Lead for Business provides the latest data on venture funds, startups and deals.

Data sample:

https://docs.google.com/spreadsheets/d/1aKmH0zuDl-ivF33ictM6f6h0hfAnHcaZScLCc6NLaG4/editgid=0

We help VC data providers deliver better insights to their clients.

Our custom-made AI algorithm to find the latest info in the venture capital market.

We analyze over 1M websites every day of venture funds, startups, media websites, blogs, and social media pages.

We directly collect information about all the changes and updates on the websites of VCs and startups.

Investment Trends in Indian Startups

An Exploration of Indian Startup Funding Rounds

By [source]

About this dataset

This project aims to explore the fascinating Indian Startup Funding Landscape. By utilizing two datasets, 'startup_cleaned' and 'startup_funding', each containing different yet complementary features, this study will analyze investment patterns across startups operating in India. With seven columns including details such as date, start-up name, verticals and sub-verticals associated with them, city of their operations and investors involved in the funding round - the ‘startup_cleaned’ dataset offers a broad overview of the Indian startup ecosystem. The ‘startup_funding’ dataset contains 10 columns which provide a more detailed look into the investments made in each startup - from investors name and investment type to amount invested & remarks offering additional insights. This analysis seeks to discover interesting trends & correlations between different industry sectors & cities which have enabled a dynamic entrepreneurship ecosystem in India that continues to attract global investments despite daunting challenges ahead

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

To use this dataset effectively you need to first become familiar with the data that has been provided. The columns labeled ‘Sr No’ and ‘Date dd/mm/yyyy’ denote the Unique serial number associated with each start-up as well as the date of investment made into it respectively. You can group all investments made around a particular date range or even view individual investments by referring to these two columns.

Research Ideas

• Investigating investor trends - Analyzing what types of startups investors often invest in, where these investments occur, and how much they typically invest can inform entrepreneurial strategy when it comes to finding potential investments.
• Mapping startup ecosystems - Compiling the data into large-scale maps can show hotspots for startup activity and help visualize the diversification of the Indian startup ecosystem.
• Analyzing impact - Examining investment patterns over time as well as in specific cities or industry verticals can provide insight into how government regulation, trade agreements, and other factors affectIndia's economy and business landscape

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: startup_cleaned.csv | Column name | Description | |:----------------|:-----------------------------------------| | date | Date of the investment round. (Date) | | startup | Name of the startup. (String) | | vertical | Industry sector of the startup. (String) | | subvertical | Sub-sector of the startup. (String) | | city | Location of the startup. (String) | | investors | Name of the investors. (String) | | amount | Amount of investment in USD. (Integer) |

File: startup_funding.csv | Column name | Description | |:----------------------|:-----------------------------------------------------------| | Sr No | A unique identifier for each startup. (Integer) | | Date dd/mm/yyyy | The date of the investment. (Date) | | Startup Name | The name of the startup. (String) | | Industry Vertical | The industry sector the startup operates in. (String) | | SubVertical | The sub-sector the startup operates in. (String) | | City Location | The city the startup is located in. (String) | | Investors Name | The name of the investor. (String) | | InvestmentnType | The type of investment made. (String) | | Amount in USD | The amount of the investment in US Dollars. (Integer) | | Remarks | Any additional remarks related to the investment. (String) |

Acknowledgements

If you use this dataset in your research, plea...

Track the global startup ecosystem: Comprehensive data on startups, founders, funding, and growth metrics worldwide.

Discover Startup Data for technology startups globally with Success.ai. Gain access to verified company data, including firmographic data, employee counts, and funding insights. Best price guaranteed.

In terms of deals over the past five years, artificial intelligence and big data was the largest VC-funded startup industry in 2022, accounting for close to 30 percent of the global deals. Meanwhile, fintech accounted for 16 percent of the deals, with life sciences and health care behind with 12 percent. Blue economy and digital media media were the smallest industries with only one percent each. However, the blue economy saw its funding deals almost doubling over the past five years.

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.

The data contains companies formed as startups during 2021-2023. The data set is relatively clean. The data set contains the company name, country, city, and year of formation along with a brief description.

There are some ideas about this data set.

• Which region in Europe has a big amount of start-ups?
• year-wise change in the growth of start-ups in each country.
• Group the similar type of industry. This requires serious work on the description column.
• Where are the most AI-related companies are formed?

These are just some ideas. You can explore more.

To celebrate the return of HackerNoon Startups of the Year we've open sourced our previous Startup of the Year votes. This dataset includes every city above half a million ppl, tens of thousands of startups with meta data like homepage and company description, as well as, 600k+ votes for these startups on HackerNoon.

Learn more about startups, tech company media coverage, and business blogging.

In 2023, San Francisco Bay, CA had the highest total startup score of any city in the United States at 546.43. The total startup score is a sum of quantity, quality, and business environment evaluations that determines the best place for startup businesses to take root. New York City, NY had the second highest score at 223.41 in 2023.

Success.ai’s Startup Data for Technology Startups Worldwide provides a comprehensive dataset to help businesses, investors, and service providers connect with innovative tech startups across the globe. With access to over 170 million verified professional profiles and 30 million company profiles, this dataset includes detailed firmographic data, funding insights, and employee information. Whether you’re targeting early-stage ventures, scaling startups, or established unicorns, Success.ai ensures your outreach and strategic planning are informed by reliable, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution empowers you to engage meaningfully with the technology startup ecosystem.

Why Choose Success.ai’s Technology Startup Data?

1. Comprehensive Startup Insights

   • Access verified data on company size, founding dates, technology focus areas, geographic locations, and funding stages.
   • AI-driven validation ensures 99% accuracy, minimizing wasted outreach and guiding confident engagement.

2. Global Coverage of Technology Startups

   • Includes profiles of tech startups specializing in SaaS, AI, FinTech, e-commerce, HealthTech, IoT, cybersecurity, and more.
   • Covers key innovation hubs such as Silicon Valley, Bangalore, London, Berlin, Singapore, and emerging markets worldwide.

3. Continuously Updated Datasets

   • Real-time updates reflect new funding rounds, product launches, leadership changes, and expansion activities.
   • Stay aligned with the fast-paced startup ecosystem to identify opportunities and build relationships at the right time.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible data usage and compliance with legal standards.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with founders, co-founders, CEOs, CTOs, and decision-makers in technology startups worldwide.
• 30M Company Profiles: Gain insights into startup firmographics, including size, industry focus, and operational footprint.
• Funding Insights: Access data on seed, Series A, Series B, and other funding rounds, along with investor details.
• Employee and Organizational Data: Understand team compositions, growth trajectories, and hiring trends for better engagement.

Key Features of the Dataset:

1. Startup Decision-Maker Profiles

   • Identify and connect with founders, product leads, technology architects, and business development managers driving growth in tech startups.
   • Engage with professionals making critical decisions about partnerships, procurement, and market expansion.

2. Funding and Investment Data

   • Access insights into recent funding rounds, investor portfolios, and funding sources (venture capital, angel investors, private equity).
   • Tailor outreach based on funding stage, ensuring relevance for services like advisory, recruitment, or technology solutions.

3. Advanced Filters for Precision Targeting

   • Filter startups by industry vertical, funding stage, geographic location, company size, or growth metrics.
   • Align campaigns with unique startup challenges, such as scaling operations, building MVPs, or achieving market fit.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow you to craft personalized messaging, showcase relevant value propositions, and enhance engagement with startup leaders.

Strategic Use Cases:

1. Investor Relations and Funding Opportunities

   • Connect with founders and executives preparing for fundraising rounds or seeking strategic investment partnerships.
   • Identify high-growth startups for venture capital, angel investing, or co-investment opportunities.

2. Sales and Lead Generation

   • Present SaaS solutions, infrastructure tools, or marketing services tailored to startup challenges, such as scaling, market entry, or user acquisition.
   • Build relationships with startups that align with your product offerings and service capabilities.

3. Strategic Partnerships and Ecosystem Building

   • Engage startups developing complementary technologies or exploring strategic alliances in innovation ecosystems.
   • Identify early-stage companies to co-develop products, expand market reach, or enhance technology stacks.

4. Recruitment and Talent Solutions

   • Offer staffing services, recruitment platforms, or HR solutions to fast-growing startups scaling their teams.
   • Engage with hiring managers or founders seeking skilled professionals to support product development or market expansion.

Why Choose Success.ai?

1. Best Price Guarantee

   • Gain access to premium-quality verified data at competitive prices, ensuring optimal ROI for outreach and strategic initiatives targeting startups.

2. Seamless Integration

   • Integrate verified startup data into your CRM or marketing platforms via APIs or d...

About

Dataset about 50 Startups' expenditures & profits

Column Description

50 startup dataset with columns - Sl. No. - R&D Spend - Administration - Marketing Spend - State - Profit

The costliest startup failure of all time was Quibi Holdings, which was shut down a mere six months after launching its online streaming service. Its total disclosed funding was 1.75 billion U.S. dollars. The second costliest startup failure of all time was the Hong Kong sports streaming arm of Chinese conglomerate LeEco, LeSports. Its total disclosed funding was 1.7 billion U.S. dollars. It was shut down due to overdue rent and 30 subscription-related complaints against the company, amid other problems.

Glavin001/startup-interviews dataset hosted on Hugging Face and contributed by the HF Datasets community

The dataset contains information on startup companies in the US from 2008, including company name, location, team size, number of founders, and other relevant information. This data can be used to empower the next wave of entrepreneurs by providing insights on what types of startups are being founded, where they are located, and how large their teams are. Additionally, this dataset can be used to understand trends in the startup industry over time.

Research Ideas Study the trend of startups over time. Track the number of startups in each industry and compare companies and industries. Identify geographies with a high concentration of startups for recruiting purposes. Map the development cycle of a startup from ideation to successful exit.

This dataset is a comprehensive collection of information about startups that have been accepted into the Y Combinator program since its inception. Each entry in the dataset represents a unique startup and contains several key pieces of information:

1. Name: The official name of the startup.

2. Location: The geographic location of the startup's headquarters or main operation base. This information is crucial for understanding the regional distribution and focus of Y Combinator startups.

3. Description: A brief summary of the startup, highlighting its main purpose or product.

4. Batch: The specific Y Combinator batch or cohort the startup was part of. This indicates the time period when the startup was involved in the Y Combinator program.

5. Industry: This field lists the industries or sectors the startup is involved in, such as "Consumer," "Content," "B2B," "Productivity," "Security," "Fintech," "Consumer Finance," "Social," and "Analytics." It provides insight into the diverse range of industries that Y Combinator invests in and supports.

6. Extended Description: This is a more detailed narrative about the startup, often including its founding story, key features or services, unique selling points, and any significant achievements or milestones. This detailed description gives a deeper understanding of the startup's mission, operations, and market impact.

For example, entries in the dataset include well-known companies like Reddit, described as "The front page of the internet," and others like Kiko, Clickfacts, TextPayMe, Loopt, and Infogami, each with unique contributions in their respective fields. These descriptions not only provide a snapshot of the company at the time of their Y Combinator involvement but also offer insights into the startup ecosystem's evolution over time.

This dataset is valuable for researchers, entrepreneurs, investors, and anyone interested in the startup world, providing a rich source of information on the types of companies Y Combinator has fostered, the geographical diversity of these companies, and the evolution of different industries within the startup ecosystem.

Introduction

Startup Failure Rate Statistics: Launching a new business can be both exciting and promising, but it also comes with its share of ups and downs. Understanding the reasons behind startup failures can help aspiring entrepreneurs navigate challenges more effectively. By analyzing data on these failures, entrepreneurs can develop strategies to mitigate risks and create adaptable business plans that increase their chances of success.

This article presents statistics on startup failures, highlighting what potential new businesses may encounter and how to prepare for these challenges. Being informed, developing a clear strategy, and stepping out with confidence are essential for overcoming obstacles in the entrepreneurial journey.

Startup

## Overview

Startup is a dataset for object detection tasks - it contains Startup annotations for 231 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset

This dataset was created by Bipul Nath

Contents

Rachmad, Yoesoep Edhie. 2020. From Startup to Scale-up: Steps to Success for Millennial Entrepreneurs. International Journal of Business and Innovation, Volume 18, No 2. https://doi.org/10.17605/osf.io/twv2m

In the 2020 study titled "From Startup to Scale-up: Steps to Success for Millennial Entrepreneurs," published in the "International Journal of Business and Innovation," Volume 18, Issue 2, Yoesoep Edhie Rachmad examines the critical stages and strategies involved in transitioning a startup into a successful scale-up, particularly focusing on the unique challenges and strengths of millennial entrepreneurs. This research highlights practical approaches and key considerations that can significantly influence the scalability and long-term viability of ventures driven by the millennial generation. Background: Situated within the context of the entrepreneurial journey, the study explores how millennial entrepreneurs can effectively navigate the transition from initial startup phase to significant growth or scale-up. It addresses the distinct mindset, technological adeptness, and innovative thinking characteristic of millennials, which can both aid and complicate the scaling process. Definition and Basic Concepts: Scale-up is defined in this context as the phase of business where significant growth is achieved following the establishment phase of a startup. This growth often involves expanding market reach, increasing product lines, enhancing operational capacities, and growing the team size. The process requires strategic planning, resource management, and market adaptation. Phenomenon: The driving phenomenon behind the research is the increasing number of millennial-led startups looking to scale operations in a highly competitive and rapidly changing business environment. Millennial entrepreneurs often face unique challenges such as limited resources, fast-evolving technological landscapes, and changing consumer behaviors. Problem Formulation: The main challenge addressed by the research is identifying the essential steps millennial entrepreneurs must take to successfully scale their startups. It seeks to delineate the strategies that are most effective in ensuring sustainable growth and overcoming the hurdles typically encountered during the scale-up phase. Research Objectives: The objectives include detailing the process of scaling a business, identifying the common pitfalls during the scale-up phase, and providing actionable strategies tailored to the strengths and challenges of millennial entrepreneurs. Qualitative Research Methodology: Utilizing a qualitative approach, the methodology involves case studies of successful scale-ups and in-depth interviews with millennial entrepreneurs who have navigated this transition. This method allows for an exploration of diverse experiences and the extraction of patterns and best practices. Criteria and Respondent Selection: Respondents were selected based on their successful navigation from startup to scale-up stages. The study includes 20 millennial entrepreneurs from various sectors, including technology, health and wellness, and consumer goods, providing a broad perspective on scaling strategies. Research Indicators: Key indicators of successful scaling include revenue growth, market expansion, operational efficiency, customer base enlargement, and employee growth. Operational Variables: These variables consist of leadership strategies, financial management, marketing and sales expansion techniques, technology utilization, and talent acquisition and development. Determining Factors: The study identifies critical factors for successful scaling, such as the ability to adapt to market changes, effective resource management, maintaining company culture amid growth, and leveraging technology for operational efficiency. Research Findings: The findings suggest that millennial entrepreneurs often leverage their tech-savviness and adaptability but need to focus more on strategic resource allocation, long-term planning, and maintaining organizational culture as they scale. Challenges frequently include managing an expanding team and maintaining product or service quality at scale. Conclusion and Recommendations: The study concludes that successful scaling for millennial entrepreneurs involves a mix of leveraging inherent strengths such as technological proficiency and innovative thinking while developing robust strategies in financial management and human resources. Recommendations for entrepreneurs include focusing on sustainable growth practices, investing in leadership development, and maintaining an agile approach to business operations. This research offers a comprehensive guide for millennial entrepreneurs aiming to transition from startup to scale-up, providing insights into the steps necessary for successful growth and the common challenges that may arise during the process.

Around 28.3 percent of German startups were in the information and communication technology industry. The source defines startups as being younger than ten years, highly innovative in terms of technology and/ or their business model, aiming for significant growth in revenue and employee numbers. The start-up scene German states varied in their startup density. Based on recent data, Berlin had the highest number of startups on its territory, followed by North Rhine-Westphalia and Bavaria. The most money was invested in software and analytics, at over 3.2 billion euros. Recently, investment volume in German startups saw a drastic increase but 2022 saw a decrease compared to the previous year. While information and communication technology was booming as far as startups were concerned, fintech is another area that has seen success in Germany. Female entrepeneurs Unfortunately, Germany did not fare well when looking at male and female entrepreneur numbers. In fact, European countries generally disappointed. Germany had a 7.1 percent rate of female entrepreneurship, followed by Poland with 1.6 percent.