For a data layer that has outgrown the platform underneath it
Data Modernization
A healthy application can still sit on top of a data layer nobody trusts. That layer might be a database groaning under load it was never sized for, pipelines that fail silently, or a schema built for a business a fraction of its current size. This practice treats the data layer as its own system, separate from any application logic running above it.
How a legacy data estate gets moved forward
Database migration, pipeline rebuilds and schema redesign, and how each stays reconciled against the original.
90 secSample frame · video pending01Before touching the schema
Migrate in place or redesign is a real decision
Teams often redesign a schema when a simpler platform migration would have solved the actual complaint. Four questions settle which move the evidence supports.
Is the pain performance, or is it the shape of the data?
Slow queries on an otherwise sound schema point at a platform migration. Constant workarounds point at the schema itself.
How many systems currently write to this data?
A single writer tolerates a bigger structural change more easily than a schema shared across a dozen applications.
How much history has to move with the migration?
Years of historical data raise the reconciliation effort regardless of which approach gets chosen, and should be sized upfront.
Can dependent systems tolerate a phased cutover?
A schema redesign is safer behind compatibility views for systems that cannot change immediately alongside the migration.
| Platform migration | Schema redesign | |
|---|---|---|
| What changes | The database engine | The structure of the data |
| Typical trigger | Load past the engine's ceiling | Workarounds piling up around the model |
| Application impact | Usually minimal | Requires coordinated updates |
| Best fit | Sound schema, wrong platform | Sound platform, wrong schema |
02Agreed before the first pipeline is touched
The ledger that keeps the migration honest
Data projects drift when reconciliation gets treated as optional under deadline pressure. This ledger fixes what you bring, what we migrate, what returns, and what stays your call to make.
- Your side
You bring
- The current database, its schema and its load patterns
- Every application and report reading from it today
- Any maintenance windows the business can tolerate
- Our side
We build
- A migration to a database platform sized for current scale
- A validated, reconciled copy of the historical data
- A cutover sequence that keeps dependent applications reading correctly
- Your side
You receive
- A production database running on a platform built for the current load
- A reconciliation report proving no data was lost in transit
- Query performance measured before and after the move
Never leaves you
- Every application and report that reads from the database
- The final decision on which platform to move to
- Ownership of the data itself throughout the migration
Where the line sitsA database migration is only finished once every dependent system is verified against the new platform, not merely once the copy completes.
03From reconciliation plan to cutover
Four stages, each closing on proof
Data migrations that lose trust usually skipped one of these stages. This system closes each one with a number you can check, not a promise.
Map sources, pipelines and dependents
Every database, pipeline and downstream report gets catalogued before a migration plan gets written, including the undocumented ones.
The current estate, mapped honestlyDefine how correctness gets proven
The method for verifying that migrated data matches its source is agreed before migration starts, not improvised afterward.
A reconciliation method agreed upfrontMigrate and reconcile in parallel
Data moves in stages, with each stage checked against the reconciliation method before the next stage begins.
Data moved and checked, not just copiedCut over with the source kept live
Dependent systems switch over in a controlled sequence, with the original source kept available until every switch is verified.
A cutover with the old source still available
04What actually moves the number
Four drivers, priced from a short assessment
Data modernization is priced against the estate mapping, because these four factors change scope by multiples.
Volume of historical data to reconcile
Years of accumulated history take longer to verify than a system that has only been in production for a short while.
Number of dependent applications and reports
Each downstream consumer adds its own verification step before it can safely read from the new source.
Whether the schema changes shape, not just platform
A like-for-like platform migration is simpler than a redesign that also changes how the data is structured.
Tolerance for a phased, longer cutover
A tighter cutover window compresses the schedule but increases the verification work needed to trust it.
05Evidence and proof
Data estates people finally stopped double-checking.
Data modernization proof is a number nobody feels the need to verify by hand anymore. Every record here waits for written client approval before production.
Explore case studiesA decade-old database migrated with every dependent report reconciled.
Nexa Foundry's core database was timing out under load a decade of growth had never been designed for. Migration to a modern platform was reconciled against every downstream report before cutover.
- Reports reconciled before cutover
- All of them
- Query timeout incidents post-migration
- Zero
Pipelines rebuilt so a broken job finally raises an alarm.
Koru Health's nightly pipelines failed silently often enough that reports were routinely double-checked by hand. Rebuilt pipelines with monitoring ended the manual verification habit.
- Silent pipeline failures after rebuild
- None recorded
- Manual report double-checks
- No longer required
For the first time in years, our finance team stopped reconciling the numbers by hand before a board meeting.
Sample · pending client approval
06Read this first
When data modernization is the wrong first move
Migrating a data estate before confirming where the real bottleneck sits risks solving the wrong problem well. The exits are marked plainly.
- Application Modernization
The data platform is fine; the application isn't
Slow screens on top of a healthy, well-performing database point at application code, not the data layer underneath it.
- Data practice
There is no legacy estate, only a new build
Standing up analytics or a data platform from scratch is new-build data engineering, not a migration off something old.
- Digital Transformation Consulting
It is unclear whether data is even the priority
When several systems compete for attention and the sequence is unclear, a roadmap should come before any migration commitment.
| Shape | Best when | You direct | We carry |
|---|---|---|---|
| Platform migration | The schema is sound, the engine isn't | Cutover timing and maintenance windows | Migration, reconciliation and verification |
| Pipeline rebuild | Trust in the numbers has broken down | Which reports matter most | Rebuilt pipelines with monitoring |
| Schema redesign | Workarounds have accumulated around the model | Business rules the new schema represents | Design, migration and compatibility views |
07Straight answers
Asked before a data migration budget is set.
01How is this different from application modernization?
Application modernization rebuilds the code that reads and writes data. This practice rebuilds the data layer itself, the database, pipelines and schema, and the two are scoped as separate projects even when they run alongside each other.
02Is this the same as your data practice for analytics work?
No. The data practice builds new analytics and data engineering capability from scratch. This practice specifically migrates a business off a legacy data estate that has outgrown its foundations.
03Will our applications need to change during a data migration?
Often minimally, especially for a platform migration where the schema stays the same. A schema redesign is more likely to require coordinated updates, and that scope gets agreed before work begins.
04How do you prove no data was lost during migration?
A reconciliation method is agreed before migration starts, comparing record counts and key values between source and destination. Every stage of the migration is checked against that method before the next stage proceeds.
05What does data modernization cost?
It is priced against the volume of historical data and the number of dependent systems discovered during mapping. A platform migration typically costs less than a schema redesign touching several applications at once.
06Can you modernise our pipelines without touching the underlying database?
Yes, pipeline rebuilds are commonly scoped on their own when the database itself is performing fine. The two workstreams share a discipline but do not have to run together.
07How long does a typical data migration take?
A straightforward platform migration can close within a few weeks. A schema redesign with many dependent systems runs considerably longer, and the mapping stage gives an honest estimate first.
08Do you work with our existing data or analytics team?
Yes, an existing team's knowledge of current pipelines and reports shortens the mapping stage considerably. The engagement is scoped around whatever that team already owns.