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.

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

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

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.

Listing of all (active and inactive) businesses registered with the Office of Finance. An "active" business is defined as a registered business whose owner has not notified the Office of Finance of a cease of business operations. Update Interval: Monthly. NAICS Codes are from 2007 NAICS: https://www.census.gov/cgi-bin/sssd/naics/naicsrch?chart=2007

Database Platform as a Service Market Outlook

The Database Platform as a Service (DBPaaS) market is poised for substantial growth, with a market size that was valued at USD 9.5 billion in 2023 and is projected to reach USD 25.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 11.5% during the forecast period. This remarkable growth is driven by factors such as the increasing adoption of cloud-based solutions, the surge in data generation across various sectors, and the need for scalable and efficient database management systems. Furthermore, the growing demand for real-time data analytics to derive actionable insights and the rising trend of digital transformation across industries are further propelling the market's expansion.

One of the critical growth drivers of the DBPaaS market is the widespread embrace of cloud technology across businesses of all sizes. As organizations increasingly migrate their operations to the cloud, the demand for flexible and cost-effective database management solutions has surged. DBPaaS allows companies to manage databases without the need for complex on-premises infrastructure, enabling them to focus more on their core business objectives. This cloud-first approach is particularly appealing to small and medium enterprises (SMEs) that may lack the resources to maintain robust IT infrastructures, thereby fueling market growth across this segment.

Moreover, the acceleration of digital transformation initiatives across various industries is another pivotal factor influencing the growth of the DBPaaS market. Industries such as BFSI, healthcare, IT and telecommunications, and retail are increasingly relying on digital solutions to optimize their operations, improve customer experiences, and gain competitive advantages. As these sectors generate vast amounts of data, the need for efficient and scalable database management systems becomes paramount. DBPaaS offers these industries the agility and scalability required to handle their data needs effectively, thereby contributing significantly to market expansion.

The ongoing advancements in real-time data analytics and the increasing importance of data-driven decision-making are also boosting the DBPaaS market. Organizations today are keen on leveraging big data and analytics to enhance business operations and customer satisfaction. DBPaaS solutions provide the necessary infrastructure and tools to manage and analyze large datasets efficiently, allowing businesses to derive insights that can drive strategic initiatives. The ability to access real-time data analytics is crucial for industries like retail and BFSI, where timely decisions can significantly impact performance and profitability.

As the DBPaaS market continues to evolve, the concept of a Database Private Cloud is gaining traction among organizations seeking enhanced security and control over their data. Unlike public cloud solutions, a Database Private Cloud offers dedicated resources and infrastructure, ensuring higher levels of data privacy and compliance with industry regulations. This model is particularly appealing to sectors such as healthcare and BFSI, where data sensitivity and confidentiality are paramount. By opting for a Database Private Cloud, businesses can maintain greater oversight of their data environments, tailoring their database management strategies to meet specific security and operational requirements. This approach not only enhances data protection but also allows for more customized and efficient database solutions, aligning with the growing demand for secure cloud-based services.

Regionally, North America dominates the DBPaaS market due to the early adoption of innovative technologies and the presence of major cloud service providers. The region's mature IT infrastructure, coupled with a strong focus on digital transformation across verticals, creates a conducive environment for DBPaaS growth. Meanwhile, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. Factors such as increasing investments in cloud infrastructure, rapid economic development, and the rising uptake of cloud services by SMEs in countries like India and China contribute to this regional surge. Europe also demonstrates steady growth, driven by stringent data protection regulations that encourage cloud adoption and database management solutions.

Service Type Analysis

The DBPaaS market is segmented based on service types into managed services and pr

Comprehensive dataset of 27 Database management companies in Greece as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

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.

This dataset provides information on 206 in Mexico 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.

Every single contact from our firmographic database with 341 million+ companies comes directly from local sources that you can trust and are GDPR proof. We can deliver 200 firmographics such as company size, industry, legal status, revenue, employee size, opening hours, geocodes, import / export. BoldData is the nr.1 supplier of firmographic data supplier because we make use of thousands of local data sources. Ask us for a quote!

LinkedIn companies use datasets to access public company data for machine learning, ecosystem mapping, and strategic decisions. Popular use cases include competitive analysis, CRM enrichment, and lead generation.

Use our LinkedIn Companies Information dataset to access comprehensive data on companies worldwide, including business size, industry, employee profiles, and corporate activity. This dataset provides key company insights, organizational structure, and competitive landscape, tailored for market researchers, HR professionals, business analysts, and recruiters.

Leverage the LinkedIn Companies dataset to track company growth, analyze industry trends, and refine your recruitment strategies. By understanding company dynamics and employee movements, you can optimize sourcing efforts, enhance business development opportunities, and gain a strategic edge in your market. Stay informed and make data-backed decisions with this essential resource for understanding global company ecosystems.

Dataset Features

• timestamp: Represents the date and time when the company data was collected.
• id: Unique identifier for each company in the dataset.
• company_id: Identifier linking the company to an external database or internal system.
• url: Website or URL for more information about the company.
• name: The name of the company.
• about: Brief description of the company.
• description: More detailed information about the company's operations and offerings.
• organization_type: Type of the organization (e.g., private, public).
• industries: List of industries the company operates in.
• followers: Number of followers on the company's platform.
• headquarters: Location of the company's headquarters.
• country_code: Code for the country where the company is located.
• country_codes_array: List of country codes associated with the company (may represent various locations or markets).
• locations: Locations where the company operates.
• get_directions_url: URL to get directions to the company's location(s).
• formatted_locations: Human-readable format of the company's locations.
• website: The official website of the company.
• website_simplified: A simplified version of the company's website URL.
• company_size: Number of employees or company size.
• employees_in_linkedin: Number of employees listed on LinkedIn.
• employees: URL of employees.
• specialties: List of the company’s specializations or services.
• updates: Recent updates or news related to the company.
• crunchbase_url: Link to the company’s profile on Crunchbase.
• founded: Year when the company was founded.
• funding: Information on funding rounds or financial data.
• investors: Investors who have funded the company.
• alumni: Notable alumni from the company.
• alumni_information: Details about the alumni, their roles, or achievements.
• stock_info: Stock market information for publicly traded companies.
• affiliated: Companies or organizations affiliated with the company.
• image: Image representing the company.
• logo: URL of the official logo of the company.
• slogan: Company’s slogan or tagline.
• similar: URL of companies similar to this one.

Distribution

• Data Volume: 56.51M rows and 35 columns.
• Structure: Tabular format (CSV, Excel).

Usage

This dataset is ideal for:
- Market Research: Identifying key trends and patterns across different industries and geographies.
- Business Development: Analyzing potential partners, competitors, or customers.
- Investment Analysis: Assessing investment potential based on company size, funding, and industries.
- Recruitment & Talent Analytics: Understanding the workforce size and specialties of various companies.

Coverage

• Geographic Coverage: Global, with company locations and headquarters spanning multiple countries.
• Time Range: Data likely covers both current and historical information about companies.
• Demographics: Focuses on company attributes rather than demographics, but may contain information about the company's workforce.

License

CUSTOM

Please review the respective licenses below:

1. Data Provider's License
   • Bright Data Master Service Agreement

Who Can Use It

• Data Scientists: For building models, conducting research, or enhancing machine learning algorithms with business data.
• Researchers: For academic analysis in fields like economics, business, or technology.
• Businesses: For analysis, competitive benchmarking, and strategic development.
• Investors: For identifying and evaluating potential investment opportunities.

Dataset Name Ideas

• Global Company Profile Database
• **Business Intellige

Database Management Services Market Outlook

The global database management services market size was estimated at USD 20.5 billion in 2023 and is projected to reach USD 40.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.6% during the forecast period. A significant growth factor propelling this market includes the increasing digital transformation initiatives across various industries, driving the need for robust database management solutions.

One of the primary growth drivers for the database management services market is the exponential growth of data generated globally. Enterprises are increasingly digitizing their operations, generating massive volumes of data that need efficient management. Furthermore, the proliferation of cloud computing has made the storage and management of data more flexible and scalable, fueling the adoption of cloud-based database management services. Another critical aspect is the advent of big data analytics, which demands advanced database management systems to handle and process large datasets effectively.

The increasing adoption of advanced technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) is also contributing significantly to the market's growth. These technologies require robust database management systems to store and analyze the vast amounts of data they generate. Businesses are recognizing the value of data-driven insights for making informed decisions, thereby accelerating the demand for sophisticated database management services. Additionally, regulatory requirements for data storage and management are becoming more stringent, compelling organizations to adopt advanced database management systems to ensure compliance.

The growing trend of remote work and the need for real-time data access also play a crucial role in the market's expansion. With more employees working remotely, the demand for seamless and secure data access has surged, leading to a higher need for effective database management solutions. Moreover, the rise of e-commerce and online services has led to an increased demand for efficient and scalable database management systems to handle customer data, transactions, and other critical information.

From a regional perspective, North America holds a significant share of the database management services market, primarily due to the presence of major technology companies and early adoption of advanced technologies. The Asia-Pacific region is expected to witness the highest growth rate during the forecast period, driven by rapid industrialization, increasing digitalization, and growing investments in IT infrastructure. Europe and Latin America are also experiencing steady growth, with organizations in these regions increasingly adopting database management solutions to enhance operational efficiency and drive business growth.

Service Type Analysis

Database management services can be segmented by service type into consulting, implementation, maintenance, and support. Consulting services involve providing expert advice and strategies for database management tailored to an organization’s specific needs. As businesses strive to integrate more sophisticated data solutions, the demand for consulting services is expected to grow. Consultants help identify the most suitable database management systems, optimize existing infrastructure, and ensure that data policies comply with regulatory standards, thus driving the segment's growth.

Implementation services encompass the deployment of database management systems and solutions within an organization. This segment is poised for significant growth as companies move towards modernizing their IT infrastructures. Implementation services ensure seamless integration of new systems with existing technologies, minimizing disruption and enhancing data accessibility and security. With the rise of cloud computing, implementation services are increasingly focused on migrating on-premises databases to cloud-based solutions, which offers scalability and cost-efficiency.

Maintenance services involve the ongoing management and upkeep of database systems to ensure their optimal performance. This includes regular updates, security patches, and troubleshooting to prevent downtime and data loss. As businesses become more reliant on data-driven operations, the importance of maintenance services cannot be overstated. These services ensure that databases remain functional, secure, and efficient, thereby supporting continuous business operations and data availabilit

Data base of large business groups and their structure. Updated: Ad hoc. Data available for 2005/06

Comprehensive dataset of 3 Database management companies in Tarragona, Spain as of June, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

Comprehensive dataset of 1 Database management companies in TAS, Australia as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

The CBT database builds on an existing database which has been created in 2006 as a multi-country database and developed over the years by various Research Fellows at the Centre, and earlier at the Institute for Fiscal Studies. The original version uses various sources such as OECD Tax Database, IBFD (International Bureau of Fiscal Documentation), World Tax Database from the University of Michigan, KPMG and E&Y and covered mainly OECD countries. The data currently in the database comes from various sources, mainly from: • The Worldwide Corporate Tax Guide published by E&Y; years available: 2002-2017 • data for 2011 - 2017 comes mainly from the online IBFD Tax Research Platform where they provide very detailed Country Surveys • G20 countries data has been updated to be consistent with IBFD "Global corporate tax handbook" (years 2007 - 2010) and "European tax handbook" (years 1990 - 2010) • ZEW Intermediate Report 2011, “Effective Tax levels using Devereux/Griffith methodology” • Deloitte Tax Highlights and International Tax and Business Guide; years available: 2009, 2010 • KPMG Tax Rate Survey; years available: 1998 - 2009 • PKF Worldwide Tax Guide; years available: 2007 - 2009

The Universal Basic Income dataset, which was indexed Scopus from 1996 to 2020. The dataset contains data authors, authors ID Scopus, title, year, source title, volume, issue, article number in Scopus, DOI, link, affiliation, index keywords, reference, correspondence address, editors, publisher, conference name, conference data, conference code, ISSN, language, document type, access type, and EID.

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.

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.

Global Cloud Database And DBaaS market size is expected to reach $63.19 billion by 2029 at 21.3%, segmented as by solution, relational cloud databases, nosql cloud databases, in-memory cloud databases, newsql cloud databases