Why Data Products are the Hidden Key to Unlocking AI's True Potential

As organizations rush to embrace AI and machine learning, many are finding their efforts unraveling due to a silent saboteur lurking in their data. This isn't just a hypothetical scenario—it's the reality I see playing out every day in my role as Senior Director of Product Management at Alation. 

The allure of AI often blinds leaders to a fundamental truth: without robust, well-governed data products, successful AI initiatives will be few and far between. 

While the industry buzzes with excitement over AI's potential, the real secret to unlocking its transformative power lies in the unglamorous yet essential groundwork of data management. To truly harness AI, we need to shift our focus from flashy algorithms to providing AI with well-governed, reusable data products to consume.  

At Alation, we have a front-row seat to the challenges and requirements organizations face in preparing their data, at scale, for AI and machine learning use cases. Time and again, I've seen that the critical oversight in many AI initiatives isn't the sophistication of the algorithms, but the quality and governance of the data feeding those algorithms.

Data is the lifeblood of AI, but most data is a mess

Here's the thing: AI algorithms require clean, well-structured data with all the necessary context easily available to produce accurate insights. But the reality is that most enterprise data landscapes are a tangled web lacking the governance needed to deliver such clean data. This messy, siloed data leads to inaccurate or unreliable AI outputs. In fact, 85% of AI projects fail due to poor data quality and volume issues. As AI adoption accelerates, this oversight is becoming a critical gap. While humans can navigate the nuances of schema quirks and interpret messy data relationships, AI systems simply can't. 

Data products help AI bridge this gap in comprehension. To unlock real AI value, organizations must prioritize high-quality, self-describing, governed data products—with clear semantics, strong metadata, data quality assurances, and business context baked in. These aren't just "nice to haves"; they're prerequisites for AI to deliver trustworthy, explainable, and repeatable results at scale.

Data products are the critical foundation

Data products are curated, reusable data assets treated as products, complete with robust metadata, ownership, and more. An ecosystem of trusted data products enables reliable AI inputs, ensuring that the data feeding AI algorithms is accurate, consistent, and well-governed. According to EY Research, 83% of senior leaders cite poor data infrastructure as a major AI adoption bottleneck. That's a huge red flag underscoring the importance of building a solid data foundation to support AI initiatives.

Data products can help to bridge the gap between data chaos and usability. Even if the underlying infrastructure is fragmented or immature, data products package usable, trustworthy datasets with helpful context and information on how to get access. This means business users don’t need to navigate raw, messy systems, while technical users can trust that data consumers are using well-governed assets.

Those who've treated data products as foundational building blocks, with strong metadata, ownership, and governance, will be in a prime position to unlock real AI value. Those who haven't may soon realize their AI ambitions are being held back by the very data infrastructure they thought was "good enough."

Those prioritizing data products will lead in AI

Make no mistake: having deployable, trusted data products is a massive competitive advantage, since these reusable assets can be leveraged in a wide variety of ways. In a survey from Accenture, “reinvention-ready” enterprises with fully AI-led processes achieved 2.5× higher revenue growth and 2.4× greater productivity than peers, along with 3.3× more success in scaling AI use cases into production​. These AI-forward organizations are deploying models faster and realizing value sooner than those without mature data/AI product capabilities. Meanwhile, laggards struggling with ungoverned data chaos are finding their AI initiatives hampered, unable to scale or deliver reliable results.

The message couldn't be clearer: to lead in AI, organizations must prioritize the creation and governance of data products. This isn't just about investing in technology; it's about fostering a culture that values data quality and governance. By doing so, organizations can unlock the full potential of AI, transforming their operations and gaining a significant competitive advantage.

Conclusion

At the end of the day, data products are the key to enabling transformative AI value. Organizations need to take a hard look at their data foundations for AI readiness and start treating data as a strategic product to unlock AI's potential. By prioritizing high-quality, governed data products, organizations can ensure that their AI initiatives are built on a solid foundation, capable of delivering trustworthy, explainable, and repeatable results at scale. The future of AI is bright, but only for those who recognize the importance of robust data management and governance.

Contact Alation today to create a thriving data product ecosystem.