The $200 Billion Secondary Data Market: How It’s Shaping Our Digital Lives and What Marketers Need to Know

Are you sitting down? Because we're gonna share a mind-blowing marketing story about a market you probably didn't know existed, and it's where a lot of the REAL MONEY is. The story starts like most modern marketing stories; with a vacuum yelling racial slurs.

A recent Gizmodo piece explained that certain Ecovac models were being hacked and the audio and video components were switched to piece of shit mode - and it is one of the BILLIONS of smart device hacks that have happened since these "awesome" technologies have been adopted by the public.

But how did it happen? You can blame security on the users and the companies, or you can start to see past the short-sighted lapses in product as a key signifier of the true value unlock for the modern era - access to the SECONDARY DATA MARKET.

And what is the secondary data market?

The Secondary Data Market Explained

As of 2024, the secondary data market—a booming industry built on data generated and sold by IoT devices, apps, and online platforms—is valued at over $200 billion and counting. This ecosystem is driven by massive demand across advertising, retail, finance, healthcare, and more. The secondary data market’s rapid growth has given rise to what many consider the “new gold rush” in data-driven insights, a rush that is quickly reshaping consumer expectations, brand strategies, and product lifecycles.

With new innovations, industries are relying on consumer data to target ads, tailor product recommendations, assess financial risk, and fuel research. It’s hard to overstate how big this market has become. Let’s take a look at its impact across sectors.

Breaking Down the Secondary Data Market by Industry

Advertising and Marketing Data is the lifeblood of digital advertising, where behavioral insights are the holy grail for targeting. About 20-30% of the $600 billion digital ad spend goes toward third-party data for hyper-targeted campaigns.

Retail and E-commerce Retailers use purchase trend data to personalize offerings, tailoring everything from email recommendations to in-store experiences. Much of this data is sourced from IoT devices like connected refrigerators, wearables, and point-of-sale systems, feeding tens of billions into the data market.

Financial Services Banks, credit companies, and insurers all pay top dollar for data. Whether it’s for credit risk assessments, personalized offers, or pricing accuracy, the industry’s heavy reliance on third-party data is only growing, with IoT devices like automotive telematics adding unique, actionable insights to their arsenal.

Healthcare With data from wearables, health apps, and even connected medical devices, the healthcare sector is projected to hit $70 billion in data value by 2025. While the data helps streamline R&D and marketing, privacy concerns in healthcare data remain sensitive.

Data Brokers and Data-as-a-Service (DaaS) Players like Experian, Oracle, and Acxiom have built businesses entirely around collecting, processing, and selling data. Their offerings, increasingly referred to as “Data-as-a-Service” (DaaS), are experiencing projected growth rates of 25-30% annually.

The business of data might be soaring, but the ethics, sustainability, and even costs of this data-driven model are coming into question. Laws like GDPR and CCPA are forcing companies to anonymize data, which adds a layer of compliance cost but also bolsters data credibility in regions with strict standards. And with consumers becoming more aware of the value of their data, marketers are learning that collecting data has its trade-offs.

How the Secondary Data Market Reshapes Consumer Expectations

The rise of the secondary data market has profoundly shaped consumer expectations, creating a landscape where people increasingly expect free or low-cost services in exchange for their data. This model, deeply embedded in our digital lives, has changed the way we perceive value, cost, and quality, especially in an era driven by convenience and the allure of accessible content.

1. “Free” as the New Standard

• Business Model of Data-for-Services: Platforms like Facebook, Google, and countless apps popularized the model of providing “free” services in exchange for user data, allowing them to monetize through advertising, analytics, and third-party data sales. This has conditioned users to expect that digital services—whether social networking, search engines, or email—should be free, as if personal data has no inherent cost.
• Expectation of Unlimited Access: As more services adopted this model, it became normalized. Consumers now expect vast libraries of music, videos, or even navigation tools without upfront payment, often unaware of the extent to which their data is being monetized. This “data-fueled free” access is perceived as a fair trade-off, despite the hidden cost.

2. Quality Sacrificed for Scale and Monetization

• Ad-Driven Content Prioritizes Engagement Over Quality: To maximize profits from data, platforms focus on content that drives engagement (clicks, shares, time on site), sometimes at the expense of quality. Algorithms are designed to promote addictive or sensational content, which, while effective for ad revenue, often lowers content quality and factual accuracy.
• Less Investment in Craftsmanship: Media, entertainment, and even product quality often suffer as companies prioritize scalable, low-cost solutions over quality craftsmanship. Think about streaming services: they often prioritize quantity over quality, producing high volumes of quickly made content to keep users engaged, leading to a “fast food” approach to content creation. Similarly, tech and appliance companies may reduce manufacturing quality as they shift focus to data monetization rather than longevity and quality of the product itself.

3. Convenience as the Selling Point, Not Quality

• Cost of Convenience: Today’s products are designed for convenience and rapid replacement cycles rather than durability. IoT appliances, for instance, often use cheaper components, assuming users will replace or upgrade quickly. This shift reduces initial costs but ends up being more expensive over time due to recurring replacements and upgrades.
• Hidden Costs: What feels like a good deal upfront (like a cheap connected appliance or a free app) often hides costs later, whether through forced upgrades, subscription models, or data-based personalization that encourages impulsive purchasing. These hidden costs mean consumers may actually pay more over time for lower-quality services and products that lack transparency about their true value.

4. Inflated Costs in the Era of Data

• Increased Subscription Models: The data market has shifted companies toward subscription-based revenue models. Initially, these services were inexpensive or “freemium,” but with time, subscription fatigue has set in. Even “free” services eventually introduce tiers, paywalls, or ads, pushing users to pay for the ad-free experience.
• Data Monetization Encouraging Inflation: To sustain their high valuations and growth, data-dependent companies continuously seek ways to increase revenues, often by inflating prices for services or devices over time. For example, smart home devices might start cheap but require costly upgrades or subscriptions for full functionality as companies try to lock in recurring revenue streams.

5. Privacy Concerns and the Realization of Value

• User Awareness and Shifting Perception of Value: As users become more aware of the true value of their data, there’s growing scrutiny around whether these “free” services are worth the privacy trade-offs. For instance, consumers are increasingly aware that even though a service is free, the data collected may be used in ways that feel invasive or may later cost them in terms of ad targeting or even security risks.

The result of this shift is a digital ecosystem where “free” and low-cost services feel like the norm, but the actual cost is higher than it appears, both financially and in terms of quality. The secondary data market has conditioned users to give up data for convenience, but as this model matures, there’s a clearer sense that we’re often getting less—and paying more.

What Marketers Can Do: Responsible Approaches to the Secondary Data Market

Marketers are beginning to understand the need for a more balanced approach—one that respects consumer privacy while delivering value. Here are some actionable steps to keep your brand on the right side of the data conversation.

1. Assess Your True Data Needs Not all data is critical. Define your core objectives, and focus on data that delivers tangible customer benefits instead of collecting information just because you can.
2. Prioritize First-Party Data Collection Relying on third-party data has become risky and costly. Invest in collecting your own data through first-party channels, giving you more control and trust with customers.
3. Communicate with Transparency Explain how you collect, store, and use customer data in plain terms. Transparent data practices build loyalty and demonstrate respect for user privacy.
4. Balance Quality with Scale Instead of flooding the market with ad-driven content, focus on creating high-quality, memorable experiences. A thoughtful, customer-first approach generates lasting brand loyalty and genuine engagement.
5. Adopt a Long-Term Approach for IoT Products Build products designed to last, not to be replaced. Customers recognize and value products that reflect durability and trust over quick, profit-driven upgrades.
6. Prepare for Emerging Regulations GDPR and CCPA are just the beginning. Be proactive in establishing privacy standards that anticipate future regulations. Anonymize, encrypt, and secure data at all stages to protect user information.
7. Rethink the Data-for-Service Model Instead of offering “free” services that rely on data monetization, explore ways to build real value for customers. Users will value brands that prioritize privacy and provide meaningful benefits without hidden trade-offs.

The secondary data market’s rapid expansion won’t slow down anytime soon. But as marketers, we have a unique opportunity—and responsibility—to harness this data without exploiting it. Balancing profitability with user respect isn’t just good business; it’s how we build the brands that will stand the test of time.