Interview: People don't want data, they want answers
Originally published on Monda
Nomad Data's CEO Brad Schneider spoke with Thani Shamsi, Co-Founder & CEO of Monda on the future of data discovery.
Read the interview below:
Brad Schneider, CEO of Nomad Data, sat down with Monda’s CEO and co-founder, Thani Shamsi, to discuss the LLMs the data industry needs, what most data marketplaces are getting wrong, and the secret sauce to procuring data successfully.
---
Brad Schneider is no stranger to the intersection of technology, business, and data. With an engineering mindset at the core of his career, he's spent years developing solutions to complex problems, first as a software engineer, then as an investor, and later as an entrepreneur. His latest venture, Nomad Data, seeks to address the dysfunctions within the data market, where data is often seen as a valuable asset but remains inaccessible, fragmented, and underutilized.
Schneider’s passion for problem-solving has deep roots. “I’ve been an engineer in one sense all my life,” he reflects. “Whether it’s building things, understanding how to put pieces together, or working with data to make informed decisions, I’ve always been driven by how things work.” That curiosity extends to his role as an investor, where he saw the potential to apply his engineering background to solve problems in business and economies. Eventually, this led him to founding Nomad, where his mission is clear: to create a more efficient and accessible data discovery platform.
The inspiration for Nomad Data came from the realization that despite the increasing importance of data, the data market itself was riddled with inefficiencies. “Data should be easy to find, easy to purchase, and easy to integrate,” says Schneider. “But it’s not. There are so many friction points, and a lot of that comes from the fact that data is multidisciplinary. It’s not just about raw numbers; it’s about understanding the context, the art of data.” This complexity, combined with the challenge of transacting data effectively, led Schneider to believe that solving these issues would be critical in the years ahead.
Skyrocketing demand for data meets challenges monetizing it
The need to fix the data market has only become more urgent with the rise of artificial intelligence (AI). As companies scramble to build the next generation of AI models, access to the right data has emerged as one of the most significant bottlenecks in AI development. "We see a lot of data out there, but the amount of usable data is much smaller," Schneider explains. "To build these AI models, we need data that can actually help train them. Without that infrastructure in place, we’re going to run into some serious challenges."
This gap between demand and supply of usable data is compounded by the difficulty in monetizing data. Schneider notes that many companies with valuable proprietary data don’t even realize its worth. “They look at their data, and they have no idea what it could be used for,” he explains. “It’s a huge disconnect.” Often, these companies don’t understand how to package and sell their data, nor do they know the right market to target. “They don’t understand who would even buy their data, let alone how to get it into the hands of those who need it,” he adds.
This problem is not unique to data sellers. On the buyer side, companies looking for data face their own set of challenges. “There’s a lot of demand for data, but finding the right data is still incredibly difficult,” Schneider says. “And even when you find it, figuring out how to legally and ethically obtain and use it is another hurdle.” The legal, compliance, and reputational risks involved in buying or selling data often discourage companies from engaging in the marketplace at all.
Data is nothing without a use case
At Nomad, Schneider and his team are attempting to bridge this gap by focusing on use cases. “Our model is use case-driven,” he explains. “We help companies identify potential applications for their data, and we match them with buyers who need it.” This approach not only helps sellers recognize the value of their data but also gives buyers a clearer idea of where they can find the data they need. “If you can show a company that their data could solve a specific problem or help someone in a meaningful way, it becomes much easier to convince them to sell,” he says.
While this process is still in its early stages, Schneider is optimistic that the data marketplace is on the verge of a transformation. He points to historical examples where critical issues were only addressed once they reached a tipping point, such as the global shortage of fertilizer in the 19th century. "When problems are big enough, people figure out how to solve them,” he says. “I think that’s what we’re seeing with data. It’s becoming so critical that it’s only a matter of time before the market catches up and the infrastructure to support it is built."
Looking ahead, Schneider believes that 2025 could be a turning point for the data industry. With AI continuing to evolve and data-driven businesses on the rise, the need for better data infrastructure will only grow. “We need faster, more efficient access to data. Otherwise, we’ll face major bottlenecks that slow down progress in AI and other industries,” he warns. For companies like Nomad, the future looks promising, as the demand for data continues to skyrocket and the infrastructure to support it slowly comes into place.
Ultimately, the success of the data marketplace hinges on overcoming the complex, multifaceted challenges that have historically hindered its growth. Whether it’s through better education on data’s value or the development of clearer frameworks for data transactions, Schneider remains hopeful that the industry is on the cusp of a breakthrough. As he puts it, "We know we have to fix this. The demand for data is only going to grow, and we need to make sure the infrastructure is there to support it."
In the data marketplace, the dynamic between data providers and buyers is inherently complex. On the one hand, providers are experts in their own datasets—often sitting on vast troves of valuable data. But, as Brad Schneider points out, “There’s no value in data if there’s no use case for it.” Buyers, on the other hand, bring their industry-specific knowledge to the table, but they often struggle to identify what data could solve their problems, whether it’s for marketing, investing, or machine learning. This disconnect between the two sides is a critical gap that Schneider believes must be addressed to help the ecosystem grow.
The problems with existing data marketplaces
While Nomad Data works on solving this matching problem, it's not the only player in the field. There are other well-known data marketplaces, such as AWS Data Exchange, Snowflake Marketplace, and Google Cloud Marketplace, but Schneider remains skeptical about their ability to truly solve the issue. “I think they’ve added a lot to the ecosystem, but none of them have been successful as marketplaces,” he argues. “These platforms are not really data marketplaces—they’re compute companies. Their core focus is on software and services, not on facilitating data transactions.”
The fundamental issue, according to Schneider, lies in a misalignment of incentives. These platforms may provide some data exchange functionality, but their primary business is not data. Take Snowflake Marketplace, for example. While it does offer data for sale, it is tailored specifically for data stored in Snowflake’s relational database. “If your data doesn’t fit in that mold, it doesn’t belong there,” Schneider explains. Even more limiting is the platform’s focus on one buyer persona—typically data engineers—leaving other potential buyers, like marketers or insurance actuaries, out of the equation. “You’ve already shrunk the market to one particular type of buyer and one type of data,” he says. “It’s a niche marketplace, not a general one.”
This bias towards technical users is something Schneider sees across other platforms as well. “Amazon’s data marketplace doesn’t really serve the non-technical crowd,” he adds. “How many people know what AWS is or have logged in to it? It’s another case of a marketplace that’s designed for a very specific audience, leaving out huge swaths of potential buyers and sellers.”
For a truly effective, all-encompassing data marketplace to emerge, Schneider believes it must cater to every kind of buyer and every type of data. This is where Nomad Data differentiates itself. "Our focus is on solving the demand side of the equation," he explains. "We know it’s easier to aggregate supply than it is to aggregate demand because there are so many different buyer personas out there—from marketers to real estate professionals to AI developers." Building a marketplace that spans every vertical requires not only time and capital but a concerted effort to engage buyers from diverse industries.
Cracking the data discovery code
The challenge of demand aggregation is one of the key reasons why existing marketplaces have struggled. It’s not just about matching data with use cases; it’s about making sure the right people even know that data exists. “There’s hundreds of different types of buyers, and getting them all in one place isn’t easy,” Schneider says. A real estate company might know where to go for its data, but what about a marketing executive or someone in insurance? “You need a marketplace that reaches all of them, and that’s a huge challenge,” he adds.
In addition to this, data discovery—one of the most fundamental aspects of a successful marketplace—remains a huge hurdle. Schneider points out that in current marketplaces, users often struggle to find the specific datasets they need. “If you’re looking for something like ‘election betting data,’ there might not be a category for that,” he explains. Even if you manage to narrow down your search to a few dozen vendors, the process is still too cumbersome to be effective. “You’d have to call each company individually, and that’s just not scalable.” In a modern data marketplace, Schneider envisions a more streamlined approach: “I want to be able to ask a question—like I would in ChatGPT—and get an answer, ‘Which data vendor has X?’”
Once data discovery is solved, the next major challenge becomes marketing to a diverse array of buyers. As Schneider notes, marketing is already noisy and saturated, and reaching the right audience is difficult without a massive budget. More importantly, the messages needed to reach different types of buyers—be it data scientists, marketing execs, or actuaries—vary significantly. “Each persona requires a different message,” he explains. “Marketing today is extremely fragmented, and without knowing where to focus your efforts, it’s hard to make any headway.”
Despite the obstacles, Schneider remains optimistic about the future of the data marketplace. He’s confident that as demand begins to converge, we’ll see solutions that solve these challenges in a way that’s scalable. “It’s going to take time and capital,” he acknowledges. “But if we have the right conditions, I think we’re on the brink of a breakthrough.” The need for a true, general-purpose data marketplace is more urgent than ever, and companies like Nomad are working to build the infrastructure needed to make it a reality.
LLMs can help people find the answers they’re searching for
At the forefront of data discovery and monetization, Large Language Models (LLMs) and AI-powered systems are transforming the way companies source, interact with, and leverage data. Brad Schneider, co-founder of Nomad, a platform that helps companies find and connect with the right data providers, believes these technologies will dramatically reshape the landscape of data procurement, making it more efficient and streamlined than ever before.
Currently, the process of discovering and buying data can be cumbersome and inefficient. Companies often find themselves sifting through countless data providers and products, participating in numerous demo calls, and still struggling to find the exact dataset they need. As Brad explains, Nomad is already leveraging LLMs to automate much of this process. "LLMs have been an incredible technology that helped us automate a lot of things," he says. However, he points out a key challenge: LLMs need data to understand how to match it with specific problems, and that understanding needs to be built over time.
To solve this, Nomad has built a system that constantly collects information about what data solves which problems. The goal is to create a framework where people no longer need to purchase entire datasets. Instead, they could simply ask an LLM for a specific answer, and the system would figure out the best way to retrieve and organize the necessary data to provide that answer. "People don’t want data, they want answers," Brad emphasizes. "If we can automate that process and just give you the answer, there’s no need for the intermediate step of buying data."
The future of data procurement, according to Brad, could look like a system that automatically generates answers—for example, creating a graph from raw data—without the user ever needing to touch the data itself. "The Holy Grail is an engine that figures out who has the data, pulls it together, organizes it, and provides the answer. Here’s your graph. Here’s how much it costs."
While this vision of data discovery may sound futuristic, Brad is already seeing its early stages in practice, especially in the context of unstructured data. Nomad has developed systems capable of parsing and processing hundreds of thousands of documents to produce actionable insights. This type of automation would significantly reduce the overhead in data procurement, particularly in areas like healthcare, where personal health information (PHI) is often required to identify trends. “The end customer just needs a trend, but they can’t create that trend without the PHI,” Brad explains. If the system can handle the complexity of managing sensitive data, it could open up significant opportunities for more efficient data sharing and insight generation.
The secret sauce for discovering and monetizing data successfully
As data marketplaces evolve, the way data providers engage with customers will also shift. Brad highlights two critical factors that make a data provider successful: clarity and speed. Providers who articulate exactly what their data is and how it can solve a specific problem tend to be more successful in Nomad’s system. This clear communication not only helps Nomad make better matches, but it also builds trust with buyers, who are increasingly looking for answers immediately.
In addition, Brad emphasizes the importance of being responsive. “The successful data providers are the ones who respond within minutes, with clear descriptions of what they’re selling.” If a vendor takes too long to respond, the buyer often moves on. The quicker and more articulate a data provider is, the better their chances of securing a deal.
When it comes to B2B data providers, Brad believes that success also hinges on understanding use cases. Rather than simply listing data attributes like company revenue or number of employees, successful B2B providers focus on how their data can be used to solve specific business challenges. “They’re always selling it for one use case, and they know exactly how to describe it,” Brad says. This focus on practical applications—such as CRM enrichment—helps buyers understand the value of the data without needing to navigate a complex dataset.
However, even with the right tools and clear communication, many companies still struggle with data discovery. Brad reveals that many businesses are still managing their data relationships using outdated methods, like spreadsheets and emails. “You’d be shocked at how many companies keep track of what they know about the data universe in Excel,” he says. As a result, critical information about potential vendors and datasets often gets lost or forgotten when employees leave or when knowledge isn’t shared properly. “The key to successful data discovery is a systematic process for tracking, cataloging, and keeping records of who has what data,” Brad explains.
For companies that want to truly succeed in data discovery, they need to develop a robust knowledge management system. This means creating a centralized repository of data-related information, perhaps using tools like Nomad’s Data Relationship Manager, which helps companies manage their data interactions and vendor relationships more efficiently.
AI will both necessitate and support efficient data discovery
Looking ahead, Brad predicts that data monetization will continue to be a hot topic, with more companies seeking to capitalize on their datasets. While platforms like Reddit and Shutterstock have already seen major deals with tech giants like Google, Brad believes that the majority of companies will still be buyers, not sellers, of data. “99% of companies will be buying data,” he states, adding that selling data will likely remain a niche activity for 40-50% of companies.
The key to unlocking data monetization, according to Brad, is creating a utility—a simple, secure way for companies to share data without worrying about compliance or privacy issues. He compares this vision to solar energy: just as you can sell excess energy back to the grid without worrying about where it goes, companies will need a seamless way to “sell” data without the complications. “There’s a huge market for solutions that help companies securely share data,” he says, acknowledging that most companies can’t afford to build this capability themselves.
Finally, Brad touches on the broader implications of AI and data monetization. As AI-powered systems become more sophisticated, they may reduce the need for large teams of employees, particularly in roles that involve repetitive tasks or manual data processing. “With AI and good data, you don’t need as many people to do certain roles,” Brad says. He predicts that companies will increasingly shift their budgets away from hiring more staff and towards purchasing data. “I could hire an army to create a dataset, or I could just buy it for a fraction of the cost of hiring one employee,” he explains. This shift, Brad believes, will lead to the emergence of smaller, AI-centric companies that can leverage data in powerful new ways.
The next few decades, Brad suggests, will be a time of tremendous change and opportunity. The combination of AI, data, and automation will redefine how companies operate, source data, and deliver insights—ushering in a new era of efficiency, innovation, and growth. As Brad concludes, “legacy systems are always at risk. A system with AI can do a lot more than an old system without it. So we could see a lot of shifts in company spending. There are competitive wins and losses there. It's gonna be an exciting couple of decades ahead, for sure.”
