For CFOs, financial data migrations are often a major challenge: inconsistent data, complicated reconciliations, and numerous Excel files and Python scripts. Done wrong, it leads to reporting errors and regulatory risk. Done right, it’s a smooth transition to cleaner, audit-ready systems.
Common Pitfalls That Derail Migrations Financial data migrations often require extensive coordination with IT teams, leading to delays and frequent miscommunications. This back-and-forth slows progress and increases the risk of errors creeping into critical financial data.
Manual GL mapping, disorganized product catalogs, mismatched schemas, and inconsistent reporting fields compound the problem. The reliance on one-off Python scripts and Excel files makes the process untracked, unauditable, and hard to reproduce. AI helps eliminate much of this friction by automating key technical tasks — from schema generation to reconciliation — streamlining handoffs between finance and IT, and ensuring a smoother, faster migration.
A CFO-Friendly Migration Framework (7 Steps) Every step should be auditable and reproducible.
1. Value Mapping Standardize key values like GL accounts, payment methods, and products. Clean up overlaps e.g., consolidate duplicate GL accounts or unify old product codes into standardized SKUs.
2. Data Preprocessing Fix dirty source data early: deduplicate accounts, normalize records, and clean internal IDs. Fewer surprises later.
3. Schema Creation Automatically extract schemas from ERPs and billing systems, whether from CSV exports or JSON dumps. Identify structural gaps upfront.
4. Schema Mapping Map fields between systems. Example: align regional codes or country names to a consistent target format.
5. Data Loading Load into test environments. AI uses system error messages to refine mappings, reducing manual trial-and-error.
6. Reconciliation Validate totals, invoices, ARR, deferred revenue, and sample test transactions for accuracy.
7. Final Cutover and Reporting Execute a clean cutover with full audit trails and CFO-ready reconciliation reports.
How AI Helps CFOs AI eliminates the guesswork by automating schema and value mappings, capturing institution-specific knowledge like GL mapping rules, and using system feedback to refine processes. It provides reconciliation dashboards out-of-the-box, meaning less manual work for your team, faster timelines, and fewer errors along the way.
What About Data Cleaning? Data cleaning is often cited as one of the most challenging aspects of migrations. The term is broad and refers to different tasks across the process. It starts with value mapping, such as consolidating duplicate GL accounts or standardizing product categories. It continues with preprocessing, where records are deduplicated and normalized early on. Schema mapping ensures data structures are properly aligned, for example, converting geographic codes into a unified format. Finally, reconciliation applies automated checks with human oversight to confirm accuracy. With Doyen, these data cleaning activities are integrated into each phase, eliminating the need for after-the-fact cleanup.
Conclusion
We've completed dozens of financial data migrations, helping accounting teams avoid common pitfalls, reduce project timelines, and achieve audit-ready results. Migrations shouldn’t be painful. With an AI-driven process, you can expect clean, reconciled financial data without all the accounting headaches.