Enterprise data migration with full cleansing, schema mapping, transformation, and rollback planning — from a Zeeland, Michigan company with 20+ years moving data between legacy databases, modern platforms, and cloud environments. SQL Server, Oracle, PostgreSQL, MySQL, AS/400, Access, and everything in between.
Gartner estimates that 83% of data migration projects either fail outright or exceed their budget and timeline. That number has held steady for over a decade, and the reasons are consistent: poor data quality in the source system, incomplete schema mapping, no rollback plan, and testing that covers the happy path but ignores the 10,000 edge cases hiding in production data. A failed migration does not just waste the project budget — it disrupts operations, corrupts downstream reporting, and in regulated industries, triggers compliance violations that carry six-figure penalties.
The most common failure point is not the migration itself — it is the source data. Companies that have been running the same database for 10, 15, or 20 years accumulate data quality problems that nobody sees until migration exposes them. Duplicate customer records with slightly different spellings. Address fields containing phone numbers. Nullable columns that were never supposed to be null but have 40,000 null values because a form validation was added in 2016 and nobody backfilled. Date fields stored as strings in three different formats across three eras of the application. A West Michigan manufacturer we assessed had 2.3 million rows in their primary customer table. After deduplication and cleansing, the true number of unique active customers was 840,000 — the rest were duplicates, test records, orphaned entries, and soft-deleted rows that never got purged.
The second failure point is schema mapping. Source and target databases almost never have matching schemas. A single 'address' field in your legacy system maps to five normalized fields in the target. Your AS/400 stores dates as 6-digit integers (YYMMDD) while PostgreSQL expects ISO 8601 timestamps. Your old system uses a 2-character state code stored in a free-text field, meaning 'MI', 'Mi', 'mi', 'Mich', 'Michigan', and 'MICHIGAN' all exist in production. Without a documented transformation ruleset for every column in every table, the migration will silently corrupt data that looks fine in spot checks but breaks business logic in production.
The third failure point is rollback. Companies that plan only for success are gambling with their entire operation. If the migration fails halfway through a 48-hour cutover window, you need to be able to restore the original system to its pre-migration state within hours, not days. Every migration FreedomDev runs includes a documented rollback procedure that has been tested at least once before the production cutover.
83% of data migration projects exceed budget, timeline, or fail entirely — most due to poor planning, not technical limitations
Legacy databases contain years of accumulated data quality issues: duplicates, format inconsistencies, orphaned records, and schema drift
Schema mapping between source and target systems requires transformation rules for every column — miss one and data silently corrupts
No rollback plan means a failed cutover can leave your business offline for days with no path back to the original system
Downtime during migration directly impacts revenue: manufacturing plants, e-commerce stores, and healthcare systems cannot afford 48-hour outages
Compliance risk in regulated industries — HIPAA, SOX, PCI-DSS — where data integrity failures during migration trigger audit findings and fines
Our engineers have built this exact solution for other businesses. Let's discuss your requirements.
FreedomDev treats data cleansing as a prerequisite to migration, not an afterthought. Every project begins with a data quality audit of the source system that produces a quantified report: total record counts per table, duplicate detection rates, null value percentages per column, format consistency scores, referential integrity violations, and orphaned record counts. This audit typically reveals that 15–30% of records in a legacy database have at least one data quality issue that will cause problems in the target system. Cleaning before migration costs 60–70% less than cleaning after, because post-migration cleanup means finding and fixing bad data that has already been loaded into a production system where it is being referenced by application code, reports, and downstream integrations.
Our cleansing process covers six categories. Deduplication uses fuzzy matching algorithms (Jaro-Winkler, Levenshtein distance, phonetic matching) to identify duplicate records that simple exact-match queries miss — the kind where 'Johnson Manufacturing LLC', 'Johnson Mfg.', and 'Johnson Mfg LLC' are the same company. Format standardization normalizes dates, phone numbers, addresses, currency values, and coded fields to the target system's format. Referential integrity repair fixes broken foreign key relationships — orders pointing to deleted customers, line items referencing discontinued products. Null value resolution applies business rules to populate required fields that are empty in the source: default values, derived values from other fields, or flagged for manual review. Orphan record cleanup removes or archives records that have no parent and no business purpose. Data type conversion maps source types to target types — converting varchar dates to proper datetime columns, numeric strings to integers, and encoded values to lookup table references.
For companies undergoing legacy modernization, the migration is the single best opportunity to fix data quality problems that have been accumulating for years. A clean migration to a well-designed target schema eliminates technical debt in the data layer that would otherwise persist indefinitely. FreedomDev's database services team designs the target schema in parallel with the cleansing phase, ensuring that the destination database enforces constraints and validations that the legacy system never had.
Before writing a single migration script, we profile every table and column in your source database. Record counts, duplicate rates, null percentages, format consistency, referential integrity violations, and data type mismatches — all quantified in a report that tells you exactly what needs to be cleaned and what it will cost to clean it. Typical audit duration: 3–5 days for databases under 50 tables, 1–2 weeks for 100+ table schemas.
Exact-match deduplication catches obvious duplicates. Our fuzzy matching engine catches the rest: misspellings, abbreviations, name variations, and records that were entered by different people at different times with slightly different formatting. We use Jaro-Winkler similarity scoring, Levenshtein distance, Soundex phonetic matching, and domain-specific rules (e.g., 'LLC' equals 'L.L.C.' equals blank for matching purposes). A typical legacy database yields a 10–25% deduplication rate after fuzzy matching.
We document every column-to-column mapping between source and target schemas with explicit transformation rules: data type conversions, format changes, value translations (code tables), concatenation or splitting of compound fields, default values for new required columns, and conditional logic for fields that map differently based on record type. This document becomes the single source of truth for the entire migration and is version-controlled alongside the migration scripts.
Migration scripts are built as repeatable ETL pipelines, not one-time throwaway scripts. We use SSIS for SQL Server environments, custom Python (pandas, SQLAlchemy) for cross-platform migrations, pgloader for PostgreSQL targets, AWS DMS for cloud migrations, and Apache NiFi or Talend for complex multi-source transformations. Every pipeline is idempotent — it can be re-run safely without creating duplicates or corrupting previously migrated data.
For systems that cannot afford extended downtime, we migrate in phases: initial bulk load of historical data (which can run while the legacy system is still live), followed by delta migrations that capture changes made after the initial load, followed by a final cutover delta that captures the last few hours of changes. This approach reduces the actual downtime window from days to hours or even minutes.
After every migration run, automated validation scripts compare source and target: row counts per table, checksum comparisons on key columns, referential integrity verification in the target, and business rule validation (e.g., every order has at least one line item, every customer has a valid state code). Discrepancies are flagged in a reconciliation report with root cause analysis before any migration is considered complete.
We had 15 years of data in an AS/400 system that three other companies told us was too messy to migrate. FreedomDev audited the source, cleaned 340,000 duplicate records, mapped every field to our new PostgreSQL schema, and migrated 8.2 million rows over a weekend with zero data loss. Our team came in Monday morning and everything just worked.
We connect to your source database (SQL Server, Oracle, MySQL, PostgreSQL, AS/400, Access, FoxPro, Progress, or flat files) and run a comprehensive data quality audit. Output: table-by-table profiling report showing record counts, duplicate rates, null percentages, format inconsistencies, referential integrity violations, and estimated cleansing effort. We also document the source schema, identify undocumented business rules embedded in the data, and catalog any stored procedures, triggers, or application-level logic that affects data integrity.
With the source profiled and the target schema defined (or designed in collaboration with our database services team), we build the complete column-to-column mapping document. Every field gets a documented transformation rule. We identify fields that require manual business decisions — cases where data does not map cleanly and someone who understands the business needs to define the rule. This phase produces the migration specification that both FreedomDev and your team sign off on before any code is written.
Cleansing runs against a copy of the source data, never against production. Deduplication, format standardization, null resolution, orphan cleanup, and referential integrity repair are applied in sequence. Each cleansing step produces a before/after report showing exactly what changed and why. Records that cannot be automatically cleaned are flagged for manual review by your team. Typical cleansing phase touches 15–30% of total records and resolves 90–95% of data quality issues identified in the audit.
We build the migration pipeline as a repeatable, idempotent process. Development happens against a staging copy of the cleansed source data and a staging instance of the target database. Testing covers full migration runs, delta migration runs, error handling (what happens when a record fails mid-batch), performance testing at production data volumes, and rollback procedure verification. Every test run produces a reconciliation report comparing source and target.
A full dress rehearsal of the production migration, run on the most current copy of source data available. This rehearsal validates the entire end-to-end process: cleansing, transformation, loading, validation, and reconciliation. It also benchmarks actual migration duration so we can plan the production cutover window with confidence. We run the rollback procedure during rehearsal to verify it works. Any issues discovered trigger a fix-and-re-rehearse cycle — we do not proceed to production until the rehearsal completes cleanly.
The production cutover follows a minute-by-minute runbook developed during rehearsal. Source system freeze, final delta extraction, cleansing of delta records, migration execution, validation and reconciliation, application connection switching, smoke testing by your team, and formal go/no-go decision. If validation fails, we execute the tested rollback procedure and revert to the source system. Post-cutover, we monitor the target system for 30 days to catch any data issues that surface during real-world use.
| Metric | With FreedomDev | Without |
|---|---|---|
| Data Quality Audit | Full profiling of every table and column with quantified quality scores | DIY/automated tools: no source analysis — you migrate dirty data as-is |
| Data Cleansing | Fuzzy deduplication, format standardization, null resolution, integrity repair | Automated tools: basic dedup at best — no business-rule-aware cleansing |
| Schema Mapping | Documented column-to-column mappings with transformation rules, reviewed and signed off | DIY: ad hoc field mapping discovered during migration — gaps found in production |
| Legacy System Support | SQL Server, Oracle, AS/400, Access, FoxPro, Progress, flat files, COBOL data stores | AWS DMS / Azure DMS: limited to supported database engines — no flat files, no AS/400 RPG |
| Rollback Planning | Tested rollback procedure rehearsed before every production cutover | DIY: hope the backup works — rollback never tested until you need it |
| Downtime Window | 4–8 hours typical (incremental + delta migration strategy) | DIY/automated tools: 24–72 hours for full dump-and-load approach |
| Post-Migration Validation | Automated reconciliation: row counts, checksums, referential integrity, business rules | DIY: manual spot checks on a handful of records |
| Ongoing Support | 30-day hypercare + optional long-term data quality monitoring | Automated tools: migration complete, you are on your own |
