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Every time you step into an elevator, check your medical records or track your fitness, you’re creating data that could be worth millions. The big question in 2025: Who gets to profit from those digital breadcrumbs you leave behind?

Ten years ago, tech companies battled for your attention. Five years ago, they competed for your engagement. Now they’re fighting over something far more valuable: your data exhaust — the trail of information you generate simply by existing in a modern world.

And one piece of data might legitimately belong to multiple parties. When an MRI machine learns to make better diagnoses from scanning thousands of patients, those improved capabilities could belong to the hospital, the device manufacturer, the AI company that processes the data or the patients themselves.

With private equity firms sitting on record amounts of uninvested capital and AI companies commanding sky-high valuations, billions of dollars hang on the answer to our earlier question. The next wave of tech fortunes won’t be made by those who collect the most data but by those who figure out who truly owns it.

The second mouse gets the cheese

Early tech investors raced to pour money into generative AI from 2021-2023. Savvy players are now eyeing a different prize.

Traditional software-as-a-service companies, especially in healthcare and financial services, are quietly incorporating AI into existing products rather than building flashy new tools. These companies understand that generic AI applications are becoming commoditized, and the real value is in specialized, industry-specific solutions.

There has been a . In 2023, global private equity dry powder soared to an unprecedented $2.59 trillion. Firms have raised record amounts of capital but have been hesitant to deploy it as valuations adjust post-2021.

Now, as seller and investor expectations finally align, we’re approaching a deal-making inflection point. The funding won’t just flow to the newest AI startups, though. Experienced investors are looking for companies that can solve real business problems, particularly those involving data privacy and security.

Private equity firms are especially interested in companies that can demonstrate clear monetization strategies for their data assets. This is a much different mindset from the “growth at all costs” mentality of recent years. Investors want to see how companies plan to protect and profit from their data resources while staying ahead of evolving regulatory requirements.

From oil wells to data wells

Think of data like oil beneath the ground. In the past, companies drilled deep in one spot, hoping to hit a gusher of valuable information. But today’s technology allows them to extract smaller amounts of data from vast areas — a process more akin to fracking than traditional drilling.

A medical device company might collect diagnostic data from thousands of MRI machines. An elevator manufacturer tracks every movement of their products worldwide. A fitness app gathers health metrics from millions of users. Each data point might seem insignificant on its own, but combined and analyzed by AI, these datasets reveal patterns, like early warning signs of equipment failure, previously unnoticed health correlations or predictive maintenance opportunities that could save repair money.

This shift from centralized data collection to distributed “data fracking” creates new questions. If an AI system gets better at diagnosing diseases by analyzing millions of anonymous patient records, who should benefit from that improvement? The hospital that collected the data? The tech company that built the model? The patients whose records made it possible?

Forward-thinking organizations are moving in the opposite direction of large language models, like GPT-4, to answer these questions. They’re developing small language models (SLMs) trained on specific, proprietary datasets. These focused AI systems might not write poetry or engage in philosophical debates, but they excel at specialized tasks within their domain.

Using specialized AI isn’t just about better performance — it’s about control. Companies that use SLMs trained on their own data don’t have to worry about their competitive advantages being diluted in large, public models. Healthcare providers, for instance, are exploring SLMs trained exclusively on their patient data to improve diagnostic accuracy and treatment recommendations.

There will be new cybersecurity challenges to worry about with SLMs, though. When your data becomes your primary competitive advantage, protecting it is essential. Traditional cybersecurity focuses on preventing personal information or financial data theft. Companies must protect the unique datasets that power their AI systems.

Data rights in 2024

The data ownership regulatory landscape is still fragmented. While Europe has GDPR and California has privacy laws, we still lack clear frameworks for monetizing data. Consider a telecommunications provider that tracks customer location data. While individual movements might seem insignificant, when anonymized and aggregated, this data becomes invaluable for targeted marketing. If a carrier sells this location data to encourage improved spot marketing, who should benefit from the revenue? The telecom company providing the service? The customers whose movements generated the data? The marketing firms creating value through analysis?

We might not have clear answers to these questions, yet, but smart companies are already:

• Auditing data assets to understand what they collect, where it comes from and who might have rights to it.
• Building infrastructure to track data lineage and attribution.
• Creating clear monetization frameworks that account for all stakeholders.
• Developing robust anonymization protocols that preserve data value while protecting privacy.
• Establishing transparent policies about how they use and profit from customer data.

Modern technology changes fast, but that doesn’t mean we can’t define who can turn these data rights challenges into opportunities. Some companies will create billions in new value by building trust-based data relationships. Others will see their business models crumble as they lose the right to monetize data they thought they owned.

In 2025, instead of focusing solely on technology innovation, let’s reimagine what fair and ethical data ownership looks like in practice.

How Wipfli can help

In a data-driven future, success requires collecting information and knowing how to protect it, monetize it and share its value fairly. As private equity reshapes the technology landscape and data rights become more complex, organizations need advisors who understand both the technical challenges and business opportunities ahead.

Wipfli can help. Our technology industry specialists can help you evaluate data ownership strategies, develop AI implementation roadmaps, assess cybersecurity needs and navigate complex regulatory requirements. Learn more about our services for the technology industry.