To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

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

Small Business Development Centers (SBDCs) provide assistance to small businesses and aspiring entrepreneurs throughout the United States and its territories. SBDCs help entrepreneurs realize the dream of business ownership and help existing businesses remain competitive in a complex, ever-changing global marketplace. SBDCs are hosted by leading universities and state economic development agencies, and funded in part through a partnership with SBA.

This Private Company Data dataset is a refined version of our company datasets, consisting of 35M+ data records.

It’s an excellent data solution for companies with limited data engineering capabilities and those who want to reduce their time to value. You get filtered, cleaned, unified, and standardized B2B private company data. This data is also enriched by leveraging a carefully instructed large language model (LLM).

AI-powered data enrichment offers more accurate information in key data fields, such as company descriptions. It also produces over 20 additional data points that are very valuable to B2B businesses. Enhancing and highlighting the most important information in web data contributes to quicker time to value, making data processing much faster and easier.

For your convenience, you can choose from multiple data formats (Parquet, JSON, JSONL, or CSV) and select suitable delivery frequency (quarterly, monthly, or weekly).

Coresignal is a leading private company data provider in the web data sphere with an extensive focus on firmographic data and public employee profiles. More than 3B data records in different categories enable companies to build data-driven products and generate actionable insights. Coresignal is exceptional in terms of data freshness, with 890M+ records updated monthly for unprecedented accuracy and relevance.

This dataset is about books. It has 1 row and is filtered where the book is Digital business : how to make money in an on-line world. It features 7 columns including author, publication date, language, and book publisher.

Success.ai offers a cutting-edge solution for businesses and organizations seeking Company Financial Data on private and public companies. Our comprehensive database is meticulously crafted to provide verified profiles, including contact details for financial decision-makers such as CFOs, financial analysts, corporate treasurers, and other key stakeholders. This robust dataset is continuously updated and validated using AI technology to ensure accuracy and relevance, empowering businesses to make informed decisions and optimize their financial strategies.

Key Features of Success.ai's Company Financial Data:

Global Coverage: Access data from over 70 million businesses worldwide, including public and private companies across all major industries and regions. Our datasets span 250+ countries, offering extensive reach for your financial analysis and market research.

Detailed Financial Profiles: Gain insights into company financials, including revenue, profit margins, funding rounds, and operational costs. Profiles are enriched with key contact details, including work emails, phone numbers, and physical addresses, ensuring direct access to decision-makers.

Industry-Specific Data: Tailored datasets for sectors such as financial services, manufacturing, technology, healthcare, and energy, among others. Each dataset is customized to meet the unique needs of industry professionals and analysts.

Real-Time Accuracy: With continuous updates powered by AI-driven validation, our financial data maintains a 99% accuracy rate, ensuring you have access to the most reliable and up-to-date information available.

Compliance and Security: All data is collected and processed in strict adherence to global compliance standards, including GDPR, ensuring ethical and lawful usage.

Why Choose Success.ai for Company Financial Data?

Best Price Guarantee: We pride ourselves on offering the most competitive pricing in the industry, ensuring you receive unparalleled value for comprehensive financial data.

AI-Validated Accuracy: Our advanced AI algorithms meticulously verify every data point to ensure precision and reliability, helping you avoid costly errors in your financial decision-making.

Customized Data Solutions: Whether you need data for a specific region, industry, or type of business, we tailor our datasets to align perfectly with your requirements.

Scalable Data Access: From small startups to global enterprises, our platform caters to businesses of all sizes, delivering scalable solutions to suit your operational needs.

Comprehensive Use Cases for Financial Data:

1. Strategic Financial Planning:

Leverage our detailed financial profiles to create accurate budgets, forecasts, and strategic plans. Gain insights into competitors’ financial health and market positions to make data-driven decisions.

1. Mergers and Acquisitions (M&A):

Access key financial details and contact information to streamline your M&A processes. Identify potential acquisition targets or partners with verified profiles and financial data.

1. Investment Analysis:

Evaluate the financial performance of public and private companies for informed investment decisions. Use our data to identify growth opportunities and assess risk factors.

1. Lead Generation and Sales:

Enhance your sales outreach by targeting CFOs, financial analysts, and other decision-makers with verified contact details. Utilize accurate email and phone data to increase conversion rates.

1. Market Research:

Understand market trends and financial benchmarks with our industry-specific datasets. Use the data for competitive analysis, benchmarking, and identifying market gaps.

APIs to Power Your Financial Strategies:

Enrichment API: Integrate real-time updates into your systems with our Enrichment API. Keep your financial data accurate and current to drive dynamic decision-making and maintain a competitive edge.

Lead Generation API: Supercharge your lead generation efforts with access to verified contact details for key financial decision-makers. Perfect for personalized outreach and targeted campaigns.

Tailored Solutions for Industry Professionals:

Financial Services Firms: Gain detailed insights into revenue streams, funding rounds, and operational costs for competitor analysis and client acquisition.

Corporate Finance Teams: Enhance decision-making with precise data on industry trends and benchmarks.

Consulting Firms: Deliver informed recommendations to clients with access to detailed financial datasets and key stakeholder profiles.

Investment Firms: Identify potential investment opportunities with verified data on financial performance and market positioning.

What Sets Success.ai Apart?

Extensive Database: Access detailed financial data for 70M+ companies worldwide, including small businesses, startups, and large corporations.

Ethical Practices: Our data collection and processing methods are fully comp...

OECD statistics contact: stat.contact@oecd.org

The OECD Secretariat collects a wide range of statistics on businesses and business activity. The Structural Business Statistics by size class dataset is part of the Structural and Demographic Business Statistics (SDBS) database featuring the harmonised data collection of the OECD Statistics and Data Directorate relating to a number of key variables, such as value added, operating surplus, employment, and the number of business units.

Data are broken down to class (4-digit) level of International Standard of Industrial Classification (ISIC Revision 4), and by enterprise size class based on the number of persons employed.

Data cover OECD member and partner countries, non-OECD countries that are members of the European Statistical System who provide data to Eurostat, as well as countries participating in OECD Regional initiatives.

Copy of https://www.kaggle.com/datasets/kisoibo/countries-databasesqlite

Updated the name of the table from 'countries of the world' to 'countries', for ease of writing queries.

Info about the dataset:

Content

Table Total Rows Total Columns countries of the world **0 ** ** 20** Country, Region, Population, Area (sq. mi.), Pop. Density (per sq. mi.), Coastline (coast/area ratio), Net migration, Infant mortality (per 1000 births), GDP ($ per capita), Literacy (%), Phones (per 1000), Arable (%), Crops (%), Other (%), Climate, Birthrate, Deathrate, Agriculture, Industry, Service

Acknowledgements

Acknowledgements Source: All these data sets are made up of data from the US government. Generally they are free to use if you use the data in the US. If you are outside of the US, you may need to contact the US Govt to ask. Data from the World Factbook is public domain. The website says "The World Factbook is in the public domain and may be used freely by anyone at anytime without seeking permission." https://www.cia.gov/library/publications/the-world-factbook/docs/faqs.html

Inspiration

When making visualisations related to countries, sometimes it is interesting to group them by attributes such as region, or weigh their importance by population, GDP or other variables.

This dataset provides values for BUSINESS INVENTORIES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

McGRAW’s US B2B Data: Accurate, Reliable, and Market-Ready

Our B2B database delivers over 80 million verified contacts with 95%+ accuracy. Supported by in-house call centers, social media validation, and market research teams, we ensure that every record is fresh, reliable, and optimized for B2B outreach, lead generation, and advanced market insights.

Our B2B database is one of the most accurate and extensive datasets available, covering over 91 million business executives with a 95%+ accuracy guarantee. Designed for businesses that require the highest quality data, this database provides detailed, validated, and continuously updated information on decision-makers and industry influencers worldwide.

The B2B Database is meticulously curated to meet the needs of businesses seeking precise and actionable data. Our datasets are not only extensive but also rigorously validated and updated to ensure the highest level of accuracy and reliability.

Key Data Attributes:

• Personal Identifiers: First name, last name
• Professional Details: Title, direct dial numbers
• Business Information: Company name, address, phone number, fax number, website
• Company Metrics: Employee size, sales volume
• Technology Insights: Information on hardware and software usage across organizations
• Social Media Connections: LinkedIn, Facebook, and direct dial contacts
• Corporate Insights: Detailed company profiles

Unlike many providers that rely solely on third-party vendor files, McGRAW takes a hands-on approach to data validation. Our dedicated nearshore and offshore call centers engage directly with data before each delivery to ensure every record meets our high standards of accuracy and relevance.

In addition, our teams of social media validators, market researchers, and digital marketing specialists continuously refine and update records to maintain data freshness. Each dataset undergoes multiple verification checks using internal validation processes and third-party tools such as Fresh Address, BriteVerify, and Impressionwise to guarantee the highest data quality.

Additional Data Solutions and Services

• Data Enhancement: Email and LinkedIn appends, contact discovery across global roles and functions

• Business Verification: Real-time validation through call centers, social media, and market research

• Technology Insights: Detailed IT infrastructure reports, spending trends, and executive insights

• Healthcare Database: Access to over 80 million healthcare professionals and industry leaders

• Global Reach: US and international GDPR-compliant datasets, complete with email, postal, and phone contacts

• Email Broadcast Services: Full-service campaign execution, from testing to live deployment, with tracking of key engagement metrics such as opens and clicks

Many B2B data providers rely on vendor-contributed files without conducting the rigorous validation necessary to ensure accuracy. This often results in outdated and unreliable data that fails to meet the demands of a fast-moving business environment.

McGRAW takes a different approach. By owning and operating dedicated call centers, we directly verify and validate our data before delivery, ensuring that every record is up-to-date and ready to drive business success.

Through continuous validation, social media verification, and real-time updates, McGRAW provides a high-quality, dependable database for businesses that prioritize data integrity and performance. Our Global Business Executives database is the ideal solution for companies that need accurate, relevant, and market-ready data to fuel their strategies.

Point of Interest (POI) is defined as an entity (such as a business) at a ground location (point) which may be (of interest). We provide high-quality POI data that is fresh, consistent, customizable, easy to use and with high-density coverage for all countries of the world.

This is our process flow:

Our machine learning systems continuously crawl for new POI data
Our geoparsing and geocoding calculates their geo locations
Our categorization systems cleanup and standardize the datasets
Our data pipeline API publishes the datasets on our data store

A new POI comes into existence. It could be a bar, a stadium, a museum, a restaurant, a cinema, or store, etc.. In today's interconnected world its information will appear very quickly in social media, pictures, websites, press releases. Soon after that, our systems will pick it up.

POI Data is in constant flux. Every minute worldwide over 200 businesses will move, over 600 new businesses will open their doors and over 400 businesses will cease to exist. And over 94% of all businesses have a public online presence of some kind tracking such changes. When a business changes, their website and social media presence will change too. We'll then extract and merge the new information, thus creating the most accurate and up-to-date business information dataset across the globe.

We offer our customers perpetual data licenses for any dataset representing this ever changing information, downloaded at any given point in time. This makes our company's licensing model unique in the current Data as a Service - DaaS Industry. Our customers don't have to delete our data after the expiration of a certain "Term", regardless of whether the data was purchased as a one time snapshot, or via our data update pipeline.

Customers requiring regularly updated datasets may subscribe to our Annual subscription plans. Our data is continuously being refreshed, therefore subscription plans are recommended for those who need the most up to date data. The main differentiators between us vs the competition are our flexible licensing terms and our data freshness.

Data samples may be downloaded at https://store.poidata.xyz/us

AZ: New Businesses Registered data was reported at 15,497.000 Number in 2022. This records an increase from the previous number of 11,532.000 Number for 2021. AZ: New Businesses Registered data is updated yearly, averaging 7,172.000 Number from Dec 2008 (Median) to 2022, with 15 observations. The data reached an all-time high of 15,687.000 Number in 2019 and a record low of 3,541.000 Number in 2010. AZ: New Businesses Registered data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Azerbaijan – Table AZ.World Bank.WDI: Businesses Registered Statistics. New businesses registered are the number of new limited liability corporations (or its equivalent) registered in the calendar year.;World Bank's Entrepreneurship Database (https://www.worldbank.org/en/programs/entrepreneurship).;;For cross-country comparability, only limited liability corporations that operate in the formal sector are included.

This collection of data includes over seventeen million global companies. The dataset has information such as a company's name, website domain, size, year founded, industry, city/state, country and the handle of their LinkedIn URL. Schema, data stats, general documentation, and other datasets can be found at: https://docs.bigpicture.io/docs/free-datasets/companies/

This dataset provides comprehensive, up-to-date information about the top 100 Software-as-a-Service (SaaS) companies globally as of 2025. It includes detailed financial metrics, company fundamentals, and operational data that are crucial for market research, competitive analysis, investment decisions, and academic studies.

Key Features

• 100 leading SaaS companies across various industries
• 11 comprehensive data points per company
• Current 2025 data including latest valuations and ARR figures
• Verified information from multiple reliable sources
• Clean, analysis-ready format with consistent data structure

Use Cases

1. Market Research: Analyze SaaS industry trends and market dynamics
2. Investment Analysis: Evaluate growth patterns and valuation multiples
3. Competitive Intelligence: Benchmark companies within sectors
4. Academic Research: Study business models and growth strategies
5. Data Science Projects: Build predictive models for SaaS metrics
6. Business Strategy: Identify successful patterns in SaaS businesses

Industries Covered

Enterprise Software (CRM, ERP, HR) Developer Tools & DevOps Cybersecurity Data Analytics & Business Intelligence Marketing & Sales Technology Financial Technology Communication & Collaboration E-commerce Platforms Design & Creative Tools Infrastructure & Cloud Services

Why This Dataset? The SaaS industry has grown to over $300 billion globally, with companies achieving unprecedented valuations and growth rates. This dataset captures the current state of the industry leaders, providing insights into what makes successful SaaS companies tick.

Sources/Proof of Data: Data Sources The data has been meticulously compiled from multiple authoritative sources:

Company Financial Reports (Q4 2024 - Q1 2025)

Official earnings releases and investor relations documents SEC filings for public companies

Investment Databases

Crunchbase, PitchBook, and CB Insights for funding data Venture capital and private equity announcements

Market Research Reports

Gartner, Forrester, and IDC industry analyses SaaS Capital Index and valuation reports

Industry Publications

TechCrunch, Forbes, Wall Street Journal coverage Company press releases and official announcements

Product Review Platforms

G2 Crowd ratings and reviews Capterra and GetApp user feedback

Data Verification

Cross-referenced across multiple sources for accuracy Updated with latest available information as of May 2025 Validated against official company statements where available

Business Analysis Services Market Outlook

As of 2023, the global market size for Business Analysis Services is estimated to be approximately USD 15 billion, with a projected growth to reach USD 30 billion by 2032, reflecting a CAGR of around 8%. Key factors driving this growth include the increasing complexity of business operations, a surge in demand for data-driven decision-making, and the rising adoption of digital transformation across various industries.

One of the notable growth factors for the Business Analysis Services market is the rapid digital transformation taking place across multiple industries. Organizations are increasingly leveraging technology to streamline operations, improve customer experience, and gain a competitive edge. Business Analysis Services play a crucial role in guiding these digital initiatives by providing insights and strategies grounded in comprehensive data analysis. As more companies embark on digital transformation journeys, the demand for business analysis services is expected to rise significantly.

Another significant driver of market growth is the heightened need for data-driven decision-making. In today's competitive landscape, organizations are relying more heavily on data to make informed decisions. Business analysts are essential in interpreting complex data sets and translating them into actionable business strategies. This trend is especially prominent in sectors such as BFSI, healthcare, and retail, where data analytics can provide critical insights into customer behavior, market trends, and operational efficiencies. Consequently, the demand for skilled business analysis services is projected to grow at a robust pace.

The increasing complexity of business operations is also propelling the demand for Business Analysis Services. As businesses expand globally, operational challenges become more intricate, necessitating expert analysis to navigate regulatory environments, optimize supply chains, and enhance overall efficiency. Business analysts provide essential support in these areas by identifying bottlenecks, recommending process improvements, and ensuring compliance with industry standards. This operational complexity underscores the indispensable role of business analysis services in achieving organizational success.

In this evolving landscape, Business-Outcome-Driven Enterprise Architecture Consulting and Solutions have emerged as pivotal in aligning business strategies with technological advancements. These solutions focus on creating a cohesive framework that not only supports current business operations but also anticipates future challenges and opportunities. By leveraging enterprise architecture, organizations can ensure that their IT infrastructure is agile, scalable, and aligned with their strategic goals. This approach not only enhances operational efficiency but also drives innovation by enabling businesses to quickly adapt to market changes and technological disruptions. As digital transformation continues to accelerate, the demand for enterprise architecture consulting is expected to grow, offering organizations a strategic advantage in navigating the complexities of modern business environments.

From a regional perspective, North America holds the largest share of the Business Analysis Services market, driven by the presence of a significant number of established enterprises and the advanced state of digital transformation in the region. Europe follows closely, with a strong emphasis on regulatory compliance and operational efficiency fueling demand. The Asia Pacific region is expected to witness the highest growth rate during the forecast period, spurred by rapid economic development and increasing adoption of technology-driven business solutions. Latin America and the Middle East & Africa are also emerging as promising markets due to growing awareness of the benefits of business analysis services.

Service Type Analysis

The Business Analysis Services market can be segmented by service type into Consulting, Training, Outsourcing, and Others. Consulting services dominate the market, accounting for the largest share. This segment involves providing expert advice to organizations on how to optimize their operations, improve efficiency, and implement effective strategies. The need for specialized knowledge and experience in addressing complex business challenges makes consulting an indispensable component of the market. Co

The global big data market is forecasted to grow to 103 billion U.S. dollars by 2027, more than double its expected market size in 2018. With a share of 45 percent, the software segment would become the large big data market segment by 2027.

What is Big data?

Big data is a term that refers to the kind of data sets that are too large or too complex for traditional data processing applications. It is defined as having one or some of the following characteristics: high volume, high velocity or high variety. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets.

Big data analytics

Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate new business insights. The global big data and business analytics market was valued at 169 billion U.S. dollars in 2018 and is expected to grow to 274 billion U.S. dollars in 2022. As of November 2018, 45 percent of professionals in the market research industry reportedly used big data analytics as a research method.

The global big data and business analytics (BDA) market was valued at 168.8 billion U.S. dollars in 2018 and is forecast to grow to 215.7 billion U.S. dollars by 2021. In 2021, more than half of BDA spending will go towards services. IT services is projected to make up around 85 billion U.S. dollars, and business services will account for the remainder. Big data High volume, high velocity and high variety: one or more of these characteristics is used to define big data, the kind of data sets that are too large or too complex for traditional data processing applications. Fast-growing mobile data traffic, cloud computing traffic, as well as the rapid development of technologies such as artificial intelligence (AI) and the Internet of Things (IoT) all contribute to the increasing volume and complexity of data sets. For example, connected IoT devices are projected to generate 79.4 ZBs of data in 2025. Business analytics Advanced analytics tools, such as predictive analytics and data mining, help to extract value from the data and generate business insights. The size of the business intelligence and analytics software application market is forecast to reach around 16.5 billion U.S. dollars in 2022. Growth in this market is driven by a focus on digital transformation, a demand for data visualization dashboards, and an increased adoption of cloud.

This dataset was created to simulate a market basket dataset, providing insights into customer purchasing behavior and store operations. The dataset facilitates market basket analysis, customer segmentation, and other retail analytics tasks. Here's more information about the context and inspiration behind this dataset:

Context:

Retail businesses, from supermarkets to convenience stores, are constantly seeking ways to better understand their customers and improve their operations. Market basket analysis, a technique used in retail analytics, explores customer purchase patterns to uncover associations between products, identify trends, and optimize pricing and promotions. Customer segmentation allows businesses to tailor their offerings to specific groups, enhancing the customer experience.

Inspiration:

The inspiration for this dataset comes from the need for accessible and customizable market basket datasets. While real-world retail data is sensitive and often restricted, synthetic datasets offer a safe and versatile alternative. Researchers, data scientists, and analysts can use this dataset to develop and test algorithms, models, and analytical tools.

Dataset Information:

The columns provide information about the transactions, customers, products, and purchasing behavior, making the dataset suitable for various analyses, including market basket analysis and customer segmentation. Here's a brief explanation of each column in the Dataset:

• Transaction_ID: A unique identifier for each transaction, represented as a 10-digit number. This column is used to uniquely identify each purchase.
• Date: The date and time when the transaction occurred. It records the timestamp of each purchase.
• Customer_Name: The name of the customer who made the purchase. It provides information about the customer's identity.
• Product: A list of products purchased in the transaction. It includes the names of the products bought.
• Total_Items: The total number of items purchased in the transaction. It represents the quantity of products bought.
• Total_Cost: The total cost of the purchase, in currency. It represents the financial value of the transaction.
• Payment_Method: The method used for payment in the transaction, such as credit card, debit card, cash, or mobile payment.
• City: The city where the purchase took place. It indicates the location of the transaction.
• Store_Type: The type of store where the purchase was made, such as a supermarket, convenience store, department store, etc.
• Discount_Applied: A binary indicator (True/False) representing whether a discount was applied to the transaction.
• Customer_Category: A category representing the customer's background or age group.
• Season: The season in which the purchase occurred, such as spring, summer, fall, or winter.
• Promotion: The type of promotion applied to the transaction, such as "None," "BOGO (Buy One Get One)," or "Discount on Selected Items."

Use Cases:

• Market Basket Analysis: Discover associations between products and uncover buying patterns.
• Customer Segmentation: Group customers based on purchasing behavior.
• Pricing Optimization: Optimize pricing strategies and identify opportunities for discounts and promotions.
• Retail Analytics: Analyze store performance and customer trends.

Note: This dataset is entirely synthetic and was generated using the Python Faker library, which means it doesn't contain real customer data. It's designed for educational and research purposes.

The Yelp Dataset is a valuable resource for academic research, teaching, and learning. It provides a rich collection of real-world data related to businesses, reviews, and user interactions. Here are the key details about the Yelp Dataset: Reviews: A whopping 6,990,280 reviews from users. Businesses: Information on 150,346 businesses. Pictures: A collection of 200,100 pictures. Metropolitan Areas: Data from 11 metropolitan areas. Tips: Over 908,915 tips provided by 1,987,897 users. Business Attributes: Details like hours, parking availability, and ambiance for more than 1.2 million businesses. Aggregated Check-ins: Historical check-in data for each of the 131,930 businesses.

The majority of respondents stated that their company used more than one database for their operations. This indicates the complexity of maintaining security of IT infrastructure at organizations. Microsoft Azure database (67 percent) and Microsoft SQL Server (50 percent) were the most commonly used databases among respondents.