Solution
The data foundation
Sources, scope and calculation rules differ from one department to the next, and the figures they report differ with them. A governed foundation keeps the provenance, lets you review the definitions and reconciles the gaps, without declaring one database true by decree.
Verifiable definitions and provenance
The debate becomes explicit
Provenance, formula, scope and ownership explain why two figures differ, then support a decision about which serves each use.
The retyping disappears
What used to be copied between the ERP, the accounts and the spreadsheets now moves on its own, on schedule, with a log of what went through and what failed.
History becomes usable material
Figures kept in one place, year after year, make comparison possible. A forest of spreadsheets never had a memory.
Sized to your scale
A 50 GB problem doesn’t justify an architecture built for 50 TB. A modest database may be enough initially, with documented capacity, thresholds and an evolution path.
In practice
- Connectors to your existing systems: ERP, accounting, tills, timesheets, exports from line-of-business software
- A central base sized to the need: for example PostgreSQL on a modest server, or a cloud warehouse when volume, workload and operations justify it
- Transformations documented and versioned, reviewed like code rather than hidden in formulas
- Sober orchestration: flows follow their schedule, replay expected failures and report the failures that require a decision
- Quality checks at the door: duplicates, gaps and drifts caught before they reach a report
- Hosting selected after reviewing constraints, operator, contract, locations, access, retention and exit
Systems involved
- ERP, CRM and accounting software, over APIs or exchange files
- Microsoft 365, Google Workspace and their exports
- PostgreSQL, BigQuery and the cloud warehouses in place
- dbt for transformations, Airflow for orchestration
- Your existing dashboards and BI tools
Service lineData →
The sources to bring together
The same work, against each sector’s own constraints. Every card opens the full sector.
Banking and insurance
Market-data platforms
Provider ingestion, documented transformations and datasets delivered on schedule, with freshness and completeness measured rather than assumed.
Energy and utilities
A unified data foundation
Meters, supervision, billing and market data brought together with one documented, defensible definition per quantity.
Healthcare and life sciences
Research data pipelines
Reproducible, versioned and documented processing, owned by the institution rather than by one person’s environment.
Manufacturing
A shop-floor data foundation
Machines, quality checks and the ERP brought into one place, with one definition per indicator that production and management share.
Retail and e-commerce
One view of the customer
Point of sale, online and loyalty brought together under a shared definition, before any personalisation project.
How it runs
Inventory
Who retypes what, where and how often. Duration depends on the sources, owners, exceptions and controls to reconcile.
Foundations
The central base and the first two connectors, on the sources that hurt most. Every figure arrives with its origin attached.
Flows
The remaining sources come in one at a time, each with its checks. A flow that fails is visible that morning, not at close.
Handover
Schema, transformations and procedures documented, so the base can be taken over without me. The foundation is yours, down to the code.
Test the fit: The data foundation
Describe the context, constraints and decision you need to make. The first conversation qualifies scope, boundaries and the next useful step.
Describe the situation