The awareness among worldwide consumers about companies selling their personal data to third parties has grown in recent years. As of July 2022, three in four consumers in selected countries worldwide said they knew that companies sell personal information. In comparison, in 2020, this share was a little over 60 percent.

According to an analysis conducted in 2023 of over 200 companies targeting children and families in the United States, only 25 percent of the businesses had a privacy-protective mindset and did not sell data. Under the California Privacy Rights Act amendment, companies are supposed to disclose if they sell users' personal data. Around 13 percent of companies did not disclose whether they engaged in such practices.

Although a majority of internet users aged between 18 and 75 years in the United Kingdom (UK) are still skeptical when it comes to personal data being collected by companies, a small share (36 percent) would be willing to share this data in return for financial compensation. These types of data mainly included purchase history and location data, while a slightly smaller percentage stated they were willing to sell their browsing history and online media consumption to companies.

According to Cognitive Market Research, the global Data Broker Services market size is USD 268154.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 8.00% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 107261.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 6.2% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 80446.26 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 61675.47 million in 2024 and will grow at a compound annual growth rate (CAGR) of 10.0% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD 13407.71 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.4% from 2024 to 2031.
Middle East and Africa held the major market ofaround 2% of the global revenue with a market size of USD 5363.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 7.7% from 2024 to 2031.
The Subscription Paid held the highest Data Broker Services market revenue share in 2024.

Market Dynamics of Data Broker Services Market

Key Drivers of Data Broker Services Market

Increasing Demand for Personalized Marketing Solutions to Increase the Demand Globally

The Data Broker Services Market is being driven by the increasing demand for personalized marketing solutions. Companies across various industries are leveraging data broker services to access valuable consumer insights and enhance their marketing strategies. Data brokers offer a wide range of data sets, including demographic, behavioral, and transactional data, which can be used to create targeted marketing campaigns. By utilizing data broker services, companies can tailor their marketing messages to specific consumer segments, leading to higher engagement and conversion rates. This trend is expected to continue driving the growth of the Data Broker Services Market as businesses increasingly prioritize personalized marketing approaches to remain competitive in the digital age.

Growing Focus on Data Monetization to Propel Market Growth

Another key driver of the Data Broker Services Market is the growing focus on data monetization. Organizations are realizing the value of their data assets and are looking for ways to monetize them. Data broker services enable companies to sell their data to third parties, such as marketers, researchers, and other businesses, generating additional revenue streams. This trend is particularly prevalent in industries with large amounts of consumer data, such as retail, finance, and healthcare. By monetizing their data, companies can unlock new revenue opportunities and offset the costs associated with data collection and management. As the demand for data-driven insights continues to grow, the Data Broker Services Market is expected to expand, driven by the increasing number of organizations looking to capitalize on their data assets.

Restraint Factors Of Data Broker Services Market

Regulatory Challenges and Data Privacy Concerns to Limit the Sales

One of the key restraints in the Data Broker Services Market is the increasing regulatory challenges and data privacy concerns. With the implementation of regulations such as the GDPR in Europe and the CCPA in California, data brokers are facing stricter requirements for data collection, processing, and sharing. Compliance with these regulations requires significant resources and can limit the ability of data brokers to collect and monetize data. Additionally, concerns about data privacy and security among consumers are leading to greater scrutiny of data broker practices, further complicating the operating environment for these companies. As regulatory pressures continue to increase, data brokers may face challenges in expanding their operations and maintaining profitability.

Impact of Covid-19 on the Data Broker Services Market

The COVID-19 pandemic has had a mixed impact on the Data Broker Services Market. On one hand, the increased reliance on digital technologies and online services during the pandemic has led to a surge in data generation, creating new opportunities for data brokers. Organizations are increasingly seeking to understand changing consumer behaviors and preferences in the digital space, ...

How to sell successfully by direct mail is a book. It was written by John William Wilberforce Cassels and published by Business Publications in 1954.

United States CSI: Home Selling Conditions: Bad Time to Sell data was reported at 21.000 % in May 2018. This records a decrease from the previous number of 25.000 % for Apr 2018. United States CSI: Home Selling Conditions: Bad Time to Sell data is updated monthly, averaging 41.000 % from Nov 1992 (Median) to May 2018, with 307 observations. The data reached an all-time high of 96.000 % in Mar 2009 and a record low of 17.000 % in May 1999. United States CSI: Home Selling Conditions: Bad Time to Sell data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H036: Consumer Sentiment Index: Home Buying and Selling Conditions. The question was: Generally speaking, do you think now is a good time or a bad time to sell a house?

Comprehensive dataset of insider trading activities for Sell Steven, including Form 4 filings and transaction visualizations across multiple companies.

How to sell to retail : the secrets of getting your product to market is a book. It was written by Clare Rayner and published by KoganPage in 2013.

India FII: Reporting Date: Turnover: Sell data was reported at 139,581.500 INR mn in 03 Dec 2018. This records an increase from the previous number of 95,873.700 INR mn for 30 Nov 2018. India FII: Reporting Date: Turnover: Sell data is updated daily, averaging 24,239.000 INR mn from Jan 1999 (Median) to 03 Dec 2018, with 4871 observations. The data reached an all-time high of 250,623.800 INR mn in 01 Jun 2015 and a record low of 0.000 INR mn in 19 Oct 2017. India FII: Reporting Date: Turnover: Sell data remains active status in CEIC and is reported by National Securities Depository Limited. The data is categorized under Daily Database’s Financial and Futures Market – Table IN.ZA025: Foreign Institutional Investors (FII) / Foreign Portfolio Investors (FPI) Investment. The data is compiled on the basis of reports submitted to SEBI by custodians on the reporting date on FII Investment of the previous trading day

Graph and download economic data for Housing Inventory: Median Days on Market in the United States (MEDDAYONMARUS) from Jul 2016 to Feb 2025 about median and USA.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Retail Sales in the United States increased 0.20 percent in February of 2025 over the previous month. This dataset provides - U.S. December Retail Sales Increased More Than Forecast - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Russia Turnover: MICEX Stock Exchange: MS: Buy & Sell: Deals for Non Residents: Individuals data was reported at 2,383,918,288.010 RUB in Jan 2019. This records an increase from the previous number of 1,803,349,508.150 RUB for Dec 2018. Russia Turnover: MICEX Stock Exchange: MS: Buy & Sell: Deals for Non Residents: Individuals data is updated monthly, averaging 3,224,748,802.555 RUB from Aug 2015 (Median) to Jan 2019, with 42 observations. The data reached an all-time high of 36,129,294,146.520 RUB in May 2017 and a record low of 1,420,750,252.680 RUB in May 2016. Russia Turnover: MICEX Stock Exchange: MS: Buy & Sell: Deals for Non Residents: Individuals data remains active status in CEIC and is reported by Moscow Exchange. The data is categorized under Russia Premium Database’s Financial Market – Table RU.ZB006: MICEX Stock Exchange: Turnover: Buy and Sell: Deals for Non Residents.

Immobiliare.it Insights' Real Estate Market Observatory offers unparalleled insights into Italy's real estate sector. This data suite harmonizes information on real estate ads, from views, leads, and saved searches to propensity of spending, Real Estate Valuation Data, Business Listings Data, Web Search Data, and Web Activity Data.

Dive deep into real estate market dynamics, including pricing trends, property types, and geographic preferences. Leverage this residential real estate data to understand market composition and customize indicator segmentation by type, number of rooms, and maintenance status.

| Dataset Details |

• 53 KPIs related to views, searches, and leads
• Monthly Updates
• Key data points including searches carried out and saved, from the viewing of individual ads and from the contacts generated by them, as a measure of the interest shown in the ads and a summary of the searches made
• Delivery via flat-file (CSV, API)

| Use Cases |

• Developers, property managers, and real estate investors: Anticipate market trends with comprehensive property market data, Real Estate Valuation Data, and - Business Listings Data to define the most attractive real estate features for users, and understand willingness to pay by floor area and asset class.
• Market analysts: Utilize real-time real estate data, Web Search Data, and Web Activity Data to identify emerging trends and dynamics in the residential real estate market.
• Urban planners: Access demand market data to support planning and development strategies tailored to current market needs.
• Financial institutions: Evaluate market risk and opportunities with accurate and timely real estate market data, and understand online search trends data for a deeper insight into user preferences.

Immobiliare.it Insights' Real Estate Market Observatory provides crucial property data for the residential and non-residential sectors, ensuring a comprehensive understanding of the real estate market data. Leveraging this real-time real estate data, Real Estate Valuation Data, Business Listings Data, Web Search Data, and Web Activity Data, stakeholders can make informed decisions based on the latest trends and metrics available in the market.

Wirestock's AI/ML Image Training Data, 4.5M Files with Metadata: This data product is a unique offering in the realm of AI/ML training data. What sets it apart is the sheer volume and diversity of the dataset, which includes 4.5 million files spanning across 20 different categories. These categories range from Animals/Wildlife and The Arts to Technology and Transportation, providing a rich and varied dataset for AI/ML applications.

The data is sourced from Wirestock's platform, where creators upload and sell their photos, videos, and AI art online. This means that the data is not only vast but also constantly updated, ensuring a fresh and relevant dataset for your AI/ML needs. The data is collected in a GDPR-compliant manner, ensuring the privacy and rights of the creators are respected.

The primary use-cases for this data product are numerous. It is ideal for training machine learning models for image recognition, improving computer vision algorithms, and enhancing AI applications in various industries such as retail, healthcare, and transportation. The diversity of the dataset also means it can be used for more niche applications, such as training AI to recognize specific objects or scenes.

This data product fits into Wirestock's broader data offering as a key resource for AI/ML training. Wirestock is a platform for creators to sell their work, and this dataset is a collection of that work. It represents the breadth and depth of content available on Wirestock, making it a valuable resource for any company working with AI/ML.

The core benefits of this dataset are its volume, diversity, and quality. With 4.5 million files, it provides a vast resource for AI training. The diversity of the dataset, spanning 20 categories, ensures a wide range of images for training purposes. The quality of the images is also high, as they are sourced from creators selling their work on Wirestock.

In terms of how the data is collected, creators upload their work to Wirestock, where it is then sold on various marketplaces. This means the data is sourced directly from creators, ensuring a diverse and unique dataset. The data includes both the images themselves and associated metadata, providing additional context for each image.

The different image categories included in this dataset are Animals/Wildlife, The Arts, Backgrounds/Textures, Beauty/Fashion, Buildings/Landmarks, Business/Finance, Celebrities, Education, Emotions, Food Drinks, Holidays, Industrial, Interiors, Nature Parks/Outdoor, People, Religion, Science, Signs/Symbols, Sports/Recreation, Technology, Transportation, Vintage, Healthcare/Medical, Objects, and Miscellaneous. This wide range of categories ensures a diverse dataset that can cater to a variety of AI/ML applications.

The files contain the replication dataset and code of the paper "Should we sell arms to human rights violators? What the public thinks" by Asif Efrat and Omer Yair (published in Defence and Peace Economics)

This dataset mainly provides the actual information of real estate transactions declared by the declared person nationwide (providing MANIFEST.CSV, schema-main.csv, schema-build.csv, schema-land.csv, schema-park)Released once on the 1st, 11th, and 21st of each month

Dataset

This dataset was created by Subhendu Ghosh

Contents

Dataset

This dataset was created by Sahabudin Ali

Contents

According to data collected in April 2022 in the United States, Telegram was the alternative social media platform that claimed to provide necessary privacy settings and a conscious approach to handling user data. Rumble appeared to have none of the mentioned online privacy control options among all the platforms. Gab and Parler were relatively neutral, claiming they wouldn't sell user data or have targeted third-party ads at the time of the research.