Data readiness: five steps to prepare for data driven decision making

AI rarely fails because of technology, it almost always fails due to a lack of coherence in data choices.

In brief:

• AI ambitions often stall due to fragmented, unreliable data and a lack of governance – not because of technology.
• Data readiness requires leadership: people, value and trust matter more than tools.
• Five concrete steps help CDOs transform data into a scalable foundation for AI-impact.

AI is no longer an experiment in Dutch organizations. Generative AI, copilots, and advanced analytics are rapidly becoming embedded in workflows, customer interaction, and decision‑making. However, once ambitions shift from pilots to scale, the real bottleneck emerges: data. Not because there is too little data, but because it is fragmented, unreliable, or difficult to access and AI exposes those weaknesses faster than ever.

Data readiness impact in Dutch organizations

This challenge is recognizable in the Dutch market. Many organizations operate a mix of legacy systems, cloud platforms, and departmental solutions, each with different definitions of reality. At the same time, privacy, compliance, and trust (GDPR, sector‑specific requirements, auditability) are non‑negotiable constraints. This makes data readiness not an IT task, but a leadership issue.

Step 1: Start with value (not data)

Data readiness does not begin with a migration plan or a new platform, but with a strategic choice: where should AI demonstrably improve performance, riskmanagement, or customer experience? The CDO roadmap emphasizes that data initiatives must explicitly connect to business objectives and operational priorities to create focus and accelerate results.

What works in the Dutch market

• Select three to five concrete value domains (for example customer interaction, supply chain, risk and fraud, finance, workforce).
• Define what “AI‑ready” means per domain: required data, timeliness, quality standards, and non‑negotiable privacy conditions.
• Stop pilots without a clear owner, KPI, or business question.

CDO role: Drive prioritization: shifting from “many ideas” to “fewer initiatives with impact.”

Step 2: Make ownership and governance unavoidable

AI can only scale safely when accountability for definitions, data quality, access, and decision‑making is clear. Governance is not an additional layer, it is the foundation that combines speed with trust. EY insights on AI‑ready data show that organizations recognize opportunity but are equally concerned about legal, ethical, and cyber risks, which can only be managed through strong governance.

What works in practice

• Appoint data owners for critical domains (customer, product, finance, risk, HR).
• Define permitted data usage, including AI use cases.
• Clearly distinguish fully automated decisions, decisions requiring human oversight, and decisions that intentionally remain human.

CDO role: Shift from “delivering data” to enabling decisions with guardrails that build trust.

Step 3: Make data quality and architecture scalable

Data quality is not a cleaning‑up activity, it is a system. AI is unforgiving: what was “good enough” for reporting can result in incorrect automated decisions at scale. EY-analysis shows that many organizations struggle to realize AI value due to limitations in data programs and infrastructure, including quality, availability, and governance.

A Dutch example illustrates the impact of addressing this properly. FrieslandCampina case shows how fragmented landscapes trapped data in silos and how a more fundamental redesign (platform plus operating model) made data accessible across the organization.

Recommendations

• Design for reuse: one definition, multiple applications.
• Make quality measurable (completeness, accuracy, freshness, lineage).
• Build architecture that supports integration rather than retrofitting.

CDO role: Focus not on tools, but on the architecture that enables repeatable value.

Step 4: Invest in capabilities and data culture

Data readiness is as much a people challenge as a technology challenge. Experience shows that culture and internal resistance are often bigger obstacles than tooling. Without trust in data and sufficient data literacy, even the best architecture remains underutilized.

People‑centric actions:

• Define data literacy by role: what must finance, marketing, risk, or operations understand to trust decisions?
• Organize around data products with product owners, not just datasets.
• Adopt a federated model: central standards and governance, decentralized domain ownership.

CDO role: Build an organization that uses data correctly and confidently scales AI responsibly.

Step 5: Run data readiness as an always‑on operating model

The data readiness landscape constantly evolves: new use cases, regulations, models, and risks. Data readiness must therefore operate as a continuous model of monitoring, improving, and steering. Leaders who want sustained impact structurally connect data initiatives to business value and mature governance.

Use cases in practice:

• Establish regular cadences for data quality, access, and model risk reviews.
• Make traceability standard — from source to decision, including logging and lineage.
• Design for scale so new AI use cases can plug in without starting over.

CDO role: Move from running a program to building a business engine — a durable data foundation that enables AI, keeps it safe, and evolves with the organization.

The Dutch CDO agenda is definitively shifting from collecting and reporting data to treating data as a strategic asset that makes AI reliable, compliant, and scalable. This requires leadership across three dimensions: prioritizing where data truly creates value, building trust through governance, quality, and ownership, and enabling scale through architecture and an operating model that supports reuse. The question is no longer whether organizations will adopt AI, but whether their data is structured to deliver sustainable impact.

CDO Roadmap: Measuring ROI

34% of organizations place the highest importance on efficiency improvements and cost savings as measures of ROI on data investments, while 25% place it on revenue growth.

Would you like to assess how AI‑ready your data truly is – and where the biggest accelerators and risks lie?

Data plays a crucial role in the success of organizations in an AI‑driven market. This report provides valuable insights to help Chief Data Officers get more out of their data. CDOs who are able to fully unlock the value of their data can better align their initiatives with key business objectives and achieve outcomes that strengthen the organization as a whole.