This DataSet to track the latest trends, we’ve compiled small business and startup statistics to better understand what makes a startup tick. If you’re looking to build a startup or just interested in diving into the numbers, check out these informative statistics on success, failure, funding and more before getting started.

Objective The objective of the project is to predict whether a startup which is currently operating turn into a success or a failure. The success of a company is defined as the event that gives the company's founders a large sum of money through the process of M&A (Merger and Acquisition) or an IPO (Initial Public Offering). A company would be considered as failed if it had to be shutdown.

This problem will be solved through a Supervised Machine Learning approach by training a model based on the history of startups which were either acquired or closed. The trained model will then be used to make predictions on startups which are currently operating to determine their success/failure.

Do an EDA and try to predict which startups and in which field achieve great success! https://www.googleapis.com/download/storage/v1/b/kaggle-forum-message-attachments/o/inbox%2F9770082%2Fd1cc4e53157d2f3f0a9f661b6f2cd28f%2FGroup%202215.jpg?generation=1674420531095211&alt=media" alt="">

You will have to answer the following questions: - How Many New Businesses Fail ? - How Many New Businesses Secsees ? - Reasons for Failing - How to Avoid Failing And many other questions...

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...

The dataset contains information about 'New business density (new registrations per 1,000 people ages 15-64)' of the Country ranging from the year 2015 to 2020. I have made the assumption that a country's New Business density is synonymous to its New Startup density.

The dataset is derived from the World Bank dataset. The startup ecosystem of a country is intricately linked to various economic, technological, and social factors. The dataset comprises indicators that, in my perspective, impact the growth of startups within a country.

Key determinants include the ease of doing business, research and development expenditure, access to electricity and the Internet, labor force participation rate, government expenditure on education, and the efficiency of startup registration procedures. These factors collectively shape the environment in which startups emerge and thrive, influencing their growth, innovation, and overall success.

This dataset provides a comprehensive look into startup growth, funding trends, valuations, and profitability across multiple industries. It includes essential business metrics such as funding rounds, revenue, employee count, market share, and profitability status, making it a valuable resource for investors, analysts, and entrepreneurs looking to understand what drives startup success.

Whether you're predicting unicorns, analyzing funding patterns, or exploring industry trends, this dataset is packed with insights to help you make data-driven decisions. 🚀

Columns Description

1. Startup Name – The name of the startup (e.g., Startup_1, Startup_2).
2. Industry – The sector in which the startup operates (e.g., AI, FinTech, HealthTech).
3. Funding Rounds – The total number of funding rounds raised by the startup (1-5).
4. Funding Amount (M USD) – The total amount of funding received in millions of USD.
5. Valuation (M USD) – The startup’s post-money valuation in millions of USD.
6. Revenue (M USD) – The estimated annual revenue in millions of USD.
7. Employees – The number of employees working in the startup (ranging from 5 to 5000).
8. Market Share (%) – The percentage of the market the startup has captured.
9. Profitable – A binary indicator (1 = Profitable, 0 = Not Profitable).

This dataset is perfect for growth analysis, investment decision-making, startup trend prediction, and machine learning applications. Let’s uncover the next unicorn! 🦄✨

📌 Do give an upvote if you find the dataset helpful :)

Unlock the potential of the global writing, editing, and publishing industry with Success.ai's Small Business Contact Data. Our extensive database provides access to verified profiles of professionals worldwide, curated from a dataset that encompasses over 700 million global entries. This specialized collection includes work emails, phone numbers, and comprehensive professional information, tailored to meet the needs of small businesses and independent professionals in the writing, editing, and publishing sectors.

Why Choose Success.ai’s Small Business Contact Data?

Targeted Professional Data: Gain access to a niche market of small business owners and freelancers in the writing, editing, and publishing industries. Global Reach: Our dataset covers professionals from all over the world, enabling you to execute international marketing campaigns and network expansion. Verified Contact Information: Ensure the reliability of your outreach with work emails and phone numbers that are regularly updated and verified for accuracy. Data Features:

Comprehensive Profiles: Detailed insights into the professional lives of industry experts, including their job roles, career history, and areas of expertise. Industry-Specific Details: Information tailored to the nuances of the writing, editing, and publishing fields, helping you to better understand and target potential leads. Segmentation Options: Easily segment data by geographic location, professional experience, or specific industry niches such as freelance writers, independent publishers, or small press editors. Customizable Delivery and Integration: Success.ai offers flexible data solutions that can be customized to fit your specific requirements. Whether you need a one-time download or continuous API access for real-time data integration, our formats are designed to seamlessly integrate into your existing business workflows.

Competitive Pricing with Best Price Guarantee: We commit to providing not only the highest quality data but also the most affordable pricing in the industry. Our Best Price Guarantee ensures you receive the best market rate for your data needs.

Ideal Use Cases for Small Business Contact Data:

Direct Marketing Campaigns: Utilize accurate contact details to send personalized email or direct mail campaigns to industry professionals. Networking and Partnership Development: Connect with key industry players to forge partnerships or collaborate on publishing projects. Event Promotion: Target industry-specific events like writing workshops, book fairs, or literary conferences with tailored invitations. Market Research: Analyze trends in the publishing industry, track the rise of independent writing professionals, or assess market needs. Quality Assurance and Compliance:

Data Quality: Our data undergoes rigorous validation processes to maintain high accuracy and usefulness. Legal Compliance: All data collection and processing are performed in strict accordance with global data protection regulations, including GDPR. Support and Professional Consultation:

Dedicated Support: Our team is ready to assist you with any queries or custom requests regarding the dataset. Expert Consultation: Leverage our expertise in data-driven marketing to enhance your outreach strategies and achieve better results. Start Reaching Writing and Publishing Professionals Today: With Success.ai’s Small Business Contact Data, you can start connecting with writing, editing, and publishing professionals globally. Enhance your marketing efforts, expand your professional network, and grow your presence in the industry with our reliable and comprehensive data solutions.

Contact us to explore our offerings and take your business to the next level with tailored data that meets your exact needs.

Bangladesh BD: Time Required to Start a Business data was reported at 19.500 Day in 2019. This stayed constant from the previous number of 19.500 Day for 2018. Bangladesh BD: Time Required to Start a Business data is updated yearly, averaging 19.500 Day from Dec 2013 (Median) to 2019, with 7 observations. The data reached an all-time high of 21.800 Day in 2013 and a record low of 19.500 Day in 2019. Bangladesh BD: Time Required to Start a Business data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Bangladesh – Table BD.World Bank.WDI: Company Statistics. Time required to start a business is the number of calendar days needed to complete the procedures to legally operate a business. If a procedure can be speeded up at additional cost, the fastest procedure, independent of cost, is chosen.;World Bank, Doing Business project (http://www.doingbusiness.org/). NOTE: Doing Business has been discontinued as of 9/16/2021. For more information: https://bit.ly/3CLCbme;Unweighted average;Data are presented for the survey year instead of publication year.

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.

Abstract (en): 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. The data are not weighted, however, this collection contains three weight variables that should be used in any analysis: WEIGHT, WEIGHT_L, and WEIGHT_A. National survey vendors implemented weights that would match the annual cohorts with the best available national data, later adjusted by matching the sample to the U.S. Census Bureau International Data Base (IDB) on national population distributions by age and gender. For more information on weights and sampling please refer to the Original P.I. Documentation section in the ICPSR Codebook. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created online analysis version with question text.; Checked for undocumented or out-of-range codes.. Adult populations of 100 countries. Smallest Geographic Unit: Country Developing representative samples of adults was a two stage process. The first step involved a random selection of households leading to a contact with an adult resident. In countries where a high proportion of households have land line telephones, this was done by creating a random set of numbers considered to be household phone numbers. In countries with a high proportion of cell-phone only adults, this has been supplemented with random samples of active cell phone numbers. Numbers were then called, generally up to three times, until an adult respondent answered the phone. In countries with low proportion of households with phones, geographic areas were selected at random for personal contacts by interviewers, who then approached households for a face-to-face interview. In some developing countries phone interviews are conducted in the major urban areas supplemented with face-to-face interviews in rural regions. Adults from each household were selected for interviews in one of two ways. In some cases it was the first adult contacted and in others a person would be randomly selected from those adults living in the household for the interview. In many developed countries there was a deliberate attempt, quota sampling, to complete half of all interviews with men and half with women. For additional information on sampling, please refer to the Original P.I. Documentation section in the ICPSR Codebook. 2016-12-14 Data have been resupplied and now in...

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.

Time series data for the statistic Starting a business: Time - Women (days) and country Dominican Republic. Indicator Definition:The time for women captures the median duration that business incorporation experts indicate is necessary for five female married entrepreneurs to complete all procedures required to start and operate a business with minimum follow-up and no extra payments. It is calulared in calendar days. The time estimates of all procedures are added to calculate the total time required to start and operate a business, taking into account simultaneity of processes. It is assumed that the minimum time required for each procedure is one day, except for procedures that can be fully completed online, for which the time required is recorded as half a day.The indicator "Starting a business: Time - Women (days)" stands at 16.50 as of 12/31/2019. Regarding the One-Year-Change of the series, the current value is equal to the value the year prior.The 1 year change in percent is 0.0.The 3 year change in percent is -10.81.The 5 year change in percent is 13.79.The 10 year change in percent is 3.12.The Serie's long term average value is 30.68. It's latest available value, on 12/31/2019, is 46.21 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2011, to it's latest available value, on 12/31/2019, is +13.79%.The Serie's change in percent from it's maximum value, on 12/31/2003, to it's latest available value, on 12/31/2019, is -79.11%.

Time series data for the statistic Starting a business: Time - Women (days) and country Cameroon. Indicator Definition:The time for women captures the median duration that business incorporation experts indicate is necessary for five female married entrepreneurs to complete all procedures required to start and operate a business with minimum follow-up and no extra payments. It is calulared in calendar days. The time estimates of all procedures are added to calculate the total time required to start and operate a business, taking into account simultaneity of processes. It is assumed that the minimum time required for each procedure is one day, except for procedures that can be fully completed online, for which the time required is recorded as half a day.The indicator "Starting a business: Time - Women (days)" stands at 14.00 as of 12/31/2019. Regarding the One-Year-Change of the series, the current value is equal to the value the year prior.The 1 year change in percent is 0.0.The 3 year change in percent is -17.65.The 5 year change in percent is -17.65.The 10 year change in percent is -61.11.The Serie's long term average value is 27.12. It's latest available value, on 12/31/2019, is 48.37 percent lower, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/2018, to it's latest available value, on 12/31/2019, is +0.0%.The Serie's change in percent from it's maximum value, on 12/31/2003, to it's latest available value, on 12/31/2019, is -68.89%.

The Panel Study of Entrepreneurial Dynamics (PSED) research program was designed to longitudinally examine the startup process with multi-year cohort tracking, so as to enhance the scientific understanding of how individuals start businesses. The project provided data on the process of business formation based on nationally-representative samples of nascent entrepreneurs, those active in business creation. PSED I (available from ICPSR as study 37203) began with screening in 1998-2000 to select a cohort of 830 with three follow-up interviews. The panel participants were identified prior to launch of their firms and were tracked through gestation, launch and eventual growth or death of the firm. A control group of those not involved in firm creation were available for comparisons. PSED II began with screening in 2005-2006, followed by six yearly interviews. The information obtained as part of the PSED research program included data on the nature of those active as nascent entrepreneurs, the activities undertaken during the start-up process, and the characteristics of start-up efforts that become new firms. The PSED II data included as part of this collection includes: Dataset 1 and 2: Screener Data (58 variables, 31,845 cases) Dataset 3 and 4: Waves A-F plus Screener Data (7,821 variables, 1,214 cases) Demographic variables included as part of this collection comprises age, race, ethnicity, gender, household income, educational attainment, employment status, marital status, citizenship, household characteristics, and business characteristics.

Company Datasets for valuable business insights!

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The current body of research on the gender-energy nexus has largely concentrated on the effects of energy poverty within households, highlighting the impact on women in domestic settings. Nonetheless, women entrepreneurs engaged in productive activities are also pivotal in adopting new energy technologies. The second version was built on the first version of the dataset and incorporated significant updates, presenting raw and processed data from 40 face-to-face interviews conducted across multiple African countries, including Nigeria, which was previously excluded. The current Version 3 includes additional data cleaning, improved consistency checks, and is the most updated and reliable version for reference or analysis compared to previous versions.

The dataset focuses on micro and small-sized enterprises with at least one female owner, offering a unified and comprehensive sample to assess energy access among women entrepreneurs in Africa and explore the potential for renewable energy adoption.

The data collection through semi-structured, face-to-face interviews occurred between February and October 2024. The interviews followed a predetermined semi-structured questionnaire designed to collect quantitative and qualitative data. The notes section explains the main methods and references used in the dataset. Distinctions are also made between primary and secondary data for appliance power ratings, ensuring transparency in cases where secondary data supplements gaps. This version (as did Version 2) includes updated technical data, such as time-of-use information for appliances, enhancing the dataset's strength in providing technical insights.

Key components of the dataset include: - Socio-economic characteristics: Enterprise location, ISIC division and industry sector classification, main production goods, gender-based ownership structures, enterprise formality (based on registration), year of establishment or business start, enterprise size (number of employees), profit margins, and business challenges related to the owner's gender.
- Energy access and use: Type of energy carriers used, subapplications, energy supply shortages, energy consumption levels, type, number, power rating of appliances used, temperature requirements, time-of-use data, and energy expenditure.
- Potential for renewable energy adoption: Type and amount of process waste, perceived barriers and drivers for renewable energy adoption, willingness to invest in or pay for new technologies, and preferred financing methods for such technologies.

Annual data on births, deaths and survival of businesses in the UK, by geographical area and Standard Industrial Classification 2007: SIC 2007 groups.

NOTE, 9/25/2025 - We have populated the Address column for additional records. Business licenses issued by the Department of Business Affairs and Consumer Protection in the City of Chicago from 2002 to the present. This dataset contains a large number of records/rows of data and may not be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Notepad or Wordpad, to view and search. Data fields requiring description are detailed below. APPLICATION TYPE: ‘ISSUE’ is the record associated with the initial license application. ‘RENEW’ is a subsequent renewal record. All renewal records are created with a term start date and term expiration date. ‘C_LOC’ is a change of location record. It means the business moved. ‘C_CAPA’ is a change of capacity record. Only a few license types may file this type of application. ‘C_EXPA’ only applies to businesses that have liquor licenses. It means the business location expanded. 'C_SBA' is a change of business activity record. It means that a new business activity was added or an existing business activity was marked as expired. LICENSE STATUS: ‘AAI’ means the license was issued. ‘AAC’ means the license was cancelled during its term. ‘REV’ means the license was revoked. 'REA' means the license revocation has been appealed. LICENSE STATUS CHANGE DATE: This date corresponds to the date a license was cancelled (AAC), revoked (REV) or appealed (REA). Business License Owner information may be accessed at: https://data.cityofchicago.org/dataset/Business-Owners/ezma-pppn. To identify the owner of a business, you will need the account number or legal name, which may be obtained from this Business Licenses dataset. Data Owner: Business Affairs and Consumer Protection. Time Period: January 1, 2002 to present. Frequency: Data is updated daily.

NEW!: Use the new Business Account Number lookup tool. SUMMARYThis dataset includes the locations of businesses that pay taxes to the City and County of San Francisco. Each registered business may have multiple locations and each location is a single row. The Treasurer & Tax Collector’s Office collects this data through business registration applications, account update/closure forms, and taxpayer filings. Business locations marked as “Administratively Closed” have not filed or communicated with TTX for 3 years, or were marked as closed following a notification from another City and County Department. The data is collected to help enforce the Business and Tax Regulations Code including, but not limited to: Article 6, Article 12, Article 12-A, and Article 12-A-1. http://sftreasurer.org/registration.HOW TO USE THIS DATASETSystem migration in 2014: When the City transitioned to a new system in 2014, only active business accounts were migrated. As a result, any businesses that had already closed by that point were not included in the current dataset.2018 account cleanup: In 2018, TTX did a major cleanup of dormant and unresponsive accounts and closed approximately 40,000 inactive businesses.To learn more about using this dataset watch this video.To update your listing or look up your BAN see this FAQ: Registered Business Locations ExplainerData pushed to ArcGIS Online on November 6, 2025 at 6:14 AM by SFGIS.Data from: https://data.sfgov.org/d/g8m3-pdisDescription of dataset columns:

 UniqueID
 Unique formula: @Value(ttxid)-@Value(certificate_number)


 Business Account Number
 Seven digit number assigned to registered business accounts


 Location Id
 Location identifier


 Ownership Name
 Business owner(s) name


 DBA Name
 Doing Business As Name or Location Name


 Street Address
 Business location street address


 City
 Business location city


 State
 Business location state


 Source Zipcode
 Business location zip code


 Business Start Date
 Start date of the business


 Business End Date
 End date of the business


 Location Start Date
 Start date at the location


 Location End Date
 End date at the location, if closed


 Administratively Closed
 Business locations marked as “Administratively Closed” have not filed or communicated with TTX for 3 years, or were marked as closed following a notification from another City and County Department.


 Mail Address
 Address for mailing


 Mail City
 Mailing address city



 Mail State
 Mailing address state



 Mail Zipcode
 Mailing address zipcode


 NAICS Code
 The North American Industry Classification System (NAICS) is a standard used by Federal statistical agencies for the purpose of collecting, analyzing and publishing statistical data related to the U.S. business economy. A subset of these are options on the business registration form used in the administration of the City and County's tax code. The registrant indicates the business activity on the City and County's tax registration forms.

See NAICS Codes tab in the attached data dictionary under About > Attachments.

 NAICS Code Description
 The Business Activity that the NAICS code maps on to ("Multiple" if there are multiple codes indicated for the business).


 NAICS Code Descriptions List
 A list of all NAICS code descriptions separated by semi-colon


 LIC Code
 The LIC code of the business, if multiple, separated by spaces


 LIC Code Description
 The LIC code description ("Multiple" if there are multiple codes for a business)


 LIC Code Descriptions List
 A list of all LIC code descriptions separated by semi-colon


 Parking Tax
 Whether or not this business pays the parking tax


 Transient Occupancy Tax
 Whether or not this business pays the transient occupancy tax


 Business Location
 The latitude and longitude of the business location for mapping purposes.


 Business Corridor
 The Business Corridor in which the the business location falls, if it is in one. Not all business locations are in a corridor.

Boundary reference: https://data.sfgov.org/d/h7xa-2xwk

 Neighborhoods - Analysis Boundaries
 The Analysis Neighborhood in which the business location falls. Not applicable outside of San Francisco.

Boundary reference: https://data.sfgov.org/d/p5b7-5n3h

 Supervisor District
 The Supervisor District in which the business location falls. Not applicable outside of San Francisco. Boundary reference: https://data.sfgov.org/d/xz9b-wyfc


 Community Benefit District
 The Community Benefit District in which the business location falls. Not applicable outside of San Francisco. Boundary reference: https://data.sfgov.org/d/c28a-f6gs


 data_as_of
 Timestamp the data was updated in the source system


 data_loaded_at
 Timestamp the data was loaded here (open data portal)


 SF Find Neighborhoods
 This column was automatically created in order to record in what polygon from the dataset 'SF Find Neighborhoods' (6qbp-sg9q) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.


 Current Police Districts
 This column was automatically created in order to record in what polygon from the dataset 'Current Police Districts' (qgnn-b9vv) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.


 Current Supervisor Districts
 This column was automatically created in order to record in what polygon from the dataset 'Current Supervisor Districts' (26cr-cadq) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.


 Analysis Neighborhoods
 This column was automatically created in order to record in what polygon from the dataset 'Analysis Neighborhoods' (ajp5-b2md) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.


 Neighborhoods
 This column was automatically created in order to record in what polygon from the dataset 'Neighborhoods' (jwn9-ihcz) the point in column 'location' is located. This enables the creation of region maps (choropleths) in the visualization canvas and data lens.

Note: If no description was provided by DataSF, the cell is left blank. See the source data for more information.

The Business Structure Database (BSD) contains a small number of variables for almost all business organisations in the UK. The BSD is derived primarily from the Inter-Departmental Business Register (IDBR), which is a live register of data collected by HM Revenue and Customs via VAT and Pay As You Earn (PAYE) records. The IDBR data are complimented with data from ONS business surveys. If a business is liable for VAT (turnover exceeds the VAT threshold) and/or has at least one member of staff registered for the PAYE tax collection system, then the business will appear on the IDBR (and hence in the BSD). In 2004 it was estimated that the businesses listed on the IDBR accounted for almost 99 per cent of economic activity in the UK. Only very small businesses, such as the self-employed were not found on the IDBR.

The IDBR is frequently updated, and contains confidential information that cannot be accessed by non-civil servants without special permission. However, the ONS Virtual Micro-data Laboratory (VML) created and developed the BSD, which is a 'snapshot' in time of the IDBR, in order to provide a version of the IDBR for research use, taking full account of changes in ownership and restructuring of businesses. The 'snapshot' is taken around April, and the captured point-in-time data are supplied to the VML by the following September. The reporting period is generally the financial year. For example, the 2000 BSD file is produced in September 2000, using data captured from the IDBR in April 2000. The data will reflect the financial year of April 1999 to March 2000. However, the ONS may, during this time, update the IDBR with data on companies from its own business surveys, such as the Annual Business Survey (SN 7451).

The data are divided into 'enterprises' and 'local units'. An enterprise is the overall business organisation. A local unit is a 'plant', such as a factory, shop, branch, etc. In some cases, an enterprise will only have one local unit, and in other cases (such as a bank or supermarket), an enterprise will own many local units.

For each company, data are available on employment, turnover, foreign ownership, and industrial activity based on Standard Industrial Classification (SIC)92, SIC 2003 or SIC 2007. Year of 'birth' (company start-up date) and 'death' (termination date) are also included, as well as postcodes for both enterprises and their local units. Previously only pseudo-anonymised postcodes were available but now all postcodes are real.

The ONS is continually developing the BSD, and so researchers are strongly recommended to read all documentation pertaining to this dataset before using the data.

Linking to Other Business Studies
These data contain IDBR reference numbers. These are anonymous but unique reference numbers assigned to business organisations. Their inclusion allows researchers to combine different business survey sources together. Researchers may consider applying for other business data to assist their research.

Latest Edition Information
For the sixteenth edition (March 2024), data files and a variable catalogue document for 2023 have been added.

Business licenses issued by the Department of Business Affairs and Consumer Protection in the City of Chicago from 2006 to the present. This dataset contains a large number of records/rows of data and may not be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Notepad or Wordpad, to view and search.

Data fields requiring description are detailed below.

APPLICATION TYPE: ‘ISSUE’ is the record associated with the initial license application. ‘RENEW’ is a subsequent renewal record. All renewal records are created with a term start date and term expiration date. ‘C_LOC’ is a change of location record. It means the business moved. ‘C_CAPA’ is a change of capacity record. Only a few license types may file this type of application. ‘C_EXPA’ only applies to businesses that have liquor licenses. It means the business location expanded.

LICENSE STATUS: ‘AAI’ means the license was issued. ‘AAC’ means the license was cancelled during its term. ‘REV’ means the license was revoked. 'REA' means the license revocation has been appealed.

LICENSE STATUS CHANGE DATE: This date corresponds to the date a license was cancelled (AAC), revoked (REV) or appealed (REA).

Business License Owner information may be accessed at: https://data.cityofchicago.org/dataset/Business-Owners/ezma-pppn. To identify the owner of a business, you will need the account number or legal name, which may be obtained from this Business Licenses dataset.

Data Owner: Business Affairs and Consumer Protection. Time Period: January 1, 2006 to present. Frequency: Data is updated daily.

** Startup Failure Prediction Dataset

This dataset helps understand why some startups succeed while others fail. It contains 5,000 startups from different industries and includes important details like funding, revenue, team size, and market conditions. **

** What’s Inside?**

This dataset has key information about startups, including:

Industry– Type of business (Tech, Healthcare, E-commerce, etc.)

Startup Age – How many years the startup has been running

Funding Amount – Total investment received

Number of Founders – How many people started the company

Founder Experience – Work experience of the founders

Employees Count – Number of employees in the startup

Revenue – How much money the startup makes

Burn Rate – How much money the startup spends per month

Market Size – Size of the industry (Small, Medium, Large)

Business Model – Does the startup sell to businesses (B2B) or customers (B2C)?

Product Uniqueness Score – How unique the startup’s product is (Scale: 1-10)

Customer Retention Rate – Percentage of customers who return

Marketing Expense – How much money is spent on marketing

Startup Status – 1 = Successful, 0 = Failed (Did the startup succeed or fail?)