Street Noise-Level Dataset — Health & Insurance Applications

Silencio’s Street Noise-Level Dataset offers health organizations, insurance companies, and wellness researchers unique access to hyper-local, real-world noise exposure data across more than 200 countries. Built from over 35 billion datapoints, collected via our mobile app and enriched with AI-powered interpolation, this dataset delivers detailed average noise levels (dBA) at the street and neighborhood level.

Chronic noise exposure is a growing public health concern linked to stress, cardiovascular risks, sleep disorders, and reduced quality of life — all of which are increasingly relevant for public health studies, insurance risk modeling, and wellness program design. Silencio’s data allows insurance and health organizations to quantify environmental noise exposure and incorporate it into risk assessments, premium modeling, urban health studies, and wellness product development.

In addition to objective noise measurements, Silencio provides access to the world’s largest noise complaint database, offering complementary subjective insights directly from communities, enabling more precise correlations between noise exposure and health outcomes.

Data is available as: • CSV exports • S3 bucket delivery • High-resolution maps, perfect for health impact assessments, research publications, or integration into insurance models.

We provide both historical and real-time data. An API is currently in development, and we welcome custom requests and early access partnerships.

Fully anonymized and GDPR-compliant, our dataset is ready to enhance health-focused research, insurance underwriting, and product innovation.

Unlock the power of 164M+ verified locations across 220+ countries with high-precision geospatial data. Featuring 50+ enriched attributes including coordinates, building type, and geometry. Our AI-powered dataset ensures unmatched accuracy through advanced deduplication and enrichment. With 30+ years of industry expertise, we deliver trusted, customizable data solutions for mapping, navigation, urban planning, and marketing, empowering smarter decision-making and strategic growth.

Key use cases of Geospatial data have helped our customers in several areas:

1. Gain a Competitive Edge with Smarter Mapping : Use geospatial data to analyse competitors, identify high-traffic zones, and optimize locations for maximum impact.
2. Enhance Navigation & Location-Based Engagement : Improve turn-by-turn navigation, EV charging station discovery, and real-time travel insights for seamless customer experiences.
3. Find High-Value Locations for Business Growth : Leverage geospatial intelligence to select profitable retail sites, franchise locations, and warehouses with precision.
4. Streamline Deliveries & Address Validation : Improve shipping accuracy, reduce failed deliveries, and optimize courier routes for better customer satisfaction.
5. Drive Smarter Decisions with Spatial Analysis : Utilize location intelligence for disaster risk assessment, public health campaigns, and agricultural planning.

Wildfire Insurance Analytics Market Outlook

According to our latest research, the global wildfire insurance analytics market size in 2024 reached USD 1.7 billion, with a robust compound annual growth rate (CAGR) of 18.2% expected from 2025 to 2033. By the end of 2033, the market is projected to attain a value of USD 8.1 billion. This growth is primarily driven by the increasing frequency and severity of wildfires globally, which has heightened the need for advanced analytics solutions to enhance risk assessment, claims management, and pricing strategies within the insurance sector. As per our latest research, the industry is witnessing a significant transformation as insurers adopt cutting-edge analytics platforms to mitigate losses and streamline operations in the wake of escalating wildfire risks.

One of the most significant growth factors for the wildfire insurance analytics market is the rising incidence and intensity of wildfires, particularly in regions such as North America, Australia, and Southern Europe. Climate change is contributing to longer fire seasons and more unpredictable fire patterns, resulting in substantial losses for property owners and insurance companies alike. This escalation in risk has prompted insurers to seek advanced analytical tools capable of integrating satellite imagery, real-time weather data, and historical loss records to better predict wildfire behavior and potential losses. The ability to leverage big data and AI-driven insights enables insurers to make more informed decisions regarding policy pricing, coverage limits, and risk mitigation strategies, thereby enhancing both profitability and customer satisfaction.

Another key driver fueling the expansion of the wildfire insurance analytics market is the growing adoption of cloud-based analytics platforms. Cloud deployment offers scalability, flexibility, and real-time data processing capabilities, which are essential for managing the vast and complex datasets associated with wildfire risk assessment. Insurance providers are increasingly migrating their analytics operations to the cloud to reduce infrastructure costs, improve collaboration, and accelerate the deployment of new analytical models. This shift is further supported by advancements in machine learning and geospatial analytics, which allow insurers to deliver personalized risk assessments and automate claims processing, ultimately improving operational efficiency and reducing fraudulent claims.

Regulatory pressures and evolving industry standards are also playing a pivotal role in shaping the wildfire insurance analytics market. Governments and regulatory bodies are mandating more rigorous risk assessment and reporting practices, compelling insurance companies to invest in sophisticated analytics solutions that ensure compliance with new guidelines. Additionally, the integration of analytics with Internet of Things (IoT) devices, such as remote sensors and drones, is enabling insurers to monitor wildfire-prone areas more effectively, gather real-time data, and initiate proactive loss prevention measures. These factors collectively underscore the critical importance of analytics in modern wildfire insurance operations, driving sustained market growth over the forecast period.

From a regional perspective, North America continues to dominate the wildfire insurance analytics market, accounting for the largest share in 2024 due to its high exposure to wildfire risks, advanced technological infrastructure, and proactive regulatory environment. Europe is also witnessing significant growth, particularly in Mediterranean countries that are increasingly vulnerable to wildfires. Meanwhile, the Asia Pacific region is emerging as a lucrative market, driven by rising awareness, expanding insurance penetration, and government initiatives to enhance disaster preparedness. Latin America and the Middle East & Africa are gradually adopting analytics solutions as wildfire risks become more pronounced, though these regions currently represent smaller shares of the global market.

Component Analysis</h2

Here are a few use cases for this project:

1. Construction Site Safety: The model can be used in a real-time monitoring system at construction sites. It can alert workers or operators if equipment fronts (either moving towards them or stationed near them) pose an immediate danger, helping to prevent accidents and improve on-site safety.

2. Autonomous Construction Machinery: This model can be integrated into self-driving construction machinery for avoiding each other as well as workers on the field. Identifying different equipment fronts can help autonomous machines navigate safely and efficiently in a dynamic construction environment.

3. Training AI and VR Simulators: The model can be used to train AI-based or VR training simulators, which can help novice operators understand the potential risks associated with different types of construction equipment. This can provide practical, risk-free training experience and promote understanding of collision prevention procedures.

4. Insurance Risk Assessment: Insurance companies can use this model to assess the potential risks on a construction site and calculate premiums more accurately. These assessments can be informed by the number and type of equipment, their potential for collision based on their location and operation, and the safety measures in place.

5. Digital Twin Creation: The model can be used in creating digital twins of real-world construction sites, which can support strategic planning, safety measures, and emergency response plans. By detecting possible anti-collision issues in the virtual environment, teams can preemptively address potential issues before they become real-world problems.

Snapshot img

Insurance Software Market Size 2025-2029

The insurance software market size is forecast to increase by USD 9.87 billion, at a CAGR of 9.3% between 2024 and 2029.

The market is experiencing significant growth and transformation, driven by increasing government regulations mandating insurance coverage in developing countries and the integration of wearables into customer engagement metrics for life insurance. These trends reflect a growing emphasis on risk management and personalized customer experiences. However, the market also faces challenges, including a tightening regulatory environment for insurance players. Compliance with evolving regulations is essential to maintain market position and mitigate potential penalties. Additionally, the integration of wearables presents opportunities for more accurate risk assessment and personalized pricing, but also raises concerns around data privacy and security.
To capitalize on market opportunities and navigate challenges effectively, insurance providers must stay informed of regulatory changes and invest in robust data security measures. By embracing technology and adapting to regulatory requirements, insurers can enhance their offerings and build stronger relationships with customers.

What will be the Size of the Insurance Software Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

The market continues to evolve, with dynamic market activities shaping its landscape. Entities reporting and analytics, user experience (UX), regulatory reporting, integration APIs, database management, machine learning (ML), data security, cloud computing, data privacy, sales management, and various other components are increasingly integrated to offer comprehensive solutions. Policy issuance, customer portals, document management, and broker management are seamlessly integrated into the policy lifecycle, enabling efficient and effective operations. Predictive analytics, microservices architecture, and agile development are transforming the industry, allowing insurers to make data-driven decisions and respond quickly to market trends. User interface (UI) and mobile applications are essential for enhancing the customer experience, while API integrations and sales force automation streamline internal processes.

Actuarial modeling, billing systems, quality assurance (QA), commission management, and premium calculation are crucial for accurate risk assessment and pricing. Data analytics, claims management, reporting & analytics, and machine learning (ML) are at the forefront of innovation, enabling insurers to detect fraud, process claims efficiently, and gain valuable insights from vast amounts of data. Data security, cloud computing, and data privacy are paramount in ensuring the protection of sensitive information. The ongoing evolution of the market reflects the industry's commitment to meeting the ever-changing needs of customers and regulatory requirements. The integration of these advanced technologies and processes will continue to reshape the market, offering new opportunities for growth and efficiency.

How is this Insurance Software Industry segmented?

The insurance software industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  On-premises
  Cloud-based


Type

  Life insurance
  Accident and health insurance
  Property and casualty insurance
  Others


End-user

  Insurance companies
  Agencies
  Brokers


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  Rest of World (ROW)

By Deployment Insights

The on-premises segment is estimated to witness significant growth during the forecast period.

The market is witnessing significant growth due to the adoption of advanced technologies such as predictive analytics, microservices architecture, and artificial intelligence (AI) in policy administration, claims management, and risk management. Customer portals and document management systems facilitate seamless interaction between insurers and policyholders, enhancing the user experience (UX). Policy issuance and renewal management are streamlined through API integrations and agile development, enabling real-time processing. Mobility is a key trend, with insurers developing mobile applications to cater to the growing demand for on-the-go access to insurance services. Data analytics and regulatory reporting are essential components, ensuring compliance with industry regulations and providing valuable insights for strategic decision-making.

Policy lifecycle

Introduction

This Egyptian Arabic Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of Arabic speech recognition, spoken language understanding, and conversational AI systems. With 40 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 40 Hours of dual-channel call center conversations between native Egyptian Arabic speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

Introduction

This Hindi Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of Hindi speech recognition, spoken language understanding, and conversational AI systems. With 30 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 30 Hours of dual-channel call center conversations between native Hindi speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

Introduction

This Tamil Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of Tamil speech recognition, spoken language understanding, and conversational AI systems. With 30 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 30 Hours of dual-channel call center conversations between native Tamil speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

Snapshot img

Big Data Security Market Size 2025-2029

The big data security market size is forecast to increase by USD 23.9 billion, at a CAGR of 15.7% between 2024 and 2029.

The market is driven by stringent regulations mandating data protection and an increasing focus on automation in big data security. With the growing volume and complexity of data, organizations are investing significantly in advanced security solutions to mitigate risks and ensure compliance. However, implementing these solutions comes with high financial requirements, posing a challenge for smaller businesses and budget-constrained organizations. Regulations, such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), have intensified the need for robust data security measures. These regulations demand that organizations protect sensitive data from unauthorized access, use, or disclosure.
As a result, companies are investing in big data security solutions that offer advanced encryption, access control, and threat detection capabilities. Another trend in the market is the automation of big data security processes. With the increasing volume and velocity of data, manual security processes are no longer sufficient. Automation helps organizations to respond quickly to threats and maintain continuous security monitoring. However, the high cost of implementing and maintaining these automated solutions can be a significant challenge for many organizations. Intruders, ransomware attacks, unauthorized users, and other threats pose a constant risk to valuable information, intellectual property (IP), and transactional data.

What will be the Size of the Big Data Security Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free Sample

The market continues to evolve, driven by the increasing volume and complexity of data being generated and collected across various sectors. Data governance is a critical aspect of this market, ensuring the secure handling and protection of valuable information. Blue teaming, a collaborative approach to cybersecurity, plays a crucial role in identifying and mitigating threats in real-time. Risk assessment and incident response are ongoing processes that help organizations prepare for and respond to data breaches. Security monitoring, powered by advanced technologies like AI in cybersecurity, plays a vital role in detecting and responding to threats. Data masking and anonymization are essential techniques for protecting sensitive data while maintaining its usability.

Network security, cloud security, and database security are key areas of focus, with ongoing threats requiring continuous vigilance. Threat intelligence and vulnerability management help organizations stay informed about potential risks and prioritize their response efforts. Disaster recovery and business continuity planning are also essential components of a robust security strategy. Cybersecurity insurance, security auditing, access control, penetration testing, and vulnerability scanning are additional services that help organizations fortify their defenses. Zero trust security and application security are emerging areas of focus, reflecting the evolving threat landscape. The market dynamics in this space are continuously unfolding, with new challenges and solutions emerging regularly.

How is this Big Data Security Industry segmented?

The big data security industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Deployment

  On-premises
  Cloud-based


End-user

  Large enterprises
  SMEs


Solution

  Software
  Services


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Spain
    UK


  APAC

    China
    India
    Japan


  Rest of World (ROW)

By Deployment Insights

The On-premises segment is estimated to witness significant growth during the forecast period. The market: Evolution and Trends in Enterprise Computing Big Data Security encompasses a range of technologies and practices designed to protect an organization's valuable data. Traditional on-premises servers form the backbone of many enterprise data infrastructures, with businesses owning and managing their hardware and software. These infrastructures include servers and storage units, located at secure sites, requiring specialized IT support for maintenance. Data security in this context is a top priority. Companies must establish user access policies, install firewalls and antivirus software, and apply security patches promptly. Network security is crucial, with vulnerability management and threat

Key Features of the Premium Dataset: In addition to the core data found in the Basic Dataset, the Premium Dataset includes the following exclusive variables:

•  Parking Availability – Information on available parking spaces, crucial for understanding accessibility and shopper convenience.
•  Shopping Center Tenants Count – The number of tenants within a shopping center, providing insights into size, tenant diversity, and business activity.
•  Actual Gross Leasable Area (GLA) in Square Footage – Accurate measurements of leasable space, allowing for better property comparisons and evaluations.
•  ICSC Shopping Center Classifications – Categorization based on International Council of Shopping Centers (ICSC) standards, helping users distinguish between different types of retail centers, from regional malls to neighborhood centers.

Benefits of the Premium Dataset:

By incorporating these additional data points, CAP’s Premium Dataset supports a wide range of use cases, including: • Retail Expansion & Site Selection – Retailers can analyze tenant distribution, parking availability, and shopping center classifications to identify ideal locations for expansion. • Real Estate Investment & Development – Investors and developers gain valuable insights into shopping center sizes, tenant compositions, and classification trends to inform property acquisition and development decisions. • Competitive & Market Analysis – Businesses and analysts can compare shopping centers across multiple metrics, assess competition, and understand local market dynamics with greater precision.

With its enhanced level of detail, the Premium USA & Canada Shopping Centers Dataset is an essential tool for retailers, real estate professionals, investors, and market researchers looking to make data-driven decisions with confidence.

Credit Assessment Sample Dataset

Overview Our Credit Opinion will provide you with a credit recommendation based on a complete risk analysis from the global leaders in Trade Credit Insurance. Our Credit Opinion: Provides credit limit recommendations based on extensive risk analysis. Supports accurate credit decisions and financial risk mitigation. Helps in selecting reliable partners to support your business growth.

Dataset Features

Structured credit assessment format with standardized decision codes Multi-currency support for international applications Company-specific credit limit recommendations Clear explanations of financial risk factors

Important Note This is a demonstration sample only. Our actual credit assessment system: Coverage of +200 markets Supports additional currencies beyond those shown

Learn More For a complete demonstration of our credit assessment capabilities or to discuss how our system can be integrated with your existing processes, please visit https://business-information.coface.com/credit-opinions to request additional information.

A. Usecase/Applications possible with the data:

1. Keep yourself updated- You can fetch and store daily updates of legal cases from multiple courts of your choice, allowing you to be informed about ongoing and pending cases.

2. Keep a check on your clients- You can make searches about your clients by using their names or case numbers to see if their legal cases are open across multiple courts. You can also build your client base as you go along.

3. Systematize your services- Fetch, store, and organize data of various legal cases from multiple sources of your choice to systematically optimize your services by searching for repeated clients or cases. You can do so by a. Searching for your client in multiple databases b. Grouping similar pending legal cases c. Putting forth your service for cases that lack attorneys

How does it work?

• Analyze sample data
• Customize parameters to suit your needs
• Add to your projects
• Contact support for further customization

Leverage high-quality B2B data with 468 enriched attributes, covering firmographics, financial stability, and industry classifications. Our AI-optimized dataset ensures accuracy through advanced deduplication and continuous updates. With 30+ years of expertise and 1,100+ trusted sources, we provide fully compliant, structured business data to power lead generation, risk assessment, CRM enrichment, market research, and more.

Key use cases of B2B Data have helped our customers in several areas :

1. Boost Lead Generation & Sales Outreach : Target the right businesses with precise, segmented contact lists for cold calling, email marketing, and industry-specific campaigns.
2. Enhance CRM & Web Data for Smarter Engagement : Enrich CRM records with instant access to detailed company profiles, visitor identification, and continuous data updates.
3. Strengthen Risk Assessment & Fraud Prevention : Evaluate supplier reliability, assess credit risk, and prevent fraud with deep firmographic and financial insights.
4. Gain a Competitive Edge with Market Research : Analyse industry trends, benchmark competitors, and identify automation-ready sectors for strategic positioning.
5. Optimize B2B Strategies with AI-Powered Insights : Leverage structured, compliant data to drive smarter business decisions across sales, marketing, and operations.

Access 43M+ high-precision building footprints across the United States of America, enabling advanced mapping, location analysis, and strategic decision-making. With 30+ years of data expertise, we provide clean, validated, and enriched datasets to power businesses worldwide.

• Expand market reach with global-scale, high-precision data.
• Enhance mapping, navigation, and spatial analysis.
• Optimize site selection, urban planning, and infrastructure development.
• Improve logistics, delivery routes, and network optimization.
• Assess property values, competitor landscapes, and demographic trends.
• Strengthen disaster management and risk assessment with reliable insights.
• Leverage AI-driven enrichment for deeper, data-driven decision-making.

Our use cases demonstrate how our data has been beneficial and helped our customers in several key areas:

1. Gain a Competitive Edge with Smarter Mapping: Use building footprint data to analyse competitors, identify high-traffic areas, and optimize locations for maximum market impact.
2. Enhance Navigation & Last-Mile Efficiency: Improve customer experiences with precise building entrances, parking areas, and optimized routes for seamless navigation and delivery.
3. Find the Perfect Site for Growth: Leverage building footprint data to select prime locations, maximize foot traffic, and drive higher sales.
4. Optimize Energy & Infrastructure Planning: Assess rooftop solar potential, utility networks, and energy distribution for smarter, more efficient urban development.
5. Improve Risk Assessment & Security: Use precise building data for insurance underwriting, security planning, and crime prevention strategies.

Access 4.7M+ high-precision building footprints across the United Kingdom, enabling advanced mapping, location analysis, and strategic decision-making. With 30+ years of data expertise, we provide clean, validated, and enriched datasets to power businesses worldwide.

• Expand market reach with global-scale, high-precision data.
• Enhance mapping, navigation, and spatial analysis.
• Optimize site selection, urban planning, and infrastructure development.
• Improve logistics, delivery routes, and network optimization.
• Assess property values, competitor landscapes, and demographic trends.
• Strengthen disaster management and risk assessment with reliable insights.
• Leverage AI-driven enrichment for deeper, data-driven decision-making.

Our use cases demonstrate how our data has been beneficial and helped our customers in several key areas:

1. Gain a Competitive Edge with Smarter Mapping: Use building footprint data to analyse competitors, identify high-traffic areas, and optimize locations for maximum market impact.
2. Enhance Navigation & Last-Mile Efficiency: Improve customer experiences with precise building entrances, parking areas, and optimized routes for seamless navigation and delivery.
3. Find the Perfect Site for Growth: Leverage building footprint data to select prime locations, maximize foot traffic, and drive higher sales.
4. Optimize Energy & Infrastructure Planning: Assess rooftop solar potential, utility networks, and energy distribution for smarter, more efficient urban development.
5. Improve Risk Assessment & Security: Use precise building data for insurance underwriting, security planning, and crime prevention strategies.

Street Noise-Level Dataset

Silencio’s Street Noise-Level Dataset offers unique access to hyper-local, real-world noise exposure data across more than 200 countries. Built from over 35 billion datapoints, collected via our mobile app and enriched with AI-powered interpolation, this dataset delivers detailed average noise levels (dBA) at the street and neighborhood level.

Chronic noise exposure is a growing public health concern linked to stress, cardiovascular risks, sleep disorders, and reduced quality of life — all of which are increasingly relevant for public health studies, insurance risk modeling, and wellness program design. Silencio’s data allows buyers to quantify environmental noise exposure and incorporate it into risk assessments, premium modeling, urban health studies, and wellness product development.

In addition to objective noise measurements, Silencio provides access to the world’s largest noise complaint database, offering complementary subjective insights directly from communities, enabling more precise correlations between noise exposure and health outcomes.

Data is available as: • CSV exports • S3 bucket delivery • High-resolution maps, perfect for health impact assessments, research publications, or integration into insurance models.

We provide both historical and real-time data. An API is currently in development, and we welcome custom requests and early access partnerships.

Fully anonymized and GDPR-compliant, our dataset is ready to enhance health-focused research, insurance underwriting, and product innovation.

At Driver Technologies, we are dedicated to harnessing advanced technology to gather anonymized critical driving data through our innovative dash cam app, which operates seamlessly on end users' smartphones. Our Tailgating Insurance Data offering is a key resource for understanding driver behavior and improving safety on the roads, making it an essential tool for various industries.

What Makes Our Data Unique? Our Tailgating Insurance Data is distinguished by its real-time collection capabilities, utilizing our built-in computer vision technology to identify and capture instances where a driver tailgates the vehicle in front. This data reflects critical safety events that are indicative of potential risks and non-compliance with traffic regulations. By providing data on these significant events, our dataset empowers clients to perform in-depth analysis.

How Is the Data Generally Sourced? Our data is sourced directly from users who utilize our dash cam app, which harnesses the smartphone’s camera and sensors to record during a trip. This direct sourcing method ensures that our data is unbiased and represents a wide variety of conditions and environments. The data is not only authentic and reflective of current road conditions but is also abundant in volume, offering millions of miles of recorded trips that cover diverse scenarios.

Primary Use-Cases and Verticals Driver Behavior Analysis: Organizations can leverage our dataset to analyze driving habits and identify trends in driver behavior. This analysis can help in understanding patterns related to rule compliance and potential risk factors.

Training Computer Vision Models: Clients can utilize our annotated data to develop and refine their own computer vision models for applications in autonomous vehicles, ensuring better decision-making capabilities in complex driving environments.

Improving Risk Assessment: Insurers can utilize our dataset to refine their risk assessment models. By understanding the frequency and context of significant events, they can better evaluate driver risk profiles, leading to more accurate premium pricing and improved underwriting processes.

Integration with Our Broader Data Offering The Tailgating Insurance Data is a crucial component of our broader data offerings at Driver Technologies. It complements our extensive library of driving data collected from various vehicles and road users, creating a comprehensive data ecosystem that supports multiple verticals, including insurance, automotive technology, and smart city planning.

In summary, Driver Technologies' Tailgating Insurance Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation across mobility. By integrating our Tailgating Insurance Data with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.

Sample Data: https://cloud.drivertechnologies.com/shared?s=85&t=1:35&token=3c7a4481-c4ae-4532-a075-30de49999a5f

At Driver Technologies, we are dedicated to harnessing advanced technology to gather anonymized critical driving data through our innovative dash cam app, which operates seamlessly on end users' smartphones. Our High Speed Insurance Data offering is a key resource for understanding driver behavior and improving safety on the roads, making it an essential tool for various industries.

What Makes Our Data Unique? Our High Speed Insurance Data is distinguished by its real-time collection capabilities, utilizing the built-in accelerometer and gyroscope sensors of smartphones to capture telematics during driving. This data reflects instances of high speed events, which are key indicators of aggressive driving behavior and potential risks on the road. Through our dataset, gain access to videos, processed through our computer vision model, of particular events and/or a telematics-only trip with an instance of a significant event. By providing data on significant events, our dataset empowers clients to perform in-depth analysis.

How Is the Data Generally Sourced? The data is sourced directly from users who use our dash cam app. As users drive, our app monitors and records telematics data, ensuring that the information is both authentic and representative of real-world driving conditions.

Primary Use-Cases and Verticals Driver Behavior Analysis: Organizations can leverage our telematics data to analyze driving habits and identify trends in aggressive driving behavior. Improving Risk Assessment: Insurers can utilize our dataset to refine their risk assessment models. By understanding the frequency and context of significant events, they can better evaluate driver risk profiles, leading to more accurate premium pricing and improved underwriting processes.

Integration with Our Broader Data Offering The High Speed Insurance Data is a crucial component of our broader data offerings at Driver Technologies. It complements our extensive library of driving data collected from various vehicles and road users, creating a comprehensive data ecosystem that supports multiple verticals, including insurance, automotive technology, and smart city planning.

In summary, Driver Technologies' High Speed Insurance Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation across mobility. By integrating our High Speed Insurance Data with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.

The Future landslides Hazard dataset offers a global, high-resolution (0.1°) view of changing landslides risk from 1981 to 2100. It provides three key indicators derived from a susceptibility map and a rainfall Index to estimate potential rainfall triggered landslides:

-Moderate hazard: Annual count of days with potential landslide occurrence over moderate hazard locations. - High hazard: Annual count of days with potential landslide occurrence over high hazard locations. - Very high hazard: Annual count of days with potential landslide occurrence over very high hazard locations.

These indicators capture both long-term trends and increasing frequency, offering actionable insights into how rainfall triggered landslides conditions may evolve with climate change.

The dataset is built from five widely used global climate models and aligned with three IPCC emissions scenarios (SSP1-2.6, SSP2-4.5, SSP5-8.5), representing a range of possible futures from low to high emissions. It supports climate risk assessments, investment planning, and resilience strategies across sectors exposed to landslides hazards, including insurance, energy, real estate, and infrastructure. Professionals can apply the dataset to assess future exposure, prioritize asset protection, design infrastructure for high-risk areas, or evaluate vulnerability in operations and supply chains.

Data are delivered in NetCDF format, with CSV or GeoTIFF versions available on request.

Success.ai’s KYB (Know Your Business) Data for Businesses Worldwide provides a reliable dataset tailored to streamline compliance processes and enable businesses to connect with small business leaders across the major markets of the world. This dataset offers verified compliance details, firmographic data, and leadership profiles to help companies meet regulatory requirements, evaluate partnerships, and build relationships with small business owners.

With access to over 170 million verified professional profiles and 30 million company profiles, Success.ai ensures that your outreach and compliance initiatives are powered by accurate, continuously updated, and AI-validated data.

Supported by our Best Price Guarantee, this solution is an essential resource for businesses engaging with the Global business community.

Why Choose Success.ai’s KYB Data?

1. Verified Compliance and Business Data

   • Access verified registration details, compliance statuses, and ownership structures for small businesses across various industries.
   • AI-driven validation ensures 99% accuracy, minimizing errors and ensuring reliable business evaluations.

2. Comprehensive Coverage of Global Businesses

   • Includes profiles of small businesses from local service providers and independent retailers to tech startups and family-owned enterprises.
   • Gain insights into regional business trends, operational structures, and growth trajectories.

3. Continuously Updated Datasets

   • Real-time updates reflect changes in ownership, compliance statuses, operational details, and leadership roles.
   • Stay aligned with evolving market conditions and regulatory requirements.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible data use and legal compliance for your campaigns.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with small business owners, managing directors, and decision-makers across the Globe.
• 30M Company Profiles: Access firmographic data, including company sizes, revenue ranges, and geographic footprints.
• Verified Compliance Data: Ensure partnerships align with regulatory requirements by verifying business registrations and compliance statuses.
• Leadership Contact Details: Connect directly with owners, partners, and directors shaping business strategy and operations.

Key Features of the Dataset:

1. KYB Compliance Profiles

   • Access detailed compliance data, including business registrations, licenses, and adherence to local and federal regulations.
   • Identify verified businesses that meet your company’s partnership and compliance standards.

2. Leadership and Decision-Maker Insights

   • Connect with owners, managing partners, and executive leaders driving business decisions in small enterprises.
   • Target professionals responsible for compliance, operations, and strategic growth.

3. Advanced Filters for Precision Targeting

   • Filter businesses by industry, geographic location, company size, or revenue range to focus on high-value prospects.
   • Tailor outreach to address specific business needs such as regulatory challenges, operational optimization, or market expansion.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and improve engagement outcomes.

Strategic Use Cases:

1. Compliance and Risk Mitigation

   • Verify small businesses’ compliance with local, state, and federal regulations to ensure secure partnerships and transactions.
   • Present tools and services that simplify compliance reporting, risk assessments, and regulatory adherence.

2. Vendor and Partnership Evaluation

   • Identify businesses with reliable compliance histories and operational structures suitable for partnerships.
   • Build relationships with verified vendors, distributors, or service providers to support business goals.

3. Sales and Lead Generation

   • Offer solutions, products, or consulting services designed for the operational needs of small businesses in compliance-heavy industries.
   • Target decision-makers managing budgets, vendor selection, and compliance requirements.

4. Market Research and Business Development

   • Analyze trends and challenges facing small businesses to refine product offerings, pricing strategies, and marketing campaigns.
   • Identify emerging industries or high-growth regions to expand your market reach effectively.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality KYB data at competitive prices, ensuring strong ROI for your compliance, marketing, and partnership initiatives.

2. Seamless Integration

   • Integrate verified KYB data into CRM systems, compliance platforms, or analytics tools via APIs or downloadable formats, simplifying workflows and enhancing produc...