PACKAGED OFFER · DATA PLATFORM FOUNDATION
Data platform foundation and initial data product
AI and analytics both fail for the same reason: data that is everywhere and trusted nowhere. This fixed engagement fixes the base before you build on it. We stand up a governed lakehouse, wire the pipelines that feed it, add the quality and governance controls that make it trustworthy, and prove it by delivering one real, trusted dataset your teams can actually use. It is the AI-ready foundation every model, dashboard, and agent quietly depends on.
- A governed lakehouse provisioned in your cloud, on open table formats
- Ingestion and transformation pipelines for one prioritised domain
- One gold-standard dataset, live and consumable by BI or an AI use case
A working foundation and one governed data product.
Rather than a two-year platform program with no visible payoff until the end, this is scoped to a working base and one trusted dataset, so value is provable early. The architecture is set up to scale from day one. Five concrete deliverables land by the end of the engagement.
A governed lakehouse
Provisioned in your cloud, on open table formats, ready to scale beyond the first domain.
Ingestion and pipelines
Ingestion and transformation pipelines for one prioritised data domain, modelled and tested.
Trust built in
Data quality checks, cataloguing, and lineage, so trust is built in rather than bolted on.
One gold-standard dataset
A trusted dataset live and consumable by BI or an AI use case, not stuck in a backlog.
A scaling blueprint
A blueprint for onboarding the next domains onto the same governed base.
AI and analytics both fail for the same reason: data that is everywhere and trusted nowhere.
Leaders whose teams reconcile numbers more than they use them.
For data and analytics leaders whose teams waste more time reconciling numbers than using them, and anyone whose AI ambitions are gated behind a shaky data base.
It is delivered through our data and analytics practice, our data engineering pods, and our data management and governance capability, on an open lakehouse architecture we tune to your cloud.
Eight weeks, four phases, one trusted dataset.
Design
Discovery, domain selection, and architecture design, producing a target design and agreed scope.
Build
Provision the lakehouse and build ingestion and pipelines, producing data flowing through bronze and silver.
Govern
Governance, quality, cataloguing, and lineage, producing live controls and the first gold dataset.
Enable
Validation, enablement, and scaling blueprint, producing a trusted dataset in use and a roadmap for the next domains.
Each phase produces a working output, so data is flowing well before the final week.
What a trusted base changes.
Fragmented pipelines replaced by one governed platform.
CASE STUDY6h to 1h
Blackwoods replaced fragmented feeds across Stibo, MSSQL, and warehouse systems with governed Azure Data Factory pipelines, cutting stock-visibility lag from more than six hours to under one and giving every team a dataset it could build on.
Show us the numbers your teams cannot agree on. We will build the base that settles them.
In eight weeks you have a governed lakehouse and one trusted dataset in use. When you are ready to onboard the next domains, the same pod scales the foundation to the blueprint it produced.