This dataset is a China-based entrepreneurship risk assessment dataset designed for business risk analysis, startup evaluation, investment decision support, and entrepreneurial performance prediction. It contains structured records of Chinese entrepreneurial ventures collected around financial records, investment profiles, market indicators, operational performance, sustainability factors, founder information, funding information, and company profile details.

The dataset includes 2,500 business records with expanded entrepreneurship-related features. Each record represents one entrepreneurial company or startup operating in a Chinese business environment. The dataset is suitable for analyzing how financial stability, founder background, funding strength, investor participation, market demand, operational efficiency, and sustainability conditions influence business risk level and decision priority.

In addition to the existing financial, market, operational, investment, and sustainability attributes, the dataset also includes founder-related and company-level information such as Founders_Counts, Founders_Degree, Founders_University, Investor_Count, Total_Funding_Amount, Facebook, Twitter, and Type_of_Company. These features help represent the entrepreneurial profile more completely by capturing founder capability, educational background, investor support, funding scale, business type, and digital/social media presence.

The dataset can be used for risk classification, investment priority prediction, business viability analysis, startup success evaluation, and decision-support studies. The output fields include financial risk score, market risk score, operational risk score, sustainability risk score, overall risk score, risk level, and decision priority. This makes the dataset useful for entrepreneurship research, startup ecosystem analysis, financial risk modeling, and intelligent business decision-making applications in the Chinese entrepreneurial context.

Column Name Description Enterprise_ID Unique identification number assigned to each entrepreneurial venture. Business_Sector Industry category of the enterprise, such as technology, healthcare, retail, manufacturing, agriculture, hospitality, or construction. Region Geographical region where the business operates. Business_Stage Current development stage of the enterprise, such as seed, startup, growth, or mature stage. Business_Age_Years Number of years the enterprise has been operating. Employee_Count Total number of employees working in the enterprise. Annual_Revenue_USD Total yearly revenue generated by the business in USD. Revenue_Growth_Rate_Percent Percentage increase or decrease in revenue over a specific period. Profit_Margin_Percent Percentage of profit earned after deducting business expenses. Cash_Flow_USD Net cash inflow or outflow available for business operations. Debt_Ratio_Percent Percentage of business assets financed through debt. Return_on_Investment_Percent Percentage return generated from business investment. EBITDA_Margin_Percent Earnings before interest, tax, depreciation, and amortization as a percentage of revenue. Current_Ratio Ratio of current assets to current liabilities, indicating short-term liquidity. Operating_Expense_Ratio_Percent Percentage of revenue spent on operating expenses. Burn_Rate_USD_Month Monthly cash spending rate of the enterprise. Runway_Months Number of months the enterprise can operate with available funds. Credit_Score Financial reliability score representing creditworthiness of the enterprise. Initial_Capital_USD Initial amount invested to start the business. Investor_Participation_Percent Percentage involvement of external investors in the enterprise. Funding_Rounds Number of investment rounds received by the enterprise. Funding_Raised_USD Total funding amount raised from investors or other sources. Investment_Growth_Percent Percentage growth in investment value over time. Company_Valuation_USD Estimated market value of the enterprise. Equity_Dilution_Percent Percentage ownership reduction due to external investment. Funding_Gap_Percent Difference between required funding and available funding. Grant_or_Subsidy_Support_Percent Percentage of financial support received through grants or subsidies. Market_Demand_Index Score representing customer demand for the product or service. Competition_Intensity_Index Score indicating the level of market competition. Customer_Growth_Rate_Percent Percentage increase in the number of customers. Market_Share_Percent Percentage of the target market captured by the enterprise. Customer_Acquisition_Cost_USD Average cost required to acquire one new customer. Customer_Lifetime_Value_USD Estimated revenue expected from a customer over their relationship with the business. Customer_Churn_Rate_Percent Percentage of customers lost during a specific period. Brand_Awareness_Ind...

This dataset comprises a hand collected market overview of the FinTech market in Germany as of December 2021. It includes various verified properties of 978 unique firms, which can be attributed to the financial technology sector and are operating in Germany. Each observation represents one company with 24 variables, including name, address, legal form, founders with corresponding LinkedIn accounts, register number or company-ID, attribution to FinTech segments and subsegments, bank cooperation, URL address, local court, former name, operating status. The dataset contains established companies as well as start-ups. Since the market in Germany and the nature of FinTech companies itself are dynamic as well as changing there is no complete overview of the market. Furthermore, the total number, the operating status as well as specific properties of FinTechs cannot be found in one accumulated data base. The dataset contains valuable information for researchers, practitioners as well as for supervising authorities. We provide the description of variables as well as a taxonomy for categorizing FinTechs. The nature of the dataset enables further cross-sectional and the possibility of longitudinal analyses of the complete market. The aim of the collection procedure was to find and identify all relevant FinTechs operating in Germany with a structured approach. Different databases and websites (see below) were used to obtain an overview of the market. The dataset was repeatedly updated and verified throughout the years within this process. An association to the segment of operations was conducted. Through structured Google searches the operating status was checked.

The corresponding paper with a detailed description of the variables and volume estimates can be downloaded here:
https://elibrary.duncker-humblot.com/article/72485/german-fintech-companies-a-market-overview-and-volume-estimates

Startup Investment Analysis 📌 Project Overview This project analyzes startup investments to uncover trends, patterns, and key insights about funding stages and company success. Using Python, we explore how investments impact a startup’s status—whether it remains operational, gets acquired, or shuts down.

💡 Problem Statement Investing in startups involves risk, and understanding investment patterns can help entrepreneurs and investors make informed decisions. This project explores how funding rounds, investment amounts, and other factors influence startup success.

📊 Dataset Information The dataset consists of company-level investment details, including:

Funding amount per startup Number of funding rounds Startup status (Operating, Closed, or Acquired) Investor details and more 🛠 Tools & Technologies Used

Python for data analysis Pandas & NumPy for data cleaning and manipulation Matplotlib & Seaborn for visualizations Exploratory Data Analysis (EDA) to uncover key trends 📌 Key Findings

Investment amount and funding stage significantly impact a startup’s future. Trends in startup success based on funding types and rounds. Data-driven insights that can help investors make better funding decisions. 🔗 Project Contents

Data Cleaning & Preprocessing Exploratory Data Analysis (EDA) Visualizations & Trend Analysis Final Insights & Conclusions

Dataset: Xstoxservices-india-finance-prediction

This dataset is an original compilation created and curated by Xstoxservices Technologies Private Limited for research, modelling, and fintech conference use.

Company financial indicators were gathered through Xstoxservices' outreach across various startup networks of India, including early-stage, growth-stage, and late-stage ventures operating in technology, fintech, healthcare, consumer, industrials, and related sectors.

The purpose of Xstoxservices-india-finance-prediction is to support financial performance prediction: estimating latest-quarter (Q0) financial outcomes from structured company identifiers and modelled financial features.

Primary source: Aggregated financial and operating signals obtained via Xstoxservices from various startup networks of India (founder networks, accelerator / incubator cohorts, and partner ecosystem datasets).

All figures in the accompanying submission file are prepared under the supervision of Xstoxservices Technologies Private Limited and are intended for analytical and educational use in the fintech conference context.

Folder: Xstoxservices-india-finance-prediction/

Xstoxservices-india-finance-prediction.csv Main data file for dataset Xstoxservices-india-finance-prediction. Each row is one company. Columns are the target financial fields for the latest reported quarter (Q0).

dataset_info.txt This document — full description of Xstoxservices-india-finance-prediction, authorship, columns, and usage notes. Authored by Xstoxservices Technologies Private Limited.

Same column schema as the Xstoxservices financial performance prediction submission format:

Id Unique company identifier assigned within Xstoxservices-india-finance-prediction.

Q0_REVENUES Total income generated by the company from primary operations in the latest reported quarter (Q0).

Q0_COST_OF_REVENUES Direct costs associated with producing goods or services sold in Q0.

Q0_GROSS_PROFIT Revenues minus cost of revenues for Q0.

Q0_OPERATING_EXPENSES Total expenses associated with operating core business functions in Q0.

Q0_EBITDA Earnings Before Interest, Taxes, Depreciation, and Amortization for Q0. Operating performance measure excluding certain non-cash / financing items.

Q0_OPERATING_INCOME Profit from normal business operations after operating expenses, before interest and taxes, for Q0.

Q0_TOTAL_ASSETS Total assets owned by the company as of Q0 (current and noncurrent).

Q0_TOTAL_LIABILITIES Total obligations or debts owed by the company as of Q0.

Q0_TOTAL_STOCKHOLDERS_EQUITY Residual interest in assets after deducting liabilities as of Q0 (shareholders' ownership equity).

Overview:

This dataset provides a comprehensive look into the financial expenditures and profits of 50 startups based in the United States. It is an invaluable resource for analysts, economists, and business strategists seeking to understand the correlation between different types of spending and profitability in startup ventures.

Attributes: 1. R&D Spend: - Description: The amount of money each company has invested in Research and Development activities. - Data Type: Numeric (US dollars) - Importance: Indicates the company's commitment to innovation and technological advancement. 2. Administration: - Description: Expenditure on administrative functions and operations. - Data Type: Numeric (US dollars) - Relevance: Reflects the overhead costs associated with managing the company. 3. Marketing Spend: - Description: Investment in marketing and promotional activities. - Data Type: Numeric (US dollars) - Significance: A key factor in revenue generation and market penetration. 4. State: - Description: The U.S. state where the company is operating. - Data Type: Categorical (California, New York, or Florida) - Purpose: Provides geographical context and allows for regional analysis. 5. Profit: - Description: The net profit earned by the company. - Data Type: Numeric (US dollars) - Utility: A direct measure of the company’s financial success.

Potential Uses: - Business Analysis: Understanding how different types of spending (R&D, administration, marketing) affect profitability. - Regional Studies: Examining the impact of geographical location on business success. - Startup Growth: Insights into the financial practices of successful startups. - Economic Research: Data-driven study of the startup ecosystem in the U.S.

Target Audience: - Business Analysts and Economists - Marketing Strategists - Startup Consultants - Data Science Enthusiasts - Academic Researchers

Conclusion: This dataset is a rich resource for anyone looking to delve into the financial dynamics of startups in the U.S. It offers a unique perspective on how different types of investments correlate with company success across various states.

Please note that the data is anonymized and does not include any confidential information about the companies listed. The dataset is intended for educational and research purposes.

Structured failure analysis data for Ofo: timeline, funding raised, root causes, IdeaProof AI Failure Score, and competitive context.

This dataset contains information on various financial metrics of startup companies, focusing on attributes such as R&D Spend, Administration costs, Marketing Spend, State of operation, and Profit. These attributes provide insights into the allocation of resources, operational expenses, geographic location, and overall profitability of startup ventures.

In financial year 2025, over 437 thousand new businesses entered the Australian market. By the end of 2025, there were over 2.5 million businesses operating in Australia.

Structured failure analysis data for Qianhe: timeline, funding raised, root causes, IdeaProof AI Failure Score, and competitive context.

Structured failure analysis data for NOX: timeline, funding raised, root causes, IdeaProof AI Failure Score, and competitive context.

Startup Investment Simulation Dataset

This dataset provides a fictional history of startup funding rounds across industries, simulating real-world patterns of investment and outcomes.

Columns

• StartupName: Simulated name of the startup.
• FoundedYear: Year the startup was founded.
• Industry: Sector the startup operates in.
• Funding(M): Total funding received (in millions USD).
• FundingStage: Current or last known funding stage.
• Outcome: Final known status of the startup.

Use Cases

• Investment prediction models
• Industry funding trend analysis
• Outcome prediction (Exit, Failure, Still Operating)
• Time-series analysis of startup life cycles

Founded in 2016, Startup Spine operates in Fintech offering peer to peer solutions across a broad spectrum of services, including accounting, business tax planning, and financial planning. The firm provides comprehensive professional consulting services, focusing on tax management, wealth management, asset management, and business operations. Additionally, Startup Spine supports business setups, mergers and acquisitions, and provides outsourcing services. The company's aim is to deliver a complete suite of financial and legal consulting solutions tailored to its clients' needs.

Entrepreneurship Services Market size was valued at $ 12.8 Bn in 2024 and is expected to reach $ 30.14 Bn by 2032, growing at a CAGR of 11.3% from 2026 to 2032.Global Entrepreneurship Services Market Drivers:The market drivers for the entrepreneurship services market can be influenced by various factors. These may include:Government Support Programs: Many governments offer tax benefits, grants, and startup-friendly policies that encourage individuals to start businesses and seek professional services for legal, financial, and operational guidance.Rising Interest in Self-Employment: A growing number of professionals prefer flexible work and autonomy, which leads them to start ventures and look for structured entrepreneurship services that help manage business planning and growth.

Technographic Data | B2B Data | 27M+ Records

Company Data, B2B Leads, Startup & Technographic Intelligence

In today’s competitive B2B landscape, data accuracy and relevance define revenue outcomes. Our comprehensive Technographic and B2B Data Platform delivers access to 27M+ verified records, enabling sales, marketing, and strategy teams to identify, prioritize, and engage the right companies with precision.

This dataset goes far beyond basic firmographics. It combines company intelligence, contact data, startup insights, and deep technographic signals to help organizations understand who to target, what technologies they use, and when they are most likely to buy.

What Is Technographic Data — and Why It Matters

Technographic data reveals the technology stack used by companies — from cloud platforms and CRM systems to marketing tools, cybersecurity solutions, AI frameworks, and developer technologies.

By understanding a prospect’s current tools and platforms, you can:

Identify high-intent buyers

Avoid mismatched or irrelevant outreach

Personalize messaging based on real technology usage

Target competitive displacement opportunities

Align sales conversations with actual infrastructure needs

Technographics turn guessing into data-driven targeting.

Dataset Overview

Our database includes 27M+ global records, continuously refreshed and validated to ensure accuracy and scale.

Coverage Includes:

Enterprises, SMBs, and mid-market companies

High-growth startups and emerging tech firms

Global and regional businesses across industries

Data Categories:

Company Data

B2B Contact & Lead Data

Startup Intelligence

Technographic Insights

Company & Firmographic Intelligence

Gain a complete view of each organization with rich firmographic attributes, including:

Company name and domain

Industry classification

Employee size and revenue range

Headquarters and global locations

Business model and company type

Growth indicators and funding signals

This data enables precise account segmentation, territory planning, and account-based marketing (ABM) strategies.

B2B Contact & Lead Data

Access decision-makers and influencers across key departments, including:

IT & Engineering

Marketing & Growth

Sales & Revenue Operations

Finance & Procurement

Product, Data, and Security

Each contact record is enriched with:

Job title and seniority

Department and role relevance

Business email verification

Company association and hierarchy

Designed to support outbound sales, demand generation, and pipeline acceleration.

Deep Technographic Insights

Our technographic intelligence identifies the tools and platforms actively used by organizations across their digital ecosystem.

Technology Categories Include:

Cloud & Infrastructure (AWS, Azure, GCP)

CRM & Sales Tools

Marketing Automation & Analytics

Cybersecurity & Compliance

Data, AI & Machine Learning

Development Frameworks & APIs

Collaboration & Productivity Tools

E-commerce & Payment Platforms

This enables:

Hyper-targeted prospect lists

Competitive takeout campaigns

Upsell and cross-sell modeling

Partner and integration targeting

Startup & Emerging Company Intelligence

For investors, accelerators, SaaS providers, and innovation teams, our startup data highlights:

Early-stage and funded startups

Technology adoption patterns

Growth velocity indicators

Market and product focus areas

Ideal for venture scouting, partnerships, and early customer acquisition.

Key Use Cases Sales & Business Development

Build high-intent prospect lists

Shorten sales cycles with relevant outreach

Improve win rates through personalization

Marketing & Demand Generation

Power ABM and ICP-based campaigns

Improve conversion with tech-aligned messaging

Reduce CPL by targeting the right accounts

Market Research & Strategy

Analyze tech adoption trends

Identify whitespace opportunities

Understand competitive positioning

Partnerships & Alliances

Identify integration-ready companies

Find ecosystem partners

Target complementary tech users

Data Quality & Compliance

We prioritize accuracy, freshness, and compliance:

Continuous data validation and enrichment

Multi-source verification

GDPR and privacy-aware processes

Scalable delivery formats (CSV, API, CRM integrations)

Our datasets are built for enterprise-grade use cases.

Why Teams Choose This Data

✔ 27M+ scalable, verified records ✔ Deep technographic granularity ✔ Global company and contact coverage ✔ Startup and enterprise intelligence in one place ✔ Designed for sales, marketing, and GTM teams

Turn Data Into Revenue Intelligence

In a world where personalization, timing, and relevance define success, technographic-driven B2B data is no longer optional — it’s a competitive advantage.

Whether you’re accelerating pipeline, launching new markets, or refining your ICP, this dataset helps you target smarter, engage better, and grow faster.

Many small businesses and startups struggle to adjust their operational plans to quickly changing market and financial situations. Traditional data-driven techniques often miss possibilities and waste resources. Our unique approach, Unified Statistical Association Validation (USAV), allows dynamic and real-time data association and improvement assessment to address this essential issue. USAV classifies and validates critical data associations based on business features to improve startup incubation and innovation decision-making. USAV analyses different financial eras using federated learning to find performance inefficiencies using a Kaggle dataset on small business success and failure. USAV recommends actionable improvements during innovation using non-recurrent statistical patterns, unlike standard models that use prior financial data. The framework allows real-time flexibility with continual statistical updates without data redundancy. The proposed approach achieved an improvement assessment score of 0.98, data association accuracy of 96%, statistical update efficiency of 0.97, modification ratio of 35%, and incubation analysis time reduction of 240 units in experimental evaluation. These findings demonstrate USAV’s ability to help strategic decision-making in dynamic corporate situations.

Startup Funding Sucess Dataset

Overview

This dataset contains startup-related financial, operational, market, and founder metrics designed for machine learning, business analytics, and predictive modeling tasks. It simulates realistic startup ecosystem trends across different industries and countries.

The dataset can be used for: - Startup success prediction - IPO and acquisition analysis - Valuation forecasting - Customer retention analysis - Funding trend analysis - Business growth modeling

Dataset Features

Possible Use Cases

• Classification and regression projects
• Startup valuation prediction
• IPO/acquisition prediction
• Business intelligence dashboards
• Financial analytics
• Data visualization projects

Notes

• The dataset is synthetically generated for educational and research purposes.
• Suitable for beginners as well as advanced ML/analytics projects.

Introduction

Lordstown Motors Statistics: The electric vehicle company Lordstown Motors has transformed from a high-growth SPAC-backed startup into a post-bankruptcy shell business, which will continue operating until 2026. The company restructured its entire business operations and financial assets after its Chapter 11 filing in June 2023 and its subsequent March 2024 launch as Nu Ride Inc.

The 2025 to 2026 period focuses on three activities, which include litigation strategy, asset preservation, and restructuring economics. The current phase demonstrates an exceptional situation where all three business elements lost value while the company shifted its financial possibilities towards legal litigation and tax-related assets.

This article will give a picture of the trending Lordstown Motors statistics and its market performance.

Founded in 2018, Refrens operates in Fintech industry. Initially focused on simplifying invoicing and payment processing, the company has expanded its offerings to include a comprehensive suite of financial tools aimed at enhancing business operations for its users. Refrens has launched additional features to support seamless financial transactions and reporting, enhancing user experience. The startup has also expanded its platform capabilities by integrating solutions that cater to a wider range of financial management needs. Competing with companies such as Khatabook and OkCredit.

Start-ups in the Philippines raised about more than ********* U.S. dollars worth of funding in 2025, higher than the value of deals from the previous year. Start-ups can seek funding through various sources, including private funders, angel investors, and venture capitalists. Stages of startup funding After successfully transforming their business concept into a product or service, startups go through funding stages to get their operations up and running. The earliest stage of funding is called pre-seed funding, followed by seed funding, which represents the first official money that the enterprise raised. After releasing its product in the market, the start-up enters early-stage funding to generate additional revenue. Finally, late-stage funding is the final round before planning an initial public offer (IPO) or acquisition. In the Philippines, late-stage venture capital (VC) was the most funded growth stage in 2021, followed by early-stage VC. Most funded startup sector Across sectors, fintech was the most funded start-up sector in the Philippines. The growth of the fintech sector was attributed to increased government efforts on financial inclusion and high digital adaptation among the Filipino population. Fintech firms in the country were mainly in the lending and payments sector.

Startup Names and Descriptions Dataset

Startup Names, Cities, Taglines, and Descriptions

By Ricky Hennessy [source]

About this dataset

The dataset provides crucial information about each startup, including its name, city of operation, tagline, and description. The name column represents the unique identifier assigned to each company. Meanwhile, the city column specifies the location where the respective startup is situated.

One of the most intriguing aspects of this dataset is the tagline column, which offers a glimpse into the brand identity and mission of each startup. This short phrase or slogan is carefully crafted to encapsulate their core values and differentiate themselves from competitors.

Additionally, this dataset includes a description column that provides concise summaries of each startup's product offering, service portfolio or business model. These descriptions offer insights into what makes each company innovative or unique in their respective industries.

By leveraging this rich dataset, researchers can study trends in startup naming conventions across different regions or industries. Business analysts can also gain valuable insights into how startups brand themselves through their taglines and descriptions.

Overall, this dataset serves as an invaluable tool for market research professionals aiming to understand emerging startups' landscape in various cities worldwide. It enables entrepreneurs to analyze successful companies' branding strategies or even inspire them when naming their own ventures

How to use the dataset

Here is a guide on how to effectively use this dataset:

• Familiarize yourself with the columns:

  • name: The name of the startup company.
  • city: The city where the startup is located.
  • tagline: A short phrase or slogan that represents the startup's brand or mission.
  • description: A brief description of the startup's product, service, or business model.

• Explore startups by name: Start by exploring different startups based on their names. Scan through the name column to find unique and intriguing names that catch your attention. You can filter or search for specific keywords within this column to narrow down your focus.

• Discover startups by location: If you are interested in discovering startups based on their location, refer to the city column. Here you can find cities from around the world where these startups are located. Use this information to explore regional trends in entrepreneurship or identify potential opportunities in specific locations.

• Uncover taglines as points of interest: Taglines often encapsulate a startup's brand identity and mission succinctly. Take a look at the tagline column for captivating phrases that pique your curiosity. These taglines can offer insights into a company's vision and purpose.

• Learn about products/services through descriptions: The description column provides brief descriptions of each startup's product, service, or business model. 6- Analyze trends across columns: By analyzing patterns across multiple columns such as name-city pairs you can identify trends and correlations that may help you gain insights into the startup landscape.

• Filter and sort data for specific criteria: Utilize the dataset's features to filter or sort the data based on specific criteria of interest. Whether you're looking for startups in a particular city, searching for innovative taglines, or analyzing businesses within a specific industry, leveraging these tools will streamline your search process.

• Formulate research questions or applications: As you explore this dataset, let it inspire questions and research projects related to startups, entrepreneurship, branding strategies, or city-level economic analysis. The possibilities are vast!

Remember that this dataset offers

Research Ideas

• Market research: This dataset can be used to analyze the startup landscape in different cities, identify trends in startup names and taglines, and gain insights into the types of products or services being offered by startups.
• Branding and marketing: Startups can use this dataset to analyze the taglines and descriptions of successful companies in order to create compelling branding and marketing strategies for their own businesses.
• Geographic analysis: By analyzing the city column, researchers or entrepreneurs can gain insights into which cities have a higher concentration of startups, identify emerging startup hubs, or compare different regions based on their startup activity

Acknowledgements

If you use this...