Data Modernization.
Warehouses, lakes, and semantic layers.
A data platform that's ready for AI, with lineage, semantics, and commercials agreed before work starts.
About Data Modernization
AI systems fail on their data foundation before they fail on the model. We build the foundation.
Data Modernization is the unsexy work that makes everything else possible: warehouses, lakes, semantic layers, lineage, catalogues, and the governance to keep them true over time.
We do the migrations, the schema harmonisation, and the semantic modelling that turns "raw tables" into "questions your business asks." Analysts and LLMs both benefit.
The output is a data platform handed over to your team on the terms in your agreement, with lineage you can inspect, freshness you can trust, and semantics designed to ground natural-language queries in your data.
Project deliverables
What we can deliver.
These are possible deliverables for this service. Your statement of work defines the selected scope, price, milestones, acceptance checks and responsible reviewers.
- 01Cloud warehouse or lakehouse, provisioned
- 02Ingest pipelines with freshness SLAs
- 03Semantic layer / metrics store
- 04Data catalogue with lineage & ownership
- 05Access controls & row-level policies
- 06Runbooks for common data ops
Business use cases
Where Data Modernization fits.
Explore practical applications of Data Modernization and the workflows each can support. We agree the implementation scope around your users, systems and priorities.
Warehouse migration
Move off a legacy warehouse with an agreed downtime and rollback plan; parity-verified against the source.
AI-ready platform
Add semantic layer + vector store so LLM queries are grounded in your data.
Multi-source unification
Reconcile customer, product, and event data from five systems into one canonical shape.
Technical approach
Capabilities your project needs.
We select tools around your requirements, existing systems and operational constraints. These capabilities guide the architecture and implementation.
- Warehouses & lakehouses
- Ingest & CDC
- Semantic layers
- Data catalogue
- Access control
- Lineage
Our delivery process
From requirements to handover.
We plan requirements, architecture, design, implementation, testing, release and handover. The proposal identifies the engineering review and client acceptance points for your project.
Service questions
Data Modernization: common questions.
Do you migrate our production data?
Yes, with a parity-verified cutover. The new system runs against the old in shadow mode until every dashboard and downstream job passes, then we cut over.
How long does a typical modernization take?
Timing depends on source quality, volume, access, transformation rules and validation needs. We assess those dependencies and agree milestones before a migration begins.
Will this work with our existing BI tools?
Yes, the semantic layer is BI-agnostic. Same metric definition, whichever tool your analysts already use.
Plan your project
Discuss your requirements.
Tell us your goals, current systems and delivery priorities. We will define the scope and provide a written quote for Data Modernization before implementation.
Timelines, ownership, third-party licences, warranty and support are agreed for your engagement.