100+ datasets found
  1. Business Database

    • kaggle.com
    Updated Feb 26, 2025
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    Himel Sarder (2025). Business Database [Dataset]. https://www.kaggle.com/datasets/himelsarder/business-database
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Feb 26, 2025
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Himel Sarder
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    This is a relational database schema for a sales and order management system, designed to track customers, employees, products, orders, and payments. Below is a detailed breakdown of each table and their relationships:

    1. productlines Table (Product Categories)

    • Represents different product categories.
    • Primary Key: productLine
    • Attributes:
      • textDescription: A short description of the product line.
      • htmlDescription: A detailed HTML-based description.
      • image: Associated image (if applicable).
    • Relationships:
      • One-to-Many with products: Each product belongs to one productLine.

    2. products Table (Product Information)

    • Stores details of individual products.
    • Primary Key: productCode
    • Attributes:
      • productName: Name of the product.
      • productLine: Foreign key linking to productlines.
      • productScale, productVendor, productDescription: Additional product details.
      • quantityInStock: Number of units available.
      • buyPrice: Cost price per unit.
      • MSRP: Manufacturer's Suggested Retail Price.
    • Relationships:
      • Many-to-One with productlines (each product belongs to one category).
      • One-to-Many with orderdetails (a product can be part of many orders).

    3. orderdetails Table (Line Items in an Order)

    • Stores details of each product within an order.
    • Composite Primary Key: (orderNumber, productCode)
    • Attributes:
      • quantityOrdered: Number of units in the order.
      • priceEach: Price per unit.
      • orderLineNumber: The sequence number in the order.
    • Relationships:
      • Many-to-One with orders (each order has multiple products).
      • Many-to-One with products (each product can appear in multiple orders).

    4. orders Table (Customer Orders)

    • Represents customer orders.
    • Primary Key: orderNumber
    • Attributes:
      • orderDate: Date when the order was placed.
      • requiredDate: Expected delivery date.
      • shippedDate: Actual shipping date (can be NULL if not shipped).
      • status: Order status (e.g., "Shipped", "In Process", "Cancelled").
      • comments: Additional remarks about the order.
      • customerNumber: Foreign key linking to customers.
    • Relationships:
      • One-to-Many with orderdetails (an order contains multiple products).
      • Many-to-One with customers (each order is placed by one customer).

    5. customers Table (Customer Details)

    • Stores customer information.
    • Primary Key: customerNumber
    • Attributes:
      • customerName: Name of the customer.
      • contactLastName, contactFirstName: Contact person.
      • phone: Contact number.
      • addressLine1, addressLine2, city, state, postalCode, country: Address details.
      • salesRepEmployeeNumber: Foreign key linking to employees, representing the sales representative.
      • creditLimit: Maximum credit limit assigned to the customer.
    • Relationships:
      • One-to-Many with orders (a customer can place multiple orders).
      • One-to-Many with payments (a customer can make multiple payments).
      • Many-to-One with employees (each customer has a sales representative).

    6. payments Table (Customer Payments)

    • Stores payment transactions.
    • Composite Primary Key: (customerNumber, checkNumber)
    • Attributes:
      • paymentDate: Date of payment.
      • amount: Payment amount.
    • Relationships:
      • Many-to-One with customers (each payment is linked to a customer).

    7. employees Table (Employee Information)

    • Stores details of employees, including reporting hierarchy.
    • Primary Key: employeeNumber
    • Attributes:
      • lastName, firstName: Employee's name.
      • extension, email: Contact details.
      • officeCode: Foreign key linking to offices, representing the employee's office.
      • reportsTo: References another employeeNumber, establishing a hierarchy.
      • jobTitle: Employee’s role (e.g., "Sales Rep", "Manager").
    • Relationships:
      • Many-to-One with offices (each employee works in one office).
      • One-to-Many with employees (self-referential, representing reporting structure).
      • One-to-Many with customers (each employee manages multiple customers).

    8. offices Table (Office Locations)

    • Represents company office locations.
    • Primary Key: officeCode
    • Attributes:
      • city, state, country: Location details.
      • phone: Office contact number.
      • addressLine1, addressLine2, postalCode, territory: Address details.
    • Relationships:
      • One-to-Many with employees (each office has multiple employees).

    Conclusion

    This schema provides a well-structured design for managing a sales and order system, covering: ✅ Product inventory
    ✅ Order and payment tracking
    ✅ Customer and employee management
    ✅ Office locations and hierarchical reporting

  2. Business Information Market Analysis North America, Europe, APAC, South...

    • technavio.com
    Updated Jan 15, 2025
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    Technavio (2025). Business Information Market Analysis North America, Europe, APAC, South America, Middle East and Africa - US, UK, China, Germany, Canada, Japan, France, India, Italy, South Korea - Size and Forecast 2025-2029 [Dataset]. https://www.technavio.com/report/business-information-market-industry-analysis
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    Dataset updated
    Jan 15, 2025
    Dataset provided by
    TechNavio
    Authors
    Technavio
    Time period covered
    2021 - 2025
    Area covered
    Global, Canada, Germany, United States
    Description

    Snapshot img

    Business Information Market Size 2025-2029

    The business information market size is forecast to increase by USD 79.6 billion, at a CAGR of 7.3% between 2024 and 2029.

    The market is characterized by the increasing demand for customer-centric solutions as enterprises adapt to evolving customer preferences. This shift necessitates the provision of real-time, accurate, and actionable insights to facilitate informed decision-making. However, this market landscape is not without challenges. The threat of data misappropriation and theft looms large, necessitating robust security measures to safeguard sensitive business information. As businesses continue to digitize their operations and rely on external data sources, ensuring data security becomes a critical success factor. Companies must invest in advanced security technologies and implement stringent data protection policies to mitigate these risks. Navigating this complex market requires a strategic approach that balances the need for customer-centric solutions with the imperative to secure valuable business data.
    

    What will be the Size of the Business Information Market during the forecast period?

    Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
    Request Free Sample

    In today's data-driven business landscape, the continuous and evolving nature of market dynamics plays a pivotal role in shaping various sectors. Data integration solutions enable seamless data flow between different systems, enhancing cloud-based business applications' functionality. Data quality management ensures data accuracy and consistency, crucial for strategic planning and customer segmentation. Data infrastructure, data warehousing, and data pipelines form the backbone of business intelligence, facilitating data storytelling and digital transformation. Data lineage and data mining reveal valuable insights, fueling data analytics platforms and business intelligence infrastructure. Data privacy regulations necessitate robust data management tools, ensuring compliance and protecting sensitive information.

    Sales forecasting and business intelligence consulting offer valuable industry analysis and data-driven decision making. Data governance frameworks and data cataloging maintain order and ethics in the vast expanse of big data analytics. Machine learning algorithms, predictive analytics, and real-time analytics drive business intelligence reporting and process modeling, leading to business process optimization and financial reporting software. Sentiment analysis and marketing automation cater to customer needs, while lead generation and data ethics ensure ethical business practices. The ongoing unfolding of market activities and evolving patterns necessitate the integration of various tools and frameworks, creating a dynamic interplay that fuels business growth and innovation.

    How is this Business Information Industry segmented?

    The business information industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

    End-user
    
      BFSI
      Healthcare and life sciences
      Manufacturing
      Retail
      Others
    
    
    Application
    
      B2B
      B2C
    
    
    Geography
    
      North America
    
        US
        Canada
    
    
      Europe
    
        France
        Germany
        Italy
        UK
    
    
      APAC
    
        China
        India
        Japan
        South Korea
    
    
      Rest of World (ROW). 
    

    By End-user Insights

    The bfsi segment is estimated to witness significant growth during the forecast period.

    In the dynamic business landscape, data-driven insights have become essential for strategic planning and decision-making across various industries. The market caters to this demand by offering solutions that integrate and manage data from multiple sources. These include cloud-based business applications, data quality management tools, data warehousing, data pipelines, and data analytics platforms. Data storytelling and digital transformation are key trends driving the market's growth, enabling businesses to derive meaningful insights from their data. Data governance frameworks and policies are crucial components of the business intelligence infrastructure. Data privacy regulations, such as GDPR and HIPAA, are shaping the market's development.

    Data mining, predictive analytics, and machine learning algorithms are increasingly being used for sales forecasting, customer segmentation, and churn prediction. Business intelligence consulting and industry analysis provide valuable insights for organizations seeking competitive advantage. Data visualization dashboards, market research databases, and data discovery tools facilitate data-driven decision making. Sentiment analysis and predictive analytics are essential for marketing automation and business

  3. d

    Investment Company Series and Class Information

    • catalog.data.gov
    Updated Jun 3, 2025
    + more versions
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    Investment Management (2025). Investment Company Series and Class Information [Dataset]. https://catalog.data.gov/dataset/investment-company-series-and-class-information
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    Dataset updated
    Jun 3, 2025
    Dataset provided by
    Investment Management
    Description

    The Series and Class Report provides basic identification information for all active registered investment company series and classes have been issued IDs by the Commission.

  4. d

    Computer Vision companies database

    • datarade.ai
    .xls
    Updated Dec 1, 2021
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    LeadChart (2021). Computer Vision companies database [Dataset]. https://datarade.ai/data-products/computer-vision-companies-database-leadchart
    Explore at:
    .xlsAvailable download formats
    Dataset updated
    Dec 1, 2021
    Dataset authored and provided by
    LeadChart
    Area covered
    Austria, Trinidad and Tobago, Burundi, Mauritius, Montenegro, Curaçao, Guam, Angola, Antigua and Barbuda, Virgin Islands (U.S.)
    Description

    This list has data of about 3000+ companies specializing in computer vision from Enterprises and Startups to Studios. The information includes:

    Location Company Name Linkedin profiles of key people Company Name No of employees Company Name LinkedIn profile Company Name List of key people Company Name Fund raised

    This list is suitable for:

    Agencies & service providers Pitch your services whether it be marketing, design or web development services.

    Freelancers & developers Up your job hunting, contract work and gig opportunities search.

    Product companies Companies with a product or service for computer vision companies.

    Staffing and recruiting agencies Companies with a product or service for computer vision companies.

  5. d

    Business Name Search

    • catalog.data.gov
    • opendata.hawaii.gov
    • +2more
    Updated Apr 10, 2024
    + more versions
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    Commerce and Consumer Affairs (2024). Business Name Search [Dataset]. https://catalog.data.gov/dataset/business-name-search
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    Dataset updated
    Apr 10, 2024
    Dataset provided by
    Commerce and Consumer Affairs
    Description

    Search for a business by name. You can obtain business information and then proceed to purchase a certificate of good standing or other documents. The purpose of this search is simply to determine whether a company/entity exists and to provide basic information on the company/entity.

  6. Dynamic Small Business Search (DSBS)

    • catalog.data.gov
    • datadiscoverystudio.org
    • +2more
    Updated Apr 11, 2023
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    Small Business Administration (2023). Dynamic Small Business Search (DSBS) [Dataset]. https://catalog.data.gov/dataset/dynamic-small-business-search-dsbs-4f0da
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    Dataset updated
    Apr 11, 2023
    Dataset provided by
    Small Business Administrationhttps://www.sba.gov/
    Description

    The Small Business Administration maintains the Dynamic Small Business Search (DSBS) database. As a small business registers in the System for Award Management, there is an opportunity to fill out the small business profile. The information provided populates DSBS. DSBS is another tool contracting officers use to identify potential small business contractors for upcoming contracting opportunities. Small businesses can also use DSBS to identify other small businesses for teaming and joint venturing.

  7. d

    Computer Vision companies - database

    • datarade.ai
    Updated Dec 23, 2022
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    LeadChart (2022). Computer Vision companies - database [Dataset]. https://datarade.ai/data-products/computer-vision-companies-database-leadchart-b373
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    Dataset updated
    Dec 23, 2022
    Dataset authored and provided by
    LeadChart
    Area covered
    Falkland Islands (Malvinas), Denmark, Chile, Jersey, Libya, Israel, Dominican Republic, Mauritania, British Indian Ocean Territory, India
    Description

    Computer vision is one of the fastest growing industries at the moment and there are a lot of opportunites associated with this industry. Whether you run a development shop, staffing agency or sales/marketing related business this list will help you find opportunities with computer vision companies.

  8. d

    B2B Data | Company Data | TOP#1 Database: 360 Million Businesses Worldwide

    • datarade.ai
    Updated Mar 5, 2025
    + more versions
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    InfobelPRO (2025). B2B Data | Company Data | TOP#1 Database: 360 Million Businesses Worldwide [Dataset]. https://datarade.ai/data-products/b2b-data-company-data-top-1-database-360-million-busi-infobelpro
    Explore at:
    .bin, .json, .xml, .csv, .xls, .sql, .txtAvailable download formats
    Dataset updated
    Mar 5, 2025
    Dataset authored and provided by
    InfobelPRO
    Area covered
    Philippines, Croatia, Netherlands, Timor-Leste, Cambodia, Congo, Hong Kong, Panama, Tunisia, Poland
    Description

    Leverage high-quality B2B data with 468 enriched attributes, covering firmographics, financial stability, and industry classifications. Our AI-optimized dataset ensures accuracy through advanced deduplication and continuous updates. With 30+ years of expertise and 1,100+ trusted sources, we provide fully compliant, structured business data to power lead generation, risk assessment, CRM enrichment, market research, and more.

    Key use cases of B2B Data have helped our customers in several areas :

    1. Boost Lead Generation & Sales Outreach : Target the right businesses with precise, segmented contact lists for cold calling, email marketing, and industry-specific campaigns.
    2. Enhance CRM & Web Data for Smarter Engagement : Enrich CRM records with instant access to detailed company profiles, visitor identification, and continuous data updates.
    3. Strengthen Risk Assessment & Fraud Prevention : Evaluate supplier reliability, assess credit risk, and prevent fraud with deep firmographic and financial insights.
    4. Gain a Competitive Edge with Market Research : Analyse industry trends, benchmark competitors, and identify automation-ready sectors for strategic positioning.
    5. Optimize B2B Strategies with AI-Powered Insights : Leverage structured, compliant data to drive smarter business decisions across sales, marketing, and operations.
  9. Data processing and information companies number Japan 2020, by company size...

    • statista.com
    Updated Jun 27, 2024
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    Statista (2024). Data processing and information companies number Japan 2020, by company size [Dataset]. https://www.statista.com/statistics/957360/japan-data-processing-and-information-services-business-number-by-company-size/
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    Dataset updated
    Jun 27, 2024
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2020
    Area covered
    Japan
    Description

    Close to 2.68 thousand businesses in the data processing and information services industry in Japan had between one to four employees in 2020. At the same time, 59 businesses employed 500 or more people.

  10. f

    Types of internal company data/information in articles using internal...

    • plos.figshare.com
    xls
    Updated Jun 1, 2023
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    L. Susan Wieland; Lainie Rutkow; S. Swaroop Vedula; Christopher N. Kaufmann; Lori M. Rosman; Claire Twose; Nirosha Mahendraratnam; Kay Dickersin (2023). Types of internal company data/information in articles using internal documents from different types of companies (n = 361 articles). [Dataset]. http://doi.org/10.1371/journal.pone.0094709.t003
    Explore at:
    xlsAvailable download formats
    Dataset updated
    Jun 1, 2023
    Dataset provided by
    PLOS ONE
    Authors
    L. Susan Wieland; Lainie Rutkow; S. Swaroop Vedula; Christopher N. Kaufmann; Lori M. Rosman; Claire Twose; Nirosha Mahendraratnam; Kay Dickersin
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    1The totals in this column equal the number of articles using a particular type of data, minus instances of duplicate classification by type of company within category of type of data. These instances were: Other types of data were used by articles classified as both tobacco and transportation, both mining and manufacturing, and both tobacco and alcohol, and quantitative data from internal company studies were used by the article classified as both mining and manufacturing. The overall column total is not shown, as it is greater than the total number of included articles (n = 361) because several articles used multiple types of internal documents.2The totals in this row equal the total number of articles for each type of company, minus instances where articles used multiple types of data, of which there are too many to list. The totals for the columns are therefore not equal to the sum of the classifications within the columns. The overall row total is not shown, as it is greater than the total number of included articles (N = 361) because three articles were classified with two types of companies.

  11. p

    Database Management Companies in Brazil - 358 Available (Free Sample)

    • poidata.io
    csv
    Updated May 30, 2025
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    Poidata.io (2025). Database Management Companies in Brazil - 358 Available (Free Sample) [Dataset]. https://www.poidata.io/report/database-management-company/brazil
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    csvAvailable download formats
    Dataset updated
    May 30, 2025
    Dataset provided by
    Poidata.io
    Area covered
    Brazil
    Description

    This dataset provides information on 358 in Brazil as of May, 2025. It includes details such as email addresses (where publicly available), phone numbers (where publicly available), and geocoded addresses. Explore market trends, identify potential business partners, and gain valuable insights into the industry. Download a complimentary sample of 10 records to see what's included.

  12. Firm-level business dynamism from the Longitudinal Business Database:...

    • ons.gov.uk
    xlsx
    Updated Dec 3, 2024
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    Office for National Statistics (2024). Firm-level business dynamism from the Longitudinal Business Database: summary statistics, UK [Dataset]. https://www.ons.gov.uk/businessindustryandtrade/changestobusiness/businessbirthsdeathsandsurvivalrates/datasets/firmlevelbusinessdynamismestimatesfromthelongitudinalbusinessdatabasesummarystatisticsuk
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    xlsxAvailable download formats
    Dataset updated
    Dec 3, 2024
    Dataset provided by
    Office for National Statisticshttp://www.ons.gov.uk/
    License

    Open Government Licence 3.0http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/
    License information was derived automatically

    Area covered
    United Kingdom
    Description

    Summary statistics of business dynamism taken from the Longitudinal Business Database (LBD), UK.

  13. China CN: Listed Company: Net Profit: Information & Technology

    • ceicdata.com
    Updated Dec 15, 2024
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    CEICdata.com (2024). China CN: Listed Company: Net Profit: Information & Technology [Dataset]. https://www.ceicdata.com/en/china/financial-data-of-listed-company-net-profit/cn-listed-company-net-profit-information--technology
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    Dataset updated
    Dec 15, 2024
    Dataset provided by
    CEIC Data
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    Dec 1, 2012 - Dec 1, 2023
    Area covered
    China
    Variables measured
    Enterprises Survey
    Description

    China Listed Company: Net Profit: Information & Technology data was reported at 195.009 RMB bn in 2023. This records an increase from the previous number of 170.144 RMB bn for 2022. China Listed Company: Net Profit: Information & Technology data is updated yearly, averaging 19.470 RMB bn from Dec 2001 (Median) to 2023, with 22 observations. The data reached an all-time high of 195.009 RMB bn in 2023 and a record low of 0.250 RMB bn in 2018. China Listed Company: Net Profit: Information & Technology data remains active status in CEIC and is reported by China Securities Regulatory Commission. The data is categorized under China Premium Database’s Business and Economic Survey – Table CN.OZ: Financial Data of Listed Company: Net Profit.

  14. Business Information Resellers in the US - Market Research Report...

    • ibisworld.com
    Updated Jun 15, 2025
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    IBISWorld (2024). Business Information Resellers in the US - Market Research Report (2015-2030) [Dataset]. https://www.ibisworld.com/united-states/market-research-reports/business-information-resellers-industry/
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    Dataset updated
    Jun 15, 2025
    Dataset authored and provided by
    IBISWorld
    License

    https://www.ibisworld.com/about/termsofuse/https://www.ibisworld.com/about/termsofuse/

    Time period covered
    2015 - 2030
    Area covered
    United States
    Description

    The Business Information Resellers industry has continued to grow as the need for market research has risen. Companies investing in product development typically invest in external information and analysis to ensure the success of perspective products. Business information resellers fill this gap, making money by gathering data, articles and research reports, then offering this content to clients, mostly through subscriptions. With research and development (R&D) on the rise across the economy, information access has become a hot commodity, strengthening core revenue channels for business information resellers. As a result, revenue is forecast to grow at a CAGR of 3.4% and is expected to reach $9.5 billion in 2025, including 11.5% in52024 alone.Business information resellers have thrived thanks to increased investment in R&D and advertising. Even though the COVID-19 pandemic triggered economic instability, the lingering uncertainty has fueled demand for their products. Recent uncertainty surrounding economic policy, namely tariffs, have had a similar effect. Software advancements have also simplified the process of obtaining and repackaging information, especially data. Additionally, favorable outsourcing trends have further bolstered their success. Consequently, profitability has continued to climb, nearing all-time highs.Robust growth in research and development spending will strengthen the core revenue channels for business information resellers, driving industry expansion. Additionally, rising total advertising expenditure and a growing percentage of online services will further enhance this growth. These trends will reshape the broader landscape, with more businesses seeking market information to fine-tune their advertising projects, particularly online. As major corporations continue to globalize, their information needs will become increasingly complex, boosting product development prospects. These positive developments are forecast to drive revenue grow at a CAGR of 1.4% to an estimated $9.5 billion. However, this growth will also intensify competition, pushing the industry toward further consolidation as smaller players get squeezed out of the fiercely competitive marketplace.

  15. Small Business Contact Data | North American Small Business Owners |...

    • datarade.ai
    Updated Oct 27, 2021
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    Success.ai (2021). Small Business Contact Data | North American Small Business Owners | Verified Contact Details from 170M Profiles | Best Price Guaranteed [Dataset]. https://datarade.ai/data-products/small-business-contact-data-north-american-small-business-o-success-ai
    Explore at:
    .bin, .json, .xml, .csv, .xls, .sql, .txtAvailable download formats
    Dataset updated
    Oct 27, 2021
    Dataset provided by
    Area covered
    United States of America, Bermuda, Saint Pierre and Miquelon, Costa Rica, Greenland, Belize, Guatemala, Honduras, Panama, Mexico
    Description

    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...

  16. Premium eCommerce Leads | Target Shopify, Amazon, eBay Stores | Verified...

    • datacaptive.com
    Updated May 23, 2022
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    DataCaptive™ (2022). Premium eCommerce Leads | Target Shopify, Amazon, eBay Stores | Verified Owner Contacts | DataCaptive [Dataset]. https://www.datacaptive.com/technology-users-email-list/ecommerce-company-data/
    Explore at:
    Dataset updated
    May 23, 2022
    Dataset provided by
    DataCaptive
    Authors
    DataCaptive™
    Area covered
    Norway, Belgium, Mexico, Bahrain, United Arab Emirates, Romania, Spain, Netherlands, Switzerland, Germany
    Description

    Discover the unparalleled potential of our comprehensive eCommerce leads database, featuring essential data fields such as Store Name, Website, Contact First Name, Contact Last Name, Email Address, Physical Address, City, State, Country, Zip Code, Phone Number, Revenue Size, Employee Size, and more on demand.

    With a focus on Shopify, Amazon, eBay, and other global retail stores, this database equips you with accurate information for successful marketing campaigns. Supercharge your marketing efforts with our enriched contact and company database, providing real-time, verified data insights for strategic market assessments and effective buyer engagement across digital and traditional channels.

    • 4M+ eCommerce Companies • 40M+ Worldwide eCommerce Leads • Direct Contact Info for Shop Owners • 47+ eCommerce Platforms • 40+ Data Points • Lifetime Access • 10+ Data Segmentations • Sample Data"

  17. d

    The over-the-counter company's corporate ESG information disclosure...

    • data.gov.tw
    json
    Updated Jan 13, 2025
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    Securities and Futures Bureau, Financial Supervisory Commission, Executive Yuan, R.O.C. (2025). The over-the-counter company's corporate ESG information disclosure collectively summarized data - risk management policy [Dataset]. https://data.gov.tw/en/datasets/172241
    Explore at:
    jsonAvailable download formats
    Dataset updated
    Jan 13, 2025
    Dataset authored and provided by
    Securities and Futures Bureau, Financial Supervisory Commission, Executive Yuan, R.O.C.
    License

    https://data.gov.tw/licensehttps://data.gov.tw/license

    Description

    The Over-the-Counter Company Enterprise's ESG Information Disclosure Summary Data - Risk Management Policy, this item is an industry sustainability indicator, and companies strengthen disclosure according to industry, so not all companies disclose relevant information.

  18. d

    Important daily information for OTC companies

    • data.gov.tw
    csv
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    Securities and Futures Bureau, Financial Supervisory Commission, Executive Yuan, R.O.C., Important daily information for OTC companies [Dataset]. https://data.gov.tw/en/datasets/18418
    Explore at:
    csvAvailable download formats
    Dataset authored and provided by
    Securities and Futures Bureau, Financial Supervisory Commission, Executive Yuan, R.O.C.
    License

    https://data.gov.tw/licensehttps://data.gov.tw/license

    Description

    Daily Summary of Important Information for this Country and the First OTC Company (GreTai Securities Market)

  19. B2B Company Data API | Gain Comprehensive Firmographic Insights | Access...

    • datarade.ai
    Updated Feb 12, 2018
    + more versions
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    Success.ai (2018). B2B Company Data API | Gain Comprehensive Firmographic Insights | Access Profiles of 70M+ Companies | Best Price Guarantee [Dataset]. https://datarade.ai/data-products/b2b-company-data-api-gain-comprehensive-firmographic-insigh-success-ai
    Explore at:
    .bin, .json, .xml, .csv, .xls, .sql, .txtAvailable download formats
    Dataset updated
    Feb 12, 2018
    Dataset provided by
    Area covered
    Tokelau, Solomon Islands, Iceland, Malta, Kuwait, Korea (Democratic People's Republic of), Mauritania, Tunisia, Italy, Armenia
    Description

    Success.ai’s B2B Company Data API provides direct, on-demand access to in-depth firmographic insights for over 70 million companies worldwide. Covering key attributes such as industry classification, company size, revenue ranges, and geographic footprints, this API ensures your sales, marketing, and strategic planning efforts are informed by accurate, continuously updated, and AI-validated data.

    Whether you’re evaluating new markets, refining your ICP (Ideal Customer Profile), or enhancing ABM campaigns, Success.ai’s B2B Company Data API delivers the intelligence needed to target the right organizations at the right time. Supported by our Best Price Guarantee, this solution empowers you to make data-driven decisions and gain a competitive edge in a complex global marketplace.

    Why Choose Success.ai’s B2B Company Data API?

    1. Comprehensive Global Coverage

      • Access profiles of over 70 million companies spanning multiple industries, sectors, and regions.
      • Confidently enter new markets, identify niche segments, and discover growth opportunities across the globe.
    2. AI-Validated Accuracy

      • Benefit from 99% data accuracy through AI-driven validation, ensuring every insight is reliable and actionable.
      • Trust that your decisions are backed by current, high-quality information, minimizing risk and guesswork.
    3. Continuous Data Updates

      • Real-time refreshes keep you aligned with evolving market conditions, organizational changes, and industry dynamics.
      • Always operate with the most relevant data, ensuring your outreach and strategies remain timely and impactful.
    4. Ethical and Compliant

      • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage for all applications.

    Data Highlights:

    • 70M+ Verified Company Profiles: Leverage a vast database to discover new accounts, refine targeting, and guide strategic initiatives.
    • Firmographic Insights: Gain visibility into industry classifications, company sizes, revenue tiers, and regional footprints.
    • Continuously Updated: Stay current with market expansions, mergers, and new entrants, seizing opportunities early.
    • Best Price Guarantee: Optimize ROI by accessing top-tier data at the most competitive prices on the market.

    Key Features of the B2B Company Data API:

    1. On-Demand Data Enrichment

      • Instantly enhance CRM records or marketing databases with verified company profiles, eliminating guesswork.
      • Maintain data hygiene and ensure teams always work with accurate, current intelligence.
    2. Advanced Filtering and Query Capabilities

      • Query the API to segment companies by industry, location, employee count, or revenue.
      • Zero in on the precise accounts that match your ideal customer profile, improving conversion and engagement rates.
    3. Real-Time Validation and Reliability

      • Rely on continuous data refreshes and AI validation for impeccable data integrity.
      • Reduce wasted effort and improve decision-making backed by trustworthy insights.
    4. Scalable and Flexible Integration

      • Seamlessly integrate the API into CRMs, analytics tools, or marketing platforms, streamlining workflows.
      • Adjust parameters as market conditions evolve, ensuring your data needs always match your strategic priorities.

    Strategic Use Cases:

    1. Account-Based Marketing (ABM)

      • Identify high-value accounts aligned with your ICP using firmographic data.
      • Deliver personalized outreach, increasing engagement, deal size, and overall ABM success.
    2. Market Expansion and Product Launches

      • Enter new markets with confidence by identifying industry leaders, rising players, and underserved segments.
      • Validate product-market fit and refine go-to-market strategies using data-driven insights.
    3. Competitive Benchmarking and Analysis

      • Monitor industry landscapes and track competitor growth to anticipate trends and pivot strategies proactively.
      • Stay ahead of market shifts by aligning solutions with evolving customer needs.
    4. Partner and Supplier Sourcing

      • Discover reliable partners, suppliers, or distributors based on firmographic filters.
      • Strengthen supply chains, reduce risks, and ensure stable growth through informed partner selection.

    Why Choose Success.ai?

    1. Best Price Guarantee

      • Access premium-quality B2B company data at industry-leading prices, maximizing ROI for data-driven initiatives.
    2. Seamless Integration

      • Incorporate the API into existing workflows easily, eliminating manual data imports and siloed processes.
    3. Data Accuracy with AI Validation

      • Rely on 99% accuracy to guide data-driven choices, refine targeting, and improve conversion rates.
    4. Customizable and Scalable Solutions

      • Tailor datasets to focus on particular industries, regions, or company sizes, adapting as your goals shift.

    Additi...

  20. The Organic INTEGRITY Database

    • agdatacommons.nal.usda.gov
    bin
    Updated Feb 9, 2024
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    USDA Agricultural Marketing Service (2024). The Organic INTEGRITY Database [Dataset]. https://agdatacommons.nal.usda.gov/articles/dataset/The_Organic_INTEGRITY_Database/24661722
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    binAvailable download formats
    Dataset updated
    Feb 9, 2024
    Dataset provided by
    Agricultural Marketing Servicehttps://www.ams.usda.gov/
    United States Department of Agriculturehttp://usda.gov/
    Authors
    USDA Agricultural Marketing Service
    License

    U.S. Government Workshttps://www.usa.gov/government-works
    License information was derived automatically

    Description

    The Organic INTEGRITY Database is a certified organic operations database that contains up-to-date and accurate information about operations that may and may not sell as organic, deterring fraud, increases supply chain transparency for buyers and sellers, and promotes market visibility for organic operations. Only certified operations can sell, label, or represent products as organic, unless exempt or excluded from certification. The INTEGRITY database improves access to certified organic operation information by giving industry and public users an easier way to search for data with greater precision than the formerly posted Annual Lists of Certified Operations. You can find a certified organic farm or business, or search for an operation with specific characteristics such as:

    The status of an operation: Certified, Surrendered, Revoked, or Suspended The scopes for which an operation is certified: Crops, Livestock, Wild Crops, or Handling

    The organic commodities and services that operations offer. A new, more structured classification system (sample provided) will help you find more of what you’re looking for and details about the flexible taxonomy can be found in the INTEGRITY Categories and Items list. Resources in this dataset:Resource Title: Organic INTEGRITY Database. File Name: Web Page, url: https://organic.ams.usda.gov/integrity/Default.aspx Find a specific certified organic farm or business, or search for an operation with specific characteristics. Listings come from USDA-Accredited Certifying Agents. Historical Annual Lists of Certified Organic Operations and monthly snapshots of the full data set are available for download on the Data History page. Only certified operations can sell, label or represent products as organic, unless exempt or excluded from certification.

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Himel Sarder (2025). Business Database [Dataset]. https://www.kaggle.com/datasets/himelsarder/business-database
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Business Database

A Comprehensive Sales and Order Management Database

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CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Dataset updated
Feb 26, 2025
Dataset provided by
Kagglehttp://kaggle.com/
Authors
Himel Sarder
License

https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

Description

This is a relational database schema for a sales and order management system, designed to track customers, employees, products, orders, and payments. Below is a detailed breakdown of each table and their relationships:

1. productlines Table (Product Categories)

  • Represents different product categories.
  • Primary Key: productLine
  • Attributes:
    • textDescription: A short description of the product line.
    • htmlDescription: A detailed HTML-based description.
    • image: Associated image (if applicable).
  • Relationships:
    • One-to-Many with products: Each product belongs to one productLine.

2. products Table (Product Information)

  • Stores details of individual products.
  • Primary Key: productCode
  • Attributes:
    • productName: Name of the product.
    • productLine: Foreign key linking to productlines.
    • productScale, productVendor, productDescription: Additional product details.
    • quantityInStock: Number of units available.
    • buyPrice: Cost price per unit.
    • MSRP: Manufacturer's Suggested Retail Price.
  • Relationships:
    • Many-to-One with productlines (each product belongs to one category).
    • One-to-Many with orderdetails (a product can be part of many orders).

3. orderdetails Table (Line Items in an Order)

  • Stores details of each product within an order.
  • Composite Primary Key: (orderNumber, productCode)
  • Attributes:
    • quantityOrdered: Number of units in the order.
    • priceEach: Price per unit.
    • orderLineNumber: The sequence number in the order.
  • Relationships:
    • Many-to-One with orders (each order has multiple products).
    • Many-to-One with products (each product can appear in multiple orders).

4. orders Table (Customer Orders)

  • Represents customer orders.
  • Primary Key: orderNumber
  • Attributes:
    • orderDate: Date when the order was placed.
    • requiredDate: Expected delivery date.
    • shippedDate: Actual shipping date (can be NULL if not shipped).
    • status: Order status (e.g., "Shipped", "In Process", "Cancelled").
    • comments: Additional remarks about the order.
    • customerNumber: Foreign key linking to customers.
  • Relationships:
    • One-to-Many with orderdetails (an order contains multiple products).
    • Many-to-One with customers (each order is placed by one customer).

5. customers Table (Customer Details)

  • Stores customer information.
  • Primary Key: customerNumber
  • Attributes:
    • customerName: Name of the customer.
    • contactLastName, contactFirstName: Contact person.
    • phone: Contact number.
    • addressLine1, addressLine2, city, state, postalCode, country: Address details.
    • salesRepEmployeeNumber: Foreign key linking to employees, representing the sales representative.
    • creditLimit: Maximum credit limit assigned to the customer.
  • Relationships:
    • One-to-Many with orders (a customer can place multiple orders).
    • One-to-Many with payments (a customer can make multiple payments).
    • Many-to-One with employees (each customer has a sales representative).

6. payments Table (Customer Payments)

  • Stores payment transactions.
  • Composite Primary Key: (customerNumber, checkNumber)
  • Attributes:
    • paymentDate: Date of payment.
    • amount: Payment amount.
  • Relationships:
    • Many-to-One with customers (each payment is linked to a customer).

7. employees Table (Employee Information)

  • Stores details of employees, including reporting hierarchy.
  • Primary Key: employeeNumber
  • Attributes:
    • lastName, firstName: Employee's name.
    • extension, email: Contact details.
    • officeCode: Foreign key linking to offices, representing the employee's office.
    • reportsTo: References another employeeNumber, establishing a hierarchy.
    • jobTitle: Employee’s role (e.g., "Sales Rep", "Manager").
  • Relationships:
    • Many-to-One with offices (each employee works in one office).
    • One-to-Many with employees (self-referential, representing reporting structure).
    • One-to-Many with customers (each employee manages multiple customers).

8. offices Table (Office Locations)

  • Represents company office locations.
  • Primary Key: officeCode
  • Attributes:
    • city, state, country: Location details.
    • phone: Office contact number.
    • addressLine1, addressLine2, postalCode, territory: Address details.
  • Relationships:
    • One-to-Many with employees (each office has multiple employees).

Conclusion

This schema provides a well-structured design for managing a sales and order system, covering: ✅ Product inventory
✅ Order and payment tracking
✅ Customer and employee management
✅ Office locations and hierarchical reporting

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