The modern Chief Data Office (CDO) must accelerate outcomes, reduce friction and turn intelligence into measurable business impact faster, cheaper and at scale. CDOs are shifting from today’s focus on control to finding new ways to liberate knowledge and create value by bringing together human and machine intelligence. Below we explore the new mandate emerging for the CDO and the key capabilities this demands.
In a recent survey of large organizations, under half of respondents (48%) believed their CDO was very successful and well established. Only half (51%) think the role is well understood.1
The confusion is because the role is still evolving – it is no longer enough for the CDO to manage data and its risks. The office is moving from control to enablement as it seeks to prove its worth commercially, operationally and strategically. As our figure shows, CDOs first emerged to manage the data and reporting risks exposed by the 2008 banking crisis, and have since evolved to help businesses access data and analytics to drive insights.
Leading CDOs are now moving through a value creation phase with an expanded focus on analytical insights, AI enablement and the delivery of data products. Tomorrow’s CDOs will use AI and shared services to liberate knowledge across the enterprise, and then move on to design entire data-driven ecosystems that leverage LLM-based applications and Agentic AI to create value. This will mean overcoming key challenges rooted in today’s approaches.
Key data challenges
Despite significant efforts to democratize data access, data often remains hard to find, trust or use. Existing data frameworks are complex, slowing down delivery and usability. Business users struggle to access the right data, at the right time, in the right way.
While progress has been made in improving data accessibility, the challenge of finding the right data remains significant. Many organizations have introduced data products to make information discoverable and usable across domains, but these initiatives often lack the consistency and structure needed to scale. A more mature approach will require the development of shared ontologies and semantic frameworks that allow both humans and large language models to navigate, interpret and consume enterprise data effectively.
Today’s lack of scalability means that too few data/AI use cases are moving from pilot to production across the firm to generate the value they promised. Organizations must invest in scalable, enterprise-wide approaches such as the deployment of robust AI frameworks and protocols, semantically-rich data catalogs, and tools to manage unstructured data.
Programs to improve data management, insights and access remain expensive, rigid, slow and disconnected from business value. In turn, businesses resist committing budget without clearly visible ROI and often view the CDO as a cost center. Despite years of investment, data quality and provenance has not improved significantly. Organizations continue to struggle with inconsistent definitions, incomplete records, timeliness issues and duplication across systems, compounded by integration challenges.
These limit the business’ ability to trust and act on insights and create operational issues, such as hampering straight-through processing in banks or claims handling by insurers. CDOs therefore need strategies, tooling and governance models that not only improve agility but deliver sustainable, proactive uplift in data quality.
Finally, while there has been a lot of talk about improving data culture, this has often focused on improving basic data literacy, rather than changing the way stakeholders use data and gain value from it. CDOs need to become a vehicle for reshaping behaviors so that data is seen as integral to decision-making, AI is embraced as a trusted companion, and business outcomes are enhanced.
The lack of scalability means that too few data/AI use cases are moving from pilot to production across the firm
A new mandate is emerging
Data and AI-driven technologies offer the promise of rapid business evolution, but CDOs need to reposition themselves and their enterprise data strategies to enable this. A new CDO mandate is therefore emerging with several key features.
The first is that the CDO takes the lead in improving how data and AI investment ROI is measured and tracked, in terms of putting the right frameworks and metrics in place. The business units retain accountability for the ROI and value created by the individual use cases they drive. However, the CDO is accountable for data capability maturity, data infrastructure, and data governance as well as for quantifying the ROI of data projects under its direct control. This will change the perception of CDOs from ‘cost center’ to ‘driver of growth and innovation’.
To enable the delivery by the business of demonstrable ROI across multiple use cases, the CDO will need to make it cheaper, faster and safer to access data and to test, use and scale new AI-driven technologies. There will need to be a renewed push at a fundamental level to simplify and modernize data management frameworks, improve data usability and increase the ease and speed of access.
The push for cost-efficient scalability will also mean rolling out proportionate and easy-to-understand governance frameworks around AI and data in line with evolving regulations. With the right enterprise frameworks in place, the CDO mindset can shift from ‘control-first’ to ‘enablement first, usage fast’.
This is more important than ever, now that business users can pose questions to GPT-like solutions and get rapid answers – potentially cutting the CDO out of the process. Only by delivering trusted, business-ready data and intelligence that is easy to use and to turn into measurable business value can CDOs remain a strategic contributor to the business’ vision.
CDOs must become the architects of trust, value and intelligence at an enterprise scale
Fulfilling the mandate: five key levers
CDOs need to uplift the skillsets and services within their teams and deploy several levers across the business to help them to reshape user experiences and mindsets, build the right tools and organizational structures, and incentivize behaviors that unlock transformation.
The key levers for fulfilling the new mandate are:
- Building a clearer line of sight from data and AI spend to business impact. In particular, the CDO should embrace its responsibility for measuring and increasing the ROI on data spend. This endeavor will involve improving approaches to data products and making sure data-linked initiatives are prioritized holistically at a portfolio level in terms of their business value. Taking account of the network effects of benefits is critical to making a strong investment case. (We discuss the key areas of CDO accountability and how to improve ROI metrics in the second article in this series.)
- Speeding up time-to-value and business adoption. This will mean creating innovation fast lanes, using AI copilots to guide businesses on how to create value, and enabling low-cost self-serve experimentation using tools such as next-generation data marketplaces. It will also demand faster, cheaper, safer AI testing in easy-to-access, well-controlled sandboxes as well as flexible ways to create synthetic data that accurately mimics business data. CDOs may need to deploy enablement teams to remove friction and help embed data and AI intelligence into day-to-day business decisions.
- Creating new data-driven revenue streams. Growth now depends on creating value through appropriately governed intelligence rather than information. Internally, CDOs are helping business lines transform data into reusable, curated revenue-generating assets by applying a ‘build once, use many times’ mindset. For instance, behavioral and transaction data captured for fraud prevention can also improve credit scoring or fuel personalized customer offers. Anonymized insights can be packaged and sold externally, e.g. via premium APIs offering aggregated spending trends, or by blending partner and institutional data to deliver richer analytics for corporates.
- Making AI scalable, safe and explainable to gain trust and regulatory confidence. Creating trusted intelligence will mean simplifying data management frameworks, setting up robust curation strategies for unstructured data, and putting in place responsible AI governance, oversight and transparency. For many institutions, the next stage of CDO evolution may well be that of AI enabler and innovator – now that secure foundations have been implemented. This will mean applying features such as improved AI-enabled frameworks for structured and unstructured data and AI-enabled data marketplaces to make enterprise knowledge easy to find, consume and use.
- Growing a culture that defaults to data intelligence and AI-augmented decisions. The culture shift will mean building data literacy into business teams through behavioral change, e.g. by embedding nudges to enable data-driven decisioning in specific workflows and decision journeys. Critically, CDOs must also purposefully increase the business literacy of data teams, ensuring their relevance and inclusion in solutioning at a time when complexity is further abstracted from the business. CDOs should incentivize the right role models and leadership by adjusting HR scorecards with metrics such as the number of data/AI innovation pilots launched and measured benefits; the percentage successfully scaled & operationalized; the number of cross-functional initiatives using shared data products; and the percentage of investment decisions supported by data analytics.
As key levers are put in place, CDOs will deliver transparency on how the institution is creating measurable data value through both business use cases and direct data spend. CDOs can partner with business leaders to demonstrate the value achieved for each line of business.
Successful CDOs will act as enterprise strategists, shaping corporate direction by embedding actionable intelligence, not just by mitigating data risks.
To begin realizing this mandate, CDOs should focus on practical actions that create immediate momentum. First, they need to identify and reduce inefficiencies in existing data management activities to release capital for higher-value initiatives. Second, CDOs should clearly communicate their services and make them easily-consumable, whilst demonstrating how these drive measurable business outcomes.
Third, they should lead by example through adopting AI and automation to improve their own processes, from metadata curation to issue management. Finally, CDOs must work in close partnership with technology leaders to align data and technology architectures – ensuring that the operating model delivers scalability, robustness and agility.
Tomorrow’s CDO: powering change
CDOs are at a critical juncture – they must establish themselves as leaders or risk being excluded from key decisions on data-driven value creation. The tasks of scaling AI safely, coping with unstructured data and democratizing the use of data are significant, but offer huge opportunities for CDOs to reshape their role within the enterprise.
The most immediate challenge is that businesses are demanding tangible results from data investments. CDOs can turn this to their advantage by demonstrating how to put in place outcome-driven data programs where business value is measured, adoption is frictionless and trust is engineered from the start – securing their seat at the strategy-making table.
Today’s Chief Data Officers suffer from short tenures – future Chief Data Officers that cannot link data investment to business outcomes will likely last no longer than their predecessors