The general taxonomy contains a default scope of data related topics, based on the user's browser and mobile app activity through last 30 days. There are classical Demographic, purchase interests, intentions.

How you can use our data?

There are two main areas where you can use our data: • marketers - targeting online campaigns With our high-quality audience data, you can easily reach specific audiences across the world in programmatic campaigns. Show them personalized ads adjusted to their specific profiles. • ad tech companies - enriching 1st party data or using our raw data by your own data science team

OAN's Third Party Audience Data provides targeted audiences for online advertising campaigns. With the ever-increasing competition in the digital advertising space, businesses must reach the right audience to maximize their marketing efforts. OAN offers a comprehensive solution by leveraging third-party data sources to identify and segment audiences based on various demographic, behavioral, and interest-based attributes.

Key features: - 1 billion unique Xandr IDs/ month, globally - 1 billion unique Mobile IDs/ month, globally - 887 IAB-compliant segments - 500 segments of players globally - We gather and provide non-cookie IDs - for example, Universal IDs, CTV IDs and Mobile Ad IDs

How you can use our data? - Marketers - targeting online campaigns With our high-quality audience data, you can easily reach specific audiences across the world in programmatic campaigns. Show them personalized ads adjusted to their specific profiles. - Ad tech companies Enriching 1st party data or using our raw data by your own data science team.

Our third party audience dataset is collected from various reliable sources, ensuring its accuracy and reliability. We provide GDPR and CCPA-compliant data that is constantly updated and enriched, providing businesses with the most up-to-date segmented information on consumer behavior and preferences.

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

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...

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.

The Gaming Taxonomy contains a broad scope of Gaming related topics, based on the user's browser and mobile app activity through last 30 days. There are classical Demographic, Game Genre, Title and Studio segments. However, we provide also plenty of specific User Types, which contain e.g. Hardcore Gamers, Big Spenders or Parents of Gamers. There are also audiences categorized by specific Hardware Products and Brands, based on the Intent of these devices' purchase. Moreover, we offer segments for Virtual Reality, interest in Gaming Subscriptions, Payments, Micropayments, Devices and Platforms. We also cover the area of E-sports Enthusiasts and Fandoms Members. In spirit of looking beyond simple game genres, we categorize Games according to their Theme (e.g. Historical), which is definitely important aspects of user experience and purchase decisions. Since Mobile Gaming is a very important part of the Gaming Industry, we distinct special Mobile Gaming segments, which are analogous to the ordinary Gaming segments, with additional categorizations of the Telecommunication Network Providers.

Our data base include millions of profiles divided into popular categories. You can choose which target groups you want to reach. Contact us to check all the possibilities: team@oan.pl

How you can use our data?

There are two main areas where you can use our data: • marketers - targeting online campaigns With our high-quality audience data, you can easily reach specific audiences across the world in programmatic campaigns. Show them personalized ads adjusted to their specific profiles. • ad tech companies - enriching 1st party data or using our raw data by your own data science team

We are ready for a cookieless era. We already gather and provide non-cookie ID - for example Universal IDs, CTV IDs or Mobile IDs.

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.

The research data utilized in this study primarily consists of responses from a survey administered to information security and risk management professionals globally. The survey was designed to elicit the relative importance of various criteria when selecting a Third-Party Risk Management (TPRM) tool. The survey employed a weighted scale, allowing respondents to assign a level of importance (e.g., not important, somewhat important, very important) to each of the criteria identified in the applied framework.

The survey sample encompassed a diverse range of roles and levels of seniority within organizations, including CEOs, VPs, auditors, and managers. This diversity aimed to capture a comprehensive view of the priorities and preferences across the industry.

The collected survey data was then analyzed using descriptive statistics and a weighted average approach to determine the mean and median scores for each evaluation criterion. This analysis allowed for a quantitative assessment of the relative importance of different features and functionalities in TPRM tools, providing valuable insights into the decision-making process of industry professionals.

Additionally, the study incorporated a review of existing literature on TPRM and tool selection. This literature review served to identify key concepts, trends, and gaps in knowledge, informing the development of the TPRMTSF framework and the selection of survey criteria.

The combination of survey data and literature review provides a comprehensive foundation for the research findings and recommendations presented in this study. By analyzing both empirical data and existing knowledge, the study offers a well-rounded perspective on the challenges and opportunities associated with TPRM tool selection.

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.

This data set includes sample information and results for third-party performance assessment samples analyzed for hormones and pharmaceuticals during the same general time period as environmental samples collected by the National Water-Quality Assessment (NAWQA) Project for a study of groundwater resources used for drinking-water supply across the United States, 2013 through 2015. Hormone and pharmaceutical results are provided for spiked and unspiked samples submitted by the Quality Systems Branch (QSB) Organic Blind Sample Project (OBSP)(https://bqs.usgs.gov/obsp/), providing information regarding analyte recovery and occurrence of false positive and false negative results.

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.

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.

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

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.

Third-Party Data Processor Liability Market Outlook

According to our latest research, the global Third-Party Data Processor Liability market size reached USD 7.2 billion in 2024, reflecting the rapid expansion and increasing complexity of data ecosystems worldwide. The market is set to grow at a robust CAGR of 12.8% from 2025 to 2033, with the market size projected to reach USD 21.1 billion by 2033. This impressive growth is primarily driven by the escalating volume of sensitive data handled by third-party processors, heightened regulatory scrutiny, and the growing need for robust data protection frameworks across industries.

A primary growth factor for the Third-Party Data Processor Liability market is the exponential increase in data outsourcing by organizations seeking operational efficiency and scalability. As businesses across BFSI, healthcare, retail, and IT sectors leverage third-party vendors for data processing, storage, and analytics, the risk of data breaches and non-compliance with stringent regulations such as GDPR, CCPA, and HIPAA rises significantly. This has compelled enterprises to invest in comprehensive liability solutions to mitigate financial and reputational damages. The growing awareness of data privacy rights among consumers and the increasing frequency of high-profile data breaches are further intensifying the demand for robust third-party data processor liability frameworks.

Another critical driver is the evolving regulatory landscape, which mandates stricter compliance requirements for data controllers and processors. Governments and regulatory bodies worldwide are continuously updating data protection laws, imposing hefty fines and penalties for non-compliance. As a result, organizations are prioritizing investments in liability solutions that ensure adherence to these regulations, minimize legal risks, and foster trust with stakeholders. The proliferation of cloud-based services and cross-border data transfers has further complicated compliance, making third-party liability solutions indispensable in today’s interconnected digital environment.

Technological advancements are also playing a pivotal role in shaping the Third-Party Data Processor Liability market. The integration of advanced security protocols, artificial intelligence, and machine learning in data processing and analytics has enhanced the ability to detect and respond to threats in real-time. However, these advancements also introduce new vulnerabilities and complexities, necessitating continuous updates to liability policies and risk management strategies. The convergence of technology and regulatory compliance is thus fueling innovation in the market, with vendors offering specialized solutions tailored to industry-specific requirements and emerging threats.

From a regional perspective, North America continues to dominate the market, driven by a mature regulatory framework, high adoption of cloud technologies, and a large base of data-centric enterprises. However, the Asia Pacific region is witnessing the fastest growth, supported by rapid digital transformation, rising awareness of data privacy, and increasing regulatory initiatives. Europe remains a key market due to the stringent enforcement of GDPR and similar regulations across member states. Latin America and the Middle East & Africa are also emerging as significant markets, as governments in these regions intensify efforts to strengthen data protection and compliance infrastructure.

Service Type Analysis

The Service Type segment in the Third-Party Data Processor Liability market encompasses data processing, data storage, data analytics, data security, and other related services. Data processing remains the largest sub-segment, accounting for a significant share of the market due to the sheer volume of personal and sensitive information processed by third-party vendors on behalf of organizations. As enterprises increasingly outsource their data management functions to specialized service providers, the r

DRAKO is a Mobile Location Audience Targeting provider with a programmatic trading desk specialising in geolocation analytics and programmatic advertising. Through our customised approach, we offer business and consumer insights as well as addressable audiences for advertising.

Mobile Location Data can be meaningfully transformed into Audience Targeting when used in conjunction with other dataset. Our expansive POI Data allows us to segment users by visitation to major brands and retailers as well as categorizes them into syndicated segments. Beyond POI visits, our proprietary Home Location Model determines residents of geographic areas such as Designated Market Areas, Counties, or States. Relatedly, our Home Location Model also fuels our Geodemographic Census Data segments as we are able to determine residents of the smallest census units. Additionally, we also have audiences of: ticketed event and venue visitors; survey data; and retail data.

All of our Audience Targeting is 100% deterministic in that it only includes high-quality, real visits to locations as defined by a POIs satellite imagery buildings contour. We never use a radius when building an audience unless requested. We have a horizontal accuracy of 5m.

Additionally, we can always cross reference your audience targeting with our syndicated segments:

Overview of our Syndicated Audience Data Segments: - Brand/POI segments (specific named stores and locations) - Categories (behavioural segments - revealed habits) - Census demographic segments (HH income, race, religion, age, family structure, language, etc.,) - Events segments (ticketed live events, conferences, and seminars) - Resident segments (State/province, CMAs, DMAs, city, county, sub-county) - Political segments (Canadian Federal and Provincial, US Congressional Upper and Lower House, US States, City elections, etc.,) - Survey Data (Psychosocial/Demographic survey data) - Retail Data (Receipt/transaction data)

All of our syndicated segments are customizable. That means you can limit them to people within a certain geography, remove employees, include only the most frequent visitors, define your own custom lookback, or extend our audiences using our Home, Work, and Social Extensions.

In addition to our syndicated segments, we’re also able to run custom queries return to you all the Mobile Ad IDs (MAIDs) seen at in a specific location (address; latitude and longitude; or WKT84 Polygon) or in your defined geographic area of interest (political districts, DMAs, Zip Codes, etc.,)

Beyond just returning all the MAIDs seen within a geofence, we are also able to offer additional customizable advantages: - Average precision between 5 and 15 meters - CRM list activation + extension - Extend beyond Mobile Location Data (MAIDs) with our device graph - Filter by frequency of visitations - Home and Work targeting (retrieve only employees or residents of an address) - Home extensions (devices that reside in the same dwelling from your seed geofence) - Rooftop level address geofencing precision (no radius used EVER unless user specified) - Social extensions (devices in the same social circle as users in your seed geofence) - Turn analytics into addressable audiences - Work extensions (coworkers of users in your seed geofence)

Data Compliance: All of our Audience Targeting Data is fully CCPA compliant and 100% sourced from SDKs (Software Development Kits), the most reliable and consistent mobile data stream with end user consent available with only a 4-5 day delay. This means that our location and device ID data comes from partnerships with over 1,500+ mobile apps. This data comes with an associated location which is how we are able to segment using geofences.

Data Quality: In addition to partnering with trusted SDKs, DRAKO has additional screening methods to ensure that our mobile location data is consistent and reliable. This includes data harmonization and quality scoring from all of our partners in order to disregard MAIDs with a low quality score.

The Gaming Taxonomy contains a broad scope of Gaming related topics, based on the user's browser and mobile app activity through last 30 days. There are classical Demographic, Game Genre, Title and Studio segments. However, we provide also plenty of specific User Types, which contain e.g. Hardcore Gamers, Big Spenders or Parents of Gamers. There are also audiences categorized by specific Hardware Products and Brands, based on the Intent of these devices' purchase. Moreover, we offer segments for Virtual Reality, interest in Gaming Subscriptions, Payments, Micropayments, Devices and Platforms. We also cover the area of E-sports Enthusiasts and Fandoms Members. In spirit of looking beyond simple game genres, we categorize Games according to their Theme (e.g. Historical), which is definitely important aspects of user experience and purchase decisions. Since Mobile Gaming is a very important part of the Gaming Industry, we distinct special Mobile Gaming segments, which are analogous to the ordinary Gaming segments, with additional categorizations of the Telecommunication Network Providers.

Our data base include millions of profiles divided into popular categories. You can choose which target groups you want to reach. Contact us to check all the possibilities: team@oan.pl

How you can use our data?

There are two main areas where you can use our data: • marketers - targeting online campaigns With our high-quality audience data, you can easily reach specific audiences across the world in programmatic campaigns. Show them personalized ads adjusted to their specific profiles. • ad tech companies - enriching 1st party data or using our raw data by your own data science team

We are ready for a cookieless era. We already gather and provide non-cookie ID - for example Universal IDs, CTV IDs or Mobile IDs.

The WZDx Specification enables infrastructure owners and operators (IOOs) to make harmonized work zone data available for third party use. The intent is to make travel on public roads safer and more efficient through ubiquitous access to data on work zone activity. Specifically, the project aims to get data on work zones into vehicles to help automated driving systems (ADS) and human drivers navigate more safely. MCDOT leads the effort to aggregate and collect work zone data from the AZTech Regional Partners. A continuously updating archive of the WZDx feed data can be found at ITS WorkZone Data Sandbox. The live feed is currently compliant with WZDx specification version 1.1.

Report Overview

The global Privacy Management Software market has become a vital sector in the technology landscape. With increasingly sophisticated cyber threats, organizations are investing heavily in advanced solutions. In 2023, the market value stood at USD 3.0 billion, and it is projected to soar to USD 83.7 billion by 2033, growing at an impressive CAGR of 39.50% between 2024 and 2033. This surge is fueled by the rapid adoption of digital transformation strategies, growing reliance on cloud infrastructure, and the ever-increasing risk of cyberattacks.

AI and ML are playing a pivotal role in automating privacy management processes. These technologies enable real-time data monitoring, identify compliance risks, and offer predictive insights to mitigate potential breaches. For instance, AI-based solutions can now detect anomalies in large data sets, improving compliance efficiency. By 2024, over 40% of privacy management tools will incorporate AI-driven analytics.

With regulations such as GDPR, CCPA, and China's Personal Information Protection Law (PIPL), companies are prioritizing consumer rights like data portability, the right to be forgotten, and opt-out preferences. Privacy management solutions are increasingly equipped with features to address these rights efficiently. For example, the demand for data subject access request (DSAR) management tools has surged by nearly 35% annually.

Privacy management software is being integrated with broader cybersecurity platforms to create unified solutions. This integration helps companies streamline compliance while protecting data from unauthorized access. Gartner predicts that by 2025, 60% of the privacy management software market will be bundled with cybersecurity suites to address overlapping challenges.

Industries like healthcare, finance, and e-commerce are seeing tailored privacy management solutions that cater to specific compliance needs. For example, healthcare providers are adopting tools to meet HIPAA compliance, while financial institutions are leveraging software that ensures data security in line with GDPR and PSD2 regulations.

Organizations are increasingly concerned about the data shared with third-party vendors. Privacy management tools now include third-party risk assessment capabilities to evaluate vendor compliance with privacy standards. According to a recent survey, 55% of organizations implemented third-party risk management in 2023, a figure expected to grow significantly in 2024.

As businesses migrate to cloud environments, cloud-based privacy management software is becoming a preferred choice due to its scalability and ease of integration. Currently, 67% of businesses prefer cloud-based solutions, a number anticipated to grow as remote work and digital transformation expand.

Governments worldwide are enforcing data localization rules, requiring businesses to store user data within specific geographic boundaries. Privacy management tools now offer features to ensure compliance with such laws, enabling organizations to align with region-specific data storage requirements.

To meet growing consumer expectations, organizations are deploying privacy dashboards that allow users to view, manage, and delete their data. These dashboards are becoming a standard feature, with 30% of companies globally adopting them in 2023 to improve transparency.

Organizatio...