Data Science
Operating
Model
Transforming inconsistent, misaligned data science delivery into a structured operating model — capturing hundreds of millions in value confirmed by Finance.
Hundreds of millions (GBP) in value captured and confirmed by Finance.
Improved delivery speed and consistency across data science teams.
Value reporting provided confidence for further investment in Data & AI.
Data science delivery was inconsistent, slow, and disconnected from the business.
A FTSE100 aviation group had data science capability but no operating model to make it work. Delivery was inconsistent and slow, priorities were misaligned with the business, and there was no mechanism to capture or report the value being created — making it impossible to justify further investment.
Inconsistent delivery — data science output was unpredictable, with no standardised process or rhythm.
Misaligned priorities — data science work was not structured around business priorities or value creation.
No value capture — there was no mechanism to measure, confirm, or report the business value being generated.
Investment at risk — without evidence of value, further investment in Data & AI was difficult to justify.
A structured operating model — built for speed, alignment, and accountability.
The engagement designed and embedded the operating model the data science function needed — from agile delivery processes to Finance-confirmed value reporting.
Data science operating model
Designed the operating model for data science delivery — clarifying roles, ways of working, and the rhythm of execution.
Agile delivery for data teams
Introduced agile delivery practices tailored specifically for data science — improving speed, predictability, and team accountability.
Backlog ownership and business alignment
Established backlog ownership and structured alignment between data teams and business priorities — ensuring the right work got done.
Value capture and Finance sign-off
Established value capture and reporting processes confirmed by Finance — creating the evidence base needed to sustain and grow investment.
Faster delivery, Finance-confirmed value, and confidence to invest further.
Hundreds of millions (GBP) in value captured and confirmed by Finance.
Stronger alignment between data teams and business stakeholders.
Improved delivery speed and consistency across data science teams.
Stronger alignment between data teams and business stakeholders.
Data science capability without an operating model is potential without output.
Having data scientists is not the same as having data science delivery. This engagement shows what changes when the operating model is right: work aligns to business priorities, delivery becomes consistent and predictable, and value can be measured and reported. The result was hundreds of millions in Finance-confirmed value — and the confidence to invest further.
Three ways to engage
For leaders who need a clear, defensible view of where the organisation stands — and what to do next.
- Readiness scorecard
- AI inventory
- Root causes and 90-day plan
- Executive defensibility pack
For organisations that know the gaps and need senior-led delivery to close them — without a large consulting team.
- Close capability gaps
- Deliver measured outcomes
- Embed operating rhythm
- Executive progress narrative
For executive teams that need named senior ownership of the Data & AI agenda without hiring a permanent CDAO immediately.
- Executive and leadership presence
- Strategy and value ownership
- Delivery and governance oversight
- Succession planning
Need to turn Data & AI ambition into measurable progress?
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