Enterprises worldwide are harboring massive amounts of data. Although data has always accumulated naturally, the result of ever-growing consumer and business activity, data growth is expanding exponentially, opening opportunities for organizations to monetize unprecedented amounts of information.
Data can be effectively monetized by transforming it into a product or service the market values, says Kathy Rudy, chief data and analytics officer with technology research and advisory firm ISG. She notes that her firm works with a variety of data-rich clients. “In the course of our work, with our clients’ permission, we collect data and enter it into our databases. We then transform that data into products that help our clients compare themselves to peers, and understand which providers are best suited to support their strategic initiatives.”
Interest in turning enterprise data into new revenue is soaring. According to the 2025 State of the CIO survey, 38% of IT leaders say monetizing company data is the most significant business initiative driving their IT investments this year — the No. 1 such initiative, more than double the 16% who said the same last year.
If you’re among those IT leaders looking to transform your company’s data into new revenue, here are several key tips and insights from data experts who’ve done it.
Emphasize product development fundamentals
“Data monetization is no different than creating and selling other products,” says Adam Yong, founder of AI-enabled content generator Agility Writer. “It requires ideation, market research, pricing analysis, and go-to-market plans.”
That’s why Young suggests developing a structured product development process first.
ISG’s Rudy concurs: “You need to define what your data product or service is, why the market would want to buy what you’re selling, who your competitors are, and why what you are selling is unique. After you’re convinced you have a data product or service the market wants, then define the technology required to manage, maintain, and govern the data.”
Rick Young, data and AI lead at professional services company Sikich, recommends undertaking straightforward analyses using systems that integrate easily with existing products — and keeping initial data monetization projects manageable, gathering customer feedback, and understanding opportunities before expanding.
“Organizations should prioritize solutions that align with their current data/technology stack and product lifecycle to ensure seamless implementation,” he says.
Ensure your data has actionable value
The most monetizable data types provide insights that can’t be found elsewhere, ISG’s Rudy says. “This includes benchmark data for comparison to peers to help drive actionable change, competitive intelligence that’s specific, predictive analytics to help drive fact-based decisions, and AI-driven insights that pull from multiple sources of data that are typically siloed.”
User behavior data is one of the most monetizable data types, says Agility Writers’ Yong, pointing to Google Analytics as an example. “It tracks user interactions, which enterprises can then use to fine-tune their website or marketing efforts,” he explains. Even deeper data, such as purchase intent or churn rates, can be sold to third parties or used for targeted marketing, making it highly valuable.
The types of data that aren’t worth monetizing include outdated data, third-party data anyone can access, data your organization lacks the specific rights to use — which could lead to potential lawsuits — or inconsistent or partial data that could lead to bad decisions, Rudy says.
Data that’s stale or lacks the context to be actionable won’t yield any real value, Yong says. “Raw traffic numbers lacking segmentation or behavioral context won’t provide insights that are as useful as personalized engagement data,” he observes.
Iliyan Paskalev, founder of MyHumanoid, a robotics and humanoids information website, says the key lies in carefully selecting data that can help customers transform insights into real value. Important to this, he says, is pinpointing the exact problem your data solves.
“Highly granular, actionable datasets — such as user behavior patterns, transaction data, and geolocation insights — are typically the easiest to monetize,” he advises. “These datasets directly help businesses refine their operations, predict market shifts, or better understand consumer preferences.”
Generalized data, such as broad demographic details, overly-anonymized datasets, or historical information that’s quickly outdated, rarely justifies monetization efforts, Paskalev observes. “Not only does this information lack a competitive edge, but compliance costs and privacy risks often outweigh the profits.”
Don’t shortchange potential risks
Data monetization can be risky, particularly for organizations that aren’t accustomed to handling financial transactions. There’s an increased threat of security breaches as other parties become aware that you’re in possession of valuable information, ISG’s Rudy says. Another risk is unintentionally using data you don’t have a right to use or discovering that the data you want to monetize is of poor quality or doesn’t integrate across data sets. Ultimately, the biggest risk is that no one wants to buy what you’re selling.
Strong security is essential, Agility Writer’s Yong says. “If you’re not careful, you could end up facing big fines for mishandling data or not getting the right consent from users,” he cautions. If a data breach occurs, it can deeply damage an enterprise’s reputation. “Keeping your data safe and being transparent with users about how you use their info can go a long way in avoiding these costly mistakes.”
Failing to meet regulatory compliance can also be costly. “If you’re not careful, GDPR, CCPA, and similar regulations carry hefty penalties that could wipe out any profit you hope to earn,” Paskalev warns. “Allocate resources generously to data security and compliance experts from the outset,” he recommends.
Select a suitable revenue model
Leverage subscription-based approaches and commercialization strategies for direct sales to businesses, research institutions, or government agencies, Sikich’s Young advises.
“Data-as-a-service, where companies compile and package valuable datasets, is the base model for monetizing data,” he notes. However, insights-as-a-service, where customers provide prescriptive/predictive modeling capabilities, can demand a higher valuation. Another consideration is offering an insights platform-as-a-service, where subscribers can securely integrate their data into the provider’s insights platform.
Young adds that it’s also possible to enhance offerings with embedded analytics to increase value and customer retention.
“This includes using data to improve internal operations, optimize customer experiences, and create personalized offerings,” he says. “Advanced AI-driven analytics now enable product customization and real-time insight generation that can transform core business offerings while maintaining competitive advantages.”
Reinvest wisely
MyHumanoid’s Paskalev recommends reinvesting a portion of data monetization proceeds into refining data collection methods, enhancing privacy safeguards, and improving analytics. “This creates a self-sustaining cycle, ensuring long-term growth rather than short-term gains,” he says.
It’s not just the data itself, but how you use it that makes the difference, Agility Writer’s Yong says. “Whether you’re building content, personalizing marketing, or optimizing sales, using data smartly and responsibly is key.”
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Source: News