They know where you live. They may know your phone number, your relatives' names and roughly what your house is worth. By now, most people have at least a vague sense that data brokers collect personal information What many don't realize is what happens next.

Data brokers also analyze that information. They use algorithms, compare records from different sources and sort people into categories that predict their interests, circumstances and behavior. Those predictions can involve your health, finances, religious beliefs and potential vulnerabilities.

Some may be wrong. Others could reveal sensitive details you never intended to share. And depending on the company and the type of information involved, those profiles may be sold or shared with businesses you have never heard of. Here's a closer look at the labels data brokers assign to you, how they come up with them and what you can do to protect your personal information

Data brokers do far more than maintain databases of addresses and phone numbers. Some build detailed profiles designed to help advertisers identify specific audiences. The Federal Trade Commission documented this practice years ago. Its investigation found that brokers combined information from multiple sources to infer sensitive characteristics, including income, religion, political views and health conditions.

Acxiom, a major data company, developed a segmentation system called PersonicX that grouped households into lifestyle categories. One documented historical category was called "Rural Everlasting." According to the FTC, it described older adults with relatively low income and educational attainment. Other categories included "Urban Scramble" and "Mobile Mixers."

These were classifications built from data, not personal descriptions written by the people involved. The FTC also documented how companies used location information to create even more specific advertising audiences. For example, InMarket offered audience categories such as "Christian church goers," "parents of preschoolers," and "wealthy and not healthy." The FTC subsequently restricted InMarket's use and sale of sensitive location data.

Then there was Oracle's BlueKai advertising business, which helped advertisers use extensive consumer data for targeting. Oracle shut down its advertising products in September 2024. Still, its history illustrates how sophisticated this industry became. The important point is that a label doesn't have to be accurate to influence how a company views you.

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Some of these classifications sound harmless. Others are surprisingly personal. Here are examples of the information brokers have historically inferred or used to create consumer segments. Data broker research and enforcement cases have documented categories involving: A major concern is that these classifications can originate outside the medical system.

HIPAA protects health information handled by covered healthcare organizations and their business associates. However, health-related information independently gathered or inferred by other businesses may fall outside HIPAA's protections.

For example, searching for information about diabetes doesn't mean you have diabetes. You could be researching the condition for a family member. Yet advertising systems may still use that activity as one of several signals when building a profile. Other federal and state privacy laws may apply, but they don't necessarily provide the same protections as HIPAA.

Data companies have also offered categories based on estimated income, spending behavior and financial circumstances. These can include people classified as affluent, price-sensitive or likely to respond to certain financial offers. One particularly troubling example comes from the Justice Department's case against Epsilon Data Management. According to federal prosecutors, Epsilon used sophisticated modeling to identify consumers likely to respond to solicitations.

The company sold information about more than 30 million Americans to operators of fraudulent schemes, including fake sweepstakes promotions Epsilon agreed to pay $150 million as part of its deferred prosecution agreement. This is a documented example of consumer data being used to help fraudsters identify potential victims.

Data brokers have also inferred people's religious interests and political leanings. These predictions may draw on information such as location patterns, purchases, public records and other available data. For example, the FTC documented the use of a "Christian church goers" advertising segment. A person visiting a church doesn't necessarily belong to that faith. Similarly, someone researching a political candidate doesn't automatically support that candidate. Nevertheless, profiling systems can use these signals to group people into audiences.

Another category focuses on what may be happening in your personal life. Data companies have developed segments involving parents with young children, empty nesters and households at different stages of life. These classifications can help businesses decide what products or services to advertise. However, they can also reveal potential pressure points. Someone approaching retirement may receive different financial offers than a young parent. A household believed to be managing health expenses may attract another type of marketing. And when those assumptions are wrong, the resulting profile may still influence what someone sees.

Consider what you've done online recently. Maybe you searched for joint pain relief. Perhaps you compared retirement plans or looked up treatment options for a relative. Individually, these activities might reveal very little. However, companies can combine information from websites, apps, purchase histories, public records and commercial databases.

Then predictive models look for patterns. For example, a company may consider your age range, estimated income and browsing activity when deciding whether you fit a particular advertising category. These classifications are often called modeled attributes or inferences. They represent predictions rather than verified personal facts. And here's the problem: a company buying access to an audience segment may use the prediction without knowing how accurately it describes each person.

Basic information helps a scammer contact you. More detailed personal information can help make the approach believable. For example, someone who knows your name and address could send a convincing-looking financial notice. Now suppose that same person also has information suggesting you're approaching retirement or researching medical expenses. A criminal could use those clues to tailor a fraudulent Medicare offer, investment pitch or fake medical service.

This doesn't mean every scammer buys sophisticated consumer profiles. However, the Justice Department's case against Epsilon Data Management shows how targeted consumer information can end up in the hands of fraudsters. The company sold consumer data to operators of fraudulent schemes, helping them identify people more likely to respond.

Meanwhile, the FBI reported that Americans age 60 and older lost approximately $7.7 billion to internet crime in 2025, up from $4.9 billion in 2024. While those losses weren't all connected to data brokers, the documented sale of targeted consumer lists shows how personal information can help criminals identify potential victims and make their scams more convincing.

Finding every profile attached to your name can be difficult. Unlike a credit report, there isn't one centralized system where you can review all the marketing predictions companies have generated about you. However, some state privacy laws give residents rights to access, correct or delete personal information, including certain inferences.

California provides one important example. Under the California Consumer Privacy Act eligible residents can request information held by covered businesses, subject to legal exceptions. California also launched its Delete Request and Opt-out Platform, known as DROP at privacy. in January 2026.

It allows eligible California residents to submit one deletion request covering registered data brokers. Those companies began processing requests in August 2026. Matching records, including inferences, generally must be deleted unless an exception applies. Other states have different privacy protections, so your options depend on where you live.

You may not be able to eliminate every prediction about yourself. However, you can take practical steps to reduce how much personal information remains available. Here's where to start: Reducing publicly accessible personal information can make it harder for unfamiliar businesses and scammers to build detailed profiles. However, removal services cannot guarantee that every existing inference will disappear or prevent companies from collecting new information.

I've warned you before about data brokers collecting your personal information, but I find the predictions they make about us even more troubling. Think about it. You could search for a medical condition on behalf of someone you love and end up in a category suggesting you have that condition yourself. Someone buying access to that profile may have no idea the assumption is wrong. And when personal information gets into the wrong hands, it can help scammers make their pitches sound much more convincing.

What concerns me is how little most of us know about the profiles being built around our names. You may never see every label a company assigns to you, but you can start reducing the information available to build those profiles. I recommend checking what appears when you search for yourself and taking advantage of the privacy protections available in your state. A reputable data removal service can also help by automatically sending removal requests to data brokers and continuing to check when your information reappears. You won't disappear from the internet, but you can make it harder for strangers to assemble a detailed profile of your life.

What would concern you more, a data broker accurately predicting something deeply personal about you or selling a completely false prediction as if it were fact? Let us know by writing to us at