What’s Keeping Data Leaders Up at Night? 2025 Priorities Revealed

In 2025, data leaders are under immense pressure to leverage data and transform it into relevant, actionable insights that drive tangible business outcomes. It’s obvious that the excitement around AI isn’t going away any time soon, but the fundamental question remains: how is it driving measurable results?

We asked over 30 senior data leaders what they’re prioritising this year, how they will deliver on their initiatives and what their biggest blockers are. Let’s see what we found out.

Top Data Priorities for 2025: What Data Leaders Are Focused On

The CDAO role has shifted dramatically in the past 5 years. Now, more than ever, data leaders are expected to show what the millions poured into technology have resulted in. Moving forward, data leaders must prioritise initiatives that drive tangible business outcomes, which means questioning how every piece of tech they invest in will drive cost reductions or deliver growth opportunities. But navigating the increasing complexities of AI and other tech, governance, and culture isn’t easy. But one thing is clear. Our survey reveals that 69% of data leaders identify "building a strong data-driven culture" as their top priority for 2025.

And that’s what it ultimately comes down to. Every pound or dollar spent must deliver better insights and decision-making that leads to an increase in the bottom line. Successful CDAOs need to think “business first, technology second.”

And when allocating budgets, they need to assess how to benchmark for success so it becomes clear what value their investments are delivering.

According to our latest survey, the top three priorities for data leaders in 2025 are the following:

1. Building a data-driven culture
2. Advancing AI and machine learning adoption
3. Enhancing data governance and compliance

Priority 1: Building a Data-Driven Culture

Building a data-driven culture emerged as the top priority for the data leaders we surveyed, reflecting the importance of bringing the whole organisation on the transformation journey, not just the data professionals. A strong data-driven culture doesn’t just mean having a team of data professionals that deal with all things data; it means embedding data-driven practices throughout the organisation and shifting mindsets to encourage the wider business to use data to inform their day-to-day decision making.  By upskilling employees that wouldn’t consider themselves “data people”, data leaders can implement long lasting change throughout their organisation, which positively contributes to overarching corporate goals.

The understanding of data and its value needs to be woven throughout each team, and a fundamental level of data literacy is key to unlocking this. If data isn’t central to decision-making on a daily basis, investing in more tools won’t move the needle.

According to Forrester’s research, 82% of decision-makers expect basic data literacy from all employees across departments, regardless of their level or status. Yet, only 40% of employees say they are being provided with the data skills their employers expect. As the value of data continues to grow, there is an interesting discrepancy between the beliefs about the importance of data literacy and the actions that follow. Despite data literacy being upheld as the most important skills for success, 86% of data leaders we surveyed perceived their organisation as having ‘poor’ or ‘fair’ data literacy.

It’s critical that data leaders continue to invest in bridging the data literacy gap. Otherwise, business teams will keep struggling to deliver the impact that data teams aspire to.

As Ashar Khan, Group Chief Data Officer, SSE Plc, shares:

“Bringing together diverse interest groups, from data privacy, security, and cloud technology to business teams, is key to making data an asset, not a bottleneck.”

But cultural change doesn’t happen overnight. It requires leadership to rethink how they support upskilling, remove data silos, and shift mindsets from “data as an IT function” to “data as a business enabler.” It also requires the wider business to embrace new ways of working and step out of their comfort zones.

Ultimately, “mindset matters more than skillset.”

Priority 2: AI and Machine Learning Adoption

The UK is already a clear leader in AI, with an added predicted GDP growth of 10% by 2030. However, without a strong data foundation, organisations will struggle to integrate AI in a way that drives business impact.

70% of businesses continue to believe that AI is critical to their long-term success, and according to the data leaders we surveyed, advancing AI and machine learning adoption remains a key priority heading into 2025. Yet many organisations struggle to implement AI effectively due to poor data readiness and a lack of strategic alignment.

AI Literacy and core data foundations related to quality are more important than ever.

A recent survey by Informatica revealed that 97% of data leaders (600 respondents) have already encountered issues with their workforce using GenAI or its outputs in their day-to-day operations. Whether it’s working with wrong or incomplete data, using sensitive data or not reviewing for biases, end-users must have critical thinking skills to question the outputs of GenAI. The balance between risk and reward is tricky to navigate, but it’s clear that successful AI adoption in the workplace requires investment in data quality, management and upskilling.

Another key question is: how fast or slow should a data leader invest in the new kid on the block? AI tools are cropping up everywhere, but it will be interesting to see which ones outlive the hype.

James Scott, Chief Data Officer, Coterie Holdings raised concerns about AI:

The global arms race around AI and rapid deployment of new tools pose a huge risk. Companies must be careful not to invest in the wrong technology without a clear ROI.

Yet on the other hand, if AI is not experimented with and guardrails are too tight, businesses risk losing out on those who are moving forward with a quicker, iterative approach.

Priority 3: Enhancing Data Governance and Compliance

The explosion of AI and regulatory scrutiny means that governance is now a boardroom-level concern. Data leaders must ensure compliance while still enabling innovation. This is a tricky balance to strike, as being too restrictive means losing out on massive opportunities, yet depending on industries, the repercussions of non-compliance and careless use of data can be detrimental.

However, there are many daily processes that can be evaluated and automated. Data leaders must understand that employees need certain freedom to experiment with how they can test this in their own roles, as a complete top-down approach will only stifle the art of the possible.

That’s why it’s key that data governance is not done in a siloed approach, but is optimised in a hub-and-spoke model where each team has members who understand the governance principles to guide daily activities. The value of this can’t be underestimated and leadership needs to get on board with its impact.

Securing investment in data governance as a strategic objective is our biggest challenge this year.‍

James Shaw, Data Governance Manager, North-Standard

Many organisations are realising that governance cannot be an afterthought. Because at the end of the day, everyone is a data owner, therefore making data secure, accurate and available across an organisation should be second nature. A proactive governance strategy can serve as an enabler, ensuring that AI, analytics, and decision-making processes are built on trustworthy, high-quality data. Strong governance helps ensure organisations can extract meaningful insights from their large data pools to truly make the most out of their data.

What’s holding back progress? The biggest challenges

Despite the clear priorities for 2025, data leaders face persistent roadblocks that threaten to derail their strategies. The most pressing challenges identified in our survey include:

• Lack of executive buy-in and investment in data literacy
• Siloed initiatives that fail to scale across the organisation
• Uncertainty in AI adoption and integration

Business priorities and financial constraints often push data initiatives to the back burner. If leadership doesn’t see immediate ROI, it’s difficult to sustain long-term investment.‍

Andy Moore, CDO, Bentley

That’s why it’s more important than ever that data leaders build a robust feedback loop, KPI and benchmarking system that ensures that the impact of new initiatives can be shared with leadership in both quantitative and qualitative measures.

If you’re unsure how to get started, we recommend you watch The ROI of Data Literacy, where we unpack our DRIVE Framework showing how to build your own.

Data is one of a company’s most valuable assets, yet data frequently operates in siloes. Working in silos complicates data management and reduces opportunity for collaboration. Data silos impede visibility and access thus limiting the value organisations can extract from their data. Implementing org-wide data initiatives and removing data silos allows for better sharing of information and collaboration between different departments and ultimately, stronger data-driven decision-making across the company.

As AI continues to evolve, the overarching concern of being replaced by “robots” continues to grow. It is less about being replaced by AI, but rather being replaced by people who understand how to use AI to improve their workflows and increase their productivity. Perceiving AI as a new team mate as opposed to a new tool that’s taking over is the first step. Being curious to experiment with AI while having the critical thinking skills to challenge its outputs will enhance adoption and usage. However, without basic data and AI literacy, workforces will remain sceptical of AI and its benefits.

How will Data Strategies transform in 2025?

So, what are the ingredients of the most successful data strategies?

1. Value and quality over quantity

Old approach: Focused on collecting vast amounts of data without clear use cases.

New approach: Prioritising data monetisation, impact tracking, and ROI measurement.

What it means: Data leaders will need to prove how data investments drive business outcomes.

2. Breaking down silos

Old approach: Centralised data lakes & warehouses controlled by IT.

New approach: Data Mesh and decentralised ownership. Teams manage their own data products while ensuring interoperability.

What it means: More self-service analytics, faster decision-making, and collaborative data ecosystems.

3. Data Literacy at every level becomes a non-negotiable

Old approach: Only data teams and analysts had deep data knowledge.

New approach: Every employee (from finance to marketing) will be expected to understand and use data & AI tools in daily decisions.

What it means: CDAOs must prioritise upskilling & cultural shifts to ensure AI and data tools are delivering the value expected from the C-Suite.

4. Real-time and dynamic data takes over

Old approach: Batch data processing with delayed insights.

New approach: Real-time, streaming, and edge AI drive instant decision-making.

What it means: More investments in real-time analytics, IoT-driven data, and AI models that can respond in milliseconds.

5. AI-first, but governance-driven

Old approach: AI was an experiment, often siloed in innovation teams.

New approach: AI is now embedded in operations but with strong governance for trust and risk management.

What it means: More AI governance frameworks that don’t stifle innovation, explainability measures, and bias audits to ensure responsible AI adoption.

6. AI-augmented data teams become the norm

Old approach: Data teams manually handled analysis, modelling, and reporting.

New approach: AI copilots and automation will take over repetitive tasks, allowing data teams to focus on strategy and innovation.

What it means: Data leaders must redefine roles to blend human expertise with AI capabilities. Over-reliance and accountability should still be monitored.

Being a proactive data leader means enabling the entire business to drive the value of its data forward. They need top prioritise people to unlock the full value, giving agency and skills across teams. This will help deliver the impact they intended, and keep data and business strategies in sync. Because at the end of the day, it’s all about creating corporately aligned data initiatives that drive the bottom line.

The Future of Data Leadership

The role of the Chief Data Officer is no longer about managing data or delivering insights; it’s about shaping the future of organisations through strategic leadership. As businesses face growing demands for innovation and AI integration, the CDO must step up as a unifying force, driving transformation and creating lasting value across the enterprise.

Jason Foster, Founder and CEO, Cynozure

The most successful data leaders of 2025 will not only manage technology projects but also ensure every initiative they plan for aligns with trackable, measurable business outcomes. As AI, governance, and culture transformation collide, those who prioritise collaboration and strategic alignment will emerge as true leaders in their field.

The question is: when looking at your day-to-day, are you leading in this way?

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