We will create a customized startups dataset tailored to your specific requirements. Data points may include startup foundation dates, locations, industry sectors, funding rounds, investor profiles, financial health, market positions, technological assets, employee counts, and other relevant metrics.

Utilize our startups datasets for a variety of applications to boost strategic planning and innovation tracking. Analyzing these datasets can help organizations grasp market trends and growth opportunities within the startup ecosystem, allowing for more precise strategy adjustments and operations. You can choose to access the complete dataset or a customized subset based on your business needs.

Popular use cases include: enhancing competitive analysis, identifying emerging market trends, and finding high-potential investment opportunities.

This dataset consists of state and industry wise number of startups that gained DPIIT's recognition since inception in 2016.

Note: According to DPIIT‚ As per the Manual for Procurement of Consultancy and other services, an entity should not have completed ten years from the date of its incorporation/registration and its turnover for any of the financial years since incorporation/registration should not have exceeded one hundred crore rupees to get recognized as startup by the department. This recognition is necessary to avail the benefits under various schemes and seek assistance of the government.

B2B Company data encompasses vital information about businesses, including company name, industry, employees, revenue, website, and more. It provides valuable insights for market analysis, competitive intelligence, and strategic decision-making. Startup data, on the other hand, focuses specifically on emerging businesses, offering crucial details such as funding rounds, founder information, growth metrics, and market presence. Both types of data play a pivotal role in understanding the business landscape and identifying opportunities for growth and innovation.

Company data and startup data serve various specific use cases and applications:

1. Market Research for Investors: Investors use company data to identify promising startups in specific industries or regions, helping them make informed investment decisions.

2. Competitor Analysis for Incumbent Companies: Established companies leverage startup data to monitor emerging competitors and identify potential disruptions to their market share.

3. Partnership Opportunities: Startups use company data to identify potential partners or investors who align with their business goals and values.

4. Recruitment Strategies: Companies use startup data to target high-growth startups as potential sources of talent, offering opportunities for strategic partnerships or acquisitions.

5. Economic Development Initiatives: Governments and economic development agencies use company data to identify high-potential startups and provide support through grants, incentives, or incubator programs.

6. Risk Assessment for Service Providers: Service providers, such as financial institutions or insurance companies, use company data to assess the risk associated with serving startups as clients or partners.

7. Product Development Insights: Startups and established companies alike use company data to identify emerging trends and consumer preferences, informing product development strategies.

8. Marketing and Sales Targeting: Companies use company data to identify potential customers or partners based on specific criteria, such as industry, size, or geographic location, enabling targeted marketing and sales efforts.

9. Mergers and Acquisitions: Corporations use company data to identify potential acquisition targets or merger partners that align with their strategic objectives, helping them expand their market reach or diversify their product offerings.

10. Entrepreneurial Education: Educational institutions and entrepreneurship programs use company data to provide real-world examples and case studies for students, helping them understand the challenges and opportunities of starting and scaling a business.

Key features of using APISCRAPY for Company Data & Startup Data include:

Comprehensive Data Extraction: APISCRAPY extracts a wide range of data points, including company name, industry, employees, revenue, website, funding rounds, and founder information.

High Accuracy: Our advanced scraping technology ensures the accuracy and reliability of the extracted data, enabling confident decision-making.

Real-Time Updates: Stay ahead of the competition with real-time data updates, providing the latest insights into the dynamic business landscape

Customized Solutions: Tailored to your specific needs, APISCRAPY offers customized scraping solutions to extract the exact data points you require for your analysis.

Ease of Integration: Our data is delivered in formats that are easy to integrate into your existing systems and workflows, saving you time and resources.

Fast Turnaround Time: Benefit from quick turnaround times, allowing you to access the data you need promptly for strategic decision-making.

Diverse Data Sources: APISCRAPY accesses data from a variety of sources, ensuring comprehensive coverage and providing a holistic view of the market.

Secure Data Handling: We prioritize data security and confidentiality, ensuring that your sensitive information is handled with the utmost care and compliance with data protection regulations.

Expert Support: Our team of experienced professionals is dedicated to providing exceptional customer support and guidance throughout the data extraction process.

Cost-Effective Solutions: APISCRAPY offers cost-effective solutions that provide maximum value for your investment, helping you achieve your business objectives efficiently and affordably.

[Related Tags: Company data, B2B Data, Company Datasets, Company Registry data, Private Company Data, Company Funding Data, Private Equity (PE) Funding Data, SIC Data Regulatory Company Data, Startup Data, Manufacturing Company Data, Venture Capital (VC) Funding Data, Company Financial Data, KYB Data, startup funding data, startup company address data, company owner data, company data scraping, company location API, company data API, startup data API, global startup database, B2b datasets, Firmographic data]

Analysis of ‘2022 Startups Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/khaiid/startups-by-valuation on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Startups are an interesting field to explore and taking a look at the field can expand your knowledge and let you think outside the box

Content

The dataset has 5 columns (Company, Valuation, Valuation_date, Industry, Country)

Company: Describes company name Valuation: Describes the valuation of the company Valuation_date: Describes the date of valuation Industry: Describes the industry of the company Country: Describes the country of the company

Data Collection

This Data uses material from ( https://en.m.wikipedia.org/wiki/List_of_unicorn_startup_companies ) which is released under the Creative Commons Attribution-Share-Alike License 3.0

Inspiration

What industry has the most startups ? What is the total value of these startups ? How many AI companies are in the top 250 startup companies ?

--- Original source retains full ownership of the source dataset ---

This dataset provides comprehensive, up-to-date information about the top 100 Software-as-a-Service (SaaS) companies globally as of 2025. It includes detailed financial metrics, company fundamentals, and operational data that are crucial for market research, competitive analysis, investment decisions, and academic studies.

Key Features

• 100 leading SaaS companies across various industries
• 11 comprehensive data points per company
• Current 2025 data including latest valuations and ARR figures
• Verified information from multiple reliable sources
• Clean, analysis-ready format with consistent data structure

Use Cases

1. Market Research: Analyze SaaS industry trends and market dynamics
2. Investment Analysis: Evaluate growth patterns and valuation multiples
3. Competitive Intelligence: Benchmark companies within sectors
4. Academic Research: Study business models and growth strategies
5. Data Science Projects: Build predictive models for SaaS metrics
6. Business Strategy: Identify successful patterns in SaaS businesses

Industries Covered

Enterprise Software (CRM, ERP, HR) Developer Tools & DevOps Cybersecurity Data Analytics & Business Intelligence Marketing & Sales Technology Financial Technology Communication & Collaboration E-commerce Platforms Design & Creative Tools Infrastructure & Cloud Services

Why This Dataset? The SaaS industry has grown to over $300 billion globally, with companies achieving unprecedented valuations and growth rates. This dataset captures the current state of the industry leaders, providing insights into what makes successful SaaS companies tick.

Sources/Proof of Data: Data Sources The data has been meticulously compiled from multiple authoritative sources:

Company Financial Reports (Q4 2024 - Q1 2025)

Official earnings releases and investor relations documents SEC filings for public companies

Investment Databases

Crunchbase, PitchBook, and CB Insights for funding data Venture capital and private equity announcements

Market Research Reports

Gartner, Forrester, and IDC industry analyses SaaS Capital Index and valuation reports

Industry Publications

TechCrunch, Forbes, Wall Street Journal coverage Company press releases and official announcements

Product Review Platforms

G2 Crowd ratings and reviews Capterra and GetApp user feedback

Data Verification

Cross-referenced across multiple sources for accuracy Updated with latest available information as of May 2025 Validated against official company statements where available

Description This dataset contains responses to a yearly panel survey among entrepreneurs in Baden-Württemberg. Based on the DEEM. research project's collected data (see the DEEM. project's website for more information), we survey founders to track the development of startups in our region and to assess the quality and performance of the local Entrepreneurial Ecosystem (see Empirical entrepreneurial ecosystem research: A guide to creating multilevel datasets for more information on this multilevel dataset). Surveys are sent out to all founders of currently active startups. Surveys were made available in German and English, with respondents being able to choose their preferred language at the start of the survey. For any questions about this survey or the underlying research project, please contact us. Aims Research Integrating data on founders, firms, regional contexts and socioeconomic indicators, this data enables deeper insights into patterns and dynamics across different levels of Entrepreneurial Ecosystems (EEs) - insights often missed in traditional single-source and cross-sectional data studies. As such, this data contributes to the understanding of EEs as multilevel phenomena crucial for understanding and promoting productive entrepreneurship and economic development. Respondents We aim for a full population survey every year, instead of drawing samples. This means that all startups with an identifiable means of contact are contacted, with every potential respondent receiving a personalized survey link. Response rates typically vary between 10-15%. To increase response rates, the following approach is used: The survey is left open for a period of two months for founders to answer at their own pace, with periodic reminders sent. While the survey is designed as a panel to track founders' perceptions over time, we cannot guarantee that founders participate in more than one wave. As such, this dataset can be more accurately viewed as a "macro-panel" on the Entrepreneurial Ecosystem of BW. Usage This repository is structured as follows: The global codebook contains information on the broad concepts addressed in each survey wave, as well as the question batteries asked to address these concepts. As such it serves as a broad overview for researchers, to understand whether the data suits their research interests, and whether the relevant questions were asked in multiple years (i.e. panel analyses are possible), or whether they were included as one-off batteries. It is only available in English. The folders include the responses obtained for each survey year, as well as a wave-specific codebook with more detailed information. In contrast to the global codebook, these codebooks contain the questions and response options in both English and German, as well as meta-information about question filters and sub-groups if applicable. Additionally, for each item, basic summary statistics (number of responses per category, number of non-responses) are reported. Data for each survey wave is made available in .csv format (Comma-Separated Values) with a header row. The columns are separated via semicolons (";"). This has been done to avoid conflicts, as some text responses and system variables included commas. Please consider this when loading and using the data with the analysis software of your choice. Should any issues arise in downloading, opening or using this data, please contact us for help.

The Forager.ai Global Private Equity (PE) Funding Data Set is a leading source of firmographic data, backed by advanced AI and offering the highest refresh rate in the industry.

| Volume and Stats |

• Every company record refreshed twice a month, offering an unparalleled update frequency.
• Delivery is made every hour, ensuring you have the latest data at your fingertips.
• Each record is the result of an advanced AI-driven process, ensuring high-quality, accurate data.

| Use Cases |

Sales Platforms, ABM and Intent Data Platforms, Identity Platforms, Data Vendors:

Example applications include:

1. Uncover trending technologies or tools gaining popularity.

2. Pinpoint lucrative business prospects by identifying similar solutions utilized by a specific company.

3. Study a company's tech stacks to understand the technical capability and skills available within that company.

B2B Tech Companies:

• Enrich leads that sign-up through the Company Search API (available separately).
• Identify and map every company that fits your core personas and ICP.
• Build audiences to target, using key fields like location, company size, industry, and description.

Venture Capital and Private Equity:

• Discover new investment opportunities using company descriptions and industry-level data.
• Review the growth of private companies and benchmark their strength against competitors.
• Create high-level views of companies competing in popular verticals for investment.

| Delivery Options |

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

Our dataset provides a unique blend of volume, freshness, and detail that is perfect for Sales Platforms, B2B Tech, VCs & PE firms, Marketing Automation, ABM & Intent. It stands as a cornerstone in our broader data offering, ensuring you have the information you need to drive decision-making and growth.

Tags: Company Data, Company Profiles, Employee Data, Firmographic Data, AI-Driven Data, High Refresh Rate, Company Classification, Private Market Intelligence, Workforce Intelligence, Public Companies.

What contender will emerge as the next big creator economy company? To find out, we've built a database of more than 500 global startups serving the millions of individuals making money off their online followings. Many founders see an opportunity to help creators connect with fans. Others have developed artificial intelligent tools or financial management services for creators. U.S. creator startups have raised more than $9.8 billion since early 2021, and creator startups based outside the U.S. have raised more than $4 billion in that period. The database comes from our reporting, founders and investors, and estimates from PitchBook.

Techsalerator's Corporate Actions Dataset in Israel offers a comprehensive collection of data fields related to corporate actions, providing valuable insights for investors, traders, and financial institutions. This dataset includes crucial information about the various financial instruments of all 473 companies traded on the Tel-Aviv Stock Exchange (XTAE).

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Top 5 used data fields in the Corporate Actions Dataset for Israel:

• Dividend Declaration Date: The date on which a company's board of directors announces the dividend payout to its shareholders. This information is crucial for investors who rely on dividends as a source of income.

• Stock Split Ratio: The ratio by which a company's shares are split to increase liquidity and affordability. This field is essential for understanding changes in share structure.

• Merger Announcement Date: The date on which a company officially announces its intention to merge with another entity. This field is crucial for investors assessing the impact of potential mergers on their investments.

• Rights Issue Record Date: The date on which shareholders must be on the company's books to be eligible for participating in a rights issue. This data helps investors plan their participation in fundraising events.

• Bonus Issue Ex-Date: The date on which a company's shares start trading without the value of the bonus issue. This information is vital for investors to adjust their portfolios accordingly.

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Top 5 corporate actions in Israel:

Technology and Startups: Corporate actions in Israel's renowned technology sector, including mergers, acquisitions, and initial public offerings (IPOs), are crucial for the country's innovation ecosystem and its reputation as the "Startup Nation."

Healthcare and Life Sciences: Corporate actions related to pharmaceuticals, medical research, and healthcare startups contribute to Israel's reputation as a hub for medical innovation and cutting-edge research.

Cybersecurity and Defense Technology: Corporate actions in the cybersecurity and defense technology sectors reflect Israel's expertise in developing advanced cybersecurity solutions and defense systems.

Renewable Energy and Cleantech: Corporate actions related to renewable energy projects and cleantech initiatives align with Israel's efforts to develop sustainable energy sources and address environmental challenges.

Financial Services and Fintech: Corporate actions involving financial technology (fintech) startups, digital payment solutions, and blockchain technology contribute to Israel's financial services sector's modernization.

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Top 5 financial instruments with corporate action Data in Israel

Israel Stock Exchange (ISE) Domestic Company Index: The main index that tracks the performance of domestic companies listed on the Israel Stock Exchange. This index would provide insights into the performance of the Israeli stock market.

Israel Stock Exchange (ISE) Foreign Company Index: The index that tracks the performance of foreign companies listed on the Israel Stock Exchange, if foreign listings were present. This index would give an overview of foreign business involvement in Israel.

SuperMart Israel: An Israel-based supermarket chain with operations in multiple regions. SuperMart focuses on providing essential products to local communities and contributing to the retail sector's growth.

FinanceIsrael: A financial services provider in Israel with a focus on promoting financial inclusion and access to banking services, particularly among underserved communities.

AgriTech Israel: A company dedicated to advancing agricultural technology in Israel, focusing on optimizing crop yields and improving food security to support the country's agricultural sector.

‍

If you're interested in accessing Techsalerator's End-of-Day Pricing Data for Israel, please contact info@techsalerator.com with your specific requirements. Techsalerator will provide you with a customized quote based on the number of data fields and records you need. The dataset can be delivered within 24 hours, and ongoing access options can be discussed if needed.

‍

Data fields included:

Dividend Declaration Date Stock Split Ratio Merger Announcement Date Rights Issue Record Date Bonus Issue Ex-Date Stock Buyback Date Spin-Off Announcement Date Dividend Record Date Merger Effective Date Rights Issue Subscription Price ‍

Q&A:

How much does the Corporate Actions Dataset cost in Israel?

The cost of the Corporate Actions Dataset may vary depending on factors such as the number of data fields, the frequency of updates, and the total records count. For precise pricing details, it is recommended to directly consult with a Techsalerator Data specialist.

How complete is the Corporate Actions Dataset coverage in Israel ?

Techsalerator provides comprehensive coverage of Corporate Actions Data for various companies and...

The Global Entrepreneurship Monitor (GEM) was designed to capture various aspects of firm creation and entrepreneurship across countries. The data have been collected over a number of years (1998-2003) and include responses from 4,685 experts in over 38 countries and three subnational regions. This study seeks to measure the national attributes considered critical for new firm births and small firm growth. The dataset is a harmonized file capturing the results from all of the surveys. The expert, or key informant, questionnaire was improved and adjusted each year to increase the reliability of multi-item indices and provide for the addition of new dimensions. For each version of the questionnaire, respondents completed 70-80 standardized items that were the basis for 12-15 multi-item indices. Respondents were initially asked a series of general questions pertaining to starting a business, such as whether they were currently trying to start a new business, whether they knew anyone who had started a new business, and whether they thought it was a good time to do so. Respondents were also asked about the process of starting up a new business; whether they had done anything to start a new business in the past 12 months; whether they would own all, part, or none of the new business; how many people would be involved with the new business; what sort of business they were starting; and what they would sell. In addition, respondents identified the total start-up costs, the various sources of the start-up money, and why they were involved in the start-up. Respondents then answered a set of questions to assess the national conditions influencing entrepreneurial activity in their own country. In this respect, respondents provided their opinions on business and entrepreneurial education, the integration of new technology in businesses, the availability of financial support through government policies and programs, the availability of subcontractors, yearly changes in the economic market, and the physical infrastructure in their country. Views were also elicited from respondents about their national cultures in regard to entrepreneurial efforts and opportunities, attitudes towards entrepreneurs in general, women entrepreneurs and the resources available to them, and citizens' knowledge and experience with new businesses. They also gave their views on the Intellectual Property Rights (IPR) legislation and its enforcement in their respective countries. Respondents were then queried on the technological strengths of their country by ranking the top five sectors in which there has been development of the greatest number of technology-intensive start-up companies in the past ten years. Finally, respondents were asked the same general questions as those used in the GLOBAL ENTREPRENEURSHIP MONITOR (GEM): ADULT POPULATION SURVEY DATA SET, 1998-2003 (ICPSR 20320) in order to ascertain whether the opinions and behaviors of the current "expert" respondents differ from those of the general population. These questions included whether they were starting a new business, if there were opportunities for new businesses, funding sources for a new business, skills required to start a new business, shutting down a business, and whether a fear of failure was preventing the start of a new business. The dataset also contains variables that describe the respondent's gender, age, educational attainment, labor force status, the entrepreneurial areas in which they feel they have strong expertise, and the month and year the survey was conducted.

Launched on April 28, 2009, Kickstarter is a Public Benefit Corporation based in Brooklyn, New York. It is a global crowdfunding platform that helps to fund new creative projects and ideas through direct support from individuals (backers) from around the world who pledge money to bring these projects and ideas to life. Kickstarter supports many different kinds of projects. Everything from films, games, and music to art, design, and technology. Funding on Kickstarter is based on the all-or-nothing model. Backers who pledge their support towards a particular project won't be charged unless the funding goal has been reached. Successfully funded projects reward their backers with one-of-a-kind experiences, e.g., limited editions, or copies of the creative work being produced. This study includes three datasets: (1) Kickstarter Project (public-use file), (2) Backer Location file, and (3) Kickstarter Project (restricted-use file). The public-use Kickstarter Project dataset contains detailed information about all successful and unsuccessful Kickstarter projects (N=610,015) from 2009-2023, including the project category and subcategory, project location (city, state (for U.S.-based projects), and country), funding goal in original and U.S. currencies, amount pledged in dollars, and the number of backers for each project. The restricted file adds the project title, 150-character project description, and the URL for the project on the Kickstarter site. The Backer Location dataset includes information about backers' country and state and the total amount pledged for each geographic location.

Companies in Hong Kong with Parent Companies Located outside Hong Kong - Report on Annual Survey of Companies in Hong Kong with Parent Companies Located outside Hong Kong [Report]

Context

This dataset is for people like me, who are highly interested in the Indian startup ecosystem. Like any other person, I first searched for similar datasets on Kaggle. However, everything I found was either outdated or unusable due to inconsistencies. Taking inspiration from them, I used Trak.in to get this data. However, the site has evolved with time and the data present there is not consistent throughout. I have taken all necessary measures to align everything into one usable format which does not require much cleaning or typecasting.

Content

It contains funding details of Indian Startups since January, 2015 to April, 2021.

Acknowledgements

The dataset is prepared using the publically available funding charts on Trak.in

Inspiration

This dataset can be used to answer interesting questions regarding the Indian startup ecosystem. One can find the similarities and differences between the startups that are being funded in India. You can probably derive patterns/preferences of different investors in India.

This dataset provides detailed bibliographic metadata records for scholarly publications related to 'Strategic Alliances' and 'Corporate Venture Capital' (CVC), as retrieved from Crossref.org. This metadata corpus facilitates in-depth exploration of the academic discourse surrounding these collaborative and investment strategies. Contextual Overview of Strategic Alliances & Corporate Venture Capital: 1. Definition and Context: Strategic Alliances are cooperative agreements between firms to achieve shared objectives, ranging from R&D partnerships to marketing JVs. Corporate Venture Capital (CVC) involves established corporations investing directly in external startups. Both gained traction as firms sought to access new markets, technologies, and capabilities, particularly from the late 20th century, driven by globalization, rapid technological change, and the need for innovation beyond internal capacities. 2. Strengths and Weaknesses: Strengths include risk sharing, access to complementary resources/knowledge, market entry facilitation (alliances), and windows on new technologies/business models (CVC). Challenges involve partner selection, governance complexities, potential for opportunistic behavior, cultural clashes in alliances, and managing the dual objectives of financial return and strategic benefit in CVC. Success depends on clear objectives, trust, and effective relationship management or investment oversight. 3. Relevance and Research Potential: Alliances and CVC are vital for navigating complex ecosystems, fostering open innovation, and accessing external growth opportunities. They are central to strategic management, entrepreneurship, and innovation studies. Research opportunities include alliance portfolio management, CVC's impact on corporate innovation and startup success, the dynamics of coopetition, governance mechanisms in inter-firm collaborations, and their role in disruptive innovation and digital platform strategies across diverse industries. Dataset Structure and Content: The dataset consists of one or more archives. Each archive contains a series of approximately 850 monthly folders (e.g., spanning from January 1950 to January 2025), reflecting a granular month-by-month process of metadata retrieval and curation for Strategic Alliances & CVC. Within each monthly folder, users will find several JSON files documenting the search and filtering process for that specific month: term_results/: A subfolder containing JSON files for results of initial broad keyword searches related to Strategic Alliances & CVC. merged_results.json: Aggregated results from these individual term searches before advanced filtering. filtered_results.json: Results after applying a more specific, complex Boolean query (e.g., ("strategic alliance" OR "corporate venture capital" ...) AND ("partnership" OR ...)) and exact phrase matching to refine relevance. The exact query used is detailed within this file. final_results.json: This is the primary file of interest for most users. It contains the curated, deduplicated (by DOI) list of unique publication metadata records deemed most relevant to 'Strategic Alliances & Corporate Venture Capital' for that specific month. Includes fields like Title, Authors, DOI, Publication Date, Source Title, Abstract (if available from Crossref). statistics_results.json: Summary statistics of the search and filtering process for the month. This granular monthly structure allows researchers to trace the evolution of academic discourse on Strategic Alliances & CVC and identify relevant publications with high temporal precision. For an overview of the general retrieval methodology, refer to the parent Dataverse description (Management Tool Bibliographic Metadata (Crossref)). Users interested in aggregated publication counts or trend analysis for Strategic Alliances & CVC should consult the corresponding datasets in the Raw Extracts Dataverse and the Comparative Indices Dataverse.

Context

Wanted to get some insights of Indian startup ecosystem. Which industry is seeing more innovation, which cities are playing major roles in attracting establishing startups. What is the pattern in number of fundings per year. Data collected from 2015 - until mid July 2020.

Content

What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Data collected through web scrapping process, though data available out there for last 5 years. you can run into lot of challenges while reading data from the website due incosistency or may be limitations of python libraries. Processing the data may be biggest hurdle as there is missing data, unorganized, inconsistency in data format. I have published my data acquiring code on github, if you want to look into how i did read, processed, organized, cleaning is done, here the link to that. github

Acknowledgements

Thanks to trak.in who are published data for free. Also i express gratitude to @sudalairajkumar for his pioneering work with respect to india start up funding data exploration.

Inspiration

How startups were funded per year ? Which cities are encouraging startup ecosystem ? Over a years how startup funding has been changed? Which industries are attracting more funding ?

The Big Data Technology Market size was valued at USD 349.40 USD Billion in 2023 and is projected to reach USD 918.16 USD Billion by 2032, exhibiting a CAGR of 14.8 % during the forecast period. Big data is larger, more complex data sets, especially from new data sources. These data sets are so voluminous that traditional data processing software just can’t manage them. But these massive volumes of data can be used to address business problems that wouldn’t have been able to tackle before. Big data technology is defined as software-utility. This technology is primarily designed to analyze, process and extract information from a large data set and a huge set of extremely complex structures. This is very difficult for traditional data processing software to deal with. Among the larger concepts of rage in technology, big data technologies are widely associated with many other technologies such as deep learning, machine learning, artificial intelligence (AI), and Internet of Things (IoT) that are massively augmented. In combination with these technologies, big data technologies are focused on analyzing and handling large amounts of real-time data and batch-related data. Recent developments include: February 2024: - SQream, a GPU data analytics platform, partnered with Dataiku, an AI and machine learning platform, to deliver a comprehensive solution for efficiently generating big data analytics and business insights by handling complex data., October 2023: - MultiversX (ELGD), a blockchain infrastructure firm, formed a partnership with Google Cloud to enhance Web3’s presence by integrating big data analytics and artificial intelligence tools. The collaboration aims to offer new possibilities for developers and startups., May 2023: - Vpon Big Data Group partnered with VIOOH, a digital out-of-home advertising (DOOH) supply-side platform, to display the unique advertising content generated by Vpon’s AI visual content generator "InVnity" with VIOOH's digital outdoor advertising inventories. This partnership pioneers the future of outdoor advertising by using AI and big data solutions., May 2023: - Salesforce launched the next generation of Tableau for users to automate data analysis and generate actionable insights., March 2023: - SAP SE, a German multinational software company, entered a partnership with AI companies, including Databricks, Collibra NV, and DataRobot, Inc., to introduce the next generation of data management portfolio., November 2022: - Thai Oil and Retail Corporation PTT Oil and Retail Business Public Company implemented the Cloudera Data Platform to deliver insights and enhance customer engagement. The implementation offered a unified and personalized experience across 1,900 gas stations and 3,000 retail branches., November 2022: - IBM launched new software for enterprises to break down data and analytics silos that helped users make data-driven decisions. The software helps to streamline how users access and discover analytics and planning tools from multiple vendors in a single dashboard view., September 2022: - ActionIQ, a global leader in CX solutions, and Teradata, a leading software company, entered a strategic partnership and integrated AIQ’s new HybridCompute Technology with Teradata VantageCloud analytics and data platform.. Key drivers for this market are: Increasing Adoption of AI, ML, and Data Analytics to Boost Market Growth . Potential restraints include: Rising Concerns on Information Security and Privacy to Hinder Market Growth. Notable trends are: Rising Adoption of Big Data and Business Analytics among End-use Industries.

The Global Entrepreneurship Monitor [GEM] research program was developed to provide comparisons among countries related to participation of adults in the firm creation process. The initial data was assembled as a pretest of five countries in 1998 and by 2012 over 100 countries had been involved in the program. The initial design for the GEM initiative was based on the first US Panel Study of Entrepreneurial Dynamics, and by 2012 data from 1,827,513 individuals had been gathered in 563 national samples and 6 specialized regional samples. This dataset is a harmonized file capturing results from all of the surveys. The procedure has been to harmonize the basic items across all surveys in all years, followed by implementing a standardized transform to identify those active as nascent entrepreneurs in the start-up process, as owner-managers of new firms, or as owner-managers of established firms. Those identified as nascent entrepreneurs or new business owners are the basis for the Total Entrepreneurial Activity [TEA] or Total Early-Stage index. This harmonized, consolidated assessment not only facilitates comparisons across countries, but provides a basis for temporal comparisons for individual countries. Respondents were queried on the following main topics: general entrepreneurship, start-up activities, ownership and management of the firm, and business angels (angel investors). Respondents were initially screened by way of a series of general questions pertaining to starting a business, such as whether they were currently trying to start a new business, whether they knew anyone who had started a new business, whether they thought it was a good time to start a new business, as well as their perceptions of the income potential and the prestige associated with starting a new business. Demographic variables include respondent age, sex, and employment status.

Success.ai’s Startup Data with Contact Data for Startup Founders Worldwide provides businesses with unparalleled access to key entrepreneurs and decision-makers shaping the global startup landscape. With data sourced from over 170 million verified professional profiles, this dataset offers essential contact details, including work emails and direct phone numbers, for founders in various industries and regions.

Whether you’re targeting tech innovators in Silicon Valley, fintech entrepreneurs in Europe, or e-commerce trailblazers in Asia, Success.ai ensures that your outreach efforts reach the right individuals at the right time.

Why Choose Success.ai’s Startup Founders Data?

1. Comprehensive Contact Information
2. Access verified work emails, phone numbers, and LinkedIn profiles for founders and key startup executives worldwide.

3. AI-driven validation ensures 99% accuracy, providing reliable data for effective outreach.

4. Global Reach Across Startup Ecosystems

5. Includes profiles of startup founders from tech, healthcare, fintech, sustainability, and other emerging sectors.

6. Covers North America, Europe, Asia-Pacific, South America, and the Middle East, helping you connect with founders on a global scale.

7. Continuously Updated Datasets

8. Real-time updates mean you always have the latest contact information, ensuring your outreach is timely and relevant.

9. Ethical and Compliant

10. Adheres to GDPR, CCPA, and global data privacy regulations, ensuring ethical and compliant use of data.

Data Highlights

• 170M+ Verified Professional Profiles: Includes startup founders and their teams across a range of industries.
• 50M Work Emails: AI-validated for precision in communication.
• 30M Company Profiles: Gain insights into the startups’ size, location, and industry focus.
• 700M Global Professional Profiles: Enriched data for comprehensive outreach and analysis.

Key Features of the Dataset:

1. Founder Decision-Maker Profiles
2. Identify and connect with founders and key executives who drive strategy, funding, and product decisions.

3. Engage with individuals who can approve partnerships, investments, and collaborations.

4. Advanced Filters for Precision Targeting

5. Filter by industry, funding stage, region, or startup size to narrow down your outreach efforts.

6. Ensure your campaigns target the most relevant contacts for your products, services, or investment opportunities.

7. AI-Driven Enrichment

8. Profiles are enriched with actionable data, offering insights that help tailor your messaging and improve response rates.

Strategic Use Cases:

1. Investor Relations and Funding Opportunities

2. Connect with founders seeking investment, pitch your venture capital or angel investment services, and establish long-term partnerships.

3. Business Development and Partnerships

4. Offer collaboration opportunities, strategic alliances, and joint ventures to startups in need of new market entries or product expansions.

5. Marketing and Sales Campaigns

6. Launch targeted email and phone outreach to founders who match your ideal customer profile, driving product adoption and long-term client relationships.

7. Recruitment and Talent Acquisition

8. Reach founders who may be open to recruitment partnerships or HR solutions, helping them build strong teams and scale effectively.

Why Choose Success.ai?

1. Best Price Guarantee

2. Enjoy top-quality, verified startup founder data at competitive prices, ensuring maximum return on investment.

3. Seamless Integration

4. Easily integrate verified contact data into your CRM or marketing platforms via APIs or customizable downloads.

5. Data Accuracy with AI Validation

6. With 99% data accuracy, you can trust the information to guide meaningful and productive outreach campaigns.

7. Customizable and Scalable Solutions

8. Tailor the dataset to your needs, focusing on specific industries, regions, or funding stages, and easily scale as your business grows.

APIs for Enhanced Functionality:

1. Data Enrichment API

2. Enrich your existing CRM records with verified founder contact data, adding valuable insights for targeted engagements.

3. Lead Generation API

4. Automate lead generation and streamline your campaigns, ensuring efficient and scalable outreach to startup founders worldwide.

Leverage Success.ai’s B2B Contact Data for Startup Founders Worldwide to connect with the entrepreneurs driving innovation across global markets. With verified work emails, phone numbers, and continuously updated profiles, your outreach efforts become more impactful, timely, and effective.

Experience AI-validated accuracy and our Best Price Guarantee. Contact Success.ai today to learn how our B2B contact data solutions can help you engage with the startup founders who matter most.

No one beats us on price. Period.

Forager.ai - Global B2B Person Data Set is a comprehensive and AI-powered collection of over 720M professional LinkedIn profiles. Our dataset is refreshed bi-weekly (2x/month) to ensure the most up-to-date and dynamic information, setting the industry standard for data accuracy and coverage. Delivered via JSON or CSV formats, it captures publicly available information on professional profiles across industries and geographies.

| Volume and Stats | 755M+ Global Records, continually growing. Each record is refreshed twice a month, ensuring high data fidelity. Powered by first-party data curation, supporting leading sales and recruitment platforms. Hourly delivery, providing near-real-time data access. Multiple data formats: JSONL, CSV for seamless integration.

| Datapoints | 150+ unique data points available, including: Current Title, Current Company, Work History, Educational Background, location and contact details. with high accuracy +95%. Linkage to other social networks and contact data for added insights.

| Use Cases | Sales Platforms, ABM Vendors, and Intent Data Companies Fuel your platforms with fresh, accurate professional data. Gain insights from job changes and update your database in real time. Enhance contact enrichment for targeted marketing and sales outreach. Venture Capital (VC) and Private Equity (PE) Firms Track employees and founders in your portfolio companies and be the first to know when they change roles. Access employee growth trends to benchmark against competitors. Discover new talent for portfolio companies, optimizing recruitment efforts. HR Tech, ATS Platforms, and Recruitment Solutions Build effective, industry-agnostic recruitment platforms with a wealth of professional data. Track job transitions and automatically refresh profiles to eliminate outdated information. Identify top talent through work history, educational background, and skills analysis.

| Delivery Options | Flat files via S3 or Snowflake PostgreSQL Shared/Managed Database REST API Custom delivery options available upon request.

| Key Features | Over 180M U.S. Professional Profiles. 150+ Data Fields available upon request. Free data samples for evaluation purposes. Bi-Weekly Updates Data accuracy +95%

Tags: LinkedIn Data, Professional Data, Employee Data, Firmographic Data, Work Experience, Education Data, Account-Based Marketing (ABM), Intent Data, Identity Resolution, Talent Sourcing, Sales Database, Recruitment Solutions, Contact Enrichment.

This CED spatial web service (ESRI ArcGIS Online Hosted Feature Layer) is a optimized quick visualization and unique value source for optimized CED web startup. The CED provides Sagebrush biome spatial representations and attribute information of conservation efforts entered into the Conservation Efforts Database (https://conservationefforts.org) by various data providers. This spatial web service is made of point and polygon layers and non-spatial tables. Feature records are group with their respective spatial feature type layers (point, polygon). The two spatial layers have identical attribute fields.Read only access to this data is ONLY available via an interactive web map on the Conservation Efforts Database website or authorized websites. Users who are interested in more access can directly contact the data providers by using the contact information available through the CED interactive map's pop-up/identify feature.The spatially explicit, web-based Conservation Efforts Database is capable of (1) allowing multiple-users to enter data from different locations, (2) uploading and storing documents, (3) linking conservation actions to one or more threats (one-to-many relationships), (4) reporting functions that would allow summaries of the conservation actions at multiple scales (e.g., management zones, populations, or priority areas for conservation), and (5) accounting for actions at multiple scales from small easements to statewide planning efforts.The sagebrush ecosystem is the largest ecosystem type in the continental U.S., providing habitat for more than 350 associated fish and wildlife species. In recognition of the need to conserve a healthy sagebrush ecosystem to provide for the long-term conservation of its inhabitants, the US Fish and Wildlife Service (Service) and United States Geological Survey (USGS) developed the Conservation Efforts Database version 2.0.0 (CED). The purpose of the CED is to efficiently capture the unprecedented level of conservation plans and actions being implemented throughout the sagebrush ecosystem and designed to capture actions not only for its most famous resident, the greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse) but for the other species that rely on sagebrush habitats. Understanding the distribution and type of conservation actions happening across the landscape will allow visualization and quantification of the extent to which threats are being addressed.The purpose of this CED spatial web service (ESRI ArcGIS Online Hosted Feature Layer) is to provide CED data for authorized web sites or authorized users.