Sports Data Low-Latency Feed Market Outlook

According to our latest research, the market size of the global Sports Data Low-Latency Feed Market reached USD 1.47 billion in 2024. Registering robust momentum, the sector is expected to grow at a CAGR of 16.8% during the forecast period, reaching a projected value of USD 4.38 billion by 2033. The primary growth driver is the surging demand for real-time analytics and instant data delivery across sports betting, broadcasting, and team performance analysis, as organizations and platforms compete to deliver the fastest, most accurate, and engaging experiences to their audiences and stakeholders.

A significant growth factor for the Sports Data Low-Latency Feed Market is the exponential rise in digital sports consumption and interactive fan engagement. As live sports streaming and digital platforms proliferate, fans expect instant access to real-time statistics, scores, and play-by-play data. This demand is particularly pronounced in the realm of sports betting and fantasy sports, where split-second data delivery can impact betting outcomes and fantasy league scores. The integration of ultra-low-latency data feeds enables platforms to offer dynamic odds, live in-play betting, and real-time fantasy updates, creating a seamless and immersive user experience. Additionally, the growing adoption of 5G networks and edge computing technologies is further enhancing the speed and reliability of data transmission, thereby fueling market expansion.

Another pivotal growth driver is the increasing integration of advanced analytics and artificial intelligence in sports team performance analysis. Professional teams and coaches are leveraging low-latency feeds to access granular, real-time data on player movements, biometrics, and in-game events. This data-driven approach allows for immediate tactical adjustments, injury prevention, and optimized training regimens, giving teams a competitive edge. The proliferation of wearable sensors and IoT devices in professional sports is generating vast volumes of actionable data, necessitating robust low-latency infrastructure to process and deliver insights instantaneously. This trend is not limited to elite leagues; even amateur and semi-professional teams are adopting these solutions to enhance performance and scouting, broadening the market’s reach.

The evolving regulatory landscape and the expansion of legalized sports betting across various jurisdictions are also propelling market growth. Governments and regulatory bodies are increasingly recognizing the economic benefits of regulated sports betting, leading to broader market access and heightened competition among betting operators. This has intensified the need for reliable, ultra-fast data feeds to ensure transparency, integrity, and fairness in betting activities. Furthermore, partnerships between sports leagues, data providers, and betting companies are becoming more prevalent, fostering innovation and the development of proprietary low-latency solutions tailored to specific sports and markets. The convergence of these factors is creating a fertile environment for sustained growth in the Sports Data Low-Latency Feed Market.

From a regional perspective, North America and Europe currently dominate the market, driven by mature sports ecosystems, high digital penetration, and early adoption of low-latency technologies. However, Asia Pacific is emerging as a high-growth region, fueled by the rapid expansion of digital infrastructure, rising sports viewership, and the legalization of sports betting in key markets. Latin America and the Middle East & Africa are also witnessing increased investment in sports technology, albeit from a smaller base, as sports organizations and broadcasters seek to enhance fan engagement and operational efficiency. The global outlook remains highly positive, with all regions poised to benefit from ongoing technological advancements and evolving consumer preferences.

Component Analysis

The Component&l

Sports API Market Outlook

According to our latest research, the global Sports API market size reached USD 2.84 billion in 2024, reflecting robust momentum driven by the digital transformation of the sports industry. The market is projected to expand at a CAGR of 9.8% from 2025 to 2033, culminating in a forecasted market size of USD 6.62 billion by 2033. This growth trajectory is underpinned by rising demand for real-time sports data integration, the proliferation of fantasy sports platforms, and increasing investments in digital fan engagement solutions. As per our latest research, technology adoption and evolving consumer expectations are the primary catalysts accelerating the global Sports API market.

The surging adoption of digital platforms across the sports ecosystem is a significant growth driver for the Sports API market. Sports organizations, broadcasters, and third-party app developers are increasingly leveraging APIs to deliver seamless access to live scores, player statistics, and other critical data. The shift towards immersive fan experiences has compelled stakeholders to prioritize real-time data delivery, which is only possible through robust API infrastructures. The proliferation of connected devices and mobile applications has further fueled the need for efficient data exchange, making Sports APIs indispensable for enhancing audience engagement and monetization strategies. As sports fans demand instant updates and interactive content, the integration of APIs ensures that data flows smoothly across multiple digital touchpoints, supporting the evolution of fan-centric business models.

Another key factor propelling the Sports API market is the exponential growth of fantasy sports and sports betting platforms. These sectors rely heavily on accurate, up-to-the-second data feeds, including player performance metrics, live scores, and match outcomes. APIs serve as the backbone for aggregating and distributing this data, enabling real-time analytics, predictive modeling, and customized user experiences. The regulatory landscape for sports betting is also evolving, with more countries legalizing online betting, which in turn increases the demand for secure and reliable Sports API solutions. Furthermore, advancements in artificial intelligence and machine learning are being integrated with Sports APIs, empowering developers to deliver smarter recommendations, automated insights, and advanced analytics for end-users. This technological convergence is expected to further amplify market growth over the forecast period.

The transformation of media and broadcasting is another pivotal growth factor for the Sports API market. Traditional sports broadcasting is being rapidly replaced by over-the-top (OTT) streaming services and digital platforms, which rely on APIs for real-time content delivery, personalization, and syndication. Media companies are investing in API-powered solutions to enhance their coverage, offer interactive features, and cater to a global audience with multilingual and multi-device support. Moreover, APIs allow broadcasters to integrate third-party content, such as betting odds, social media feeds, and user-generated content, creating a comprehensive and engaging viewing experience. The growing competition among broadcasters to provide differentiated services is expected to drive sustained investments in Sports API technologies, solidifying their role as a critical enabler of digital transformation in sports media.

From a regional perspective, North America continues to dominate the Sports API market, accounting for the largest share in 2024, followed by Europe and Asia Pacific. The region’s leadership is attributed to the presence of established sports leagues, advanced digital infrastructure, and a vibrant ecosystem of technology providers and sports organizations. Europe is witnessing rapid adoption due to the popularity of football, cricket, and other sports, coupled with increasing investments in digital fan engagement. Asia Pacific is emerging as a high-growth market, driven by the rising popularity of eSports, expanding internet penetration, and the proliferation of mobile devices. Latin America and the Middle East & Africa are also showing promising growth, albeit from a smaller base, as sports organizations in these regions embrace digital innovation to reach wider audiences and unlock new revenue streams.

Whilst the media and other data sources track reputational and ESG behaviours, NGOs drive them. Changes in government policy, corporate strategic direction or consumer spending habits are frequently a consequence of collective and sustained NGO campaigning.

Hence, tracking NGO signals presents multiple opportunities: - Early discovery of ESG themes that will shape future government, corporate and consumer behaviour. - Early warning of sector and corporate behaviours that are likely to erode shareholder value - Identification of positive sector and corporate behaviours that build shareholder value.

Where other ESG signals are limited to self-reported corporate data, and further limited to those companies compelled to report, NGO data remains an independent source spanning all corporations, regardless of ownership structure.

SIGWATCH is the only data provider to focus exclusively on this critical market signal. SIGWATCH tracks over 9,000 NGOs, with 60,000+ NGO signals spanning the last 10 years, targeting over 18,000 corporations. By going straight to source, rather than media, SIGWATCH’s provides the most extensive and timely NGO data feed on the market.

All NGO signals are tagged, taxonomized and quantified with the end user having access to either scores and/or underlying signal. NGO campaigns are assessed both by sector and by entity – our robust and transparent methodology adjusts signal score based on factors such as NGO influence, signal sentiment and prominence. Tagging includes Tickers (where applicable), ISINs and FIGI codes.

SIGWATCH is used for several use cases: - By quant funds in the search for fresh alpha - By ESG funds for thematic research, screening and monitoring - By corporations for reputation management and third-party monitoring.

SIGWATCH provides both a desktop service and data feeds with several transfer options. Our archive data spans 10+ years, with new signals added daily. The desktop service provides deep analytics capability, facilitating early detection of emerging ESG themes and sector and corporate performance with respect to ESG and broader reputational-impacting activities.

SIGWATCH is offered as multiple products focusing on sector or corporate signals with the option to take just the quantitative assessment or full access to the underlying data.

About this product This specific product provides all quantitative NGO scores by sector. This is offered as a live, daily data service with the facility to choose specific sectors. Historic data can also be purchased, by year.

$25,000 per annum with all S-Ray scores (ESG, GC, Preferences Filter and Temperature Score); $45,000 per annum with scores on 22 sustainability topics

Arabesque’s data-feed service for licensees aiming to integrate the S-Ray scores within investment management, risk management, compliance, and reporting. S-Ray scores include the GC score, ESG score, Preference Filters and Temperature score.

Daily data transfer of Arabesque S-Ray 8000+ companies ~95% of global market capitalization Historic data for 10 years Flat fee, not AuM based

1. OC Waze Partner Hub GeoRSS Cumulative Alert Data from Velocity feed analytics. The data are updated in regular (5-minute) intervals. OC Waze Partner Hub data provide information about traffic jams and events that affect road conditions, either from drivers using Waze, a.k.a. Wazers, or from external sources. Wazers may issue reports from the location at which they are currently located or, if no longer at the location, within 30 minutes after the event occurred. We are also able to provide automatic alerts for what we call Unusual Traffic (or Irregularities) - incidents that affect a large number of users and fall outside the normal traffic patterns for a given day and time.Waze generates traffic jam information by processing the following data sources:Data: includes all traffic data reported by Waze users through the Waze mobile application. Reliability: Each alert gets a reliability score based on other user reactions (‘Thumbs up’, ‘Not there’ etc.) and the level of the reporter (Wazers gain levels by contributing to the map, starting at level 1 and reaching up to level 6. The higher the level, the more experienced and trustworthy the Wazer.) The score (0-10) indicates how reliable the report is. Confidence: Each alert gets a confidence score based on other user reactions (‘Thumbs up’, ‘Not there’). The score ranges between 0 and 10. A higher score indicates more positive feedback from Waze users.2. OC Waze Partner Hub GeoRSS Cumulative Traffic Jam Data from Velocity feed analytics. The data are updated in regular (5-minute) intervals.The traffic jams feed includes data gathered in real time about traffic slowdowns on specific road segments. Waze generates traffic jam information by processing the following data sources: GPS location-points sent from user phones (users who drive while using the app) and calculations of the current average speed vs. free-flow speed (maximum speed measured on the road-segment). For Unusual traffic (irregularities) Waze uses historic average speeds (on 30 minute time-slots). User generated reports - reports shared by Waze users who encounter traffic jams. These appear as regular alerts, and also affect the way we identify and present traffic jams.Original data provided by Waze App. Learn more at Waze.com.

CFA provides data feeds for Incidents, Total Fire Bans & Fire Danger Ratings and latest news.\r
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To receive CFA RSS feeds you need a feed reader and must also subscribe to CFA feeds.\r
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To subscribe to one of the CFA RSS feeds:\r
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Click on the RSS button next to the feed you want (or ctrl, click for Mac users).\r
Copy the URL of the page that is displayed.\r
Paste the address in the appropriate place in your feed reader.\r
Important Note: Some third party readers will not refresh as frequently as is required for live updates. It is recommended that if your reader cannot be set to update at least every 5 minutes that you check the respective web pages (Incidents and Total Fire Bans & Fire Danger Ratings) for the most up to date version.\r
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These RSS feeds conforms to RSS 2.0 specification and update every minute.

Credits - Marques, Brenda ; Rezende, Gabriel; Ferreira, Sabrina; C Maia, Brian; Edwirds, Mathews; Júnior, Valdo; Maciel, Luiz; Chaves, Amália (2022), “Feed Bunk Score Images - FBSI”, Mendeley Data, V1, doi: 10.17632/p9cr67s6jp.1

The Feed Bunk Score Images - FBSI dataset is a set of images of leftover rations on feed bunks in feedlot cattle. These images are used for reading and classification of the bunk score. This technique is a visual assessment and consists of analyzing the amount of feed left in the bunk over the last 24 hours. For each volume of leftovers, specific scores are assigned suggesting adjustments in the amount of feed to be provided during the day, according to the needs of the animals in each herd. The dataset is composed of 1511 images of 6 different scores. These images represent different diet compositions according to the farms. Furthermore, in this dataset, the feed bunks differ in terms of their material and model. Most feed bunks are made of cement, but some are made of wood with the background covered in rubber or plastic material.

ABSTRACT This study evaluated the apparent digestibility coefficients (ADC) of essential (EAA) and non-essential (NEAA) amino acids of 13 ingredients for tambaqui (Colossoma macropomum) diets. Proteic and energetic ingredients were analyzed separately. The trial with energetic and proteic ingredients were arranged in a randomized block design, with four replicates: energetic ingredients (corn, wheat bran, broken rice, and sorghum) with four treatments, whereas proteic ingredients (corn gluten meal, soybean meal, poultry byproduct meal, salmon meal, fish meal [tilapia processing residue], wheat gluten meal, feather meal, cottonseed meal, and alcohol yeast [spray dried]) with nine treatments. Each block was considered as one round of fecal collection. A total of 420 tambaqui juveniles (mean initial weight: 70±8.58 g) were used. Among energetic ingredients, corn (94.6%) and wheat bran (91.9%) had the highest ADCEAA, followed by broken rice (75.7%), and sorghum (72.8%). On average, ADCEAA and ADCNEAA values of proteic ingredients were 79.5-98.5%, except for alcohol yeast (ADCEAA: 68.4 and ADCNEAA: 76.7%). Tryptophan was the first limiting amino acid in most ingredients tested and had the lowest chemical scores (0.06-0.51), except for wheat bran, corn gluten meal, and soybean meal, in which lysine was the first limiting amino acid. Soybean meal had the highest digestible essential amino acid index (EAAI: 1.02) and the most balanced amino acid profile, whereas wheat gluten meal had the lowest EAAI (0.48). Overall, tambaqui was very efficient to digest proteic and energetic ingredients.

About this Dataset

Ratings and reviews data extracted from Sephora. Crawl Feeds team extracted from research and analysis purposes. Last crawled on 3 aug 2022. Total records: 100K+

Category

Ratings & Reviews

Keywords

reviews and ratings,reviews and ratings datasets,reviews dataset,sephora datasets

Row Count

117513

Price

$145.00

This feed contains the fire danger ratings information for today and tomorrow as received from the Bureau of Meteorology as well as total fire ban information.

Title: Peer-to-peer dialogue about teachers’ written feedback enhances students’ understanding on how to improve writing skills A short description of the study set-up: Second-year university students (N=84) participated in a mixed-method study that included questionnaires and focus groups. The intervention comprised face-to-face dialogue in small groups about the participants’ written peer feedback on a draft report. Instruments Questionnaires A pre-intervention questionnaire before the start of the face-to-face dialogue measured students’ beliefs about written peer feedback (part 1). For this purpose, a validated questionnaire by Huisman, Saab, van Driel, et al. (2019) was used to measure four components: 1) degree to which peer feedback is perceived as meaningful and useful (3 items), 2) the degree to which peer feedback is considered an important skill (3 items), 3) confidence in quality of provided peer feedback (2 items) and 4) confidence in quality of received peer feedback (2 items). A five-point Likert scale was employed, ranging from 1 (=‘Completely disagree’ or ‘Completely not applicable to me’) to 5 (=‘Completely agree’ or ‘Completely applicable to me’). In part 2, students rated the presence of written peer feedback in terms of feed-up, feed-back and feed-forward information for which an adjusted version of a validated questionnaire by De Kleijn, Bronkhorst, Meijer, Pilot, and Brekelmans (2016) was used. This part of the questionnaire was also on a five-point Likert scale, ranging from 1 (=‘Agree not at all’) to 5 (=‘Agree a lot’) and contained four items about Feed up, six items about Feed back and five items about Feed forward. The pre-intervention questionnaire also measured the overall instructiveness of the written feedback on a 10-point scale (1=lowest, 10=highest). A post-intervention questionnaire measured students’ perception of improved understanding of the written feedback through face-to-face peer dialogue and the quality of this dialogue in terms of overall instructiveness, which was measured on a 10-point scale. The post-intervention questionnaire also contained items about Feed up (4 items), Feed back (6 items) and Feed forward (5 items). As in the pre-intervention questionnaire, these items were answered on a five-point Likert scale. A pilot study was conducted to test clarity of both pre- and post-intervention questionnaires items. Focus group Students were invited to participate in a focus group, which resulted in two groups of volunteers: N=9 (3 males, 6 females) and N=7 (4 males, 3 females). The participants all originated from different dialogue groups. Semi-structured, post-measurement interviews were conducted to search for explanations as to why dialogue improved students’ understanding and to distinguish important conditions for better understanding. The focus group sessions lasted one hour and were guided by a moderator (first author) while a second member of the research team (fourth author) acted as observer. The moderator and observer did not know the focus group members. Both interviews were audiotaped. Analysis Quantitative analysis Reliability analysis was performed for each subscale of ‘student beliefs’, as well as for Feed up, Feed back and Feed forward. The reliability of the subscales varied from 0.72 to 0.85, which was considered acceptable (Tavakol & Dennick, 2011). For all pre- and post-intervention variables, the median (Mdn) and interquartile range (IQR) was calculated, besides mean (M) and standard deviation (SD). The authors considered a median score equal or above 4.0 (scale 1–5) or 8.0 (scale 1–10) as very positive. A median score equal or below 3.0 (scale 1–5) or 6.0 (scale 1–10) was considered insufficient, while all the other scores were considered to be positive. A non-parametric Wilcoxon signed-rank test was performed to compare scores on ‘Instructiveness of written feedback’ and ‘Instructiveness of face-to-face dialogue’. Non-parametric Wilcoxon signed-rank tests were also performed to compare scores on pre- and post-intervention subscales of Feed up, Feed back and Feed forward. All tests were performed on the 5% level of significance. Qualitative analysis Both focus groups sessions were transcribed verbatim and two authors (moderator and observer) first analysed the transcripts in a theoretically thematic way (Braun & Clarke, 2006). This method involves deductive or top-down analysis, led by the research questions. Making an inventory of phrases related to the explanations and conditions for an improved understanding by dialogue led the analysis of the transcripts. To this end, in the first phase of the analysis and in an iterative process of three separate rounds, both authors formulated a set of themes comprising explanations and conditions. In the next phase, all authors discussed the formulated themes and reached consensus through discussion. Explanation of the data files: what data is stored in what file? The data files contain 84...

Incorporating Combinations of Sentiment Scores Of Financial News And MLP-Regressor For Stock Prediction.#

Junaid Maqbool, Preeti Aggarwal, Ravreet Kaur maqbooljunaid@gmail.com

Abstract.

The stock market is very volatile as it depends on political, financial, environmental, and various internal and external factors along with historical stock data. Such information is available to people through microblogs and news and predicting stock price merely on historical data is hard. The high volatility emphasizes the importance to check the effect of external factors on the stock market. In this paper, we have proposed a machine learning model where the financial news is used along with historical stock price data to predict upcoming prices. The paper has used three algorithms to calculate various sentiment scores and used them in different combinations to understand the impact of financial news on stock price as well the impact of each sentiment scoring algorithm. Experiments have been conducted on ten-year historical stock price data as well financial news of four different companies from different sectors to predict next day and next week stock trend and accuracy metrics were checked for a period of 10, 30, and 100 days. Our model was able to achieve the highest accuracy of 0.90 for both trend and future trend when predicted for 10 days. This paper also performs experiments to check which stock is difficult to predict and which stocks are most influenced by financial news and it was found Tata Motors an automobile company stock prediction has maximum MAPE and hence deviates more from actual prediction as compared to others.

Complete research paper can be found at

Incorporating Financial News Sentiments and MLP-Regressor with Feed-Forward for Stock Market Prediction

Stock Prediction by Integrating Sentiment Scores of Financial News and MLP-Regressor: A Machine Learning Approach

Also the pdf of paper is available in the code file as well the data for citation and references

1. Maqbool, Junaid, Preeti Aggarwal, and Ravreet Kaur. "Incorporating Financial News Sentiments and MLP-Regressor with Feed-Forward for Stock Market Prediction." Emerging Technologies for Computing, Communication and Smart Cities: Proceedings of ETCCS 2021. Singapore: Springer Nature Singapore, 2022. 55-67.
2. Maqbool, Junaid, et al. "Stock prediction by integrating sentiment scores of financial news and MLP-regressor: a machine learning approach." Procedia Computer Science 218 (2023): 1067-1078.

Code is publicly available at Github

Permutable AI’s Global Macro Sentiment API provides aggregated sentiment data for global macroeconomic topics, including inflation, GDP, monetary policy, fiscal policy, geopolitics, and natural disasters. With support for Python, R, and Java client libraries, plus webhook integration, the API allows developers and analysts to retrieve structured insights from news sources within custom date ranges. Parameters include start and end dates (30-day lookback), filtering by sources, and strict real-time extraction options. Data outputs include sentiment scores, topic classifications, and aggregated publication timestamps—ideal for market insights, trading strategies, and research applications. Full API reference documentation is available at copilot-api.permutable.ai/redoc .

Training time in seconds per epoch for feed-forward network and recurrent neural network models.

1. OC Waze Partner Hub GeoRSS Cumulative Alert Data from Velocity feed analytics. The data are updated in regular (5-minute) intervals. OC Waze Partner Hub data provide information about traffic jams and events that affect road conditions, either from drivers using Waze, a.k.a. Wazers, or from external sources. Wazers may issue reports from the location at which they are currently located or, if no longer at the location, within 30 minutes after the event occurred. We are also able to provide automatic alerts for what we call Unusual Traffic (or Irregularities) - incidents that affect a large number of users and fall outside the normal traffic patterns for a given day and time.Waze generates traffic jam information by processing the following data sources:Data: includes all traffic data reported by Waze users through the Waze mobile application. Reliability: Each alert gets a reliability score based on other user reactions (‘Thumbs up’, ‘Not there’ etc.) and the level of the reporter (Wazers gain levels by contributing to the map, starting at level 1 and reaching up to level 6. The higher the level, the more experienced and trustworthy the Wazer.) The score (0-10) indicates how reliable the report is. Confidence: Each alert gets a confidence score based on other user reactions (‘Thumbs up’, ‘Not there’). The score ranges between 0 and 10. A higher score indicates more positive feedback from Waze users.2. OC Waze Partner Hub GeoRSS Cumulative Traffic Jam Data from Velocity feed analytics. The data are updated in regular (5-minute) intervals.The traffic jams feed includes data gathered in real time about traffic slowdowns on specific road segments. Waze generates traffic jam information by processing the following data sources: GPS location-points sent from user phones (users who drive while using the app) and calculations of the current average speed vs. free-flow speed (maximum speed measured on the road-segment). For Unusual traffic (irregularities) Waze uses historic average speeds (on 30 minute time-slots). User generated reports - reports shared by Waze users who encounter traffic jams. These appear as regular alerts, and also affect the way we identify and present traffic jams.Original data provided by Waze App. Learn more at Waze.com.

Our Geospatial Dataset connects people's movements to over 200M physical locations globally. These are aggregated and anonymized data that are only used to offer context for the volume and patterns of visits to certain locations. This data feed is compiled from different data sources around the world.

It includes information such as the name, address, coordinates, and category of these locations, which can range from restaurants and hotels to parks and tourist attractions

Location Intelligence Data Reach: Location Intelligence data brings the POI/Place/OOH level insights calculated on the basis of Factori’s Mobility & People Graph data aggregated from multiple data sources globally. In order to achieve the desired foot-traffic attribution, specific attributes are combined to bring forward the desired reach data. For instance, in order to calculate the foot traffic for a specific location, a combination of location ID, day of the week, and part of the day can be combined to give specific location intelligence data. There can be a maximum of 56 data records possible for one POI based on the combination of these attributes.

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method at a suitable interval (daily/weekly/monthly).

Use Cases: Credit Scoring: Financial services can use alternative data to score an underbanked or unbanked customer by validating locations and persona. Retail Analytics: Analyze footfall trends in various locations and gain an understanding of customer personas. Market Intelligence: Study various market areas, the proximity of points or interests, and the competitive landscape Urban Planning: Build cases for urban development, public infrastructure needs, and transit planning based on fresh population data. Marketing Campaign Strategy: Analyzing visitor demographics and behavior patterns around POIs, businesses can tailor their marketing strategies to effectively reach their target audience. OOH/DOOH Campaign Planning: Identify high-traffic locations and understand consumer behavior in specific areas, to execute targeted advertising strategies effectively. Geofencing: Geofencing involves creating virtual boundaries around physical locations, enabling businesses to trigger actions when users enter or exit these areas

Data Attributes Included: LocationID
name
website BrandID Phone streetAddress
city
state country_code zip lat lng poi_status
geoHash8 poi_id category category_id full_address address additional_categories url domain rating price_level rating_distribution is_claimed photo_url attributes brand_name brand_id status total_photos popular_times places_topics people_also_search work_hours local_business_links contact_info reviews_count naics_code naics_code_description sis_code sic_code_description shape_polygon building_id building_type building_name geometry_location_type geometry_viewport_northeast_lat geometry_viewport_northeast_lng geometry_viewport_southwest_lat geometry_viewport_southwest_lng geometry_location_lat geometry_location_lng calculated_geo_hash_8

Rhetorik, a Lightcast Company provides the largest known database of globally compliant profiles of professionals, companies, and contacts—continuously updated to ensure accuracy. Powered by AI, the platform supports a wide range of use cases, including sales, marketing, recruiting, and investment banking, with flexible delivery options through platforms, APIs, and services.

Rhetorik360 is the ultimate tool to both segment your market and target, and find your ideal customer. Access detailed Firmographics and Technographics for more than 200 million+ companies globally to power your sales and marketing efforts and increase your business revenues.

Available for lead lists, data enrichment, account and contact data hygiene and validation, company technographics, leads, ABM, recruiting and other uses. One time and annual use licensing available.

Use the Rhetorik360 Company DB with its linked sister database, the Neuron 360 Global B2B Professionals Profiles Database to get the best global coverage of Companies, Offices and Professionals.

230 Million Companies 800 Million Professional Profiles 109 Company Attributes 192 Professional Profile Attributes

This is a new to market, uniquely sourced data set using the power of Rhetorik's proprietary AI. We amalgamate billions of data points from scores of sources to create a world class BTB Company and Contact data asset.

Company Profile Information: Micro-target and reach your ideal customer faster by gaining access to your complete company profile. Our global company profile data feed is always clean, accurate, up to date and compliant. Target by Technographics, Firmographics and much more.

Technographics: Our extensive technographics data sets allow you to understand the tech stack of your prospects and their interest in your products. Look forward to enhanced insight to power your company’s organizational and segmentation efforts, improve your qualification process, increase the effectiveness of your account base marketing, and shorten your sales cycles. Rhetorik's technology data organizes installed enterprise technologies across all major hardware and software product categories, allowing easy searching and filtering on buyers’ technology assets. We track:

26 Million+ technology installs 20,000+ technology products 7,900+ technology vendors 180+ technology categories

Firmographics: Our Firmographics will help you to more efficiently and effectively segment your company through comprehensive data-analysis of your target markets! Determining if a business is the right fit for your company has never been easier. Experience the power of targeted messaging which can be adapted to each and every target audience; taking into account business size, budget, and much more!

Access it where and when you need it. Rhetorik360-Profiles is available via APIs, Snowflake Marketplace, or bulk delivery in JSON and CSV formats and supports a wide range of use cases. Data is refreshed weekly, so you can be sure your information is always up to date!

North America: 55M+ Companies EMEA: 70M+ Companies APAC: 45M+ Companies LATAM: 30M+ Companies