Is Data no longer the new oil?

Remember when data was dubbed "the new oil"? Those days felt simpler, as organizations that had spent years amassing proprietary datasets felt their data moat was impenetrable. A data moat traditionally refers to the competitive advantage organizations achieve by safeguarding proprietary data. Companies that prioritized building their data ecosystems saw their assets as an almost unbeatable edge over competitors Here’s how data moat helps unlock the value of data.

Today, this paradigm is being challenged. The rapid evolution of generative AI models—trained on vast troves of public knowledge—means that access to powerful AI capabilities is now within reach for all. How, then, can data-rich organizations maintain their edge?

I recently attended a training led by LinkedIn's Chief Product Officer, Tomer Cohen , where this issue was discussed in depth. It made me realize that industry trends suggest we’re entering a new era where data strategy needs a fundamental reimagining.

The new landscape of Data's value

As Cohen highlighted, “The latest AI models are challenging competitive advantage… they're already trained on the world's public knowledge. And now that's accessible to everyone, which will make it a commodity over time.” This democratization raises a pressing question for data-rich companies: will this disrupt their hard-earned edge?

The implications are profound. No longer limited to technology departments, these discussions now involve business leaders across marketing, sales, operations, and finance, each adapting to a new landscape where competitive advantage comes not from data alone but from how effectively they can integrate AI into their strategies. Let’s dive into how these changes impact each function.

For marketing leaders, AI presents both a challenge and an opportunity.

Smaller competitors can now leverage AI to get insights similar to those that data-rich companies gain from their extensive customer data warehouses. The real edge lies in combining AI capabilities with unique market insights. Think of it as having a Masterchef’s recipe book (AI) but knowing how to blend the ingredients (data) uniquely. First-party data adds the right “flavor,” creating accurate, customized persona profiles. Without this data, marketing strategies can lack precision.

In sales, the democratization of AI levels the playing field for smaller players.

AI’s predictive power allows even companies with limited historical data to anticipate customer behavior with accuracy. Sales teams, whether from large enterprises or startups, can leverage AI to analyze behavioral patterns and forecast needs without years of customer interaction history.?

Operations leaders find that AI can bolster capabilities irrespective of data maturity.

For example, a manufacturing plant with limited historical data can still optimize processes thanks to transfer learning and synthetic data generation. During my conversations with Ducati’s engineers, their use of AI to simulate sensors—where physical ones can’t be installed—is a prime example. Similarly, automotive companies, including Tesla, have pioneered AI to make autonomous driving a reality.

The shift in data value is also reshaping finance. Traditional institutions, relying on vast historical transaction datasets, have held an edge in areas like risk assessment. But now, fintech firms can leverage AI to develop similar capabilities with less data, focusing on quality rather than quantity. This represents a major shift, emphasizing relevance over volume.

Finally AI democratization extends into HR as well, transforming talent development.AI-powered tools for training enable organizations of all sizes to offer robust learning opportunities. Synthesia’s AI-generated training videos, for example, can deliver professional development programs with minimal investment. This is especially beneficial for smaller organizations that previously lacked resources to develop content in-house Have a look at Synthesia’s AI-powered video creation here.

So, is the Data Advantage Gone?

In my view, data culture still confers an advantage, even as AI narrows the gap.

For data-rich organizations, this democratization can feel threatening at first. However, it also creates opportunities to focus on aspects that truly differentiate them. With a solid data culture, these companies understand their data’s origins, transformations, and contexts, enabling them to leverage generative AI more effectively. Furthermore, these companies can build custom AI applications beyond generic generative AI.

For organizations with less historical data, the game has changed. They can derive value from AI without years of data accumulation by collecting specific, high-quality data for their use cases. It’s akin to having a “smart assistant” that offers guidance even with limited experience.

The New Competitive Edge: Moving Beyond Big Data

Today, success in AI isn’t about collecting the most data; it’s about using data strategically to create unique value. Leading organizations are finding new ways to stand out in a world where vast datasets are increasingly common. 3 innovative approaches are helping companies build a sustainable competitive edge.

Precision Fine-Tuning: Tailoring AI for Specific Needs

With general AI models like GPT becoming accessible to many, the real value now lies in fine-tuning these models to serve specialized needs. But as LinkedIn’s Chief Product Officer Tomer Cohen warns, "creating a custom, fine-tuned AI model can be both complex and costly."

For example, Citi has recently made great improvements in customer service with a fine-tuned AI model customized to handle the nuances of financial support inquiries. By training their AI with extensive data on customer inquiries, Citi’s system quickly resolves issues like credit card disputes and loan queries, cutting response time by 40% and improving customer satisfaction. The advantage wasn’t just data access; it was Citi’s focused effort on refining their AI to understand specific customer pain points in finance.

Synthetic Data Innovation: Filling the Gaps

For many organizations, real-world data can be scarce or restricted by privacy concerns. Sophisticated synthetic data generation is solving this problem by simulating the real thing. At Tesla, synthetic data generation has been crucial in self-driving technology development, where access to all possible real-world scenarios is impossible. Tesla 0,

ha0,s created virtual driving scenarios to simulate rare, dangerous, or unusual driving conditions. This approach helps train the autonomous vehicle systems to respond to situations they’d rarely encounter in the real world, making the technology both safer and more robust.

Data Quality and Integration: Smarter Connections Over More Data

Rather than simply gathering more data, smart companies focus on combining high-quality data streams to generate actionable insights. General Electric (GE) has been a pioneer in this with its “Digital Thread” approach. By integrating real-time performance data from manufacturing equipment with historical data and environmental variables, GE creates predictive maintenance solutions that identify potential machine failures before they happen. This integrated data approach has reduced downtime by 50% across various industries, saving costs and improving reliability for GE’s customers.

The Winning Formula

The future of competitive advantage lies in combining these approaches creatively. Companies that balance fine-tuning, synthetic data, and intelligent data integration will stay ahead. Quoting Cohen ( yes, I loved that piece on AI and Data) "It’s not about who has the biggest data lake anymore—it’s about who has the smartest approach."