Last Updated: October 15, 2026
At a global industrial company, a data load that took seven days now runs in one hour, and every record lands correctly.
Data modernization services move a company’s data, and the pipelines and reports that depend on it, from aging databases and warehouses onto platforms that are faster, cheaper to run and ready for analytics and AI. The hard part is proving the new numbers match the old ones before anything gets switched off. This guide covers what the services include, the main approaches, what they cost, and how to choose a provider.
What’s in this article: What they are · What’s included · Why now · Approaches · Provider types · Cost · How to choose · Regulated industries · Results · What to do next · Scadea services · FAQ
What are data modernization services?
They are outside teams that assess, migrate and rebuild a company’s data stores, pipelines and reports on current platforms, then validate the results and retire the old systems.
Data modernization services help a company move data and data pipelines from legacy databases, warehouses and file systems to current platforms such as Snowflake, Databricks, Google BigQuery or Microsoft Fabric. A provider inventories what exists, chooses an approach for each workload, migrates and rebuilds it, reconciles the results against the old system, and decommissions what is no longer needed.
Data modernization also covers what moves with the data: the ETL jobs, stored procedures, schedules, access rules and reports built on top. Those pieces hold years of business logic, often undocumented, and they take more effort than copying the tables.
What do data modernization services include?
Most engagements run through inventory, target design, migration, validation, parallel running, cutover, decommissioning and ongoing support.
- Inventory. List every database, pipeline, report and downstream consumer, with owners, volumes and how often each one is used.
- Target design. Pick the platform and architecture, and decide which workloads move first.
- Migration and rebuild. Move the data, and rewrite or convert pipelines and stored procedures for the new platform.
- Validation. Compare row counts, totals and key records between old and new systems, table by table.
- Parallel run. Run both systems side by side long enough for business users to confirm their reports match.
- Cutover and decommissioning. Switch consumers to the new platform, then shut down the old one so you stop paying for both.
- Run and optimize. Monitor pipelines, tune performance and keep cloud spend in check.
Decommissioning is the step most often left unfinished. Until the old system is off, the company pays twice and people keep using whichever version they trust.
Why are companies modernizing their data now?
Older database versions are leaving support, hardware is due for replacement, and AI and analytics projects need clean, current data that legacy systems struggle to serve.
Support deadlines are the most concrete trigger. Microsoft’s lifecycle page for SQL Server 2016 shows extended support ended in July 2026. Microsoft now sells Extended Security Updates for it in yearly terms that run to July 2029, which buys time at a price.
AI is the other driver. Models and agents need current, well-governed data, and a warehouse that refreshes overnight and lives on a single server limits what they can do. Our guide to building a modern data platform for enterprise AI covers the target architecture in more depth.
What are the main data modernization approaches?
The main options are retire, retain, rehost, replatform, refactor and repurchase. Most programs use several, chosen workload by workload.
| Approach | What it means for data | Best for | Watch for |
|---|---|---|---|
| Retire | Archive the data and shut the system down | Reports and databases nobody uses | Record-retention rules before deleting anything |
| Retain | Leave it where it is for now | Systems with residency limits or recent upgrades | Retained systems still need support and patches |
| Rehost (lift and shift) | Move the database to cloud servers unchanged | Getting off aging hardware quickly | Cloud prices for an on-premises design |
| Replatform | Move to a managed service with modest changes, such as SQL Server to a managed cloud database | Cutting maintenance work without a rewrite | Version and feature differences that break old code |
| Refactor or re-architect | Rebuild pipelines and models for a cloud warehouse or lakehouse | Core analytics and AI workloads | The most effort, cost and risk of any option |
| Repurchase | Replace a custom system with a SaaS product and migrate its data | Commodity functions such as HR or CRM data | Data mapping and the vendor’s export terms |
The categories come from AWS’s migration strategy guidance, which also covers a seventh option, relocate, for moving whole virtualized environments. For large migrations, AWS recommends “rehosting, relocating, or replatforming the application and then modernizing the application after the migration is complete.” That sequence lowers risk: move first, then improve. The exception is a workload whose design is the problem, where a rebuild pays for itself.
For the target platform, the choice usually comes down to Snowflake, Databricks, BigQuery, Amazon Redshift or Microsoft Fabric. Our comparison of Databricks and Snowflake covers the two most common picks.
What types of data modernization providers are there?
Buyers choose among embedded data teams, cloud and platform vendors’ services arms, large consultancies, migration factories and freelance data engineers.
| Provider type | How it works | Best for | Watch for |
|---|---|---|---|
| Embedded data team (Scadea’s model) | Data engineers and architects join your teams, migrate in waves, and the provider answers for quality and continuity | Multi-year programs with many workloads, especially in regulated industries | Takes some of your managers’ time in the first weeks |
| Cloud or platform vendor services | The platform maker’s own team plans and supports the move | Complex setups on that one platform | Every answer tends to be that platform |
| Large consultancy | A broad firm runs strategy, architecture and migration together | Enterprise-wide programs with a large budget | Higher rates, and senior people who leave after the pitch |
| Migration factory | An offshore team converts code and moves data in bulk with automated tools | High volumes of similar, well-documented objects | Converted code can carry old problems across unchanged |
| Freelance data engineer | An individual migrates one database or pipeline set | Small, contained moves | No bench, so the migration stalls when that person is unavailable |
Scadea publishes this guide and runs the first model. Its teams work across AWS, Azure, Google Cloud, Databricks and Snowflake, so the platform recommendation follows your workloads and existing contracts. After cutover, some companies keep a team on to run the platform; our guide to managed data services covers that option.
How much do data modernization services cost?
Budget for the migration project, a period of running two systems at once, and the new platform’s usage-based charges, which depend heavily on how workloads are designed.
The project cost scales with the number of objects to move and how much logic sits in them. A few hundred tables with simple loads is a short project. Thousands of stored procedures with undocumented business rules is a long one, and that logic is where most of the budget goes. Scadea’s typical timelines run four to eight weeks for one workload, four to eight months for a full data infrastructure migration, and 12 to 18 months for an enterprise-wide program in phases.
Run costs change shape after the move. Cloud warehouses bill for usage. Snowflake, for example, bills warehouse compute “per-second, with a 60-second (i.e. 1-minute) minimum,” and a Small warehouse uses 2 credits an hour while it runs. That rewards jobs that start, finish and shut down. It punishes queries that run all day because nobody set limits, so cost controls belong in the design from the start.
Remember the overlap too. During the parallel run you pay for both systems, and if you need Extended Security Updates on an old database, add those to the bridge budget.
How do you choose a data modernization provider?
Choose a provider that inventories before quoting, plans validation in detail, runs systems in parallel, designs for cost control, secures data in transit and hands over everything it builds.
Ask for an inventory before a fixed quote
A credible provider wants to see your databases, pipelines and reports before it commits to a price. A fixed quote from a slide about your environment is a guess that will come back as change orders.
Ask how they will prove the data matches
Ask for the validation plan: which counts, totals and sample records they compare, and who signs off. Tooling helps here. AWS Database Migration Service, for example, “compares each row in the source with its corresponding row at the target, verifies the rows contain the same data, and reports any mismatches,” according to AWS’s documentation. Business users still need to confirm their own reports.
Check the parallel run and rollback plan
Ask how long both systems will run side by side, what triggers a rollback, and how they will move consumers over in waves.
Look at cost controls
Ask how they will size compute, set auto-suspend and spending alerts, and report cloud spend each month after go-live.
Confirm security and ownership
Data should be encrypted in transit and at rest, with access limited to named accounts during the move. You should own the code, models, documentation and run books, and the provider should support a handover if you take the platform in-house.
How does data modernization work in banking and other regulated industries?
Regulated firms add data lineage, retention rules, residency limits and third-party risk reviews to every step, and they keep evidence that migrated numbers match.
Banks, insurers and healthcare organizations need to show where each reported number came from, before and after the move. Lineage and reconciliation evidence matter as much as the migration itself. Some data must stay in a given country or on premises, which is why many regulated programs retain a few systems by design.
When an outside firm handles the migration, it falls under third-party risk management. US banks follow the interagency guidance on third-party risk management, so ask any provider for its security certifications and how it controls access to production data. Our post on making integration auditable covers the governance side.
What results should you expect from data modernization?
Expect loads and reports that took days or hours to finish in minutes, more data sources fed automatically, and validated numbers that match the old system.
Here is what changed in recent Scadea engagements, each measured against the client’s own baseline:
- At a global industrial company, the data load in the opening went from seven days to one hour, with 100% load accuracy.
- At a global bank, the compliance function now has 90% of its risk data sources fed automatically, at 95% accuracy.
- The same bank’s manual report, built from spreadsheets and databases, went from two hours to under ten minutes after the team rebuilt it as a KNIME workflow.
- At the bank, a process that copied API data into Excel by hand went from two hours to five minutes.
More results are in our write-up of what embedded specialists deliver.
What to do next
Build a one-page inventory: your five largest data stores, what version each runs, when its support ends, which reports depend on it and who owns it. Add the monthly cost of keeping each one running. Share it with any provider you are considering. Ask each one which workload they would move first and how they would prove the numbers match. The answers will separate a plan from a pitch.
Scadea services for data modernization
Scadea’s data teams work inside client teams in banking, healthcare, gaming and manufacturing, with 300+ consultants across 8 countries and ISO 9001 and ISO 27001 certification. Cloud data solutions covers migration, modernization and managed infrastructure on AWS, Azure and Google Cloud, and data strategy and architecture covers target design.
Data engineering and pipelines rebuilds the loads and transformations, and cloud migration and re-platforming moves the applications that sit on the data.
Frequently Asked Questions
What is data modernization?
Data modernization is moving data, pipelines and reports from legacy systems to current platforms, and redesigning them so the data is easier to use, govern and scale.
How long does data modernization take?
One workload takes about four to eight weeks. A full data infrastructure migration takes about four to eight months, and enterprise-wide programs run 12 to 18 months in phases.
What is the difference between data migration and data modernization?
Data migration moves data from one system to another. Data modernization includes the move and also redesigns pipelines, models, governance and access so the new platform works better than the old one.
Is lift and shift a good idea for data warehouses?
It works when you need off aging hardware fast. Plan a second phase to redesign for the cloud, or you pay cloud prices for an on-premises design.
Should we choose Snowflake or Databricks?
Snowflake suits SQL-heavy warehousing and reporting teams. Databricks suits data engineering and machine learning on large, varied data. Many companies run both for different workloads.
How do you validate a data migration?
Compare row counts, totals and sample records between source and target for every table, run both systems in parallel, and have business owners sign off on their key reports.
What is data warehouse modernization?
It is moving a legacy warehouse, such as an on-premises SQL Server, Oracle or Teradata system, to a cloud warehouse or lakehouse and rebuilding its loads and models there.
Can we modernize without stopping our reporting?
Yes. Move workloads in waves, keep the old system running during a parallel period, and switch each group of reports only after its numbers match.
Read next: Building a Modern Data Platform for Enterprise AI
