A leader’s guide for building a modern data strategy for AI

In the era of AI, data is your most strategic asset. But many C-suite leaders have discovered their organizations are not equipped to capitalize on AI due to longstanding data challenges​. In fact, nearly half of organizations in a recent survey said poor data quality or insufficient data could impede their AI initiatives.

The lesson is clear: AI’s success depends on having the right data foundation. This blog post explores what a modern data strategy is, why it’s critical for AI, and how to assess if your data infrastructure is AI-ready. We’ll conclude with a practical checklist to evaluate your data strategy’s readiness for AI.

A data strategy is a comprehensive plan that defines how an organization will collect, manage, govern, and use data to generate value​. Put simply, it’s a roadmap aligning data-related activities with broader business goals. 

A modern data strategy goes beyond just storing data—it ensures your data is treated as a strategic asset, enabling actionable insights through analytics and AI. 

A modern data strategy: 

• Addresses data governance and security (such as tracking data lineage, quality, and usage to build trustworthy AI models​)
• Outlines the steps towards gathering, securing, and analyzing organizational data
• Explains how to break down data silos so data can be shared among the whole company
• Unlocks new business value by empowering employees with the right data and tooling to make better decisions

In summary, a modern data strategy defines how an organization turns raw data into business value. 

AI systems are only as good as the data that fuels them. The saying “garbage in, garbage out” applies. If your AI is trained on poor-quality or irrelevant data, it will produce poor results​. For example, low-quality data can lead to AI models giving inaccurate recommendations or biased, irrelevant outputs​. 

Conversely, high-quality, well-contextualized data is the linchpin of successful AI outcomes​. In practice, organizations must invest in data preparation and quality assurance before expecting AI to deliver meaningful insights.

What’s more, without sufficient quantity and variety of data, AI models may underperform. Think of an AI system that has only seen a narrow slice of information. It won’t be able to handle real-world variation. 

Moreover, data must be prepared and structured for AI to use. It’s estimated that 80–90% of enterprise data is unstructured​. This unstructured data (like documents, images, or emails) often needs to be converted into structured formats before AI models can train on it​. That conversion and cleaning process is a critical part of an AI-focused data strategy.

Any issues with data quality, completeness, or bias will directly manifest in the AI’s behavior. This is why forward-looking companies treat data as the foundational layer of AI projects, often spending considerably more time on data preparation and management than on model development. In short, AI relies on data—and not just any data, but data that is accurate, well-governed, and relevant.

As you build your organization’s modern data strategy for AI, be sure to include these core components.

An effective data strategy starts with business objectives. Try to answer questions like: What do you want to achieve? What do you want to learn from your data? 

Your data strategy should support key company goals rather than hoarding data for its own sake. To give an example, you can use your collected data to improve the customer experience, optimize operations, or enable new AI-driven products. In short, start with a business question or problem.

Data misuse or data bias leads to unreliable AI models. Data governance makes data trustworthy and provides the backbone of any data strategy. It involves policies and processes to ensure high data quality and security.

Strong governance establishes who “owns” the data, who can access it, and how it can be used. If you intend to use customer or user data, remember to cover data privacy compliance like GDPR or CCPA regulations.

Data consistency is crucial for AI algorithms to work, but many organizations struggle with data silos. An AI data strategy aims to break down silos and consolidate all data into a unified view. 

For example, Rivian (an electric vehicle maker) found that siloed data and different systems were a major bottleneck. They responded by building a unified data architecture to create a single source of truth​. The result was a more scalable data foundation for AI development.

Quality is king in data strategy. After all, ensuring data is accurate and free of errors is essential before sending it to AI models. However, it’s not easy to maintain all the data your organization collects. Therefore, organizations have adopted DataOps techniques. 

DataOps is the data equivalent of DevOps. DataOps focuses on continuously testing and monitoring data quality. For instance, you might run automated tools to detect anomalies or inconsistencies in incoming data. As your data landscape continues to grow, DataOps should be part of your modern AI data strategy to save you time and improve your data quality.

More than half of organizations (52%) are still in the process of upgrading their data infrastructure to support AI use. How do you plan on ingesting, storing, or processing large volumes of data? Outline your data architecture for AI and infrastructure choices. 

Today, most organizations choose cloud-based data lakes and data warehouses to store their data for AI. Regardless of what you choose, make sure your data architecture supports different integrations so you can collect data from all kinds of sources. It’s also important that you can easily integrate AI models or analytics tooling into your data architecture for quick access to models and insights. 

Last but not least, a data strategy is about the people just as much as the technology. Successful data-driven organizations invest in culture and change management. This means cultivating a culture where decisions are based on data and analytics rather than gut feeling. Train your employees on data literacy and AI tools to help build this culture.

You should also define data-related roles, such as data owners. These roles are in charge of overseeing correct data storage for the assets in their domain. By defining data owners, you create a sense of responsibility that positively impacts your organizational culture around data.

With these elements in place, organizations build a solid foundation to develop and scale AI solutions.

Building a modern data strategy for AI is a journey—but how do you know if you’re on the right track? Below is a practical checklist to evaluate your organization’s data strategy readiness for AI.

1. Clear business alignment: Have you defined clear business questions that you want to solve using AI models trained on your organizational data? Every data strategy should map to a business outcome. Avoid collecting random data that serves no purpose and only costs time and resources to maintain.
2. Data culture: A strong data culture is important to the success of your data strategy for AI. Have you gotten C-level buy-in, made a plan to improve the data literacy of employees, and built a data-driven culture? If people and processes are not part of your strategy, technology alone will not deliver results​.
3. Data governance and ethics framework: Your strategy should include a governance framework that addresses questions like: Who is responsible for data quality? How do you ensure compliance with GDPR regulations? What data can’t be used to train AI models?
4. Data quality assurance: Do you have mechanisms to monitor and improve data quality continuously? These might include data profiling tools, validation rules, and automated anomaly alerts. Remember, AI amplifies data issues—any lack of quality will appear in AI results.
5. Integration capabilities: Is there a clear path to bring new data sources on board to further train your AI models? Your data strategy should be ready for data integration from internal and external sources. Evaluate if you have the right ETL/ELT tools and data pipeline for AI processes. When a valuable new dataset appears (like a third-party tool your team uses), how quickly can you connect it to your analytics environment?
6. Data security and data privacy for AI: Does your data strategy have robust security controls at every layer? Use the principle of least privilege. Restrict access to sensitive datasets to authorized employees and systems. Encrypt data in transit and at rest where appropriate. 

Few organizations will tick every box at first. The goal is to identify your gaps so you know where to improve your strategy. As you tick more boxes, you’ll notice your AI models deliver more impactful results.

Remember: A modern data strategy for AI is as much about people and process as it is about technology. It requires executive vision, cultural change, and disciplined execution of data management fundamentals. But the reward is significant: Organizations with an AI-ready data infrastructure can unlock insights and automation that drive innovation.

Learn if your organization is ready for AI.