Dataset

This dataset was created by Anne Ezeh

Released under Apache 2.0

Contents

The Office of Advocacy’s Small Business Profiles are an annual portrait of each state’s small business. They gather the latest federal data into state-by-state snapshots of small business health and economic activity. Limited economic data is also provided for the U.S. territories. This year’s profiles report on state economic growth and employment, income and finance, employer business owner demographics, turnover among establishments with employees, international trade, small business employment by industry and county, and self-employment by county, and small businesses by industry.

Monthly dataset showing change in sales and jobs recorded by Xero, an online accounting software platform. This dataset is updated on a quarterly basis. These are official statistics in development. Source: Xero.

📊 Coffee Shop Financial Dataset (Synthetic, 2022–2023)

📝 Overview

This dataset simulates the financial records of a small-town coffee shop over a two-year period (Jan 2022 – Dec 2023).
It was designed for data science, bookkeeping, and analytics projects — including financial dashboards, revenue forecasting, and expense tracking.

The dataset contains 5 CSV files representing different business accounts:
1. checking_account_main.csv - Daily sales deposits (hot drinks, cold drinks, pastries, sandwiches) + operating expenses
2. checking_account_secondary.csv - Monthly transfers between accounts + payroll funding
3. credit_card_account.csv - Weekly credit card expenses (supplies, utilities, vendor charges) and payments
4. gusto_payroll.csv - Payroll data for 3 employees + 1 contractor
5. gusto_payroll_bc.csv - Payroll data for 3 full-time employees + 1 contractor + 1 seasonal employee, with actual tax breakdown for the province of British Columbia, Canada

📂 File Details

checking_account_main.csv

• date
• description
• category (Sales, Utilities, Rent, Supplies, etc.)
• amount (positive = inflow, negative = outflow)
• balance

checking_account_secondary.csv

• date
• description
• amount
• balance

credit_card_account.csv

• date
• vendor
• category (Supplies, Marketing, Utilities, etc.)
• amount (negative = charge, positive = payment)
• balance

gusto_payroll.csv

• date
• employee_id
• employee_name (Owner, Barista 1, Barista 2, Contractor)
• role (Owner, Barista, Manager, Contractor)
• gross_pay

gusto_payroll_bc.csv

This file simulates bi-weekly payroll data for a small coffee shop in British Columbia, Canada, covering January 2022 – December 2023.
It reflects realistic Canadian payroll structure with federal and provincial tax breakdowns, CPP, EI, and additional factors.

Columns: - date → Pay date (bi-weekly schedule)
- employee_id → Unique identifier for each employee
- employee_name → Owner, Barista 1, Barista 2, Manager, Contractor, plus a seasonal Barista (June–Aug 2022)
- role → Role within the coffee shop (Owner, Barista, Manager, Contractor)
- gross_pay → Total earnings before deductions (wages + tips + reimbursements)
- federal_tax → Federal income tax withheld
- provincial_tax → British Columbia income tax withheld
- cpp_employee → Employee CPP contribution
- ei_employee → Employee EI contribution
- other_deductions → Placeholder for possible deductions (e.g., garnishments, union dues)
- net_pay → Take-home pay after deductions
- tips → Declared tips (taxable, included in gross pay)
- travel_reimbursement → Non-taxable reimbursement for travel expenses (if applicable)
- cpp_employer → Employer portion of CPP contributions
- ei_employer → Employer portion of EI contributions

Notes: - Payroll data is synthetic but modeled on Canadian payroll rules (2022–2023 rates).
- A seasonal barista employee is included (employed June 1 – Aug 31, 2022).
- Travel reimbursements are non-taxable and recorded separately.
- This file allows users to practice payroll accounting, deductions analysis, and tax reconciliation.

📈 Business Context

• The coffee shop experiences higher sales September–February (holiday season & winter drinks).
• Sales dip March–June due to seasonality in a small town.
• Pastries are sourced from a local bakery, while sandwiches are made in-house.
• Payroll includes 3 employees (baristas, manager) and 1 independent contractor.

🎯 Possible Use Cases

• Build a financial health dashboard
• Forecast revenue and expenses
• Create a profit & loss statement
• Test SQL queries for accounting workflows
• Explore data visualization with Python, R, or BI tools
• Educational projects for small business analytics

📜 License

This dataset is released under the MIT License, free to use for research, learning, or commercial purposes.

⭐ If you use this dataset in your project or notebook, please credit and share your work, it helps the community!

📷 Photo Credits: freepik

The congressional district profiles are part of Advocacy’s state profile series, which provides user-friendly snapshots of national, state, and congressional district small business statistics. The district profiles focus on the impact of small employers. Each profile provides the district’s total number of small employers and their industry breakout, plus the number of people employed and payroll expended. The profiles also show the total number of self-employed workers and a map showing how they are distributed in the district.

Access B2B Contact Data for North American Small Business Owners with Success.ai—your go-to provider for verified, high-quality business datasets. This dataset is tailored for businesses, agencies, and professionals seeking direct access to decision-makers within the small business ecosystem across North America. With over 170 million professional profiles, it’s an unparalleled resource for powering your marketing, sales, and lead generation efforts.

Key Features of the Dataset:

Verified Contact Details

Includes accurate and up-to-date email addresses and phone numbers to ensure you reach your targets reliably.

AI-validated for 99% accuracy, eliminating errors and reducing wasted efforts.

Detailed Professional Insights

Comprehensive data points include job titles, skills, work experience, and education to enable precise segmentation and targeting.

Enriched with insights into decision-making roles, helping you connect directly with small business owners, CEOs, and other key stakeholders.

Business-Specific Information

Covers essential details such as industry, company size, location, and more, enabling you to tailor your campaigns effectively. Ideal for profiling and understanding the unique needs of small businesses.

Continuously Updated Data

Our dataset is maintained and updated regularly to ensure relevance and accuracy in fast-changing market conditions. New business contacts are added frequently, helping you stay ahead of the competition.

Why Choose Success.ai?

At Success.ai, we understand the critical importance of high-quality data for your business success. Here’s why our dataset stands out:

Tailored for Small Business Engagement Focused specifically on North American small business owners, this dataset is an invaluable resource for building relationships with SMEs (Small and Medium Enterprises). Whether you’re targeting startups, local businesses, or established small enterprises, our dataset has you covered.

Comprehensive Coverage Across North America Spanning the United States, Canada, and Mexico, our dataset ensures wide-reaching access to verified small business contacts in the region.

Categories Tailored to Your Needs Includes highly relevant categories such as Small Business Contact Data, CEO Contact Data, B2B Contact Data, and Email Address Data to match your marketing and sales strategies.

Customizable and Flexible Choose from a wide range of filtering options to create datasets that meet your exact specifications, including filtering by industry, company size, geographic location, and more.

Best Price Guaranteed We pride ourselves on offering the most competitive rates without compromising on quality. When you partner with Success.ai, you receive superior data at the best value.

Seamless Integration Delivered in formats that integrate effortlessly with your CRM, marketing automation, or sales platforms, so you can start acting on the data immediately.

Use Cases: This dataset empowers you to:

Drive Sales Growth: Build and refine your sales pipeline by connecting directly with decision-makers in small businesses. Optimize Marketing Campaigns: Launch highly targeted email and phone outreach campaigns with verified contact data. Expand Your Network: Leverage the dataset to build relationships with small business owners and other key figures within the B2B landscape. Improve Data Accuracy: Enhance your existing databases with verified, enriched contact information, reducing bounce rates and increasing ROI. Industries Served: Whether you're in B2B SaaS, digital marketing, consulting, or any field requiring accurate and targeted contact data, this dataset serves industries of all kinds. It is especially useful for professionals focused on:

Lead Generation Business Development Market Research Sales Outreach Customer Acquisition What’s Included in the Dataset: Each profile provides:

Full Name Verified Email Address Phone Number (where available) Job Title Company Name Industry Company Size Location Skills and Professional Experience Education Background With over 170 million profiles, you can tap into a wealth of opportunities to expand your reach and grow your business.

Why High-Quality Contact Data Matters: Accurate, verified contact data is the foundation of any successful B2B strategy. Reaching small business owners and decision-makers directly ensures your message lands where it matters most, reducing costs and improving the effectiveness of your campaigns. By choosing Success.ai, you ensure that every contact in your pipeline is a genuine opportunity.

Partner with Success.ai for Better Data, Better Results: Success.ai is committed to delivering premium-quality B2B data solutions at scale. With our small business owner dataset, you can unlock the potential of North America's dynamic small business market.

Get Started Today Request a sample or customize your dataset to fit your unique...

Findings of the Small Business Survey on the performance of small and medium-sized businesses with employees and with no employees. Official Statistics

The SME data warehouse is based on existing administrative data sources from Statistics Canada and Canada Revenue Agency. Data covers tax year 2001 to tax year 2006. The SME Data Warehouse contains a complete, up to date and unduplicated list of all businesses in Canada based on Statistics Canada's Business Register for tax years 2001-2006. This product currently produces data for Small and Medium Sized Enterprises (SMEs). SMEs are defined as enterprises with less than 250 employees and less than $50 million in total revenue.

Nonemployer Statistics is an annual series that provides statistics on U.S. businesses with no paid employees or payroll, are subject to federal income taxes, and have receipts of $1,000 or more ($1 or more for the Construction sector). This program is authorized by the United States Code, Titles 13 and 26. Also, the collection provides data for approximately 450 North American Industry Classification System (NAICS) industries at the national, state, county, metropolitan statistical area, and combined statistical area geography levels. The majority of NAICS industries are included with some exceptions as follows: crop and animal production; investment funds, trusts, and other financial vehicles; management of companies and enterprises; and public administration. Data are also presented by Legal Form of Organization (LFO) (U.S. and state only) as filed with the Internal Revenue Service (IRS). Most nonemployers are self-employed individuals operating unincorporated businesses (known as sole proprietorships), which may or may not be the owner's principal source of income. Nonemployers Statistics features nonemployers in several arts-related industries and occupations, including the following: Arts, entertainment, and recreation (NAICS Code 71) Performing arts companies Spectator sports Promoters of performing arts, sports, and similar events Independent artists, writers, and performers Museums, historical sites, and similar institutions Amusement parks and arcades Professional, scientific, and technical services (NAICS Code 54) Architectural services Landscape architectural services Photographic services Retail trade (NAICS Code 44-45) Sporting goods, hobby, and musical instrument stores Sewing, needlework, and piece goods stores Book stores Art dealers Nonemployer Statistics data originate from statistical information obtained through business income tax records that the Internal Revenue Service (IRS) provides to the Census Bureau. The data are processed through various automated and analytical review to eliminate employers from the tabulation, correct and complete data items, remove anomalies, and validate geography coding and industry classification. Prior to publication, the noise infusion method is applied to protect individual businesses from disclosure. Noise infusion was first applied to Nonemployer Statistics in 2005. Prior to 2005, data were suppressed using the complementary cell suppression method. For more information on the coverage and methods used in Nonemployer Statistics, refer to NES Methodology. The majority of all business establishments in the United States are nonemployers, yet these firms average less than 4 percent of all sales and receipts nationally. Due to their small economic impact, these firms are excluded from most other Census Bureau business statistics (the primary exception being the Survey of Business Owners). The Nonemployers Statistics series is the primary resource available to study the scope and activities of nonemployers at a detailed geographic level. For complementary statistics on the firms that do have paid employees, refer to the County Business Patterns. Additional sources of data on small businesses include the Economic Census, and the Statistics of U.S. Businesses. The annual Nonemployer Statistics data are available approximately 18 months after each reference year. Data for years since 2002 are published via comma-delimited format (csv) for spreadsheet or database use, and in the American FactFinder (AFF). For help accessing the data, please refer to the Data User Guide.

Percentage of VAT registered businesses showing year-on-year employment growth. This indicator will include those businesses registered for VAT with less than 50 employment (around 98% of all VAT registered enterprises). It will measure the proportion of those businesses showing year on year employment growth, where employment is measured as the number of employees (full and part-time) plus the number of self-employed people that run the business.

In March 2020, Mayor Carter announced the Saint Paul Bridge Fund to provide emergency relief for families and small businesses most vulnerable to the economic impacts of the COVID-19 pandemic. The program was funded through $3.25 million dollars from the Saint Paul Housing and Redevelopment Authority along with contributions from philanthropic, corporate and individual donors. Through these additional contributions, the fund provided $4.1 million to families and small businesses in Saint Paul.Data previously shared in this space included only the 380 recipients funded through "Phase 1". This dataset includes all three phases that were ultimately rolled out through the Bridge Fund for Small Business program.Nearly 2,000 unique applications applied for a small business grant of $7,50036% were from ACP50 areas (Areas of Concentrated Poverty where 50% or more of the residents are people of color)The applications were reviewed in order of a random number assigned at application close. Of these applications:633 small businesses were awarded a $7,500 grant36% of applications in the city were from ACP50 areas86% of applicants in the city cited they were ordered closed under one of the Governor’s Executive OrdersThis is a dataset of the small businesses that applied for the Bridge Fund and includes:Self-reported survey responsesAward informationGeographic information Additional information about the Saint Paul Bridge Fund may be found at stpaul.gov/bridge-fund.

Data collected as part of the City of Melbourne's Census of Land Use and Employment (CLUE). The data covers the period 2002-2023. It shows number of jobs and number of business establishments by business size, classified by their CLUE industry, ANZSIC1 and CLUE small area allocation.Business size is determined by the total number of jobs at ech business establishment and is categorised as follows:Non employing, no jobs allocated to the establishment.Small business, 1 to 19 jobs employed at a business establishment.Medium business, 20 to 199 jobs employed at a business establishment.Larger business, 200 or more jobs employed at a business establishment.This dataset has been confidentialised to protect the commercially sensitive information of individual businesses. Data in cells which pertain to two or fewer businesses have been suppressed and are shown as a blank cell. The 'City of Melbourne' row totals refer to the true total, including those suppressed cells.Non-confidentialised data may be made available subject to a data supply agreement. For more information contact cityfacts@melbourne.vic.gov.auFor CLUE small area spatial files see https://data.melbourne.vic.gov.au/explore/dataset/small-areas-for-census-of-land-use-and-employment-clue/mapFor more information about CLUE see http://www.melbourne.vic.gov.au/clueFor more information about the ANZSIC industry classification system see http://www.abs.gov.au/ausstats/abs@.nsf/mf/1292.0

The "Enterprise Counts Dataset" provides valuable information on the number of enterprises operating within a specific region or country. This dataset is essential for understanding the business landscape, economic vitality, and entrepreneurial activity within the given area.

Key Features of the Dataset:

Enterprise Classification: The dataset may categorize enterprises based on industry sectors, business sizes (e.g., small, medium, large), ownership types (e.g., private, public), or other relevant classification criteria, allowing for a detailed analysis of the enterprise landscape. Geographic Coverage: The dataset covers a specific region or country, providing insights into the enterprise counts at the local, regional, or national level, depending on the scope of the data collection. Time-Series Data: The dataset may encompass multiple years, enabling researchers to analyze enterprise trends over time, identify growth or decline patterns, and assess the overall business climate. Size or Scale Analysis: By examining the enterprise counts across different size categories, researchers can gain insights into the distribution of businesses by scale, such as the prevalence of small and medium-sized enterprises (SMEs) versus large corporations. Additional Metrics: The dataset may include supplementary metrics, such as employment figures, revenue data, or industry-specific indicators, providing a comprehensive understanding of the enterprise landscape beyond the number of enterprises alone. Potential Applications and Benefits of the Dataset:

Economic Development and Policy Planning: Governments and policymakers can utilize this dataset to assess the health and growth of the business sector, identify potential gaps or opportunities for economic development, and formulate policies and strategies to support entrepreneurship and job creation. Business Environment Analysis: Entrepreneurs, investors, and market researchers can leverage this dataset to understand the competitive landscape, market saturation, and industry concentrations within a specific region or country, aiding in business planning and investment decisions. Socioeconomic Research: Researchers and social scientists can use this dataset to study the relationship between enterprise counts, economic indicators, and socioeconomic outcomes, contributing to research on topics such as income inequality, regional development disparities, or entrepreneurial ecosystems. Benchmarking and Comparisons: The dataset allows for comparisons across different regions or countries, enabling benchmarking exercises and identifying best practices in fostering a conducive business environment.

Small business transactions and revenue data aggregated from several credit card processors, collected by Womply and compiled by Opportunity Insights. Transactions and revenue are reported based on the ZIP code where the business is located.

Data provided for CT (FIPS code 9), MA (25), NJ (34), NY (36), and RI (44).

Data notes from Opportunity Insights: Seasonally adjusted change since January 2020. Data is indexed in 2019 and 2020 as the change relative to the January index period. We then seasonally adjust by dividing year-over-year, which represents the difference between the change since January observed in 2020 compared to the change since January observed since 2019. We account for differences in the dates of federal holidays between 2019 and 2020 by shifting the 2019 reference data to align the holidays before performing the year-over-year division.

Small businesses are defined as those with annual revenue below the Small Business Administration’s thresholds. Thresholds vary by 6 digit NAICS code ranging from a maximum number of employees between 100 to 1500 to be considered a small business depending on the industry.

County-level and metro-level data and breakdowns by High/Middle/Low income ZIP codes have been temporarily removed since the August 21st 2020 update due to revisions in the structure of the raw data we receive. We hope to add them back to the OI Economic Tracker soon.

More detailed documentation on Opportunity Insights data can be found here: https://github.com/OpportunityInsights/EconomicTracker/blob/main/docs/oi_tracker_data_documentation.pdf

This sample dataset includes information for different SMEs across various locations. The data includes the SME's capital, turnover, pre-tax profit, tax, and the number of employees for both 2018 and 2023.

Please note that the data provided is completely random and is for illustrative purposes only. In practice, the data for a particular SME would need to be based on actual financial and business metrics

📊 70 Small Business Ideas to Start in 2025

This dataset features a curated list of 70 small business ideas that are relevant and potentially profitable for aspiring entrepreneurs in 2025. Each entry includes the business name, difficulty rating, and its corresponding category to help users analyze and choose ideas based on their interests, expertise, and available resources.

📁 Dataset Structure

Filename: small_business_ideas_2025.csv

🗂 Categories Overview

The dataset covers diverse categories including:

• 💼 Financial Services
• 🛠️ Manual Labor
• 🎨 Creative Work
• 🏘️ Property & Real Estate
• 📈 Planning & Coaching
• 🍽️ Hospitality
• 🐾 Other Services
• 🌐 Online Business

🎯 Use Cases

• 📚 Market research for startup consultants
• 💡 Inspiration for new entrepreneurs
• 🤖 Training data for idea recommendation models
• 📊 Exploratory data analysis (EDA) on industry trends
• 📝 Project or portfolio ideas for business/data students

🏁 Sample Preview

Numbers of enterprises and local units produced from a snapshot of the Inter-Departmental Business Register (IDBR) taken on 14 March 2025.

This dataset on the income of micro and small enterprises covers multiple years of panel data and includes basic firm characteristics and economic indicators. The main variables comprise a unique firm identifier (firm_id), year (year), enterprise income (income), the level of digital financial development (digital_finance), firm size (firm_size), number of employees (employee_num), founder's education level (founder_edu), financing difficulty (financing_difficulty), regional GDP (region_gdp), urbanization status of the firm's location (urban), and whether the firm belongs to the high-tech industry (high_tech). By comprehensively considering both firm-specific characteristics and external economic environments, this dataset provides rich and detailed information to analyze the impact of digital finance on the income of micro and small enterprises.

This publication provides the only official government statistics that attempt to the estimate the total number of UK private sector businesses in the UK and their contribution to employment and turnover. Also provided is an estimate of the total number of businesses in the UK whole economy. The publication supercedes the publication 'Small and Medium-sized Enterprise Statistics for the UK and Regions'. National Statistics

The dataset exists to observe the entrepreneurial activity of Austin over a long time period. The data comes from the U.S. Census County Business Pattern table and is capturing data at the Travis County level. It contains the cumulative count of firms by employee size and count of firms by employee size by industry. This data can be used to see changes of employer growth by industry; to project where workforce growth could be occurring; or to simply see how many small businesses there are in Austin.

View more details and insights related to this data set on the story page: data.austintexas.gov/stories/s/ndb5-si22