Zero-Party Data Campaign Assistant Market Outlook

According to our latest research, the global Zero-Party Data Campaign Assistant market size reached USD 2.14 billion in 2024, reflecting a robust increase driven by the surging demand for privacy-centric marketing solutions. The market is expected to grow at a CAGR of 16.8% from 2025 to 2033, resulting in a projected market value of USD 10.33 billion by 2033. This exceptional growth is primarily fueled by the increased regulatory scrutiny on third-party data usage and the growing emphasis on consumer data privacy, compelling brands to adopt innovative zero-party data strategies for more transparent and effective customer engagement.

One of the primary growth drivers of the Zero-Party Data Campaign Assistant market is the evolving regulatory landscape surrounding data privacy. With the implementation of stringent regulations such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States, organizations are under immense pressure to ensure compliance and build trust with their customers. Zero-party data, which is voluntarily shared by consumers, offers a compliant and transparent alternative to traditional data collection methods. As organizations prioritize privacy-first marketing strategies, the adoption of zero-party data campaign assistants is accelerating, enabling brands to deliver personalized experiences while respecting user consent. This shift is not only enhancing customer trust but also providing marketers with more accurate and actionable insights, thus driving overall market growth.

Another significant factor contributing to the growth of the Zero-Party Data Campaign Assistant market is the increasing demand for personalized customer experiences. In today’s digital era, consumers expect brands to understand their preferences and deliver tailored interactions across channels. Zero-party data, which includes explicit preferences, intentions, and feedback directly provided by users, empowers marketers to create hyper-personalized campaigns. The integration of advanced analytics and artificial intelligence within campaign assistants further enhances the ability to interpret zero-party data, enabling real-time personalization and improved campaign effectiveness. As businesses across industries recognize the value of customer-centric approaches, investments in zero-party data solutions are witnessing a substantial uptick, driving market expansion.

Technological advancements and the proliferation of digital touchpoints are also catalyzing the adoption of Zero-Party Data Campaign Assistants. The rise of omnichannel marketing, mobile applications, and interactive digital experiences has opened new avenues for collecting and leveraging zero-party data. Modern campaign assistants are equipped with sophisticated tools for data collection, consent management, and analytics, enabling seamless integration with existing marketing ecosystems. Furthermore, the growing adoption of cloud-based solutions is making zero-party data platforms more accessible and scalable, particularly for small and medium enterprises. These technological innovations are facilitating the widespread implementation of zero-party data strategies, thereby underpinning the sustained growth of the market.

From a regional perspective, North America continues to dominate the Zero-Party Data Campaign Assistant market, accounting for the largest share in 2024. This leadership is attributed to the advanced digital infrastructure, early adoption of privacy regulations, and the presence of leading technology providers in the region. Europe follows closely, driven by strict regulatory frameworks and a high level of consumer awareness regarding data privacy. Meanwhile, the Asia Pacific region is emerging as a high-growth market, fueled by rapid digitalization, expanding e-commerce sectors, and increasing investments in marketing technologies. As businesses worldwide strive to enhance customer trust and compliance, the demand for zero-party data solutions is expected to rise across all major regions.

Demographics Analysis with Consumer Edge Credit & Debit Card Transaction Data

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Transact Signal is an aggregated transaction feed that includes consumer transaction data on 100M+ credit and debit cards, including 14M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 12K+ merchants and deep demographic and geographic breakouts. Track detailed consumer behavior patterns, including retention, purchase frequency, and cross shop in addition to total spend, transactions, and dollars per transaction.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

This data sample illustrates how Consumer Edge data can be used to compare demographics breakdown (age and income excluded in this free sample view) for one company vs. a competitor for a set period of time (Ex: How do demographics like wealth, ethnicity, children in the household, homeowner status, and political affiliation differ for Walmart vs. Target shopper?).

Inquire about a CE subscription to perform more complex, near real-time demographics analysis functions on public tickers and private brands like: • Analyze a demographic, like age or income, within a state for a company in 2023 • Compare all of a company’s demographics to all of that company’s competitors through most recent history

Consumer Edge offers a variety of datasets covering the US and Europe (UK, Austria, France, Germany, Italy, Spain), with subscription options serving a wide range of business needs.

Use Case: Demographics Analysis

Problem A global retailer wants to understand company performance by age group.

Solution Consumer Edge transaction data can be used to analyze shopper transactions by age group to understand: • Overall sales growth by age group over time • Percentage sales growth by age group over time • Sales by age group vs. competitors

Impact Marketing and Consumer Insights were able to: • Develop weekly reporting KPI's on key demographic drivers of growth for company-wide reporting • Reduce investment in underperforming age groups, both online and offline • Determine retention by age group to refine campaign strategy • Understand how different age groups are performing compared to key competitors

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

Public and private investors can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights, marketing, and retailers can gain visibility into transaction data’s potential for competitive analysis, understanding shopper behavior, and capturing market intelligence.

Most popular use cases among public and private investors include: • Track Key KPIs to Company-Reported Figures • Understanding TAM for Focus Industries • Competitive Analysis • Evaluating Public, Private, and Soon-to-be-Public Companies • Ability to Explore Geographic & Regional Differences • Cross-Shop & Loyalty • Drill Down to SKU Level & Full Purchase Details • Customer lifetime value • Earnings predictions • Uncovering macroeconomic trends • Analyzing market share • Performance benchmarking • Understanding share of wallet • Seeing subscription trends

Fields Include: • Day • Merchant • Subindustry • Industry • Spend • Transactions • Spend per Transaction (derivable) • Cardholder State • Cardholder CBSA • Cardholder CSA • Age • Income • Wealth • Ethnicity • Political Affiliation • Children in Household • Adults in Household • Homeowner vs. Renter • Business Owner • Retention by First-Shopped Period ...

This data release includes tables and plots of results for pesticide compounds (pesticides and degradates) analyzed in groundwater samples collected by the USGS National Water-Quality Assessment Project during water years 2013-18 and in associated quality-control samples that are used to assess the quality of the reported pesticide results. All samples were analyzed by the USGS National Water Quality Laboratory (NWQL) using laboratory schedule 2437. The table of groundwater data includes pesticide results as reported by the laboratory, along with results that represent the application of censoring levels at the 90-percent upper confidence limit of the 95th percentile of laboratory blank concentrations determined by water year. The other seven tables included in this data release contain pesticide results for the following types of quality-control samples: field blanks, matrix spikes, and replicates collected at field sites; laboratory blanks and reagent spikes prepared by the NWQL; and third-party blind blanks and blind spikes prepared by the USGS Quality Systems Branch. The table of pesticide results for field matrix spikes includes the paired groundwater results and other fields needed to calculate spike recovery as described in the data processing steps of the metadata file. The table of pesticide results for field replicates includes the paired groundwater results and other fields needed to calculate variability in detection and (or) concentration as described in the data processing steps of the metadata file. Results included in this data release for laboratory reagent spikes are for water year 2018 only; results for laboratory reagent spikes analyzed in water years 2013-15 are available in Shoda and others (2017) and in water years 2016-17 are available in Wieben (2019). Useful graphical representations of data in the tables are provided in various plots that compare detections and concentrations for groundwater and blank samples, compare recovery results for the different spike types, and illustrate variability in replicate-sample results across concentration ranges. Shoda, M.E., Nowell, L.H., Bexfield, L.M., Sandstrom, M.W., Stone, W.W., 2017, Recovery data for surface water, groundwater and lab reagent samples analyzed by the USGS National Water Quality Laboratory schedule 2437, water years 2013-15: U.S. Geological Survey data release, https://doi.org/10.5066/F7QZ28G4. Wieben, C.M., 2019, Pesticide recovery data for surface-water and lab reagent samples analyzed by the USGS National Water Quality Laboratory schedule 2437, water years 2016-17: U.S. Geological Survey data release, https://doi.org/10.5066/P93MWMVF. There are 8 tables included in this data release: Table1_GroundwaterData2013_2018.xlsx -- Pesticide results for groundwater samples collected by the National Water-Quality Assessment Project, 2013-18. This table includes pesticide results as reported by the laboratory, along with results that represent the application of censoring levels at the 90-percent upper confidence limit of the 95th percentile of laboratory blank concentrations determined by water year. Results that were rejected for data analysis for reasons described in the metadata document and in the associated Scientific Investigations Report are flagged. Table2_FieldBlankData2013_2018.xlsx -- Pesticide results for field blanks collected at groundwater sites by the National Water-Quality Assessment Project, 2013-18. Results that were rejected for data analysis for reasons described in the metadata document and in the associated Scientific Investigations Report are flagged. Table3_FieldSpikeData2013_2018.xlsx -- Pesticide results for field matrix spikes collected at groundwater sites by the National Water-Quality Assessment Project, 2013-18. Results of paired groundwater samples are included. Results that were rejected for data analysis for reasons described in the metadata document and in the associated Scientific Investigations Report are flagged. Fields needed to calculate spike recovery as described in the data processing steps of the metadata file are included. Table4_FieldRepData2013_2018.xlsx -- Pesticide results for field replicates collected at groundwater sites by the National Water-Quality Assessment Project, 2013-18. Results of paired groundwater samples are included. Results that were rejected for data analysis for reasons described in the metadata document and in the associated Scientific Investigations Report are flagged. Fields needed to calculate variability in detection and (or) concentration as described in the data processing steps of the metadata file are included. Table5_LabBlankData2013_2018.xlsx -- Pesticide results for laboratory blanks prepared by the National Water Quality Laboratory, 2013-18. Results that were rejected for data analysis for reasons described in the metadata document and in the associated Scientific Investigations Report are flagged. Table6_LabReagentSpikeData2018.xlsx -- Pesticide results for laboratory reagent spikes prepared by the National Water Quality Laboratory, 2018. Table7_QSBBlindBlankData_2018.xlsx -- Pesticide results for third-party blind blanks prepared by the Quality Systems Branch, 2018. Table8_QSBBlindSpikeData2013_2018.xlsx -- Pesticide results for third-party blind spikes prepared by the Quality Systems Branch, 2013-18. Results that were rejected for data analysis for reasons described in the metadata document and in the associated Scientific Investigations Report are flagged. There are 5 sets of graphical representations of the data. Detailed descriptions of the plots included in this data release are provided in the associated Scientific Investigations Report: PlotGroup1_TimeSeries.pdf – Plots of reported detections and concentrations in groundwater samples (Table 1), field blanks (Table 2), and laboratory blanks (Table 5) for individual compounds by analysis date, showing the frequency, timing, and magnitude of detections among these sample types. Nondetections are plotted as open circles at the standard laboratory reporting level in effect at the time of analysis (identified on each graph) or, if applicable, at the raised reporting level specified for an individual sample. PlotGroup2_EDFsByWY.pdf – Empirical distribution functions illustrating upper percentiles of concentrations for groundwater samples (Table 1) relative to field blanks (Table 2) and laboratory blanks (Table 5) for selected pesticides and water years. Plots are provided for compounds and water years with at least one groundwater detection and a quantifiable (detected) 99th percentile of concentration for laboratory blanks. PlotGroup3_SpikeTimeSeries.pdf – Plots of recoveries for laboratory reagent spikes (Table 6), field matrix spikes (Table 3), and third-party blind spikes (Table 8) for individual pesticides by analysis date, illustrating the range of typical recoveries. Lowess (locally weighted scatterplot smoothing) curves are included to illustrate general changes in recovery through time. Results for laboratory reagent spikes analyzed in water years 2013-15 are available in Shoda and others (2017) and in water years 2016-17 are available in Wieben (2019). PlotGroup4_LabFieldSpikes.pdf – Box plots comparing recoveries for laboratory reagent spikes (Table 6) and field matrix spikes (Table 3). Results for laboratory reagent spikes analyzed in water years 2013-15 are available in Shoda and others (2017) and in water years 2016-17 are available in Wieben (2019). PlotGroup5_FieldRepVar.pdf – Plots of standard deviation and relative standard deviation against mean concentration of field replicate samples (Table 4) for selected pesticides, including assigned boundaries between the lower concentration range where standard deviation generally is more uniform and the upper concentration range where relative standard deviation generally is more uniform. Plots are provided for pesticides that had 10 or more replicate pairs with detections in both samples of the pair.

From October 2017 through September 2022, the National Water Quality Network (NWQN) monitored 110 surface-water river and stream sites and more than 1,800 groundwater wells for a large number of water-quality analytes, for which associated quality-control data and corresponding statistical summaries are included in this data release. The quality-control data—for samples that were collected in the field (at all 110 surface-water sites, 350 groundwater wells, and 16 quality-control-only sites), prepared in the laboratory, or prepared by a third party—can be used to assess the quality of environmental data collected by the NWQN through the estimation of bias and variability in reported results. The general analyte groups that were monitored at NWQN surface-water and (or) groundwater sites and have associated quality-control data in this data release include major ions, nutrients, trace elements, pesticides, volatile organic compounds, hormones, pharmaceuticals, radionuclides, microbial indicators, sediment, and environmental tracers. For each analyte group, the data tables contain results for one or more of the following types of quality-control samples, where relevant: blanks, matrix spikes, and replicates collected at field sites; laboratory blanks, reagent spikes, and matrix spikes prepared by the USGS National Water Quality Laboratory (NWQL) (quality-control samples prepared by other analyzing laboratories are not included in the current data release); and third-party blanks, spikes, and reference samples prepared by the USGS Quality Systems Branch (QSB). For each relevant analyte, tables of summary statistics characterize the frequency and concentrations of blank detections, the typical magnitude of and variability in spike and reference-sample recoveries, and the typical variability between replicate concentrations. Tables included in this data release: Table1_SiteList.txt: Information about National Water Quality Network sites that have associated quality-control data. Table2_AnalyteList.txt: Information about National Water Quality Network analytes that have associated quality-control data, including available aquatic-life and (or) human-health benchmarks and selected information regarding analytical methods. Table3_BlankData.txt: For all relevant analytes, results for blanks collected at field sites, prepared in the laboratory, or prepared by a third party. Table4_SpikeData.txt: For all relevant analytes, results for matrix spikes prepared in the field, matrix spikes prepared in the laboratory, reagent spikes prepared in the laboratory, or reagent spikes prepared by a third party. For matrix spikes, results of paired environmental samples are included. Table5_ReplicateData.txt: For all relevant analytes, results for field replicates and paired environmental samples. Table 6_ReferenceData.txt: For all relevant analytes, results for third-party reference samples. Table7_BlankStats.txt: For all relevant analytes, summary statistics for each type of available blank sample. Table8_SpikeStats.txt: For all relevant analytes, summary statistics for each type of available spike sample. Table9_ReplicateStats.txt: For all relevant analytes, summary statistics for field replicates. Table10_ReferenceStats.txt: For all relevant analytes, summary statistics for reference samples.

NIST SP 800-171 for Third-Party Data Market Outlook

According to our latest research findings, the global NIST SP 800-171 for Third-Party Data market size reached USD 3.98 billion in 2024. The market is experiencing robust expansion, supported by a CAGR of 17.6% from 2025 to 2033. By the end of 2033, the market is forecasted to attain a value of USD 14.08 billion. This remarkable growth is primarily fueled by the increasing stringency of regulatory compliance requirements, surging volumes of sensitive data exchanged with third parties, and the growing threat landscape that necessitates advanced data protection frameworks.

The primary growth driver for the NIST SP 800-171 for Third-Party Data market is the escalating need for organizations to comply with evolving cybersecurity mandates, particularly within the defense, government, and critical infrastructure sectors. The proliferation of cyberattacks targeting third-party vendors has heightened awareness about the vulnerabilities associated with external data sharing. As a result, enterprises are increasingly investing in comprehensive compliance management and risk assessment solutions to ensure that their third-party partners adhere to the stringent standards outlined by NIST SP 800-171. The rising adoption of digital transformation initiatives and cloud-based ecosystems further amplifies the urgency for robust data protection protocols, as organizations extend their digital perimeters and expose themselves to new vectors of risk.

Another significant factor contributing to market expansion is the growing complexity of supply chains and the corresponding need for secure data collaboration. With global supply chains involving a multitude of third-party vendors, subcontractors, and service providers, maintaining consistent security postures across all entities has become a formidable challenge. NIST SP 800-171 serves as a critical framework for standardizing security practices and ensuring that all parties involved in the data exchange process meet minimum cybersecurity requirements. This trend is particularly prominent in sectors such as manufacturing, IT & telecom, and BFSI, where sensitive intellectual property, customer data, and financial information are routinely shared with external partners. The widespread adoption of these standards is expected to drive sustained demand for compliance management, risk assessment, and monitoring solutions.

The market is also benefiting from advancements in automation, artificial intelligence, and analytics, which are being integrated into NIST SP 800-171 compliance solutions to streamline processes and enhance threat detection capabilities. Automated tools can rapidly identify compliance gaps, monitor third-party activities in real-time, and generate actionable insights for remediation. This technological evolution is making it easier for organizations of all sizes to implement and maintain compliance, thereby broadening the addressable market. Furthermore, the introduction of managed security services and cloud-based compliance platforms is lowering the barrier to entry for small and medium enterprises, enabling them to achieve regulatory alignment without significant capital outlays or specialized in-house expertise.

From a regional perspective, North America continues to dominate the NIST SP 800-171 for Third-Party Data market, accounting for the largest share in 2024 due to the high concentration of defense contractors, government agencies, and technology firms subject to federal cybersecurity regulations. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid digitalization, expanding regulatory frameworks, and increasing cross-border data flows. Europe is also witnessing substantial growth, driven by GDPR compliance and the integration of NIST standards into broader data protection initiatives. The global market landscape is thus characterized by a dynamic interplay of regulatory pressures, technological innovation, and evolving threat vectors, all of which are shaping the future trajectory of NIST SP 800-171 adoption for third-party data security.

Demographic and PII data (including emails, phone numbers, and addresses) for the US Millennial and Gen Z population segments. Fully opt-in and CCPA compliant (direct submission from the individuals). 30 million+ population.

High success and conversion rates for direct marketing, targeted ads, identity verification, and demographic research.

This data can be merged into the BIGDBM Consumer dataset or have specific data fields appended from the BIGDBM Consumer dataset.

BIGDBM Privacy Policy: https://bigdbm.com/privacy.html

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Vision Europe includes consumer transaction data on 6.7M+ credit cards, debit cards, direct debit accounts, and direct transfer accounts, including 5.3M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 5K+ merchants, 3K+ brands mapped to 600 global parent companies (500 publicly traded), and deep geographic breakouts with demographic breakouts coming soon for UK. Brick & mortar and ecommerce direct-to-consumer sales are recorded on transaction date and purchase data is available for most companies as early as 5 days post-swipe.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

Private equity and venture capital firms can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights teams and retailers can gain visibility into transaction data’s potential for competitive analysis, shopper behavior, and market intelligence.

CE Vision Benefits • Discover new competitors • Compare sales, average ticket & transactions across competition • Evaluate demographic and geographic drivers of growth • Assess customer loyalty • Explore granularity by geos • Benchmark market share vs. competition • Analyze business performance with advanced cross-cut queries

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

In 2024, financial media networks (FMNs) accounted for *** percent of digital advertising spending in the United States. The share is expected to quadruple by 2026. FMNs are defined as financial institutions with their own ad networks using their own first-party data to target their customers with third-party ads. Examples include Chase Bank, PayPal, or Klarna.

Insurance Third-Party Data Enrichment Market Outlook

According to our latest research, the global insurance third-party data enrichment market size reached USD 2.56 billion in 2024, demonstrating the sector’s robust expansion fueled by the increasing demand for advanced analytics in the insurance industry. With a compelling compound annual growth rate (CAGR) of 13.4% projected for the forecast period, the market is expected to achieve a value of USD 7.87 billion by 2033. The primary growth factor driving this market is the insurance sector’s accelerating shift towards data-driven decision-making, leveraging third-party data to enhance risk assessment, streamline claims management, and personalize customer experiences.

The surge in digital transformation initiatives across the insurance industry is a pivotal growth catalyst for the insurance third-party data enrichment market. Insurers are increasingly seeking ways to differentiate their offerings and improve operational efficiencies in a highly competitive landscape. By integrating external data sources—such as demographic, behavioral, and technographic data—insurers gain deeper insights into customer needs, risk profiles, and emerging market trends. This enables more accurate underwriting, proactive fraud detection, and tailored product recommendations, which collectively boost customer satisfaction and retention rates. Furthermore, the proliferation of connected devices, IoT, and big data analytics platforms is expanding the pool of actionable data, empowering insurers to make more informed decisions across the value chain.

Another significant growth factor is the rising incidence of insurance fraud and the corresponding need for robust fraud detection mechanisms. Third-party data enrichment solutions empower insurers to cross-verify applicant information, identify anomalies, and flag suspicious activities in real-time. Advanced machine learning algorithms and AI-powered analytics are increasingly being integrated into these solutions, enhancing their ability to detect complex fraud patterns that traditional methods may overlook. As regulatory scrutiny intensifies and insurers face mounting pressure to minimize losses, investment in sophisticated data enrichment tools is becoming indispensable for maintaining profitability and compliance.

The evolving regulatory landscape is also shaping market growth, as insurers must navigate a complex web of data privacy laws and compliance requirements. The adoption of third-party data enrichment solutions facilitates adherence to these regulations by ensuring data accuracy, enhancing transparency, and supporting robust audit trails. In addition, partnerships between insurers and data providers are fostering the development of innovative enrichment solutions tailored to specific insurance segments such as life, health, and property & casualty insurance. These collaborations are accelerating the adoption of enriched data across diverse applications, further propelling market expansion.

From a regional perspective, North America continues to dominate the insurance third-party data enrichment market, accounting for the largest revenue share in 2024, driven by the presence of leading insurance providers, advanced data infrastructure, and a strong regulatory framework. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid digitalization, increasing insurance penetration, and a burgeoning middle class. Meanwhile, Europe is witnessing steady growth, supported by stringent regulatory mandates and a mature insurance ecosystem. Latin America and the Middle East & Africa are also experiencing gradual adoption, with insurers in these regions increasingly recognizing the value of third-party data enrichment to enhance competitiveness and operational efficiency.

Component Analysis

The insurance third-party data enrichment market is segmented by component into solutions and services, each playing a c

Context

The Kymera Data Fabrication machine has a feature names "Reverse Score" to address the following use case:

A data Owner, has a data set containing customer data, and wishes to use a 3td party solution for credit score. The data owner is wery of the confidentiality of the data and would not like to expose it to the 3rd party provider. The data owner would like to get the credit scores for his list of customers without compromising confidentiality.

Using the Kymera Machine, the data owner would follow these steps: 1. Run a fabrication process and generate a synthetic data set from the confidencial set. 2. Send the synthetic data set to the 3rd party and receive back a score set based on synthetic data 3. Run the received score set again through the Kymera Machine in reversed score process 4. End result: a score file true to the original confidencial set.

This Data set contains the data input and output of this entire process, with the addition of an extra set that has been created by running the same model on the original confidencial set, for result comparison.

Third-Party Data Enrichment for Insurance Market Outlook

According to our latest research, the global market size for Third-Party Data Enrichment for Insurance reached USD 2.1 billion in 2024, with a robust year-on-year growth momentum. The market is expected to expand at a CAGR of 13.2% from 2025 to 2033, culminating in a projected value of USD 6.2 billion by 2033. This dynamic growth is primarily driven by the increasing need for insurance companies to enhance customer profiling, risk assessment, and fraud detection through advanced data analytics and external data sources. As per our latest research, insurers are rapidly adopting third-party data enrichment solutions to gain a competitive edge, improve operational efficiency, and deliver personalized services in a highly regulated and customer-centric environment.

A key growth factor propelling the Third-Party Data Enrichment for Insurance market is the exponential increase in the volume and variety of data available from external sources. Insurers are leveraging demographic, firmographic, technographic, and behavioral data to gain deeper insights into customer needs, preferences, and risk profiles. The integration of third-party data allows for more accurate underwriting, dynamic pricing, and targeted marketing strategies, thereby reducing loss ratios and improving profitability. Furthermore, the proliferation of digital channels and the rise of insurtech startups have intensified competition, compelling traditional insurers to invest in advanced data enrichment solutions to stay relevant and agile in a rapidly evolving marketplace.

Another significant driver is the growing prevalence of digital fraud and cyber threats, which has heightened the need for robust fraud detection and risk assessment mechanisms. Third-party data enrichment empowers insurers to validate customer identities, detect anomalies, and flag suspicious activities in real time. This capability is particularly crucial in the context of online policy issuance and claims management, where the risk of fraudulent transactions is substantially higher. Additionally, regulatory requirements such as Know Your Customer (KYC) and Anti-Money Laundering (AML) have made it imperative for insurers to access comprehensive and up-to-date external data sources to ensure compliance and mitigate financial crime risks.

The ongoing digital transformation across the insurance industry is further accelerating the adoption of third-party data enrichment solutions. As insurers transition from legacy systems to cloud-based platforms, they are increasingly seeking scalable and flexible data enrichment tools that can seamlessly integrate with their core systems. The emergence of artificial intelligence, machine learning, and big data analytics has enabled insurers to extract actionable insights from vast and disparate datasets, thereby enhancing decision-making processes across the value chain. Moreover, partnerships between insurers and data providers are fostering innovation and enabling the development of tailored solutions that address specific industry challenges and customer expectations.

Regionally, North America commands the largest share of the Third-Party Data Enrichment for Insurance market, driven by the presence of leading insurance companies, advanced IT infrastructure, and a high degree of digital adoption. Europe follows closely, with stringent regulatory frameworks and a strong focus on data privacy and security. The Asia Pacific region is witnessing the fastest growth, fueled by rising insurance penetration, rapid urbanization, and increasing investments in digital technologies. Latin America and the Middle East & Africa are also emerging as promising markets, supported by ongoing regulatory reforms and the growing adoption of insurtech solutions. Overall, the global market is characterized by intense competition, continuous innovation, and a strong emphasis on data-driven decision-making.

Component Analysis

The Component segmen

In 2024, advertising spending on financial media networks (FMNs) was estimated at 350 million U.S. dollars in the United States. The value is expected to double in 2025, and then to double again in 2026. FMNs are defined as financial institutions with their own ad networks using their own first-party data to target their customers with third-party ads. Examples include Chase Bank, PayPal, or Klarna.

According to Cognitive Market Research, The global third-party risk management market size is USD 5.5 billion in 2023 and will expand at a compound annual growth rate (CAGR) of 17.20% from 2023 to 2030.

The demand for third party risk managements is rising due to Resource optimization to protect the interests of millions of digital financial service consumers.
Demand for cloud remains higher in the third party risk management market.
The BFSI category held the highest third party risk management market revenue share in 2023.
North American third party risk management will continue to lead, whereas the European third party risk management market will experience the most substantial growth until 2030.

Rising Instances of Cyber-attacks and Frauds in Digital Financial Services to Provide Viable Market Output

With greater internet penetration, the deployment of smart technology has enhanced the appeal of digital financial services such as mobile banking and digital payments. Because of the growth of digital services, businesses must adapt and incorporate sophisticated technologies into their offerings. However, as the use of digital payment systems in the BFSI sector has grown, so have the risks of cyber-attacks and fraud. BFSI stakeholders are investing heavily to protect their clients from such disasters. The market for third-party risk management will develop as resources are optimized to protect the interests of millions of users of digital financial services.

Growing digitization of Businesses to Propel Market Growth

Industry automation and digitization have exacerbated data privacy and security breaches. With growing digitization, various stakeholders become involved, heightening safety issues. This spike in third-party involvement is propelling the third-party risk management market, raising associated hazards. As industries increasingly rely on external partners and vendors, the need for robust risk management solutions to protect against potential vulnerabilities and ensure the integrity of sensitive data becomes critical in the midst of an evolving landscape of technological advancements and increased interconnectivity.

Market Dynamics of

Third Party Risk Management Market

Key Drivers of

Third Party Risk Management Market

Increasing Regulatory Compliance Demands : Organizations are encountering heightened regulatory pressures to ensure that third parties adhere to legal and compliance standards, particularly in sectors such as finance, healthcare, and technology. Regulations like GDPR, HIPAA, and SOX require comprehensive risk assessments and ongoing monitoring. As the consequences of non-compliance become more severe, businesses are allocating resources to third-party risk management platforms to protect their operations and ensure regulatory compliance.

Escalating Outsourcing and Supply Chain Complexity : As organizations expand their global reach and outsource essential services, the intricacy of managing third-party vendors, suppliers, and partners significantly increases. This escalation results in greater exposure to cybersecurity threats, operational interruptions, and data breaches. The demand for real-time visibility, thorough due diligence, and risk profiling across multi-tier vendor ecosystems is a key factor driving the need for effective TPRM solutions.

Increase in Cybersecurity Threats from Third Parties : Third-party vendors frequently represent the most vulnerable aspect of an organization’s cybersecurity framework. Notable breaches associated with third-party failures have raised awareness regarding vendor-related cyber risks. Companies are now pursuing comprehensive tools to continuously monitor vendor activities, implement security measures, and proactively address vulnerabilities, leading to substantial growth in the market for third-party risk management software and services.

Key Restraints in

Third Party Risk Management Market

High Implementation and Operational Costs : Implementing a successful Third-Party Risk Management (TPRM) program often necessitates a significant initial investment in software, training, and resources. For small to medium-sized enterprises, these expenses can be overwhelming. Beyond the initial setup, continuous risk monitoring and compliance audits further elevate operational costs, which can deter adoption among organizations with limited budgets or those lack...

First-Party Data Onboarding Hubs Market Outlook

According to our latest research, the global First-Party Data Onboarding Hubs market size reached USD 3.2 billion in 2024, reflecting robust industry momentum fueled by the rising demand for privacy-compliant data solutions. The market is expected to experience a compound annual growth rate (CAGR) of 13.7% from 2025 to 2033, projecting the market to reach approximately USD 9.1 billion by 2033. This remarkable growth trajectory is primarily attributed to the surge in digital transformation initiatives, the phasing out of third-party cookies, and tightening data privacy regulations, all of which are compelling enterprises to invest in advanced first-party data onboarding solutions.

The primary growth factor driving the First-Party Data Onboarding Hubs market is the global shift towards consumer privacy and regulatory compliance. With regulations such as the General Data Protection Regulation (GDPR) in Europe, the California Consumer Privacy Act (CCPA), and similar laws emerging worldwide, organizations are increasingly compelled to manage and activate data in a privacy-centric manner. First-party data onboarding hubs enable businesses to securely collect, match, and activate customer data across digital platforms without violating privacy norms. This shift is not only a compliance necessity but also a strategic advantage, as brands that effectively leverage first-party data can deliver more relevant and personalized customer experiences while maintaining trust and transparency.

Another key driver for the market is the rapid evolution of digital advertising and marketing technologies. As the digital ecosystem becomes more complex and third-party cookies are deprecated by major browsers, marketers are seeking new ways to connect offline and online customer data. First-party data onboarding hubs provide a seamless solution for integrating disparate data sources, enabling accurate audience segmentation, advanced analytics, and real-time personalization. The proliferation of omnichannel marketing strategies in industries such as retail, BFSI, and media & entertainment further accelerates the need for robust onboarding platforms that can unify customer identities and deliver actionable insights at scale.

In addition, the increasing adoption of cloud-based solutions and advancements in artificial intelligence are significantly propelling market growth. Cloud deployment offers scalability, agility, and cost-effectiveness, making it an attractive option for both large enterprises and SMEs. AI-powered onboarding hubs can automate identity resolution, enhance data quality, and generate predictive analytics, thereby optimizing marketing ROI and operational efficiency. As organizations prioritize digital maturity, the demand for integrated, intelligent, and secure first-party data onboarding hubs is expected to rise sharply, driving sustained market expansion over the forecast period.

Regionally, North America currently dominates the First-Party Data Onboarding Hubs market, accounting for the largest share due to its advanced digital infrastructure, high adoption of marketing technologies, and stringent privacy regulations. Europe follows closely, driven by GDPR compliance and a mature data management landscape. The Asia Pacific region is emerging as a high-growth market, fueled by rapid digitalization, increasing internet penetration, and a burgeoning e-commerce sector. Latin America and the Middle East & Africa, while currently representing smaller shares, are anticipated to witness accelerated growth as businesses in these regions intensify their focus on data-driven strategies and customer engagement.

Component Analysis

The Component segment of the First-Party Data Onboarding Hubs market is bifurcated into software and services, each playing a pivotal role in the overall ecosystem. Software solutions constitute the core of onboarding hubs, providing the infrastructure for data ingestion, identity resolution, audience

Data Access for Third-Party DER Market Outlook

According to our latest research, the global data access for third-party DER (Distributed Energy Resources) market size reached USD 2.4 billion in 2024, reflecting the rapid digitalization and decentralization of the energy sector. The market is poised for robust expansion, projected to grow at a CAGR of 18.7% from 2025 to 2033, reaching a forecasted value of USD 12.5 billion by 2033. This growth is primarily driven by the increasing integration of renewable energy assets, regulatory mandates for grid flexibility, and the rising need for real-time data exchange between utilities and third-party DER operators.

A key growth factor for the data access for third-party DER market is the accelerating adoption of renewable energy sources such as solar, wind, and battery storage systems across both developed and emerging economies. As the global energy mix shifts toward cleaner alternatives, the number and diversity of DER assets proliferate, necessitating seamless data access and interoperability. Third-party operators, including aggregators and energy service companies, require granular, real-time data to optimize performance, participate in energy trading, and deliver value-added services. Regulatory frameworks, particularly in North America and Europe, are increasingly mandating open data access and interoperability standards, further fueling market demand. These regulations not only ensure fair competition but also foster innovation by enabling new business models centered around distributed energy.

Another significant driver is the digital transformation of grid infrastructure, which is unlocking new possibilities for data-driven decision-making and grid management. Advanced metering infrastructure (AMI), IoT sensors, and cloud-based platforms are being widely deployed to collect, process, and share vast volumes of DER data. This digital ecosystem empowers utilities and third-party stakeholders to enhance grid reliability, balance supply and demand, and reduce operational costs. Furthermore, the growing prevalence of smart homes, electric vehicles, and demand response programs is amplifying the need for robust, secure, and scalable data access solutions. These technological advancements are not only improving operational efficiency but also enabling the transition to a more resilient, flexible, and sustainable energy system.

The evolving landscape of energy markets and the emergence of peer-to-peer energy trading platforms are also contributing to the expansion of the data access for third-party DER market. As prosumers—entities that both produce and consume energy—become more active market participants, there is a heightened requirement for transparent, real-time data exchange to facilitate transactions and settlement processes. This trend is particularly pronounced in regions with deregulated energy markets, where competition and consumer choice are driving the adoption of innovative DER solutions. The convergence of data access technologies with blockchain, artificial intelligence, and advanced analytics is expected to further accelerate market growth by enabling more sophisticated and automated energy trading mechanisms.

From a regional perspective, North America currently dominates the global data access for third-party DER market, accounting for approximately 39% of total revenue in 2024. The region’s leadership is underpinned by progressive regulatory initiatives, high DER penetration, and substantial investments in grid modernization. Europe follows closely, driven by ambitious decarbonization targets and strong policy support for digital energy solutions. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by rapid urbanization, expanding renewable energy capacity, and government incentives for smart grid deployment. Latin America and the Middle East & Africa, though comparatively smaller in market share, are expected to gain momentum as energy access and sustainability become increasingly prioritized.

Compon

RedCircle API provides reliable, real-time Target product, category, reviews, offers, and search results data. All data includes comprehensive coverage of each of the search results in a cleanly structured output.

You can originate your request from any zip code (US) to see results as they would appear to customers in the specified location i.e. shipping info. RedCircle APIs high-capacity, global infrastructure assures you the highest level of performance and reliability. For easy integration with your Target data apps and services, data is delivered in JSON or CSV format.

Data is retrieved by search term, or for single products, by the Target product ID (TCIN), or other unique Target identifier: department-class-item ID (DPCI). You can also submit a product page URL (product results) or a category ID (category search results) instead.

So what's in the data from RedCircle API?

Product: - 1st party, 2nd party & 3rd party seller data - Product specifications - Imagery - Product videos - Description - Buy Box winner: price, availability and fulfillment info - Top reviews - Promotions

Search results: - Product details per search result: - Position - Related queries - Pagination - Categories - Facets

...and more, depending on your request parameters or the search result.

How can RedCircle API be used? - Product listing management - Price monitoring - Category & product trends monitoring - Market research & competitor intelligence - Location-specific shipping data - Rank tracking on Target

Who uses RedCircle API? This data is leveraged by software developers, marketers & business owners, sales & business development teams, researchers, and data analysts & engineers, in ecommerce, other retail business, agencies and SaaS platforms.

Anyone in your organization who works with your digital presence can develop business intelligence and strategy using this advanced product data.

Zero‑Party Data Collection for Hotels Market Outlook

According to our latest research, the Zero-Party Data Collection for Hotels market size stood at USD 1.08 billion in 2024, reflecting the growing importance of guest-driven data strategies in the hospitality industry. The market is projected to expand robustly at a CAGR of 21.4% from 2025 to 2033, reaching an estimated USD 7.62 billion by 2033. This surge is primarily fueled by the increasing demand for personalized guest experiences, stringent data privacy regulations, and the shift away from third-party data dependency, as hotels strive to enhance loyalty and engagement through direct, consent-based data collection methods.

The primary growth factor for the Zero-Party Data Collection for Hotels market is the rising emphasis on guest personalization. As travelers increasingly expect tailored experiences, hotels are leveraging zero-party data—which is data that guests intentionally share with the brand—to curate unique offerings, recommendations, and services. This shift is a strategic response to the evolving digital privacy landscape, where traditional data tracking methods are becoming less effective due to regulatory and technological changes. By directly soliciting preferences, interests, and feedback, hotels can enhance guest satisfaction, increase repeat bookings, and foster a sense of trust and transparency, all of which are critical to driving long-term profitability in a competitive industry.

Another significant driver is the tightening of global data privacy regulations, such as GDPR in Europe and CCPA in California, which have compelled hotels to rethink their data collection strategies. These regulations restrict the use of third-party cookies and mandate explicit consent for data usage, making zero-party data an attractive, compliant alternative. Hotels adopting zero-party data collection solutions can not only mitigate legal risks but also position themselves as privacy-conscious brands. This approach enhances brand reputation and appeals to a growing segment of privacy-aware travelers, further accelerating market adoption and technological innovation in this space.

The proliferation of advanced digital platforms and omnichannel engagement tools has also played a pivotal role in market expansion. Modern software and service solutions enable hotels to seamlessly integrate zero-party data collection into their websites, mobile apps, loyalty programs, and on-property experiences. These technologies facilitate real-time data capture, analysis, and activation, empowering hotels to make informed decisions and deliver hyper-personalized marketing, promotions, and services. As competition intensifies and guest expectations rise, the adoption of these solutions is expected to increase, further driving the growth of the Zero-Party Data Collection for Hotels market over the forecast period.

Regionally, North America leads the market due to its advanced hospitality sector, high digital adoption rates, and proactive regulatory environment. The region accounted for the largest share in 2024, with significant investments in guest personalization and loyalty programs. Europe follows closely, driven by stringent data privacy laws and a mature hotel industry. The Asia Pacific region is poised for the fastest growth, supported by a booming tourism sector, rapid digital transformation, and increasing awareness of privacy-centric data strategies among hotel operators. Latin America and the Middle East & Africa are also witnessing steady adoption, albeit at a slower pace, as digital infrastructure and regulatory frameworks continue to evolve.

Component Analysis

The Component segment of the Zero-Party Data Collection for Hotels market is bifurcated into Software and Services. Software solutions dominate the market, accounting for more than 60% of the total revenue in 2024. Thes

First‑Party Data Collaboration Hubs Market Outlook

According to our latest research, the global First‑Party Data Collaboration Hubs market size reached USD 2.47 billion in 2024, reflecting robust demand across key industries. The market is projected to expand at a CAGR of 16.8% from 2025 to 2033, reaching an anticipated value of USD 11.86 billion by 2033. The primary growth factor driving this impressive trajectory is the increasing prioritization of data privacy and the phasing out of third-party cookies, which is compelling organizations to invest in secure, scalable data collaboration solutions.

The growing emphasis on data privacy regulations such as GDPR, CCPA, and other regional mandates is fundamentally reshaping how organizations collect, store, and utilize customer information. As third-party data sources become less reliable and more restricted, enterprises are turning to first-party data collaboration hubs to unlock actionable insights while maintaining compliance. These platforms enable secure sharing and analysis of customer data across departments and with trusted partners, without exposing sensitive information or breaching regulatory frameworks. The ability to facilitate privacy-centric data collaboration is emerging as a critical differentiator, especially for sectors like BFSI, healthcare, and retail, where customer trust and regulatory adherence are paramount.

Another significant growth driver is the accelerating digital transformation across industries, which is fueling the adoption of advanced analytics and artificial intelligence. Organizations are increasingly leveraging first-party data collaboration hubs to unify disparate data sources, break down silos, and generate holistic customer profiles. This unified approach empowers marketing, sales, and product teams to create personalized experiences, optimize campaigns, and drive data-driven decision-making. The proliferation of cloud-based solutions and the integration of machine learning algorithms have further enhanced the scalability and effectiveness of these hubs, making them indispensable for large enterprises as well as agile SMEs seeking to compete on customer experience.

Moreover, the growing trend of data monetization is propelling the demand for robust collaboration platforms. Enterprises recognize the untapped value in their proprietary data assets and are seeking secure ways to exchange and monetize data with external partners, advertisers, and ecosystem collaborators. First-party data collaboration hubs provide the necessary infrastructure to facilitate these exchanges while ensuring governance, transparency, and compliance. This not only opens new revenue streams but also fosters innovation in product development, customer engagement, and business models. The shift from isolated data management to collaborative ecosystems is expected to be a defining trend in the market’s evolution over the next decade.

From a regional perspective, North America remains the dominant market for first-party data collaboration hubs, accounting for over 38% of global revenue in 2024. This leadership is attributed to the region’s advanced digital infrastructure, strong regulatory environment, and high adoption rates among retail, BFSI, and technology sectors. Europe follows closely, driven by stringent data protection laws and a mature enterprise landscape. Meanwhile, Asia Pacific is witnessing the fastest growth, with a CAGR of 19.2% through 2033, as organizations in emerging economies accelerate digital initiatives and invest in data-driven strategies. Latin America and the Middle East & Africa are also experiencing steady adoption, supported by increasing investments in digital transformation and evolving regulatory frameworks.

Component Analysis

The component segment of the First‑Party Data Collaboration Hubs market is bifurcated into software and services, each playing a pivotal role in market expansion. Software solutions

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. CE Vision USA includes consumer transaction data on 100M+ credit and debit cards, including 35M+ with activity in the past 12 months and 14M+ active monthly users. Capturing online, offline, and 3rd-party consumer spending on public and private companies, data covers 12K+ merchants, 800+ parent companies, 80+ same store sales metrics, and deep demographic and geographic breakouts. Review data by ticker in our Investor Relations module. Brick & mortar and ecommerce direct-to-consumer sales are recorded on transaction date and purchase data is available for most companies as early as 6 days post-swipe.

Consumer Edge’s consumer transaction datasets offer insights into industries across consumer and discretionary spend such as: • Apparel, Accessories, & Footwear • Automotive • Beauty • Commercial – Hardlines • Convenience / Drug / Diet • Department Stores • Discount / Club • Education • Electronics / Software • Financial Services • Full-Service Restaurants • Grocery • Ground Transportation • Health Products & Services • Home & Garden • Insurance • Leisure & Recreation • Limited-Service Restaurants • Luxury • Miscellaneous Services • Online Retail – Broadlines • Other Specialty Retail • Pet Products & Services • Sporting Goods, Hobby, Toy & Game • Telecom & Media • Travel

Private equity and venture capital firms can leverage insights from CE’s synthetic data to assess investment opportunities, while consumer insights teams and retailers can gain visibility into transaction data’s potential for competitive analysis, shopper behavior, and market intelligence.

CE Vision Benefits • Discover new competitors • Compare sales, average ticket & transactions across competition • Evaluate demographic and geographic drivers of growth • Assess customer loyalty • Explore granularity by geos • Benchmark market share vs. competition • Analyze business performance with advanced cross-cut queries

Corporate researchers and consumer insights teams use CE Vision for:

Corporate Strategy Use Cases • Ecommerce vs. brick & mortar trends • Real estate opportunities • Economic spending shifts

Marketing & Consumer Insights • Total addressable market view • Competitive threats & opportunities • Cross-shopping trends for new partnerships • Demo and geo growth drivers • Customer loyalty & retention

Investor Relations • Shareholder perspective on brand vs. competition • Real-time market intelligence • M&A opportunities

Most popular use cases for private equity and venture capital firms include: • Deal Sourcing • Live Diligences • Portfolio Monitoring

Use Case: Apparel Retailer, Enterprise-Wide Solution

Problem A $49B global apparel retailer was looking for a comprehensive enterprise-wide consumer data platform to manage and track consumer behavior across a variety of KPI's for use in weekly and monthly management reporting.

Solution The retailer leveraged Consumer Edge's Vision Pro platform to monitor and report weekly on: • market share, competitive analysis and new entrants • trends by geography and demographics • online and offline spending • cross-shopping trends

Impact Marketing and Consumer Insights were able to: • develop weekly reporting KPI's on market share for company-wide reporting • establish new partnerships based on cross shopping trends online and offline • reduce investment in slow channels in both online and offline channels • determine demo and geo drivers of growth for refined targeting • analyze customer retention and plan campaigns accordingly

This data release includes tables and time-series plots of results for volatile organic compounds (VOCs) analyzed in samples of groundwater used for public supply that were collected by the USGS National Water-Quality Assessment (NAWQA) Project and the California State Water Resources Control Board’s Groundwater Ambient Monitoring and Assessment Program Priority Basin Project (GAMA-PBP) during 2013-19; results for associated quality-control samples also are included. All samples were analyzed by the USGS National Water Quality Laboratory (NWQL) using laboratory schedules 4436 and 4437. The table of groundwater data includes VOC results as reported by the laboratory, along with results that represent the application of censoring approaches described in the metadata file and associated journal article. The other seven tables included in this data release contain VOC results for the following types of quality-control samples: field blanks and replicates collected at field sites; laboratory blanks, reagent spikes, and matrix spikes prepared by the NWQL; and third-party blind blanks and blind spikes prepared by the USGS Quality Systems Branch. The tables of VOC results for matrix spikes and field replicates include the paired groundwater results. For convenience, plots are provided of reported VOC detections and concentrations in groundwater samples, field blanks, and laboratory blanks for individual compounds by analysis date. Plots also are provided of recoveries for laboratory reagent spikes, laboratory matrix spikes, and third-party blind spikes for individual VOCs by analysis date. This data release includes 8 tables and 2 series of laboratory results plots: Table1_GroundwaterData2013_2019.csv: VOC results for samples collected by NAWQA and GAMA-PBP of groundwater used for public supply, 2013-19. This table includes VOC results as reported by the laboratory, along with results that represent the application of censoring approaches described in the associated journal article. Results that were rejected or censored for data analysis for reasons described in the metadata document and in the associated journal article are identified using attribute values described in the process steps for this table. Table2_FieldBlankData2013_2019.csv: VOC results for field blanks collected at applicable groundwater sites by NAWQA and GAMA-PBP, 2013-19. Results that were rejected for data analysis for reasons described in the metadata document and in the associated journal article are identified using attribute values described in the process steps for this table. Table3_MatrixSpikeData2013_2019.csv: VOC results for samples collected for laboratory matrix spikes at applicable groundwater sites by NAWQA and GAMA-PBP, 2013-19. Results of paired groundwater samples are included. Results that were rejected for data analysis for reasons described in the metadata document and in the associated journal article are identified using attribute values described in the process steps for this table. Fields needed to calculate spike recovery as described in the data processing steps of the metadata file are included. Table4_FieldRepData2013_2019.csv: VOC results for field replicates collected at groundwater sites by NAWQA and GAMA-PBP, 2013-19. Results of paired groundwater samples are included. Fields needed to calculate variability in detection and (or) concentration as described in the data processing steps of the metadata file are included. Table5_LabBlankData2013_2019.csv: VOC results for laboratory blanks prepared by the National Water Quality Laboratory, 2013-19. Table6_LabReagentSpikeData2013_2019.csv: VOC results for laboratory reagent spikes prepared by the National Water Quality Laboratory, 2013-19. Table7_QSBBlindBlankData2016_2019.csv: VOC results for third-party blind blanks prepared by the Quality Systems Branch, 2016-19. Table8_QSBBlindSpikeData2013_2019.csv: VOC results for third-party blind spikes prepared by the Quality Systems Branch, 2013-19. Results that were rejected for data analysis for reasons described in the metadata document are flagged. PlotGroup1_GW_Blank_TimeSeries.pdf: Plots of laboratory-reported (uncensored) detections and concentrations in groundwater samples (Table 1), field blanks (Table 2), laboratory blanks (Table 5), and third-party blind blanks (Table 7) for individual VOCs by analysis date, showing the frequency, timing, and magnitude of detections among these sample types. Nondetections are plotted as open circles at the standard laboratory reporting limit in effect at the time of analysis (identified on each graph) or, if applicable, at the raised reporting limit specified for an individual sample. PlotGroup2_SpikeTimeSeries.pdf: Plots of recoveries for laboratory reagent spikes (Table 6), laboratory matrix spikes (Table 3), and third-party blind spikes (Table 8) for individual VOCs by analysis date, illustrating the range of typical recoveries. Kernel regression smoothing curves are included to illustrate general changes in recovery through time. False-negative results from third-party blind samples also are shown.