Solution 06

Data platform modernisation

One agreed set of numbers, governed access and dashboards people trust — the unglamorous work that makes every later AI project cheaper.

Outcome

Single source of truth

Outcome

Governed access

Outcome

Self-serve dashboards

Inside the solution

Single source of truth

Core entities and metrics defined once, with the definition visible next to the number.

Row and column level controls, data classification and access reviews that can be evidenced.

Source contracts, incremental loads and tests that stop bad data at the door.

Certified datasets and templates so analysts stop rebuilding the same joins.

Every field traceable to its source, which is what makes an audit or a migration survivable.

Storage tiers, partitioning and query patterns chosen with the monthly bill in view.

Scope of delivery

Indicative timeline

8-14 weeks for a first governed domain, then domain by domain.

Questions we are asked

Warehouse or lakehouse?: Warehouse for mostly structured, SQL-driven analytics; lakehouse when you have large volumes of semi-structured or ML training data. Many clients end up with both, deliberately.
Do we have to replace our BI tool?: No. We build the governed layer underneath and keep Power BI, Tableau, Looker or Metabase on top.
How do you handle the migration risk?: Domain by domain, running old and new in parallel and reconciling the numbers until the business signs off. No cutover weekend.
Is this necessary before doing AI?: Not always, but it is usually why the second and third AI project run so much faster than the first.

Invoices, claims, purchase orders and contracts read, validated and pushed into your ERP with a review queue for edge cases.

A grounded assistant for voice and chat that drafts replies, resolves routine tickets and escalates cleanly with full context.

Forecasting and anomaly detection over sensor, sales or logistics data so teams act before an exception becomes a cost.