If you last reviewed your data strategy more than 18 months ago, chances are it’s already outdated. In 2025, a modern data strategy needs to account for the rapid amount of change in data and analytics that has fast moved beyond the traditional pace of enterprise planning cycles. Cloud data platforms are maturing. Generative AI has become more than a novelty. And business stakeholders are no longer asking why data matters, they’re demanding to see tangible value from it.
So, what exactly does this mean? And how should Heads of IT and Finance adapt their approach? A modern data strategy is a comprehensive plan that guides how an organisation collects, manages, governs and utilises data to achieve its business objectives. Here’s what’s reshaping modern data strategies in 2025 and what to do about it.
1. From Technology-First to Value-First
Until recently, many data strategies focused on building cloud data platforms in the absence of business drivers. When looking to migrate data warehouses to the cloud, integrating BI tools and setting up governance frameworks, organisations are shifting from just data platform building to understanding value delivered. This means asking sharper questions at the outset:
- What business problems are we trying to solve with data?
- How does our data strategy enable faster, better decisions across the organisation?
- Are we measuring ROI on data initiatives?
This should guide the creation of a data and analytics roadmap that acts as the blueprint for your organisation when investing in capability. It presents a real opportunity to bring financial discipline and business need to data investments. This should link spend to outcomes and prioritising initiatives that drive operational efficiency, risk reduction or revenue growth.
2. GenAI Is Changing the Game
The introduction of Generative AI into the enterprise analytics stack has been nothing short of transformative. From automated report generation to natural language querying and AI copilots embedded into tools like Power BI and Excel, users now expect to “talk to their data” and receive contextual, real-time insights without technical know-how.
For CIOs and CFOs, this creates both opportunity and risk. Opportunity to empower non-technical teams with self-service insights, but can create an overreliance on AI output and give potential data security blind spots.
A modern data strategy should include a GenAI data governance framework, which covers:
- Controlled access to sensitive models and data
- Accuracy testing and feedback loops
- Clear policies on where GenAI can (and cannot) be used
- Upskilling teams to ask the right questions of AI, not just any question
3. Data Products Are Becoming the New Standard
We’re seeing a major shift toward data product thinking, that is treating data not just as an asset, but as something that’s packaged, maintained and delivered with a clear purpose and user in mind to solve a specific business problem.
This is particularly relevant for finance and IT leaders managing data outputs across multiple business units or subsidiaries. Instead of building monolithic platforms that serve everyone (and often no one well), smart organisations are creating modular, reusable data products, like a “Customer 360 API” or a “Weekly Revenue Forecast Model”, that can be consumed across teams. It includes metadata, documentation, unified schemas, quality controls and defined interfaces to make data usable, shareable and maintainable.
This leads to faster time to insight, reduced duplication of effort and a more scalable, resilient data architecture. A modern data strategy should address:
- Who owns each data product?
- How is quality assured and documented?
- What SLAs and access controls apply?
4. Data Governance Must Be Embedded, Not Imposed
Data governance in the past could have been viewed as a blocker rather than an enabler, something that slows down innovation in the name of compliance.
Behind every successful data and analytics deployment is a robust data and governance strategy that understands how to manage your data through all stages of its lifecycle. Now, modern data governance models are designed to be embedded in workflows, not layered on top. Think of automated data classification, real-time lineage tracking and proactive anomaly detection, running in the background, surfacing only when needed. This means less time chasing data issues or reconciling reports and gives more confidence in the data being used for key decisions.
Data governance must now evolve from static policy documents to live systems that adapt to changing business rules and regulations, especially around AI usage and consumer data privacy.
5. The Talent Mix Is Evolving
Finally, a modern data strategy must take into account the changing skillsets needed within teams to succeed. In addition to traditional roles like data engineers and analysts, we now see a growing demand for data and analytics translators who can bridge the gap between business questions and data solutions and data governance leads who also understand compliance and ethics around AI.
For mid-market and enterprise organisations without large internal data and analytics teams, this also means reassessing the partners you work with. You can read more here on how to build an effective data and analytics project team structure or download the ebook.
A modern data strategy is no longer just about building infrastructure, dashboards or cleaning data. It’s about having a clear data roadmap that delivers value fast and repeatedly. Building a data and analytics capability that measures success by business impact not just technical milestones, while balancing innovation with governance and risk management.
