The development of information technology such as the world wide web and the growing number of devices capable of recording and transmitting data has turned data into a commodity in the 21st century.
Although previously, only a few data types were analyzed by businesses and investors, including a combination of traditional and public web data.
Nowadays, businesses and data companies collect huge volumes of public web data. As a result, third-party data providers can enrich the data businesses already have.
Over time, this practice gave rise to data brokerage: the collection of individual, sensor, social media, and company data.
Businesses and investors leverage this data for various business purposes.
What are data brokers?
The term data brokers is defined as private persons or, more often than not, firms that specialize in gathering information from various public web sources.
Data brokers then process, clean, and structure the collected data and then license it for businesses and financial firms.
Data brokers gather information from publicly available sources, especially those on the internet. Sometimes data brokers might buy the information to supplement the datasets they already have, consequently increasing their value.
Data brokers in most contexts are known as data suppliers or data providers.
Who uses data brokers?
Data brokers play a crucial role in today’s data-driven economy, supplying organizations across various industries with valuable insights. From businesses and advertisers to government agencies and financial institutions, many rely on data brokers to enhance decision-making, improve targeting, and manage risks. Here’s a breakdown of the key industries that use data brokers and why.
Building your own tools or platforms with data from brokers
Companies that purchase data from brokers can go beyond using pre-built analytics platforms. They can build their own proprietary tools and products that integrate, analyze, and generate unique insights from raw data. By combining alternative data sources, machine learning, and automation, businesses can develop custom solutions tailored to their industry’s specific needs, giving them a competitive edge in decision-making and operational efficiency.
One approach is to develop internal business intelligence platforms that aggregate and visualize brokered data in a way that aligns with company goals. For example, a hedge fund or investment firm might acquire job posting data, hiring trends, and financial disclosures from data brokers to build a predictive analytics tool for tracking market movements and investment opportunities. Instead of relying on general third-party platforms, this custom solution processes raw datasets into actionable financial models, giving the firm unique, proprietary insights.
Similarly, B2B companies and SaaS providers can integrate brokered data into sales intelligence tools that enhance lead generation. For instance, a custom CRM system could be designed to automatically ingest firmographic and technographic data from sources like Coresignal. This platform could rank potential leads based on company size, hiring activity, and tech stack usage, providing sales teams with real-time, high-intent prospects something that off-the-shelf platforms may not offer.
For companies in risk management and cybersecurity, building proprietary fraud detection engines using identity verification, credit risk, and online behavior data can drastically improve security. A fintech company, for example, could acquire brokered transactional and behavioral data to power an AI-based fraud detection system that flags unusual financial activities in real time. Instead of depending on general fraud detection tools, this custom-built solution adapts to the company’s specific risk models and business processes.
Owning a platform powered by brokered data enables businesses to differentiate themselves in a data-driven economy. Whether it’s a custom market intelligence dashboard, an AI-powered sales engine, or a proprietary risk assessment system, companies that develop their own data-driven products gain deeper insights, stronger competitive advantages, and more control over how data is leveraged for growth and innovation.
Training AI agents and AI robots with data from data brokers
One of the primary applications is in business intelligence and market analysis AI agents. AI systems trained on company financials, firmographic data, hiring trends, and industry shifts can help businesses identify emerging competitors, predict market movements, and optimize investment strategies. For example, an AI-powered corporate intelligence assistant can analyze company financial reports and job posting trends to forecast industry disruptions or high-growth sectors.
In HR and recruitment automation, AI models trained on employee and job market data can revolutionize hiring processes. Data brokers aggregate publicly available employment data, job postings, skills demand, and workforce demographics, which can be used to build AI-driven resume screening tools, talent-matching systems, and salary benchmarking models. For instance, an AI recruiting bot could analyze millions of job descriptions and hiring patterns to predict which skills will be in demand and suggest ideal candidates based on work history, online professional profiles, and industry trends. AI can also automate outreach, using brokered employment data to create personalized engagement strategies for recruiters looking to attract top talent.
AI robots and automation systems in corporate workflow optimization can also benefit from company and employee data. HR chatbots trained on organizational structures, employee movement data, and HR policy trends can automate onboarding, answer employee queries, and streamline internal processes. Meanwhile, AI-driven workforce analytics tools can use brokered job market data to predict employee turnover, suggest retention strategies, and recommend workforce restructuring plans based on industry-wide patterns.
By leveraging company, employee, and job data from brokers, businesses can train AI agents and robots to enhance decision-making, automate workflows, and optimize workforce management. As AI-driven automation continues to evolve, integrating structured corporate data into training models will allow companies to build intelligent business systems, predictive workforce solutions, and cutting-edge recruitment technology that improve efficiency and scalability
Investment companies: the growing demand for better data sources
Investment firms and hedge funds are constantly seeking more accurate, real-time data sources to improve their investment decision-making and risk assessment. Traditional financial reports and stock market trends are no longer enough—investors are now turning to alternative data from data brokers, web scraping, and AI-driven analytics platforms to gain a competitive edge.
Data-driven investing has evolved beyond traditional financial statements and stock price movements. Today, investors look at real-time job market data, firmographic insights, and employee sentiment analysis to evaluate the health and future potential of a company. A surge in job postings for AI engineers in a tech firm, for example, might indicate a company doubling down on innovation, while a sharp decline in hiring activity could be an early warning sign of financial struggles.
Hedge funds and venture capital firms are now buying data from brokers that track supply chain activities, hiring trends, and industry-specific business expansions. For example, knowing that a major retailer is increasing logistics hires or expanding fulfillment centers before it’s officially announced can signal expected growth in consumer demand and sales performance. Likewise, tracking patent filings, employee movements, and vendor contracts can help investors predict whether a tech company is about to launch a breakthrough product.
As AI-driven investment models become more sophisticated, the need for structured, high-quality alternative data will only grow. Investment firms building proprietary machine learning models for risk assessment and stock predictions are increasingly relying on real-time employment data, corporate hiring behaviors, and firm expansion indicators to refine their strategies. The future of investing belongs to those who can integrate diverse, real-time datasets into predictive models, giving them an unparalleled ability to spot high-value opportunities and mitigate risks ahead of the competition
Financial and insurance companies
Financial institutions and insurers use data brokers to assess risks and optimize customer interactions. Common use cases include:
- Credit risk assessment – determining a person’s creditworthiness before issuing loans.
- Fraud detection – identifying patterns that signal fraudulent activities.
- Pricing and underwriting – analyzing personal data to determine insurance premiums.
Example: A bank may analyze spending behaviors to detect potential cases of identity theft.
Healthcare and pharmaceutical companies
Data brokers help healthcare providers and pharmaceutical firms understand patient behaviors and trends. Common applications include:
- Medical research and drug development – analyzing patient data to predict health trends.
- Healthcare marketing – targeting potential patients with relevant healthcare services.
- Predictive analytics for disease prevention – using AI-driven insights to prevent health crises.
Example: A pharmaceutical company may study patient prescription data to track medication adherence trends.
How do data brokers collect information?
Data brokers collect information through various third-party companies and public records.
Online activity
Your online behavior leaves a digital footprint that data brokers can track and analyze. This includes browsing habits, social media interactions, and app usage.
Methods used to collect online activity data:
- Tracking cookies and pixels – Websites track your actions and preferences for targeted advertising.
- Social media activity – Scraping likes, shares, comments, and public profiles contribute to behavioral analysis.
- Search history and website visits – Browsing behavior reveals interests and intent.
- E-commerce transactions – Purchase history and product preferences inform marketing strategies.
Public data scraping
One of the primary ways data brokers collect information is through public data scraping, a process that involves extracting information from publicly available websites, directories, and government databases. By using automated tools such as web crawlers and data parsers, brokers can continuously scan and retrieve structured data from millions of online sources. Public data scraping allows data brokers to collect valuable business intelligence, consumer insights, and financial trends without direct access to private databases.
Sensor & device data
With the rise of the Internet of Things (IoT), smart devices constantly collect and transmit data, some of which is accessible to data brokers.
Examples of data collected from smart devices:
- Mobile GPS tracking – Location data is gathered from apps, navigation services, and Wi-Fi connections.
- Wearable technology – Smartwatches and fitness trackers monitor health metrics, step counts, and sleep patterns.
- Smart home devices – Voice assistants, security cameras, and smart thermostats collect behavioral data.
Third-party companies
When you open a website and agree to give permission to share your consumer data with third-party partners, chances are that your personal data will end up being sold to a data broker.
Public sources
Also, data brokers collect information from various social media sites. Billions of people use social media around the world and share their personal data such as person's age, name, email, location, and more.
It's also abundant in non-sensitive data, such as education level, gender, skills, and so on.
Through public databases, data brokers can also access such information as court records, criminal records, driver's license, motor vehicle records, birth certificates, marriage licenses, census data, credit card company, and more.
Protection of collected consumer data
While it may seem that data brokers know everything about you, there are certain laws and government agencies preventing them from freely distributing your data. For example, the Federal Trade Commission in the U.S. overlooks the entire economic life of the U.S. and in cases of data breaches, this institution will help the affected individuals get compensation for the leak.
Also, there is a General Data Protection Regulation law in Europe that limits and regulates the distribution of personal data.
For these reasons, data brokers must abide by a set of rules that define safe and regulated data collection and exchange.
What information do data brokers collect?
Data brokers operate in almost every data-collection site out there. Above, we discussed the personal data that they can access.
Here is a short list of some public information that data brokers collect:
- Company firmographics
- Professional employee data
- Job posting data
- Technographic data
- Community and repository data
- Funding data
- Product reviews data
- Sentiment data
All these data types are beneficial to investors, HR tech companies, and lead generation companies. They can use a combination of these datasets to:
- Enrich their existing data
- Boost deal sourcing
- Track and monitor selected companies
- Enhance talent sourcing
- Build recruiting platforms
- Improve and scale lead generation
These are several examples of what you can do with these data types.
Types of data brokers
Data vendors usually specialize in the kind of information they collect and manage. For example, some data brokers provide information to scientific or government organizations rather than businesses and financial organizations, collecting the kind of data that would interest such entities.
Commercial data brokers dealing with business-relevant data may also cover specific areas, industries, or topics.
Below are four common types of data brokers, categorized by the kind of information they provide and their purpose.
Marketing and sales
Among the most commonly recognized data brokers are those that provide information for the purpose of targeted marketing or ABM.
Such providers create databases with information such as a professional's age, income, buying habits, internet activity, and similar, helping to create their consumer profile.
For certain companies, this information might become a sales lead that they could then target through specific marketing strategies.
Alternatively, data brokers working with B2B companies collect information such as firmographics, company funding data, community and repository data, and product review data for data enrichment purposes.
B2B companies compile multiple data sources, sometimes from numerous data vendors, to create dashboards and automated marketing tools for their customers, often B2C companies.
This method is practiced by lead generation companies and by HR tech companies that enhance talent acquisition.
Fraud detection
Some data brokers specialize in double-checking the information on people or businesses in order to prevent possible fraud.
For example, banks or financial firms might turn to a data broker to find out more about an entity before granting a loan.
The data that the broker holds or can collect might help establish the accuracy of the information provided by the load claimer, thus preventing granting a fraudulent claim.
Risk mitigation
Banks and loan firms also use data brokers to calibrate the loan offers for particular applicants.
Such a data broker would then collect financial data and information such as online purchase history, from which companies could determine the individual's financial situation and predict buying intent.
This would let the bank know what size of loan could be risked with that person and the interest rate that should be set.
Information brokers are also used for the purposes of risk mitigation by insurance companies, as particular website visits or purchases of medical items might indicate higher medical risks, thus making the insurer raise the interest rates of health insurance.
People search
A data broker of this type would create a database about private people that may be accessed through the broker's website.
The website might provide general biographic data such as date of birth, education and employment history, marital status, and such personal information as affiliations and interests.
Professional profile websites, also known as people search websites, are used by private individuals to retrace lost contacts or simply find out more about acquaintances.
Companies use these websites for various ranking purposes, such as job candidate ranking and lead scoring.
What do data brokers do with your information?
In contemporary business, data is a necessary asset for most companies to handle various workflow challenges effectively.
However, because not all firms can collect the needed data themselves, data vendors have a crucial role in knowledge and data management.
The value that data brokers bring is of multiple kinds from the business perspective, as data is utilized in many different areas.
The most common examples of the added value data companies provide to businesses and investors include the aspects discussed below.
Data for investment models
Investors traditionally have used limited sources and types of data for investment decisions.
After the proliferation of public web data types, it is beyond the capabilities of most financial firms to gather all the necessary data for investment models themselves.
According to the report published by Alternative Investment Management Associated, over 50% of hedge funds are already using some public web data to estimate that the percentage will reach a maximum within five years.
Data brokers come in to provide the data needed for investment models.
Market analysis
Before launching a product, service, or a whole new startup, entrepreneurs might want to conduct a market analysis to answer relevant questions about demand and competition.
However, even for a small industry sector, such as market analysis, companies may require a considerable volume of data.
This volume is enormous when global markets are to be taken into consideration.
Usually, there are no other ways to get all the necessary information other than turning to a data company.
Profiling leads and job candidates
The data-driven approach to hiring has recently gained momentum, with more companies allowing algorithms to screen potential candidates.
People making final hiring decisions would expect to have extensive profiles of the candidates before the interview.
This mirrors the practice of lead profiling, which analyses, scores, and ranks sales leads in order to approach them correctly.
Companies usually use more than the aforementioned professional profile search websites to get the data for these practices.
When B2B companies can't find the necessary information for contacting new leads, they contact a data provider.
Targeted advertising
One of the most common practices that data brokers are associated with is ad targeting. This is most noticeable to private customers who see targeted ads online or receive email offers based on their online activity.
Allowing companies to run targeted advertising, data providers save their time and effort, connecting businesses with the people most likely to need what they offer.
The risks and ethical concerns of data brokerage
The goal of the data broker is to have the kind of data that is of value for businesses. To achieve this, information brokers collect data from multiple sources.
These are usually public sources, such as online databases. Additionally, data broker companies might utilize various techniques of coming into possession of consumer data, such as surveying and web tracking.
The responsibilities of data brokers are of two main kinds.
Firstly, data brokers deal with sensitive information, and it must be handled responsibly, respecting individuals' privacy.
For this reason, data brokers install security measures to protect the information from falling into the wrong hands and preventing data breaches. Additionally, companies often license data for a particular usage instead of selling information.
As a result, companies dealing with information on other firms would usually raise fewer concerns from the general public than those licensing consumer information.
Secondly, a data broker is responsible for following various guidelines regarding data processing. Like all kinds of laws, data governance laws differ by country.
Thus how information brokers are allowed to operate varies by jurisdiction. As business is becoming increasingly globalized, there are attempts to regulate data governance on a global level.
A relatively recent example is the General Data Protection Regulation (GDPR) introduced by the European Union.
Laws & regulations on data brokers
Data brokers operate in a rapidly evolving legal landscape, where privacy laws vary significantly across regions. While some countries have strict regulations to protect consumer data, others lack comprehensive oversight, creating gaps in accountability.
- General data protection regulation (GDPR). Data brokers operating in Europe must ensure transparency, provide opt-out mechanisms, and comply with stringent data security requirements. Many have had to adjust their data collection practices to align with GDPR.
- California consumer privacy Act (CCPA). Data brokers must provide opt-out links, disclose data collection practices, and ensure compliance with consumer requests. The law has forced many companies to revise their data-handling policies to avoid fines and legal action.
How to protect your personal information from data brokers
Data brokers collect information from a wide range of public records and sources. Fortunately, there are ways to limit the personal data they can access.
- Opt-Out from major data brokers. Many data brokers allow users to request data removal.
- Use privacy-focused tools & browsers. Many websites and apps track your online activity for advertising and data collection. Using privacy-enhancing tools can limit exposure.
- Regularly Delete & Limit Data Sharing. Data brokers scrape information from social media, websites, and online services. Reducing your digital footprint can prevent unnecessary exposure.
Concluding thoughts
The data broker industry is a product of its time, created by the growing demand for data in business.
Although the discussion regarding privacy concerns related to sharing personal information is still very much in progress, the value brought by information brokers both to businesses and to consumers indicates that this industry is here to stay.
Therefore, an important goal for the international political community is to create such data regulations that would simultaneously protect consumers' privacy and retain the business value of data brokerage.
