicting approaches, and gaps where no one realized something was missing. Treat the migration backlog like a project management task — with owners, status, and priority — and track it in a centralized catalog rather than spreadsheets circulating by email.
A practical workflow for each prioritized report: document the legacy version (capturing its purpose, data items, calculation logic, and glossary references), then create a new specification for the SaaS version that retains all the functional requirements while mapping to the new data sources. When someone on your team figures out how a particular field is calculated in the new data warehouse or API, that knowledge gets recorded and becomes reusable — the next person building a report with that same field doesn't have to start from scratch.
While assessing your legacy environment, begin cataloging the new one. Your SaaS platform will include a new data warehouse, APIs, analytics layers, ETL pipelines, and possibly new reporting tools. All these need to be documented — not as a post-project exercise, but as an active part of the implementation.
A critical part of this cataloging work is identifying where definitions have changed. When a field, metric, or calculation exists in both the legacy system and the new SaaS environment, ask: does it mean the same thing? Is it calculated the same way? Often the answer is no — and that discrepancy, if undocumented, becomes a trust problem the moment users notice that numbers don't match what they expected.
For each significant metric or data element, document both the legacy calculation and the new one. If they differ, document why. This transparency is what allows users to understand and accept differences in reported figures, rather than concluding that the new system is broken or untrustworthy. A well-maintained glossary that maps old definitions to new ones — with clear explanations for any changes — is one of the highest-value artifacts you can produce during a migration.
Effective data governance does not require a massive bureaucracy. During a migration, the goal is to connect the right data stewards (subject-matter experts) to data decisions quickly — not to build layers of review and approval that slow the project down.
Start by identifying data stewards — the people in each functional area who own data definitions and can answer authoritative questions about how data is used. In a higher education context, this might mean a registrar who owns enrollment data definitions, a financial aid director who owns aid calculation logic, and an HR analyst who defines active employee status. These are the people who need to be empowered to make and document data decisions during migration.
Keep approval workflows lean during the migration phase. When a report writer needs to know how to define a term or pull a field from the new data warehouse, they need an answer quickly — not a routing process that takes weeks. Empower stewards to define and document terms with minimal friction, with the understanding that definitions can be refined over time as more people review them. Speed of content creation matters more than perfection during this phase.
The data governance structure you build during the migration does not need to be dismantled afterward. These same stewards, workflows, and accountability structures become the foundation of your ongoing data governance program — making the migration a catalyst for long-term data maturity rather than a one-time exercise.
The moment users access the new system for the first time is a critical trust event. If reports produce numbers that look wrong — or even just different — without any explanation, users lose confidence in the entire platform. Rebuilding that trust is far harder than preserving it in the first place.
Several practices help protect trust at go-live.
First, ensure that high-visibility reports are fully documented and vetted before launching, not just technically correct, but accompanied by clear explanations of what they show and how they are calculated.
Second, build easy pathways for users to ask questions. Embedding a link from a report directly to its catalog documentation lets users get context without leaving the tool. Providing a mechanism to report potential data quality issues — with a fast-turnaround response commitment — prevents uncertainty from festering into distrust.
Third, be proactive about communicating known differences. If a calculation changed between the legacy system and the new one, tell users before they discover the discrepancy on their own. A clear, plain-language explanation of why a number might look different — along with confidence that the new calculation is correct and intentional — goes a long way toward maintaining user trust through the transition.
The documentation, glossary terms, report specifications, and data lineage maps you produce during the migration are not just tools for getting through the project — they are the beginning of an organizational knowledge base that will serve your organization for years.
When a new analyst joins the team and needs to understand how enrollment is measured, that definition should be available in the catalog — not buried in email threads or locked in a colleague's memory. When a department requests a new report, the catalog should give the report writer a head start: existing definitions, field mappings, prior calculations, and examples of how similar data has been used before. When auditors or leadership ask questions about data quality or methodology, the catalog provides an authoritative, versioned record of decisions made and when.
This long-term value is best realized when the catalog is kept alive after the migration — updated as new reports are built, new integrations are added, and definitions evolve. Organizations that treat the catalog as a living resource rather than a project deliverable consistently report faster onboarding, fewer data quality incidents, and greater confidence in their reporting and analytics.
A SaaS migration is never just a technology project. It is a data project, a change management project, and an opportunity to build the knowledge infrastructure your organization should have had all along. The organizations that navigate these migrations most successfully are the ones that recognize this early — and invest in data governance as a force multiplier for everything else they are trying to accomplish.
Assess your legacy environment thoroughly. Prioritize ruthlessly. Document as you build. Connect your data stewards to the decisions that matter. Catalog your new environment proactively. Design for trust at go-live. And maintain the knowledge base you create long after the project is over.
Done well, a SaaS migration doesn't just replace an old system — it leaves your organization with better data literacy, more trusted analytics, and a foundation for data governance that will pay dividends for the life of the platform.
Want to learn more?
Hope this blog post was of assistance to you and your organization. All our data governance and data intelligence resources (blog posts, videos, and recorded webinars) can be accessed from our data governance resources page. IData has a solution, the Data Cookbook, that can aid the employees and the organization in its data governance, data intelligence, data stewardship and data quality initiatives. IData also has experts that can assist with data governance, reporting, integration and other technology services on an as needed basis. Feel free to contact us and let us know how we can assist.
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