Over the past decade or so, two questions have fundamentally reshaped organisational design:
- “Who owns Customer Experience?” Creating the rise of the Chief Customer Officer
- “Who owns Digital?” Creating the rise of the Chief Digital Officer
Now organisations face the next big inflection point:
- Who should own Data, and, by extension, AI?
Now that businesses and consumers have realised how precious data is, this is no longer a theoretical debate. It is a strategic design decision that determines whether companies unlock value for business and consumers, or create fragmentation, duplication, and shadow capabilities with lack of ownership and focus, that can dilute meaningful impact.
Data Under the Chief Customer Officer
There is a strong logic for this in customer-led organisations.
For many organisations, the most value-rich use cases for data sit close to customer, commercial, and marketing functions:
- Personalisation & lifecycle management
- Loyalty & retention programmes
- Media and monetisation initiatives
- Product, service, and experience optimisation
- Demand, usage or behavioural insights
If the strategic agenda is centred on customer value creation, then placing data under the CCO can be a powerful model.
It ensures analytics teams are embedded where commercial decisions are made, increases responsiveness, and keeps the data agenda grounded in customer and business outcomes.
But this comes with a caveat:
If other functions (operations, finance, HR, procurement, product, etc.) feel underserved, they may build shadow data/AI teams, creating duplication and fragmentation.
Other Emerging Models:
- Reporting to the COO
Data is part of a data-first business wide and people culture strategy.
Under this model, data becomes a horizontal capability that enables the entire business. Either the COO controls product build and operational excellence, or the COO acts as the enterprise integrator, enabling all functions to consume data effectively.
- Reporting to the CIO or CTO
A technology-led integration approach, which can be linked to business wide digital transformation.
When the organisational priority is modernising cloud platforms, building cross-company data pipelines, or ensuring technology coherence, data is often placed under the CIO/CTO.
Pros:
> Strong alignment with data engineering and technical change
> Improved prioritisation for platform investment
Risks:
> Analytics teams become more distant from the business
> Business context can weaken
This sort of structure often represents a shift from business-embedded priorities to technology-centric transformation.
- Reporting to the CDO
Where the monetisation of digital and data is a strategic priority. This is increasingly common where e-commerce and digital trade is central.
A Chief Digital Officer owning data creates alignment across digital channels, digital product, transformation initiatives, and emerging AI use cases, especially when ‘technology’ is centralised elsewhere.
- Split Ownership Models
Technical enablement to leverage actionable business insight.
Some organisations place data enablement (platforms, pipelines, governance) under the CIO or CTO, while insights and analytics sit under the COO or CCO (or other commercial leadership functions).
Care needs to be taken here so that priorities and ownership do not become misaligned, and teams then struggle to join-up towards a single agenda and business outcome.
- CFO Ownership
Can be effective in heavily regulated industries.
Under the CFO, data benefits from strong governance, investment discipline, and a single, often more statistically driven, version of the truth.
However, many CFOs can lack ‘deep data’ or AI experience, which can potentially limit strategic ambition, business effectiveness and value creation, unless balanced by strong technical leadership beneath them, and strong alliances with the business cross functionally.
Key Questions to Determine the Right Model:
Before deciding where data (and AI) should report, organisations should ask:
- How broad is the intended scope of data? Is it primarily customer/commercial, or fully business-wide?
- Which business outcomes will data be used for? And does the chosen function own the levers responsible for those outcomes?
- How will funding, prioritisation and delivery be managed? Matrixed models require strong strategy alignment with clear accountability to avoid competing agendas.
- How mature is the organisation’s AI ambition? AI requires connected, governed, high-quality data foundations with accountability for tangible outputs.
Conclusion
There is no universal answer to this, with a lot depending on company priorities and what the overarching business strategy is.
The real question seems to be, rather than, “Where should data sit?”, instead:
“Which leader and operating model will most effectively convert data into measurable business value?”
Just as organisations once had to define who owned customer and digital, they must now define who owns data (and subsequently AI), because the next decade of competitive advantage will depend on getting this right.
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