Moving bad data faithfully reproduces the problem in a system bought to solve it. We fix the source first, reconcile throughout, and keep a way back at every stage.
Data migration services move data from one platform to another: a new database, a new business system, a new warehouse or a new provider. The transfer is rarely the hard part. What consumes migration programs is the discovery that source data was never as clean as the business believed, and that the target system enforces rules the source never did.
Remediation happens at source before transfer, because migrating poor data accurately reproduces the problem somewhere more expensive. Reconciliation between source and target runs continuously rather than at cutover, and a tested return path exists at every stage until the target has operated through a full business cycle including period close.
What the data actually contains rather than what the schema permits: the nulls in mandatory fields, the duplicates, the free-text entries in coded columns. This is discovered by profiling, never by asking.
What must match between source and target for the migration to be accepted, agreed with the business before transfer rather than argued after it.
How long the source stays live and what triggers a return. Programs that decommission on a schedule rather than on evidence discover the gap during the first period close.
A business system is being replaced and historical data has to move with it, usually on a timetable set by the application project rather than by the data work.
A database version has reached end of support and the upgrade path requires a migration rather than a patch.
Analytical workloads are moving to a new platform and years of modeled history must arrive intact and reconcilable.
Entities are being combined or separated on a transaction timetable, with data ownership changing hands.
A migration was attempted, encountered data nobody expected, and was rolled back. The estate now carries the original problem and the memory of the attempt.
Data must move to a specific jurisdiction or platform to satisfy an obligation, on a deadline that is not negotiable.
Several systems hold overlapping records and the business has decided to consolidate onto one, which makes entity resolution part of the migration rather than a follow-on.
Source data is profiled for completeness, validity, duplication and referential integrity before a plan exists. Migration estimates built from row counts rather than from data condition are the most common cause of overrun.
Moving between database engines or versions with schema conversion, stored procedure translation and behavior verification against the original.
Moving master and transactional data into a replacement ERP, CRM or operational system, where the target enforces rules the source never did.
Moving modeled data and history between analytical platforms, with query and report output reconciled against the outgoing system.
Merging overlapping records from several systems into one authoritative set, with matching rules, survivorship logic and a stewardship path for exceptions.
Moving data estates into cloud platforms with residency, encryption and transfer method chosen against volume and sensitivity.
Retaining what must be kept for obligation without carrying it into the new system, and retiring the source once traffic reaches zero.
| Problem | What profiling finds | Why fixing it in flight fails |
|---|---|---|
| Missing mandatory values | Fields the target requires that the source permitted to be blank for years | In-flight defaults populate the target with values nobody chose and cannot later distinguish |
| Duplicate records | The same entity present several times with different spellings, identifiers or contact details | Deduplication at transfer decides survivorship silently; the business never sees what was discarded |
| Referential breakage | Child records pointing at parents that no longer exist | Orphans either fail the load or arrive detached, producing totals that understate quietly |
| Free text in coded fields | Status, category or country columns holding values outside the intended set | Mapping to a default hides the exception; mapping to null loses the information entirely |
| Encoding and format drift | Dates in several formats, mixed character encodings, numeric fields holding text | Silent coercion produces plausible wrong values that nobody detects for months |
| History nobody can explain | Records that violate current business rules because the rules changed and history was not revised | Migrating them fails validation; excluding them loses the audit trail. Both need a decision, not a default |
What must match for the migration to be accepted, agreed with the business before transfer: record counts, financial control totals, business-level aggregates and sampled record comparison.
Automated reconciliation running throughout the migration rather than as a single verification at the end, so a discrepancy surfaces while there is still time to act.
Full rehearsals into a target replica, timed and measured, so the cutover window is known rather than estimated and the runbook is exercised rather than written.
Changes made in the source during the transfer window captured and applied, so a long-running migration does not arrive stale.
Sequenced steps with owners, timings, verification points and a stated go or no-go decision, rehearsed before the window.
A tested return to source retained until the target has completed a full business cycle including period close, retired on evidence rather than on a date.
A defined post-cutover period with elevated monitoring, daily reconciliation and a staffed escalation path.
| Engagement | What it covers | Timeline | Team |
|---|---|---|---|
| Source Profiling and Assessment | Data profiling across sources, quality findings quantified, mapping complexity assessed, remediation plan and effort estimate | 3 to 5 weeks | 1 architect, 2 data engineers |
| Remediation Support | Correcting data at source with the business, including stewardship workflow and exception handling for records requiring a decision | 4 to 12 weeks | 2 to 3 engineers, business stewards |
| Mapping and Build | Transformation and mapping logic built and tested, reconciliation implemented, trial runs executed | 6 to 16 weeks | 3 to 5 engineers, 1 QA |
| Cutover Execution | Rehearsed cutover, delta application, reconciliation, verification and hypercare | 2 to 6 weeks | Full team plus business validators |
| Consolidation and Entity Resolution | Matching rules, survivorship logic and stewardship for merging overlapping records across systems | 8 to 20 weeks | 3 to 5 engineers, 1 data steward lead |
| Archival and Decommission | Retention-compliant archive of data not migrated, and source retirement on evidence of zero use | 3 to 6 weeks | 2 engineers |
Cost depends on the requirement: the condition of the source data rather than its volume, the number of source systems and whether records overlap between them, how strictly the target validates, the depth of history required, the downtime the business can tolerate at cutover, and whether the source data owner is available to make remediation decisions. Innogent Technologies issues an estimate after source profiling.
Ten million clean rows from a well-structured source move faster than two hundred thousand rows carrying duplicates, free-text status fields and referential breakage. Migration effort tracks data condition and mapping complexity, not row count. Any provider quoting from a record volume before profiling the source is pricing the transfer and excluding the work.
Every source profiled for completeness, validity, duplication, referential integrity and format consistency. AI-assisted anomaly detection across large estates
2 to 4 weeks
Answers about migration scope, cost, timelines, source remediation, reconciliation, reversal and historical data.
Data migration services move data from one platform to another, whether a new database, business system, warehouse or provider. Innogent Technologies profiles source data before planning, remediates at source rather than in flight, reconciles continuously between source and target, and retains a tested reversal path until the target has completed a full business cycle.
Cost depends on the requirement: the condition of the source data rather than its volume, the number of source systems and whether records overlap, how strictly the target validates, history depth required, tolerable downtime at cutover, and whether a data owner is available to make remediation decisions. Innogent Technologies estimates after source profiling.
Source profiling runs 3 to 5 weeks, remediation 4 to 12 weeks depending on data condition, and mapping and build 6 to 16 weeks. Cutover itself is usually 1 to 3 days after rehearsal, with hypercare running 4 to 12 weeks. Consolidation involving entity resolution across systems adds 8 to 20 weeks.
Because effort is estimated from data volume rather than data condition, and the condition is only discovered once profiling starts. Horváth Partners found only 8 percent of major system migrations finished on schedule, with the average running 30 percent longer. Profiling before planning is the single practice that most changes that outcome.
Before, at source, wherever possible. A correction applied during transfer exists only in the target and only for the records that moved, while the source keeps creating the same problem until it is switched off. Remediation at source is slower and requires business decisions, and it is the reason a migration produces a clean system rather than a relocated mess.
Through reconciliation criteria agreed with the business before transfer: record counts, financial control totals, business-level aggregates and sampled record comparison. Reconciliation runs continuously rather than once at cutover, so a discrepancy surfaces while there is still time to act on it.
It reverses. A tested return path exists at every stage and the source stays live until the target has operated through a full business cycle including period close. Innogent Technologies retires a source on evidence of zero use rather than on a scheduled date.
Frequently not. Where a portion of history is required for obligation but not for operation, archiving it in a retention-compliant form is cheaper and lower risk than migrating it into a system that will validate it. Innogent Technologies raises this during profiling, and it usually reduces the size of the engagement.
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