Why SMEs Need a Better Data Strategy

The real AI arms race is about data

Businesses everywhere are racing to adopt advanced AI, from generative to agentic systems. But the difference between leaders and followers will not be who has the newest model. It will be who can reliably operationalize AI.

That comes down to one thing: data readiness.

The popular phrase is “without data, there is no AI.” The more practical truth is sharper: without quality data, you cannot get quality AI. Data is the fuel behind every AI use case, and most companies have plenty of “fuel” that they cannot actually use.

Why most companies can’t scale advanced AI

Organizations struggle to produce AI that creates real business value because their data estates were designed for an earlier era. Many data strategies are relics: built for structured reporting, not for modern AI systems that thrive on unstructured information.

Two forces are colliding:

• Advanced AI needs broad, high-quality data, especially unstructured data.

• Most enterprises do not trust their own data, and trust is a prerequisite for scaling AI.

Unstructured data is the primary training ground for advanced AI, and a large majority of enterprise data lives in unstructured formats (documents, emails, chats, PDFs, images, recordings). Historically, that data was difficult to validate, understand, and convert into usable assets. Governance and architecture were not built for it.

The data challenges blocking AI

Even when companies have “lots of data,” several persistent issues undermine AI adoption.

1) Data silos and fragmentation

Unstructured data sits across disconnected systems: cloud, on-prem, applications, and edge environments. Most IT architectures were not intentionally designed to manage large volumes of raw, heterogeneous data. The result is:

• incomplete datasets

• duplication

• bias

• inconsistent definitions

2) Weak accountability for data ownership

Data ownership becomes precarious when no one is explicitly accountable for how data is collected, used, and processed. Ownership must include internal teams and external vendors. Without clear accountability, risks grow:

• breaches

• corruption

• inconsistencies

• compliance failures

3) New security threats in the advanced AI era

Advanced AI introduces unpredictable data security risks that older policies were not designed to handle. Highly regulated sectors (government, healthcare) face amplified exposure due to the sensitivity of the data they hold. Controls have to evolve with the technology.

4) Bad data becomes bad AI

When AI behaves unexpectedly, it is often amplifying what it learned from the data. Any inaccuracies, inconsistencies, or biases in your data will surface in model outputs.

Why data strategy is the lever

A data strategy is a roadmap for how an organization collects, stores, secures, and uses data. More importantly, it is how you build trust in the data foundation so AI outputs become reliable enough to deploy at scale.

A strong data strategy ensures:

• the right data exists for the use case

• it is accessible to the right people and systems

• it is governed and secure

• it aligns with business goals, not just technical ambitions

How to design an AI-ready data strategy

The key is to work backwards from outcomes.

1) Start with a specific business outcome

Begin with the use case and the result you want. Ask stakeholders:

• What specific problem are we solving?

• What is the high-ROI opportunity?

• What measurable metrics can we improve quickly?

This forces alignment between data initiatives and business value, and it helps you pick use cases that can demonstrate returns early.

2) Inventory your data estate for that use case

Do not boil the ocean. You do not need all data at once. You need the right data for the chosen use case.

Assess:

• accuracy

• accessibility

• relevance

• security posture

This step is also where AI can help. Large language models can decipher and transform unstructured data, accelerating data cleanup and preparation.

A practical example of this approach is using gen AI scripts to correct inconsistencies and errors before migration, then automating mapping between source and target structures to speed migration dramatically compared to traditional methods.

3) Modernize governance through updated policies

Policy is the control layer of governance. It defines the rules for:

• collection

• ownership

• storage

• processing

• usage

• access controls

Modernizing policy is collaborative work. Data teams need to link datasets, reduce duplication, and update controls so data is usable and safe.

4) Evaluate and upgrade the IT and data architecture

Unstructured data introduces new formats, new pipelines, and new demands on storage and compute. This can require tech stack upgrades.

A practical architectural direction is to adopt hybrid-by-design principles and use architectures that can accommodate multiple data formats wherever they live. Open data lakehouse patterns are one example of enabling broad access across formats and locations.

5) Build a data-first culture

Technology and policy alone do not scale. Culture does.

A data-first culture means:

• data literacy initiatives

• data democratization with guardrails

• shared responsibility for privacy and security

• leadership alignment across the C-suite

A CDO cannot succeed without strong support from other executives. Data has to be treated as a business asset, not a back-office function.

Conclusion: quality data is the differentiator

A successful data strategy turns data into an asset that creates business value through operational efficiency, product improvements, and competitive advantage. Most importantly, it provides the high-quality data foundation needed for advanced AI to work reliably.