Forager.ai's Global Telemarketing Data provides access to 89M mobile phone numbers with an industry-leading 95% accuracy rate. Our phone number database sees comprehensive refreshes every three weeks, ensuring the highest quality of data for all business needs.

| Volume and Stats |

Access to 89M mobile phone numbers, with continuous growth. Unprecedented industry refresh rate of every three weeks for each record. First-party data curation, underpinning many leading sales and marketing platforms. Delivery formats: JSONL, PostgreSQL, CSV.

| Datapoints |

Over 150+ unique datapoints available! Key fields include Mobile Phone Number, Current Title, Current Company Data, Work History, Educational Background, Location, Address, and more. Unique linkage data to other social networks or contact data available.

| Use Cases |

Sales & Marketing Platforms, Linkedin data, Linkedin Database, Data Vendors, Data Purchase, B2B Tech, VCs & PE firms, Data for Marketing Automation, ABM & Intent:

Buy data that powers your customer experiences. Stay updated when professionals change roles, and relay these insights to your customers. Enjoy the benefits of our industry-leading data accuracy. Connect our professional contact data online to your existing database, uncovering new connections to other social networks and contact data. Hashed records also available for advertising use-cases.

| Delivery Options |

Flat files via S3 or GCP PostgreSQL Shared Database PostgreSQL Managed Database REST API Other options available at request, depending on scale required

| Other key features |

150+ Data Fields (available upon request) Free data samples, and evaluation.

Tags: B2B Contact Data, B2C Contact Data, Email Data, Direct Dial Data, Sales & Marketing Data, Professional Contacts, Verified Contact Data, People Data, Work Experience History, Education Data, Workforce Intelligence, Identity Resolution, Talent, Candidate Database, Sales Database, Account Based Marketing, Intent Data, Mobile number data, mobile number database, startup data.

The stakeholder analysis related to automated and autonomous heavy-duty mobile machines (HDMMs) performed in the context of the doctoral research of ESR 1 in the MORE project funded by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 858101

The dataset was part of a mixed methods multiple case study and contains the results of the short survey as well as the interview transcripts. The dataset has been anonymised and/or pseudonymised to maintain context.

The dataset was collected between March 2022 and September 2022.

We seek to mitigate the challenges with web-scraped and off-the-shelf POI data, and provide tailored, complete, and manually verified datasets with Geolancer. Our goal is to help represent the physical world accurately for applications and services dependent on precise POI data, and offer a reliable basis for geospatial analysis and intelligence.

Our POI database is powered by our proprietary POI collection and verification platform, Geolancer, which provides manually verified, authentic, accurate, and up-to-date POI datasets.

Enrich your geospatial applications with a contextual layer of comprehensive and actionable information on landmarks, key features, business areas, and many more granular, on-demand attributes. We offer on-demand data collection and verification services that fit unique use cases and business requirements. Using our advanced data acquisition techniques, we build and offer tailormade POI datasets. Combined with our expertise in location data solutions, we can be a holistic data partner for our customers.

KEY FEATURES - Our proprietary, industry-leading manual verification platform Geolancer delivers up-to-date, authentic data points

• POI-as-a-Service with on-demand verification and collection in 170+ countries leveraging our network of 1M+ contributors

• Customise your feed by specific refresh rate, location, country, category, and brand based on your specific needs

• Data Noise Filtering Algorithms normalise and de-dupe POI data that is ready for analysis with minimal preparation

DATA QUALITY

Quadrant’s POI data are manually collected and verified by Geolancers. Our network of freelancers, maps cities and neighborhoods adding and updating POIs on our proprietary app Geolancer on their smartphone. Compared to other methods, this process guarantees accuracy and promises a healthy stream of POI data. This method of data collection also steers clear of infringement on users’ privacy and sale of their location data. These purpose-built apps do not store, collect, or share any data other than the physical location (without tying context back to an actual human being and their mobile device).

USE CASES

The main goal of POI data is to identify a place of interest, establish its accurate location, and help businesses understand the happenings around that place to make better, well-informed decisions. POI can be essential in assessing competition, improving operational efficiency, planning the expansion of your business, and more.

It can be used by businesses to power their apps and platforms for last-mile delivery, navigation, mapping, logistics, and more. Combined with mobility data, POI data can be employed by retail outlets to monitor traffic to one of their sites or of their competitors. Logistics businesses can save costs and improve customer experience with accurate address data. Real estate companies use POI data for site selection and project planning based on market potential. Governments can use POI data to enforce regulations, monitor public health and well-being, plan public infrastructure and services, and more. A few common and widespread use cases of POI data are:

• Navigation and mapping for digital marketplaces and apps.
• Logistics for online shopping, food delivery, last-mile delivery, and more.
• Improving operational efficiency for rideshare and transportation platforms.
• Demographic and human mobility studies for market consumption and competitive analysis.
• Market assessment, site selection, and business expansion.
• Disaster management and urban mapping for public welfare.
• Advertising and marketing deployment and ROI assessment.
• Real-estate mapping for online sales and renting platforms.About Geolancer

ABOUT GEOLANCER

Quadrant's POI-as-a-Service is powered by Geolancer, our industry-leading manual verification project. Geolancers, equipped with a smartphone running our proprietary app, manually add and verify POI data points, ensuring accuracy and authenticity. Geolancer helps data buyers acquire data with the update frequency suited for their specific use case.

The Internet of things (IoT) has emerged as a topic of intense interest among the research and industrial community as it has had a revolutionary impact on human life. The rapid growth of IoT technology has revolutionized human life by inaugurating the concept of smart devices, smart healthcare, smart industry, smart city, smart grid, among others. IoT devices’ security has become a serious concern nowadays, especially for the healthcare domain, where recent attacks exposed damaging IoT security vulnerabilities. Traditional network security solutions are well established. However, due to the resource constraint property of IoT devices and the distinct behavior of IoT protocols, the existing security mechanisms cannot be deployed directly for securing the IoT devices and network from the cyber-attacks. To enhance the level of security for IoT, researchers need IoT-specific tools, methods, and datasets. To address the mentioned problem, we provide a framework for developing IoT context-aware security solutions to detect malicious traffic in IoT use cases. The proposed framework consists of a newly created, open-source IoT data generator tool named IoT-Flock. The IoT-Flock tool allows researchers to develop an IoT use-case comprised of both normal and malicious IoT devices and generate traffic. Additionally, the proposed framework provides an open-source utility for converting the captured traffic generated by IoT-Flock into an IoT dataset. Using the proposed framework in this research, we first generated an IoT healthcare dataset which comprises both normal and IoT attack traffic. Afterwards, we applied different machine learning techniques to the generated dataset to detect the cyber-attacks and protect the healthcare system from cyber-attacks. The proposed framework will help in developing the context-aware IoT security solutions, especially for a sensitive use case like IoT healthcare environment.