# Data Migration Solutions 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 d... ## Data Migration Services: Clean, Transform & Move Your Data Safely 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. --- ## Our Process 1. **Discovery & Source System Assessment (1–2 Weeks)** — 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. 2. **Schema Mapping & Transformation Design (1–2 Weeks)** — 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. 3. **Data Cleansing Execution (1–3 Weeks)** — 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. 4. **ETL Pipeline Development & Testing (2–4 Weeks)** — 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. 5. **Rehearsal Migration (3–5 Days)** — 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. 6. **Production Cutover & Validation (1–3 Days)** — 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. --- ## Frequently Asked Questions ### How long does a data migration project take? Timeline depends on three factors: the size and complexity of the source database, the data quality condition, and the downtime tolerance. A straightforward migration between two modern databases with clean data and compatible schemas — for example, moving from MySQL to PostgreSQL with 50 tables and under 10 million rows — takes 4–6 weeks from kickoff to production cutover. That includes 1 week for discovery and profiling, 1 week for schema mapping, 1 week for cleansing, 1–2 weeks for pipeline development and testing, and a rehearsal-plus-cutover window. Complex migrations involving legacy systems (AS/400, FoxPro, Access), significant data quality issues requiring extensive cleansing, or multi-source consolidation (merging data from 3–5 separate databases into one target) run 8–16 weeks. The longest projects we run are ERP migrations for manufacturers with 20+ years of data in systems like Epicor, MAPICS, or SAP — those can take 3–6 months because the source data contains decades of accumulated business logic that has to be understood, documented, and correctly translated to the new schema. We publish a detailed timeline during the discovery phase so there are no surprises. Every timeline includes buffer for the cleansing phase, because data quality issues always take longer to resolve than initial estimates suggest. ### What happens to my data during migration? Your production data is never touched until the final cutover. All cleansing, transformation, and testing work happens on copies. Here is the sequence: we extract a full copy of your source database to our staging environment. All profiling, cleansing, and transformation work runs against this copy. We build and test the migration pipeline against staging copies of both source and target. When we run the rehearsal migration, we use the most current copy available. Only during the final production cutover do we touch the live source system, and even then, the first step is a verified backup. During the actual migration, your data moves through four stages: extraction from the source (read-only against source), cleansing and transformation in a staging layer (no connection to either production system), loading into the target database (write operations against target only), and validation and reconciliation (read-only comparison of source and target). At no point is your source data modified in place. If the migration fails or validation reveals issues, we execute the rollback procedure and your original system is restored from the verified backup taken before cutover. We maintain backups of the source data, the intermediate staging data, and the target data at each stage of the process so we can trace any discrepancy back to its origin. ### Can you migrate data from legacy databases? Yes — legacy database migration is the core of what FreedomDev does and the area where we have the most experience. We have migrated data from SQL Server (all versions back to SQL Server 2000), Oracle (9i through 19c), PostgreSQL, MySQL, IBM AS/400 (DB2 for i), Microsoft Access (97 through current), FoxPro, Progress OpenEdge, Informix, Sybase, dBASE, FileMaker, and flat file formats including CSV, fixed-width, EDI, XML, and COBOL copybook-defined data files. For AS/400 systems specifically, we work with RPG programs, physical files, logical files, and data queues — extracting data either through ODBC connections, file transfer via FTP, or direct SQL against DB2 for i. For Access databases, we handle the particular challenges that Access migrations present: memo fields with inconsistent encoding, linked tables pointing to network shares that no longer exist, and VBA-embedded business logic that affects data integrity. The key to legacy migration is understanding not just the data but the business rules embedded in the application layer. A 20-year-old ERP system has decades of logic in stored procedures, triggers, and application code that silently shapes the data. We reverse-engineer those rules during discovery so the migration preserves the implicit constraints that the legacy system enforced but never formally documented. ### How do you handle data cleansing during migration? Data cleansing runs as a distinct phase between source extraction and target loading — never skipped, never combined with the migration itself. The process begins with the data quality audit, which quantifies every issue in the source: duplicate record rates, null value percentages per column, format inconsistencies, referential integrity violations, and data type mismatches. This audit typically reveals that 15–30% of records in a legacy database have at least one issue that will cause problems in the target. Cleansing then proceeds through six categories in order. First, deduplication: we run fuzzy matching algorithms (Jaro-Winkler, Levenshtein, Soundex) against entity tables — customers, vendors, contacts, products — to identify records that represent the same real-world entity but differ in spelling, formatting, or abbreviation. A typical legacy system yields 10–25% duplicate rates after fuzzy matching. Merge rules are defined with your team (which record is the master, how to combine data from duplicates). Second, format standardization: dates, phone numbers, addresses, postal codes, state codes, and currency values are normalized to the target system's format. Third, referential integrity repair: broken foreign keys are either repaired (reassigned to the correct parent record) or flagged for review. Fourth, null resolution: required fields that are null in the source get default values, derived values, or manual review flags. Fifth, orphan cleanup: records with no parent and no business purpose are archived. Sixth, data type conversion: varchar dates become datetime, numeric strings become integers, and encoded values become lookup references. Every cleansing step produces a log showing exactly which records were modified and what changed, giving you a complete audit trail. ### What is the risk of data loss during migration? The risk of data loss during a properly executed migration is near zero, but it is never actually zero — which is why every precaution matters. Industry data from Bloor Research shows that 30–40% of data migration projects experience some form of data loss or corruption, almost always due to inadequate testing, missing transformation rules, or absent rollback procedures. FreedomDev has completed over 150 enterprise migration projects with zero data loss incidents, and that record exists because of specific practices, not luck. First, we never modify source data in place — all work happens on copies, and the source system is backed up and verified before cutover. Second, every migration pipeline is idempotent and includes transaction-level error handling: if a batch fails, it rolls back cleanly without partial writes. Third, post-migration validation is automated and exhaustive — row counts per table, column-level checksums, referential integrity verification, and business rule validation (e.g., every order must have line items, every employee must have a department). Fourth, the rehearsal migration catches issues before production — we run the entire process end-to-end on current data and verify the results before touching the live system. Fifth, the rollback procedure is tested during rehearsal so we know it works before we need it. The largest risk factor in any migration is not the tools or the process — it is undocumented business logic in the source system that creates implicit data relationships not captured in the schema. Our discovery phase specifically targets this risk by interviewing users, analyzing application code, and running data pattern analysis to surface hidden rules. If you want to reduce your migration risk to the lowest possible level, invest time in the discovery and cleansing phases. Every dollar spent on pre-migration preparation saves $5–$10 in post-migration firefighting. --- ## Before-and-After: Schema Mapping and Data Transformation - **99.97%**: Data accuracy rate across completed migrations (verified by post-migration reconciliation) - **15–30%**: Records cleansed or corrected during pre-migration data quality phase - **0**: Data loss incidents across 150+ enterprise migration projects - **60–70%**: Cost savings from cleaning data before migration vs. after - **4–8 hrs**: Average production downtime window (vs. 48–72 hrs industry average) - **30 days**: Post-migration monitoring and hypercare included with every project --- **Canonical URL**: https://freedomdev.com/solutions/data-migration _Last updated: 2026-05-14_