Nothing downstream can be more reliable than the data it starts with
Most stalled automation programmes are not blocked by technology. They are blocked by three customer numbers that disagree, a master data list maintained by hand, and a report that takes nine days to produce because it stitches six exports together. That layer gets fixed before anything is built on top of it.
- 1–3 weeks
- to a defensible picture of the data estate
- 70%+
- of manual reconciliation and report assembly removed
- 100%
- of published metrics carrying end-to-end lineage
- One
- agreed definition per metric, replacing departmental variants
What it looks like today
The pattern we find in almost every operation
- The same customer exists four times across four systems with four different identifiers.
- Half the inputs a process needs live in a spreadsheet and a shared mailbox rather than in a system.
- Reporting is assembled by hand from exports, so nobody trusts the numbers enough to act on them.
- Every AI initiative rediscovers the same problem: the model is fine and the inputs are not.
- Data quality problems surface during the automation build, which is the most expensive possible time to find them.
- No figure in the board pack can be traced back to its source, so no auditor will accept it.
How we do it
The approach, step by step
- 01
Inventory the truth
We map which systems hold which facts, how each is keyed, where the copies disagree and how stale each one is. For most organisations this is the first single picture of its own data estate, and the disagreements are usually worse than assumed.
- 02
Resolve identity before anything else
Master and reference data sit at the root of most downstream disagreement. Customer, supplier, product and employee identity are reconciled first, because every analytical and automated decision afterwards depends on them.
- 03
Build the integration layer
Event-driven or batch, whichever the systems actually support: API connectors, change data capture, file ingestion and a queue with retry semantics, so a source system being unavailable delays data rather than corrupting it.
- 04
Make quality measurable
Validation rules with named owners, automated expectations for freshness and completeness, and an alert when a source silently stops producing. Data quality stops being an opinion and becomes a number somebody is accountable for.
- 05
Publish it with lineage
Curated datasets with documented definitions and end-to-end lineage, so an analyst, an auditor and an automation all work from the same agreed version of a metric instead of three departmental variants of it.
What you receive
Deliverables, stated up front
- Data estate inventory: systems, owners, keys, volumes, freshness and known conflicts
- Master and reference data model with survivorship rules
- Integration layer with connectors, change capture and an ingestion queue
- Retry, replay and dead-letter handling for every feed
- Quality rules with named owners and automated expectations
- Curated datasets with documented definitions and a semantic layer
- End-to-end lineage and an audit trail from source to report
- Data catalogue and business glossary agreed with the functions in scope
- Pipeline monitoring, alerting and a runbook
You are a fit if
- Two departments produce different numbers for the same metric
- Reporting is assembled by hand from multiple exports
- An automation or AI project stalled because the inputs were unreliable
- The same entity is keyed differently across systems
- Nobody can explain where a figure in the board pack came from
We will tell you it is a fit problem if
- There is no shared acceptance that the data needs fixing — automation will simply be blamed for its flaws
- You need one dashboard next month, which is a reporting problem rather than a foundation problem
- The source systems are being replaced within the year — sequence the replacement first
Systems we work with
Not on the list? We integrate against anything with an API, a database, a file interface or a documented import format.
Questions we get on this
Data foundation: the practical answers
Is this just a data warehouse project under a different name?
How is this different from hiring a data engineer?
Do we need to replace our ERP?
How do you handle access control?
Also consider
All 11 capabilities
Bring us the process you already know is costing too much
Thirty minutes with an engineer is usually enough to tell whether it is worth automating, roughly what it would save, and whether the payback is inside a window your finance team will accept. If the answer is no, we will say so on the call.