A Fractional Chief Data Architect, with a team behind them
The technical blueprint for how data actually moves, connects and holds up under scrutiny — data platform architecture, integration and quality engineering. One operator in the seat. Our delivery bench behind them.
Strategy tells you what to do with data. This seat makes it actually possible.
A data strategy is a set of decisions. A data architecture is the thing those decisions get built on — the platforms, the integration patterns, the modelling standards, the pipelines that move information between systems without quietly corrupting it along the way. Most businesses have the first without ever properly commissioning the second, which is why so many good strategies stall on delivery.
This is deliberately distinct from the Fractional CTO/CDO seat, which owns data strategy and governance direction. This seat owns the technical blueprint that strategy gets built on. Businesses with a genuinely complex data estate often need both. Most start with CTO/CDO and bring this seat in once the strategy has real technical weight behind it.
If more than two of these are true, the foundation is already shaky
Data lives in systems that were never designed to talk to each other, connected by manual exports if at all.
Every new analytics or AI initiative starts with a multi-month data-cleaning project nobody budgeted for.
Two reports that should show the same number for the same metric routinely don't.
Pipelines break silently, and the first anyone hears about it is when a decision's already been made on bad numbers.
Nobody could confidently trace a number in a board report back to where it actually came from.
A data strategy exists on paper, but the systems underneath it haven't changed in years.
Four areas of ownership, not a vague advisory retainer
Data Platform & Integration Architecture
Owns the technical architecture for how data is stored, moved and connected across systems — the actual plumbing behind the strategy.
Data Modelling & Quality Standards
Sets the modelling standards and quality rules that make "trust the data" something people can actually do, not a hopeful phrase in a strategy deck.
Pipeline & Engineering Oversight
Independent oversight of how data pipelines are built and maintained — catching silent failures before they become a board-level embarrassment.
Foundation for Analytics & AI
Makes sure the technical foundation can actually support whatever comes next — whether that's better reporting, an AI initiative, or neither.
When there's a platform to build or an integration to fix — our team does it
The seat sets the technical blueprint. When that calls for defined delivery work — a data platform build, a legacy integration project, a quality-remediation programme — our delivery bench executes it under the same accountable relationship, against the standards the seat-holder has already set.
Before you take this to the board
CTO/CDO sets data strategy and governance priorities. This seat owns the technical architecture that strategy gets built on. Most businesses need the strategy conversation first — this seat is usually the second seat filled, not the first.
Before, in almost every case. An AI initiative built on an ungoverned, poorly integrated data estate inherits every one of its problems. Getting the architecture right first is usually the difference between an AI pilot that scales and one that quietly dies.
No — it gives them senior architectural direction and a standard to build to. Most data teams are relieved to have someone senior finally own the standard, rather than each person inventing their own.
The evidence behind this seat
Take the AI Readiness Assessment
Data foundation is one of five scored dimensions — see where yours actually stands.
Start the assessmentProblem before platform
The same sequencing discipline, applied to data platforms specifically.
Read the articleSee the CTO / CDO seat
Where data strategy and priorities get set — upstream of this seat.
View the seatTalk to us about the Chief Data Architect seat
A direct conversation about whether your data foundation can bear the weight of what you're planning to build on it.