Chapter 1 - Data Products in the AI Strategy
Series: The Role of Data Products in Agentic AI - Strategic Benefits and Opportunities
Over the past 16 years as a data professional, I’ve seen projects soar and stumble. Technologies changed, architectures evolved, and we’ve had no shortage of paradigm shifts. But one thing stayed the same: the need to deliver the right insights, at the right time, to the right people - and now, to the right machines.
This blog series is written from the lens of an AI optimist, one who believes in the power of AI, but also knows that without the right data foundations, even the smartest agents will fall short.
I draw from real-world experience, wins and failures alike, to explore how data products play a critical role in the age of agentic AI. You’ll find reflections from the trenches, practical perspectives, and a strong case for why our data needs to be more than just available - it needs to be packaged, contextual, and built with the consumer in mind. And increasingly, that consumer is not human.
Let’s dive in.
Introduction
We have started imagining (or already living in) a world where digital assistants, virtual agents, and intelligent bots are our teammates, working together to handle everything from simple errands to complex decisions. It’s a future that’s not so far off, and it’s made possible by autonomous agentic AI systems, where many different agents collaborate to get things done. However, just like humans need reliable information to do their jobs well, these agents need high-quality, contextual data. That’s where data products come in - carefully crafted resources that agents rely on to understand the world, make decisions, and act independently. By taking a closer look at how these data products work, we can see what makes them so essential and how they’re shaping the next generation of AI.
What is a Data Product?
A data product is a curated, structured set of information designed to be easily accessed and used. It’s often a combined package of one or multiple cleaned datasets, supported with documentation, quality controls, and metadata so it can be trusted, reused, and reviewed. Data products simplify data consumption, ensuring that users and systems spend less time wrangling data and more time generating insights.
I often cite the example of a product, like a shoe on Amazon.com, to explain the concept of a data product. Data products, like your favorite Amazon shoe listing, are purposeful, searchable, documented, reliable, reviewed, customizable, and valued.
Autonomous agents need data that is not only accurate and timely but also interpretable within their operational environment. This means data products must come with relationships, ontologies, and metadata that allow agents to understand the context and meaning of the information they consume. Since agents often act independently and in real-time, these products must be continuously updated, easily interoperable, and maintained to prevent degradation in quality that could disrupt agentic workflows.
If you would like a technical perspective, I like Zhamak’s famous blog that explains the logical architecture of data products.
Examples:
To understand this better, let’s look at a few examples of data products.
Predictive maintenance dataset: A curated collection of sensor readings, maintenance logs, and environmental conditions that agents can use to forecast equipment failures and schedule repairs autonomously.
Customer behavior data service: A structured dataset containing purchase histories, browsing patterns, and customer feedback that agents use to anticipate purchasing trends and personalize marketing campaigns.
Corporate knowledge graph: A network of employees, projects, skills, and training materials that allows agents to recommend team compositions, identify skill gaps, and suggest training resources.
Supply chain knowledge graph: A graph mapping suppliers, materials, transportation routes, and inventory levels that agents leverage to dynamically adjust sourcing strategies, reroute shipments, and ensure compliance with regional regulations.
Legal precedent vector store: A curated, continuously updated database containing legal precedents, contracts, and case summaries, designed so legal research agents can retrieve similar cases or relevant clauses quickly, assisting with contract drafting and case preparation.
Connecting Data Products to AI Strategy
Data is the foundation of AI, and companies that treat data as a strategic asset set themselves up for success. To succeed with AI, enterprises must tightly align a data product strategy with their AI strategy.
Build data and AI maturity concurrently: Studies show that success of any AI initiative depends directly on the level of maturity achieved in data management practices. Advanced data management capabilities are present in 90% of leading AI organizations but completely absent in organizations at the beginning of their AI development journey (digitally.cognizant.com). The development of AI maturity depends both on the progress of AI technologies and the concurrent development of data infrastructure systems. Businesses that focus on updating their data systems to make their data accessible and reliable achieve better results from their AI projects because data maturity propels AI maturity.
Treat data as an asset: Leading organizations now view data as an asset instead of something that occurs as a byproduct of IT activities. Data management through a product mindset requires assigning ownership responsibilities, identifying target consumers, and setting quality standards for essential datasets. Within this framework each department like Marketing or Finance owns its datasets as products and other organizational units serve as their customers. This structure centers around user needs to create data assets that both support AI initiatives and enable business decisions by understanding data consumers. IBM notes that few organizations have made the strategic shift to data as a product, but those that have, are creating new business models and even see data monetization contributing 20%+ of company profits. Wow, additional revenue model. I will dedicate a seapare post on this in the coming weeks.
Build easy integration mechanisms: Data products can come in many forms like datasets, APIs, or pre-trained AI models and should be provided as services (such as downloads, APIs, or streams) for easy access by AI workflows. A well-organized collection of data products helps AI agents quickly find and trust the data they need. This speeds up the process and helps AI workflows deliver value more quickly. By organizing these data products in a structured catalog, businesses can create a unified data layer that supports all AI projects, making sure data is always available, reliable, and ready to be used. Much like the product catalog on Amazon.com.
Balance control and agility: Aligning data strategy with AI requires establishing governance that balances agility with control. Successful AI projects often need to pull data from multiple sources, and a strong data strategy helps remove data silos while ensuring common standards and compliance. For example, a semantic layer or consistent metadata can make sure that AI agents in different departments, like finance and sales, interpret terms like “customer” or “revenue” the same way. When AI agents operate under clear governance, they can innovate without risking confusion or conflict between systems. For instance, in an e-commerce business, an AI agent in marketing could use customer data to suggest personalized promotions, while an AI agent in sales uses the same customer data to recommend the right products. With proper governance, both agents would refer to the same “customer” data consistently, preventing the risk of the marketing agent offering irrelevant promotions based on outdated customer insights. This balance allows AI systems to be innovative while ensuring they work accurately across the business.
Summary
Data products have a natural place in AI strategy because AI doesn’t just need data - it needs the right data, in the right form, at the right time. Agentic AI systems rely on access to trusted, contextual, and reusable data to reason, act, and adapt. That’s exactly what data products offer: clearly defined, well-governed, and user-oriented assets that remove ambiguity and speed up decision-making. When treated as part of the AI stack, not just the data stack, data products help shift AI from scattered experimentation to scalable, reliable outcomes.
The successful companies not only treat their data like a product but integrate it fully into their AI strategy, ensuring that both mature together.
Call to Action
Now is the time to rethink how your organization handles data. By transforming your data into trusted, reusable products, you can build a more effective AI strategy. Start by assessing your data quality, implementing governance frameworks, and aligning data with AI workflows. Take your next step today and prepare your business for the future of Agentic AI.
In the next chapter I talk about how data products maximize ROI in Agentic AI systems.
Stay tuned!