Data migration

Data migration

We offer data migration services for warehouse, database, and cloud transitions. We transfer your data onto a governed foundation and reconcile each row to guarantee accuracy.

Overview

Moved. Proven. Kept.

Our data migration services transfer your data from any location, legacy databases, on-premise warehouses, SaaS platforms, spreadsheets, into a governed cloud data foundation, reconciling each row against its source before any system is turned off. Migration, staging, validation, cutover, and post-migration support are all handled by a single team.

Data migration is generally seen as a difficult and unpopular undertaking. Such projects frequently run past their schedules and expose long-standing data-quality problems. Moreover, the vendors who first bid for the work are often not the ones responsible for accounting for missing records once the project ends. These issues usually arise because the migration was treated as a simple copy operation. Transferring disorganised data into a new system retains existing errors and raises overall costs.

We consider migration a foundational step. The data is arranged using a medallion architecture. The bronze layer holds the raw data, the silver layer holds cleaned and standardised data, and the gold layer holds data ready for business use. This approach guarantees that a migration aimed at system maintenance yields a platform for reporting and AI. You obtain two results for the same level of effort.

What you get

What our migration services include

Warehouse and database migration

Legacy warehouses and databases are moved to Snowflake, Databricks, BigQuery, or Fabric, and are restructured for the platform instead of being simply pasted into it.

Cloud data migration

We handle migrations from on-premise systems to the cloud, or between cloud providers when your current service is no longer appropriate. We compute all costs before you commit to a plan so you avoid unexpected expenses later.

Retiring the legacy systems

We pull data from the systems you keep for historical purposes. We archive that data in a searchable format so you can discontinue the licences.

Pipelines and models

We rebuilt the ETL jobs, reports, and machine learning models on the new platform. This guarantees that these assets stay functional after the cutover date. Refer to the data foundation.

Proof

The evidence of effectiveness

It was always a manual, non-automated process. As soon as the button's pushed, that data's out of date.

Jason Southwell, CEO, Smarter Services, on life before the move
Read the Smarter Services case study

7 systems

We migrated and unified seven systems into a single governed cloud platform at Smarter Services.

14 weeks

from the start of the project to the launch, including the data migration, the model, and the final dashboard.

1.5 days a week

returned to the team once the manual export ritual ended.

Method

The migration process steps

  1. Conduct an audit and create a map

    We map each source, table, and owner before moving any data. We spot potential issues during this phase. We aim to discover these problems in the first week rather than the tenth week.

  2. Plan before you act

    Decide whether to adopt a phased or parallel-run approach. Determine which components migrate first, which data needs cleaning during the transition, and what will be intentionally discarded. Record these decisions in writing and add a specific rollback plan.

  3. Prepare and clean

    Data enters the bronze layer in its original format. It is then cleaned and standardised for the silver layer. Every change made during this process is logged.

  4. Verify and reconcile

    We supply counts, checksums, and line-level comparisons against the source data. You will receive a reconciliation report that satisfies the specific requirements of your auditors.

  5. Cut over and stay

    We transition once the data is verified instead of on a fixed date. We stay involved after the migration to ensure the new platform operates correctly.

Strategy

A reliable data migration strategy

Many data migration problems start with a plan that only covers ideal scenarios. A strong data migration strategy tackles difficult questions before they turn into critical issues. These questions include how to treat records that fail validation checks, who is responsible for approval in each area, the length of the parallel run period, and the conditions that will trigger a rollback.

We use phased migration with a parallel run as our standard method. We move one business domain at a time while the old and new systems run simultaneously. Each domain is reconciled and approved before the next one begins. This process takes a bit longer than a single-weekend migration, but it avoids a scenario where the entire system fails and the original version becomes unavailable.

The strategy also decides which data will not be migrated. It is unnecessary to transfer old columns, duplicate customer entries, or abandoned test records that have built up over ten years. Choosing deliberately with business stakeholders to exclude this data is more cost-effective than paying to move and govern information that will never be used.

Phased data migration strategy diagram showing the old platform and new governed foundation running in parallel while domains move one at a time, each reconciled, with cutover only when the data is proven
We will use a phased migration. Both platforms will operate simultaneously. We will reconcile each domain before moving to the next one. The final cutover will happen only after we have proof that the system works.
The destination

Cloud migration that prepares you for AI

A lift and shift migration moves your data infrastructure to the cloud without altering its organisation. This approach preserves existing data silos and conflicting metrics while moving them to rented hardware. It is the most common method of cloud migration and the one that organisations most often regret.

Moving to a governed foundation demands a comparable amount of effort to other migrations but yields better results. This process involves setting up the data warehouse, medallion layers, and a semantic layer with consistent business definitions. As a result, the migration project becomes the base for your dashboards, forecasts, and AI tools. Every project we build, including forecasting and conversational AI, relies on this foundation. You will not have to rebuild these components when you choose to add new features.

When you compare data migration consultants, you should ask how the data will appear the day after the cutover. If a consultant says the data will be "the same as before, but in the cloud", they are only offering a basic transfer service. Our data consultancy aims to ensure that the data is "better than it went in".

FAQ

Frequently asked questions

What is included in data migration services?

We manage the entire process between your current system and your target system. This covers auditing and mapping source data, designing the migration strategy, and staging and cleaning data during transit. We reconcile each row against the source, handle the cutover, and provide support for the new platform. Shipshape Data builds a governed cloud foundation instead of replicating existing data issues in a new location.

Should we migrate all at once or in stages?

We recommend a phased approach in most cases. A big-bang migration puts the entire business at risk during a single weekend. A phased data migration strategy moves one domain at a time. This lets us verify each domain against the source and keep a path to roll back changes. The only exception is a small or simple estate where the administrative cost of phasing exceeds the risk it removes. We will determine which category your project belongs to.

Will there be any period when the service is unavailable?

The duration of cutover downtime depends on the period your systems can run simultaneously. When the source and target systems are able to operate in parallel, migrations happen without user downtime, as the switch is performed only after data verification. If a hard cutover is required, we plan the process, practice it, and record the exact time constraints beforehand.

How can you prevent data loss?

We carry out reconciliation rather than offering general reassurance. We tally each migrated table, generate checksums, and match the outcomes to the source data. We detail any discrepancies line by line prior to shutting down any systems. The reconciliation report serves as a deliverable for your records and also meets auditor requirements.

Which platforms do you support for migration?

Snowflake, Databricks, Google BigQuery, and Microsoft Fabric are supported, with dbt and Fivetran in the pipeline layer. Sources may include nearly any type: legacy databases, on-premise warehouses, SaaS platforms, spreadsheets, or another cloud. We suggest the target that matches your estate; we do not resell licences.

How much does a data migration cost?

Costs are based on data volume, source count, and the present condition of the information. We outline the scope prior to delivering a written price. Migration usually takes place during the initial phase of a foundation build. Consequently, we price our work on fixed outcomes instead of daily rates.

The optimal moment to plan a migration is before the old platform's renewal date begins to dictate decisions for you. Talk to us and we will plainly explain what a clean move would require and what it depends on.

Next steps

What to expect when you contact us

  1. You tell us what's moving

    Provide the systems involved, the estimated data volume, and the reasons for the migration. A few lines in the form will be enough. We will respond personally, usually within one working day.

  2. A conversation that defines the scope

    Allocate thirty minutes to discuss your sources, target, and constraints. Avoid using a slide deck or a pitch.

  3. A direct answer

    Below is the written migration strategy we recommend, covering the phasing, timeline, and cost.

If you are not prepared to talk, you may read the Smarter Services case study. It outlines a seven-system migration from start to finish and details the difficult parts of the process.

Start at your core.

Tell us where your data is today and what you want AI to do. We'll come back with a straight answer on what your foundation needs and where the quickest real win is.

Talk to us