Salutary Data is a boutique, B2B contact and company data provider that's committed to delivering high quality data for sales intelligence, lead generation, marketing, recruiting / HR, identity resolution, and ML / AI. Our database currently consists of 148MM+ highly curated B2B Contacts ( US only), along with over 4M+ companies, and is updated regularly to ensure we have the most up-to-date information.

We can enrich your in-house data ( CRM Enrichment, Lead Enrichment, etc.) and provide you with a custom dataset ( such as a lead list) tailored to your target audience specifications and data use-case. We also support large-scale data licensing to software providers and agencies that intend to redistribute our data to their customers and end-users.

What makes Salutary unique? - We offer our clients a truly unique, one-stop aggregation of the best-of-breed quality data sources. Our supplier network consists of numerous, established high quality suppliers that are rigorously vetted. - We leverage third party verification vendors to ensure phone numbers and emails are accurate and connect to the right person. Additionally, we deploy automated and manual verification techniques to ensure we have the latest job information for contacts. - We're reasonably priced and easy to work with.

Products: API Suite Web UI Full and Custom Data Feeds

Services: Data Enrichment - We assess the fill rate gaps and profile your customer file for the purpose of appending fields, updating information, and/or rendering net new “look alike” prospects for your campaigns. ABM Match & Append - Send us your domain or other company related files, and we’ll match your Account Based Marketing targets and provide you with B2B contacts to campaign. Optionally throw in your suppression file to avoid any redundant records. Verification (“Cleaning/Hygiene”) Services - Address the 2% per month aging issue on contact records! We will identify duplicate records, contacts no longer at the company, rid your email hard bounces, and update/replace titles or phones. This is right up our alley and levers our existing internal and external processes and systems.

McGRAW’s US B2B Data: Accurate, Reliable, and Market-Ready

Our B2B database delivers over 80 million verified contacts with 95%+ accuracy. Supported by in-house call centers, social media validation, and market research teams, we ensure that every record is fresh, reliable, and optimized for B2B outreach, lead generation, and advanced market insights.

Our B2B database is one of the most accurate and extensive datasets available, covering over 91 million business executives with a 95%+ accuracy guarantee. Designed for businesses that require the highest quality data, this database provides detailed, validated, and continuously updated information on decision-makers and industry influencers worldwide.

The B2B Database is meticulously curated to meet the needs of businesses seeking precise and actionable data. Our datasets are not only extensive but also rigorously validated and updated to ensure the highest level of accuracy and reliability.

Key Data Attributes:

• Personal Identifiers: First name, last name
• Professional Details: Title, direct dial numbers
• Business Information: Company name, address, phone number, fax number, website
• Company Metrics: Employee size, sales volume
• Technology Insights: Information on hardware and software usage across organizations
• Social Media Connections: LinkedIn, Facebook, and direct dial contacts
• Corporate Insights: Detailed company profiles

Unlike many providers that rely solely on third-party vendor files, McGRAW takes a hands-on approach to data validation. Our dedicated nearshore and offshore call centers engage directly with data before each delivery to ensure every record meets our high standards of accuracy and relevance.

In addition, our teams of social media validators, market researchers, and digital marketing specialists continuously refine and update records to maintain data freshness. Each dataset undergoes multiple verification checks using internal validation processes and third-party tools such as Fresh Address, BriteVerify, and Impressionwise to guarantee the highest data quality.

Additional Data Solutions and Services

• Data Enhancement: Email and LinkedIn appends, contact discovery across global roles and functions

• Business Verification: Real-time validation through call centers, social media, and market research

• Technology Insights: Detailed IT infrastructure reports, spending trends, and executive insights

• Healthcare Database: Access to over 80 million healthcare professionals and industry leaders

• Global Reach: US and international GDPR-compliant datasets, complete with email, postal, and phone contacts

• Email Broadcast Services: Full-service campaign execution, from testing to live deployment, with tracking of key engagement metrics such as opens and clicks

Many B2B data providers rely on vendor-contributed files without conducting the rigorous validation necessary to ensure accuracy. This often results in outdated and unreliable data that fails to meet the demands of a fast-moving business environment.

McGRAW takes a different approach. By owning and operating dedicated call centers, we directly verify and validate our data before delivery, ensuring that every record is up-to-date and ready to drive business success.

Through continuous validation, social media verification, and real-time updates, McGRAW provides a high-quality, dependable database for businesses that prioritize data integrity and performance. Our Global Business Executives database is the ideal solution for companies that need accurate, relevant, and market-ready data to fuel their strategies.

From our comprehensive US Data Lake, we proudly present 23M+ high-quality US decision-makers and influencers.

Take your ABM strategy to the next level, build a strong pipeline and close deals by laser targeting key decision-makers and influencers based on their department, job functions, job responsibilities, interest areas and expertise, then utilise essential prospect information, including verified work email addresses and business phone and social links.

Our data is sourced directly from executives, businesses, official sources and registries, standardised, de-duped, and verified, and then processed through vigorous compliance procedures for GDPR/PECR on a legitimate interest basis and RTBI etc. This results in a highly accurate single source of quality and compliant B2B data.

It is with our B2B Live Data Lake that we can enrich your CRM data, supply new prospect data, verify leads, and provide you with a custom dataset tailored to your target audience specifications. We also cater for big data licensing to software providers and agencies that intend to supply our data to their customers and use it in their software solutions.

and much more

Why Choose 1 Stop Data?

• We offer our clients a unique, single source of quality and compliant data.
• We don't rely on 3rd party vendors.
• We utilise extensive verification processes to help ensure phone numbers and emails are accurate and connect to the right person.
• We are budget-friendly and our team are highly experienced.

Products and Services:

The oscar4.io web platform for self-service data on demand Bulk data feeds Data hygiene, standardisation, cleansing and enrichment Know Your Business (KYB)

Keywords:

B2B,Prospect Data,Validated Work Emails,Personal Emails,Email Enrichment,Company Data,Lead Enrichment,Data Enhancement,Account Based Marketing (ABM),Customer Data,Phone Enrichment,LinkedIn URL,Market Intelligence,Business Intelligence,Data Append,Contact Data,Lead Generation,360-Degree Customer View,Data Cleansing,Lead Data,Email and Phone Validation,Data Augmentation,Segmentation,Data Enrichment,Email Marketing,Data Intelligence,Direct Marketing,Customer Insights,Audience Targeting,Audience Generation,Mobile Phone,B2B Data Enrichment,Social Advertising,Due Diligence,B2B Advertising,Audience Insights,B2B Lead Retargeting,Contact Information,Demographic Data,Consumer Data Enrichment,People-Based Marketing,Contact Data Enrichment,Customer Data Insights,Prospecting,Sales Intelligence,Predictive Analytics,Email Address Validation,Company Data Enrichment,Audience Intelligence,Cold Outreach,Analytics,Marketing Data Enrichment,Customer Acquisition,Data Cleansing,B2C Data,People Data,Professional Information,Recruiting and HR,KYC,B2B List Validation,Lead Information,Sales Prospecting,B2B Sales,B2B Data,Lead Lists,Contact Validation,Competitive Intelligence,Customer Data Enrichment,Identity Resolution,Identity Validation,Data Science,B2C Data Enrichment,B2C,Lead Data Enrichment,Social Media Data.

Market leader Facebook was the first social network to surpass one billion registered accounts and currently sits at more than three billion monthly active users. Meta Platforms owns four of the biggest social media platforms, all with more than one billion monthly active users each: Facebook (core platform), WhatsApp, Facebook Messenger, and Instagram. In the third quarter of 2023, Facebook reported around four billion monthly core Family product users. The United States and China account for the most high-profile social platforms Most top ranked social networks with more than 100 million users originated in the United States, but services like Chinese social networks WeChat, QQ or video sharing app Douyin have also garnered mainstream appeal in their respective regions due to local context and content. Douyin’s popularity has led to the platform releasing an international version of its network: a little app called TikTok. How many people use social media? The leading social networks are usually available in multiple languages and enable users to connect with friends or people across geographical, political, or economic borders. In 2025, social networking sites are estimated to reach 5.42 billion users and these figures are still expected to grow as mobile device usage and mobile social networks increasingly gain traction in previously underserved markets.

Alternative Data Market size was valued at USD 16.13 Billion in 2024 and is projected to reach USD 408.72 Billion by 2031, growing at a CAGR of 54.92% from 2024 to 2031.

Global Alternative Data Market Drivers

Growing Need for Alpha Generation: Investors are continuously looking for fresh sources of alpha, or excess returns over a benchmark, in the fiercely competitive financial markets. Insights from alternative data are distinct from those from traditional sources, which helps investors spot opportunities and obtain a competitive advantage. Technological Developments: The mass gathering and examination of alternative data has been made easier by technological developments, especially in fields like artificial intelligence, machine learning, and big data analytics. These technologies improve the value proposition of alternative data for investors by enabling complex data processing, pattern detection, and predictive modeling. Proliferation of Data Sources: Beyond traditional financial and economic indicators, there is a proliferation of data sources due to the internet and digital technology. Web traffic, satellite imagery, social media feeds, consumer transactions, and sensor data are just a few examples of the many sources that make up alternative data, which offers deep and varied insights into a number of fields and industries. Regulatory Environment: The gathering, storing, and use of alternative data may be affected by changes in regulations, such as the General Data Protection Regulation (GDPR) of the European Union and other comparable data protection legislation across the globe. Adherence to regulatory mandates is crucial for alternative data providers and consumers, as it molds the market environment and impacts data procurement tactics.

With the phone book era far in the past, database and directory publishers have been forced to transform their business approach, focusing on their digital presence. Despite many publishers rapidly moving away from print services, they are experiencing immovable competition from online search engines and social media platforms within the digital space, negatively affecting revenue growth potential. Industry revenue has been eroding at a CAGR of 4.4% over the past five years and in 2024, a 3.9% drop has led to the industry revenue totaling $4.4 billion. Profit continues to drop in line with revenue, accounting for 4.7% of revenue as publishers invest more in their digital platforms. Interest in printed directories has disappeared as institutional clients and consumers have continued their shift to convenient online resources. Declining demand for print advertising has curbed revenue growth and online revenue has only slightly mitigated this downturn. Though many traditional publishers, such as Yellow Pages, now operate under parent companies with digital resources, directory publishers remain low on the list of options businesses have to choose from in digital advertising. Due to the convenience and connectivity that Facebook and Google services offer, traditional directory publishers have a limited ability to compete. Many providers have rebranded and tailored their services toward client needs, though these efforts have only had a marginal impact on revenue growth. The industry is forecast to decline at an accelerated CAGR of 5.2% over the next five years, reaching an estimated $3.4 billion in 2029, as businesses and consumers continually turn to digital alternatives for information and advertising opportunities. As AI and digital technology innovation expands, social media company products will likely improve at a faster rate than the digital offerings that directory publishers can provide. Though these companies will seek external partnerships to cut costs, they face an uphill battle to boost their visibility and reverse consumer habit trends.

The Influencer Marketing Platform market is experiencing explosive growth, projected to reach $22.19 billion in 2025 and exhibiting a remarkable Compound Annual Growth Rate (CAGR) of 35.8% from 2025 to 2033. This surge is driven by several key factors. Businesses are increasingly recognizing the effectiveness of influencer marketing in reaching target audiences organically and building brand trust, particularly among younger demographics heavily engaged with social media. The rise of short-form video content on platforms like TikTok and Instagram Reels further fuels this trend, creating new avenues for influencer collaborations and campaign formats. Technological advancements within these platforms, offering enhanced analytics and campaign management tools, are streamlining the process and improving ROI for brands. Furthermore, the diversification of influencer marketing strategies beyond traditional social media, encompassing live streaming, podcasts, and even gaming platforms, expands the market's potential. The segmentation into various end-users (large enterprises, SMEs) and applications (fashion, food, travel, fitness) highlights the market's versatility and adaptability across diverse industries. Growth is particularly strong in North America and APAC, reflecting the high concentration of social media users and the significant adoption of influencer marketing strategies in these regions. However, Europe also represents a substantial market, with significant growth potential in countries like Germany, the UK, and France. While challenges exist, such as influencer authenticity concerns, brand safety issues, and the need for robust measurement frameworks, the market's overall trajectory points towards sustained, high-growth potential. The expanding list of platform providers indicates a competitive landscape, encouraging innovation and improved services. Looking ahead, the market is expected to see increasing sophistication in targeting and measurement, leveraging AI and data analytics to optimize campaign performance. This will be a critical driver of future market expansion and sustained high CAGR.

From our comprehensive UK Data Lake, we proudly present 5M+ high-quality UK decision-makers and influencers.

Take your ABM strategy to the next level, build a strong pipeline and close deals by laser targeting key decision-makers and influencers based on their department, job functions, job responsibilities, interest areas and expertise, then utilise essential prospect information, including verified work email addresses and business phone and social links.

Our data is sourced directly from executives, businesses, official sources and registries, standardised, de-duped, and verified, and then processed through vigorous compliance procedures for GDPR/PECR on a legitimate interest basis and RTBI etc. This results in a highly accurate single source of quality and compliant B2B data.

It is with our B2B Live Data Lake that we can enrich your CRM data, supply new prospect data, verify leads, and provide you with a custom dataset tailored to your target audience specifications. We also cater for big data licensing to software providers and agencies that intend to supply our data to their customers and use it in their software solutions.

and much more

Why Choose 1 Stop Data?

• We offer our clients a unique, single source of quality and compliant data.
• We don't rely on 3rd party vendors.
• We utilise extensive verification processes to help ensure phone numbers and emails are accurate and connect to the right person.
• We are budget-friendly and our team are highly experienced.

Products and Services:

The oscar4.io web platform for self-service data on demand Bulk data feeds Data hygiene, standardisation, cleansing and enrichment Know Your Business (KYB)

Keywords:

B2B,Prospect Data,Validated Work Emails,Personal Emails,Email Enrichment,Company Data,Lead Enrichment,Data Enhancement,Account Based Marketing (ABM),Customer Data,Phone Enrichment,LinkedIn URL,Market Intelligence,Business Intelligence,Data Append,Contact Data,Lead Generation,360-Degree Customer View,Data Cleansing,Lead Data,Email and Phone Validation,Data Augmentation,Segmentation,Data Enrichment,Email Marketing,Data Intelligence,Direct Marketing,Customer Insights,Audience Targeting,Audience Generation,Mobile Phone,B2B Data Enrichment,Social Advertising,Due Diligence,B2B Advertising,Audience Insights,B2B Lead Retargeting,Contact Information,Demographic Data,Consumer Data Enrichment,People-Based Marketing,Contact Data Enrichment,Customer Data Insights,Prospecting,Sales Intelligence,Predictive Analytics,Email Address Validation,Company Data Enrichment,Audience Intelligence,Cold Outreach,Analytics,Marketing Data Enrichment,Customer Acquisition,Data Cleansing,B2C Data,People Data,Professional Information,Recruiting and HR,KYC,B2B List Validation,Lead Information,Sales Prospecting,B2B Sales,B2B Data,Lead Lists,Contact Validation,Competitive Intelligence,Customer Data Enrichment,Identity Resolution,Identity Validation,Data Science,B2C Data Enrichment,B2C,Lead Data Enrichment,Social Media Data.

Overview

This dataset of medical misinformation was collected and is published by Kempelen Institute of Intelligent Technologies (KInIT). It consists of approx. 317k news articles and blog posts on medical topics published between January 1, 1998 and February 1, 2022 from a total of 207 reliable and unreliable sources. The dataset contains full-texts of the articles, their original source URL and other extracted metadata. If a source has a credibility score available (e.g., from Media Bias/Fact Check), it is also included in the form of annotation. Besides the articles, the dataset contains around 3.5k fact-checks and extracted verified medical claims with their unified veracity ratings published by fact-checking organisations such as Snopes or FullFact. Lastly and most importantly, the dataset contains 573 manually and more than 51k automatically labelled mappings between previously verified claims and the articles; mappings consist of two values: claim presence (i.e., whether a claim is contained in the given article) and article stance (i.e., whether the given article supports or rejects the claim or provides both sides of the argument).

The dataset is primarily intended to be used as a training and evaluation set for machine learning methods for claim presence detection and article stance classification, but it enables a range of other misinformation related tasks, such as misinformation characterisation or analyses of misinformation spreading.

Its novelty and our main contributions lie in (1) focus on medical news article and blog posts as opposed to social media posts or political discussions; (2) providing multiple modalities (beside full-texts of the articles, there are also images and videos), thus enabling research of multimodal approaches; (3) mapping of the articles to the fact-checked claims (with manual as well as predicted labels); (4) providing source credibility labels for 95% of all articles and other potential sources of weak labels that can be mined from the articles' content and metadata.

The dataset is associated with the research paper "Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims" accepted and presented at ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '22).

The accompanying Github repository provides a small static sample of the dataset and the dataset's descriptive analysis in a form of Jupyter notebooks.

Options to access the dataset

There are two ways how to get access to the dataset:

1. Static dump of the dataset available in the CSV format
2. Continuously updated dataset available via REST API

In order to obtain an access to the dataset (either to full static dump or REST API), please, request the access by following instructions provided below.

References

If you use this dataset in any publication, project, tool or in any other form, please, cite the following papers:

@inproceedings{SrbaMonantPlatform, author = {Srba, Ivan and Moro, Robert and Simko, Jakub and Sevcech, Jakub and Chuda, Daniela and Navrat, Pavol and Bielikova, Maria}, booktitle = {Proceedings of Workshop on Reducing Online Misinformation Exposure (ROME 2019)}, pages = {1--7}, title = {Monant: Universal and Extensible Platform for Monitoring, Detection and Mitigation of Antisocial Behavior}, year = {2019} }

@inproceedings{SrbaMonantMedicalDataset, author = {Srba, Ivan and Pecher, Branislav and Tomlein Matus and Moro, Robert and Stefancova, Elena and Simko, Jakub and Bielikova, Maria}, booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '22)}, numpages = {11}, title = {Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims}, year = {2022}, doi = {10.1145/3477495.3531726}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3477495.3531726}, }

Dataset creation process

In order to create this dataset (and to continuously obtain new data), we used our research platform Monant. The Monant platform provides so called data providers to extract news articles/blogs from news/blog sites as well as fact-checking articles from fact-checking sites. General parsers (from RSS feeds, Wordpress sites, Google Fact Check Tool, etc.) as well as custom crawler and parsers were implemented (e.g., for fact checking site Snopes.com). All data is stored in the unified format in a central data storage.

Ethical considerations

The dataset was collected and is published for research purposes only. We collected only publicly available content of news/blog articles. The dataset contains identities of authors of the articles if they were stated in the original source; we left this information, since the presence of an author's name can be a strong credibility indicator. However, we anonymised the identities of the authors of discussion posts included in the dataset.

The main identified ethical issue related to the presented dataset lies in the risk of mislabelling of an article as supporting a false fact-checked claim and, to a lesser extent, in mislabelling an article as not containing a false claim or not supporting it when it actually does. To minimise these risks, we developed a labelling methodology and require an agreement of at least two independent annotators to assign a claim presence or article stance label to an article. It is also worth noting that we do not label an article as a whole as false or true. Nevertheless, we provide partial article-claim pair veracities based on the combination of claim presence and article stance labels.

As to the veracity labels of the fact-checked claims and the credibility (reliability) labels of the articles' sources, we take these from the fact-checking sites and external listings such as Media Bias/Fact Check as they are and refer to their methodologies for more details on how they were established.

Lastly, the dataset also contains automatically predicted labels of claim presence and article stance using our baselines described in the next section. These methods have their limitations and work with certain accuracy as reported in this paper. This should be taken into account when interpreting them.

Reporting mistakes in the dataset The mean to report considerable mistakes in raw collected data or in manual annotations is by creating a new issue in the accompanying Github repository. Alternately, general enquiries or requests can be sent at info [at] kinit.sk.

Dataset structure

Raw data

At first, the dataset contains so called raw data (i.e., data extracted by the Web monitoring module of Monant platform and stored in exactly the same form as they appear at the original websites). Raw data consist of articles from news sites and blogs (e.g. naturalnews.com), discussions attached to such articles, fact-checking articles from fact-checking portals (e.g. snopes.com). In addition, the dataset contains feedback (number of likes, shares, comments) provided by user on social network Facebook which is regularly extracted for all news/blogs articles.

Raw data are contained in these CSV files (and corresponding REST API endpoints):

sources.csv

articles.csv

article_media.csv

article_authors.csv

discussion_posts.csv

discussion_post_authors.csv

fact_checking_articles.csv

fact_checking_article_media.csv

claims.csv

feedback_facebook.csv

Note: Personal information about discussion posts' authors (name, website, gravatar) are anonymised.

Annotations

Secondly, the dataset contains so called annotations. Entity annotations describe the individual raw data entities (e.g., article, source). Relation annotations describe relation between two of such entities.

Each annotation is described by the following attributes:

category of annotation (annotation_category). Possible values: label (annotation corresponds to ground truth, determined by human experts) and prediction (annotation was created by means of AI method).

type of annotation (annotation_type_id). Example values: Source reliability (binary), Claim presence. The list of possible values can be obtained from enumeration in annotation_types.csv.

method which created annotation (method_id). Example values: Expert-based source reliability evaluation, Fact-checking article to claim transformation method. The list of possible values can be obtained from enumeration methods.csv.

its value (value). The value is stored in JSON format and its structure differs according to particular annotation type.

At the same time, annotations are associated with a particular object identified by:

entity type (parameter entity_type in case of entity annotations, or source_entity_type and target_entity_type in case of relation annotations). Possible values: sources, articles, fact-checking-articles.

entity id (parameter entity_id in case of entity annotations, or source_entity_id and target_entity_id in case of relation annotations).

The dataset provides specifically these entity annotations:

Source reliability (binary). Determines validity of source (website) at a binary scale with two options: reliable source and unreliable source.

Article veracity. Aggregated information about veracity from article-claim pairs.

The dataset provides specifically these relation annotations:

Fact-checking article to claim mapping. Determines mapping between fact-checking article and claim.

Claim presence. Determines presence of claim in article.

Claim stance. Determines stance of an article to a claim.

Annotations are contained in these CSV files (and corresponding REST API endpoints):

entity_annotations.csv

relation_annotations.csv

Note: Identification of human annotators authors (email provided in the annotation app) is anonymised.

According to a survey on digital usage in China, around 91.8 percent of respondents had used WeChat as of the third quarter of 2023. The short video app Douyin, the social commerce site Xiaohongshu, and the Twitter-like site Weibo, were among the most popular social media platforms in China. How successful is WeChat in China? The country’s most popular messaging app, WeChat, boasts over one billion monthly active users. For many Chinese consumers, WeChat is part of daily life, helping them to organize tasks such as paying bills, ordering food delivery, and calling a taxi. On average, WeChat users spent about an hour and 20 minutes per day on the app. Large user base with high engagement The social media scene in China is huge and mobile-centric. In 2023, the social media penetration rate amounted to 74 percent in China, slightly higher than in countries like the United States and Japan. While the user number has shown signs of reaching a saturation point, Chinese consumers have been spending more time online, with a considerable daily usage of social media.

The overall goal of the project was to establish an innovative information system, which can automatically detect and extract content related to side effect of drugs from user reviews, determine whether they are talking about effectiveness or adverse drug events, extract keywords or phrases related to effectiveness or adverse drug events, and visualize the resulting information to doctors and patients. Our group was provided with crawled Twitter reviews and social network forum reviews on drugs that are used to treat diabetes. The raw data were manually labeled in four different label for named entity recognition in order to create training, testing, and validation sets. Using the training data set, a side effect dictionary was created using PamTAT. Side effect dictionary was then refined by removing neutral words to increase accuracy. To validate the accuracy of the generated side effect dictionary, the results of side effect analysis based on the generated dictionary and two other general negative word dictionaries were compared. The generated side effect dictionary performed better in recognizing side effect entities. After validation, the generated dictionary was further tested with a set of user reviews on a drug that is used to treat stroke. Using generated dictionary, the project accomplished to accurately determine if any reviews relates to the mention of side effect of specific drugs. The project successfully delivered to accurately detect mention of side effect from the reviews in > 90% accuracy. Resulting algorithm can be used to create innovative information system to detect and extract content related to side effect of drugs for any other drugs with creation of problem specific dictionary. The project should be further developed to incorporate automatic extraction of user reviews, analysis of data, and visualization of results. DrugEventsSocialMediaReport.docx - Presentation file in docx format DrugEventsSocialMediaReport.pdf - Presentation file in pdf format DrugEventsSocialMediaPresentation.pptx - Presentation file in pptx format DrugEventsSocialMediaPresentation.pdf - Presentation file in pdf format PamTaT4VTWorks - CSV and XML files to use PamTAT

The local listing management software market is experiencing robust growth, driven by the increasing importance of online presence for businesses of all sizes. The need to manage online listings across multiple platforms like Google My Business, Yelp, Facebook, and others efficiently and accurately fuels this demand. Businesses are realizing that consistent and accurate information across all listings directly impacts local search rankings, driving foot traffic and sales. A projected CAGR of, let's assume, 15% (a reasonable estimate for a rapidly evolving tech sector like this) from 2025 to 2033 indicates a significant expansion of the market. This growth is further fueled by trends such as the rising adoption of mobile devices and the increasing sophistication of local SEO strategies. The market is segmented by software features (e.g., review management, citation building, reporting and analytics), deployment type (cloud-based vs. on-premise), and business size (SMB, enterprise). Competition is strong, with established players like Reputation, Moz, and Yext competing with emerging solutions. However, the market is large enough to support multiple providers as the need for sophisticated local listing management continues to grow. Challenges, such as the complexity of managing multiple platforms and the constant evolution of search engine algorithms, are continuously being addressed by the software providers, leading to improved functionalities and user experience. The market's expansion is fueled by the rising adoption of location-based services and the increasing demand for improved customer experience. This necessitates accurate and up-to-date business information across various online platforms. The increasing use of data analytics within the software allows businesses to track performance, identify areas for improvement, and measure ROI on their local SEO strategies. This data-driven approach further encourages adoption. Furthermore, the integration of social media management and review monitoring into these platforms is a significant factor in the market’s continuous growth. The continued expansion of e-commerce and online ordering also contributes to the importance of accurate local listings, linking online profiles directly to physical locations and facilitating easy transactions. We can anticipate continued innovation within the sector, with new features and integrations emerging to meet the evolving needs of businesses in the digital landscape.

Access detailed insights with our Instagram datasets, featuring follower counts, verified status, account types, and engagement scores. Explore post information including URLs, descriptions, hashtags, comments, likes, media, posting dates, locations, and reel URLs. Perfect for understanding user engagement and content trends to drive informed decisions and optimize your social media strategies. Over 750M records available Price starts at $250/100K records Data formats are available in JSON, NDJSON, CSV, XLSX and Parquet. 100% ethical and compliant data collection Included datapoints:

Account Fbid Id Followers Posts Count Is Business Account Is Professional Account Is Verified Avg Engagement External Url Biography Business Category Name Category Name Post Hashtags Following Posts Profile Image Link Profile URL Profile Name Highlights Count Highlights Full Name Is Private Bio Hashtags URL Is Joined Recently And much more

Snapshot img

India Data Center Cooling Market Size 2025-2029

The data center cooling market size in India is forecast to increase by USD 1.69 billion at a CAGR of 22.5% between 2024 and 2029.

The data center cooling market is experiencing significant growth due to several key trends. The increasing number of data centers, particularly in regions like India, is driving market demand. Additionally, the rise in the adoption of high-performance computing systems, which generate more heat, is increasing the need for efficient cooling solutions. Furthermore, environmental concerns are pushing data center operators to adopt more sustainable cooling technologies, such as liquid cooling and free cooling, to reduce carbon emissions. These trends are expected to continue shaping the market dynamics in the coming years.
High-density server racks and power-hungry IT equipment necessitate advanced cooling systems, as artificial intelligence, big data, and the Internet of Things (IoT) drive digital transformation efforts. However, challenges such as high capital and operational costs, complex installation processes, and the need for regular maintenance remain significant barriers to market growth. Despite these challenges, the market is expected to witness steady growth due to the increasing importance of data centers in the digital economy.

What will be the India Data Center Cooling Market Size During the Forecast Period?

To learn more about the market report, Request Free Sample

The market is experiencing significant growth due to the increasing demand for IT and telecommunication services, media and entertainment content, and the proliferation of smart devices and social media. With the internet population continuing to expand and cloud computing and digital transformation efforts gaining momentum, the need for reliable and efficient cooling solutions is more crucial than ever. Two primary cooling technologies dominate the market: air-based cooling and liquid-based cooling. Air-based cooling systems use traditional air conditioning units to regulate temperature, while liquid-based cooling, which includes economizers, cooling towers, and chillers, utilizes liquid to absorb heat and transfer it outside the data center.
Evaporative cooling is another emerging technology that utilizes water to cool data centers through the evaporation process. This method is particularly effective in hot and dry climates, making it an attractive option for colocation DC providers and third-party managed services. The rise of high-density server racks and power-hungry IT equipment has put additional pressure on cooling systems to perform efficiently. Cooling technologies must adapt to meet the demands of the market, which includes managing the heat generated by artificial intelligence and other power-intensive applications. The increasing number of internet-connected devices and the growth of social media require data centers to be constantly available and cooled effectively. As such, cooling technology continues to be a critical component of the IT infrastructure landscape.

How is this market segmented and which is the largest segment?

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

Technology

  Liquid-based cooling
  Air-based cooling


Product

  Air conditioning
  Economizers
  Cooling towers
  Chillers
  Others


Type

  Room cooling
  Rack and row cooling


Geography

  India

By Technology Insights

The liquid-based cooling segment is estimated to witness significant growth during the forecast period.

Liquid-based cooling, a sub-segment of cooling technologies, is the most widely adopted method in data centers due to its higher efficiency compared to air-based cooling. Water-cooled racks and servers, which utilize this technique, are common in the data center space. In this method, water is circulated through the hot sides of the cabinet to reduce temperature without coming into contact with the actual components. The water is then stored in basins and circulated through pipes to cooling tower pumps, ultimately lowering the temperature. Advancements in liquid-based cooling technology have led to the emergence of immersion-based cooling and direct-to-chip cooling. This cooling solution is increasingly used in sectors such as smart cities, last-mile connectivity, digital services, disaster recovery services, ecommerce, and cryptocurrency, as they require high-performance and energy-efficient cooling solutions. Water-based cooling is also employed in power lines and servers to maintain optimal operating temperatures.

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Market Dynamics

Our market researchers analyzed the data with 2024 as the base

The purpose of the present study was to examine how information source (control—no source, USDA, fictitious hospital, or fictitious social media) impacts perceptions of diet information. Participants included 943 American adults who were aged 18-74 years (M = 37.51, SD = 9.50) and were recruited from across the United States through Amazon Mechanical Turk (MTurk). As a manipulation check we assessed whether participants accurately completed the manipulation by ensuring their response to the question of who made the flyer. Participants who answered the question incorrectly were excluded from the analysis. In total, 537 answered correctly and were included in the analyses (Control = 113, Hospital = 144, Social Media = 121, USDA = 159). The majority of our eligible sample identified as men (N = 350), while the remainder identified as women (N = 185), nonbinary (N = 1), or “other” (N = 1).Participants completed an online survey in which they viewed one flyer containing dietary information and guidance on consuming pulses. The purported source of the flyer information was manipulated to create the 4 conditions. Participants rated the flyer in terms of perceived accuracy, trustworthiness, reliability, desirability for learning more from the source, and likelihood of following the advice. Attitudes, perceived control and norms, and past behavior were used to measure components of the Theory of Planned Behavior (TPB). ANOVA results indicated that the USDA and hospital sources were perceived as more accurate, trustworthy, reliable, and more desirable to learn more from relative to control and social media. There were no differences in likelihood of following guidance depending on source. Multiple regression showed that measures of the TPB were predictors of likelihood of following advice. Participants also ranked their top 3 most trusted sources for health information from a list of 29 sources. Doctors, scientists, nurses, and family and friends were among the most frequently trusted sources. Overall, these findings suggest that trust in the source of information does not influence perceived likelihood of following dietary recommendations for pulses. Resources in this dataset:Resource Title: Effect of Source on Trust of Pulse Nutrition Information and Perceived Likelihood of Following Dietary Guidance. File Name: EffectofSource_Data.xlsxResource Description: One-way analyses of variance (ANOVA) were used to assess between-condition differences for ratings of each of the 5 primary dependent variables (i.e., perceptions of the flyer; variables named Flyer_InfoAccuracy, Flyer_TrustInSource, Flyer_SourceReliability, Flyer_LearnMore, Flyer_FollowAdvice). Tukey tests were used to examine all pairwise comparisons for each of the significant ANOVA effects. A bivariate Pearson correlation was used to examine the relationship between trust in source and likelihood of following advice (variables Flyer_TrustInSource and Flyer_FollowAdvice). Multiple regression/correlation (MRC) was used to assess whether components of the TPB (TPB_Attitudes1, TPB_Attitudes2, TPB_PerceivedNorms1, TPB_PerceivedNorms2, TPB_PerceivedControl1, TPB_PerceivedControl2, TPB_PastBehavior) were predictive of likelihood of following advice (Flyer_FollowAdvice). Finally, frequency data was used to assess percentage with which participants selected sources as being in their top 3 most trusted (Trust_Ald_2_0_GROUP1-Trust_Ald_2_0_29_RANK). Sources that were selected are noted as either 1, 2, or 3 depending on rank, and the sources participants did not select are listed as #NULL!. Data was analyzed using SPSS statistical software, version 28. Resource Software Recommended: SPSS,url: https://www.ibm.com/products/spss-statistics?utm_content=SRCWW&p1=Search&p4=43700050715561164&p5=e&gclid=EAIaIQobChMI2fnV4I6e-AIVErfICh00pwcfEAAYASAAEgIkHfD_BwE&gclsrc=aw.ds

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In 2021, there were 1.21 billion monthly active users of Meta's Instagram, making up over 28 percent of the world's internet users. By 2025, it has been forecast that there will be 1.44 billion monthly active users of the social media platform, which would account for 31.2 percent of global internet users.

How popular is Instagram?

Instagram, as of January 2022, was the fourth most popular social media platform in the world in terms of user numbers. YouTube and WhatsApp ranked in second and third place, respectively, whilst Facebook remained the most popular, with almost three billion monthly active users worldwide.

India had the largest number of Instagram users as of January 2022, with a total of over 230 million users in the country. The second-largest Instagram audience could be found in the United States, with almost 160 million people subscribing to the photo and video sharing app.

Gen Z and Instagram

As of September 2021, Gen Z users in the United States spent an average of five hours per week on Instagram. Although Instagram ranked third in terms of hours per week spent on the platform, Gen Z users spent considerably more time on TikTok, amounting to a weekly average of over 10 hours being spent on the mobile-first video app.

Most followed accounts on Instagram

As of May 2022, Instagram’s own account had 504.37 million followers. In terms of celebrities, Portuguese footballer Cristiano Ronaldo (@chistiano) had over 440.41 million followers on the social network. Moreover, the average media value of an Instagram post by Ronaldo was over 985,000 U.S. dollars.

The most liked post on Instagram as of May 2022 was Photo of an Egg, which was posted in 2019 by the account @world_record_egg. Photo of an Egg has not only exceeded 55 million likes on the platform, but it also has nearly 3.5 million comments, and the account itself has over 4.5 million Instagram followers. After mysterious posts published by the account, World Record Egg revealed itself as part of a mental health campaign aimed at the difficulties and demands of using social media.

The number of Reddit users in Africa was forecast to continuously increase between 2024 and 2028 by in total 4.7 million users (+66.67 percent). After the eighth consecutive increasing year, the Reddit user base is estimated to reach 11.78 million users and therefore a new peak in 2028. Notably, the number of Reddit users of was continuously increasing over the past years.User figures, shown here with regards to the platform reddit, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once. Reddit users encompass both users that are logged in and those that are not.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Reddit users in countries like North America and Asia.

This statistic shows a ranking of the estimated number of LinkedIn users in 2020 in Africa, differentiated by country. The user numbers have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in more than 150 countries and regions worldwide. All input data are sourced from international institutions, national statistical offices, and trade associations. All data has been are processed to generate comparable datasets (see supplementary notes under details for more information).

The number of Pinterest users in Africa was forecast to continuously increase between 2024 and 2028 by in total 15 million users (+68.81 percent). After the ninth consecutive increasing year, the Pinterest user base is estimated to reach 36.81 million users and therefore a new peak in 2028. Notably, the number of Pinterest users of was continuously increasing over the past years.User figures, shown here regarding the platform pinterest, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Pinterest users in countries like Europe and Australia & Oceania.