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.

With 56 Million Businesses in the United States of America, Techsalerator has access to the highest B2B count of Data/ Business Data in the country.

Thanks to our unique tools and large data specialist team, we are able to select the ideal targeted dataset based on the unique elements such as sales volume of a company, the company's location, no. of employees etc...

Whether you are looking for an entire fill install, access to our API's or if you are just looking for a one-time targeted purchase, get in touch with our company and we will fulfill your international data need.

We cover all states and cities in the country : Example covered.

All states :

Alabama Alaska Arizona Arkansas California Colorado Connecticut Delaware Florida Georgia Hawaii Idaho IllinoisIndiana Iowa Kansas Kentucky Louisiana Maine Maryland Massachusetts Michigan Minnesota Mississippi Missouri MontanaNebraska Nevada New Hampshire New Jersey New Mexico New York North Carolina North Dakota Ohio Oklahoma Oregon PennsylvaniaRhode Island South Carolina South Dakota Tennessee Texas Utah Vermont Virginia Washington West Virginia Wisconsin Wyoming

A few cities : New York City NY Los Angeles CA Chicago IL Houston TX Phoenix AZ Philadelphia PA San Antonio TX San Diego CA Dallas TX Austin TX San Jose CA Fort Worth TX Jacksonville FL Columbus OH Charlotte NC Indianapolis IN San Francisco CA Seattle WA Denver CO Washington DC Boston MA El Paso TX Nashville TN Oklahoma City OK Las Vegas NV Detroit MI Portland OR Memphis TN Louisville KY Milwaukee WI Baltimore MD Albuquerque NM Tucson AZ Mesa AZ Fresno CA Sacramento CA Atlanta GA Kansas City MO Colorado Springs CO Raleigh NC Omaha NE Miami FL Long Beach CA Virginia Beach VA Oakland CA Minneapolis MN Tampa FL Tulsa OK Arlington TX Wichita KS Bakersfield CA Aurora CO New Orleans LA Cleveland OH Anaheim CA Henderson NV Honolulu HI Riverside CA Santa Ana CA Corpus Christi TX Lexington KY San Juan PR Stockton CA St. Paul MN Cincinnati OH Greensboro NC Pittsburgh PA Irvine CA St. Louis MO Lincoln NE Orlando FL Durham NC Plano TX Anchorage AK Newark NJ Chula Vista CA Fort Wayne IN Chandler AZ Toledo OH St. Petersburg FL Reno NV Laredo TX Scottsdale AZ North Las Vegas NV Lubbock TX Madison WI Gilbert AZ Jersey City NJ Glendale AZ Buffalo NY Winston-Salem NC Chesapeake VA Fremont CA Norfolk VA Irving TX Garland TX Paradise NV Arlington VA Richmond VA Hialeah FL Boise ID Spokane WA Frisco TX Moreno Valley CA Tacoma WA Fontana CA Modesto CA Baton Rouge LA Port St. Lucie FL San Bernardino CA McKinney TX Fayetteville NC Santa Clarita CA Des Moines IA Oxnard CA Birmingham AL Spring Valley NV Huntsville AL Rochester NY Cape Coral FL Tempe AZ Grand Rapids MI Yonkers NY Overland Park KS Salt Lake City UT Amarillo TX Augusta GA Columbus GA Tallahassee FL Montgomery AL Huntington Beach CA Akron OH Little Rock AR Glendale CA Grand Prairie TX Aurora IL Sunrise Manor NV Ontario CA Sioux Falls SD Knoxville TN Vancouver WA Mobile AL Worcester MA Chattanooga TN Brownsville TX Peoria AZ Fort Lauderdale FL Shreveport LA Newport News VA Providence RI Elk Grove CA Rancho Cucamonga CA Salem OR Pembroke Pines FL Santa Rosa CA Eugene OR Oceanside CA Cary NC Fort Collins CO Corona CA Enterprise NV Garden Grove CA Springfield MO Clarksville TN Bayamon PR Lakewood CO Alexandria VA Hayward CA Murfreesboro TN Killeen TX Hollywood FL Lancaster CA Salinas CA Jackson MS Midland TX Macon County GA Kansas City KS Palmdale CA Sunnyvale CA Springfield MA Escondido CA Pomona CA Bellevue WA Surprise AZ Naperville IL Pasadena TX Denton TX Roseville CA Joliet IL Thornton CO McAllen TX Paterson NJ Rockford IL Carrollton TX Bridgeport CT Miramar FL Round Rock TX Metairie LA Olathe KS Waco TX

• Dataset Title: Company Purchasing Dataset
• Rows: 500 | Columns: 9
• Domain: Supply Chain, Procurement, Finance
• Use Case: Spend Analytics, Supplier Performance, Cost Optimization

This synthetic dataset simulates procurement transactions for a mid-sized organization over 2024. It includes purchases across multiple categories (electronics, furniture, stationery, etc.) from various suppliers and buyers. Ideal for practicing descriptive analytics, spend analysis, and supplier performance evaluation.

With 1.2 Million Businesses in Turkey, Techsalerator has access to the highest B2B count of Data/Business Data in the country. .

Thanks to our unique tools and large data specialist team, we can select the ideal targeted dataset based on the unique elements such as sales volume of a company, the company's location, no. of employees etc...

Whether you are looking for an entire fill install, access to our API's or if you are just looking for a one-time targeted purchase, get in touch with our company and we will fulfill your international data need.

We cover all regions and cities in the country:

Aegean Region Aegean Region Aegean Section Edremit Area Bakirçay Area Gediz Area Izmir Area Küçük Menderes Area Büyük Menderes Area Mentese Area Inner Western Anatolia Section

Blacksea Region Black Sea Region Western Black Sea Section Inner Black Sea Area Küre Mountains Area Central Black Sea Section Canik Mountains Area Inner Central Black Sea Area Eastern Black Sea Section Eastern Black Sea Coast Area Upper Kelkit - Çoruh Gully

Central Anatolia Region Central Anatolia Region Konya Section Obruk [tr] Plateau Konya - Eregli Vicinity Upper Sakarya Section Ankara Area Porsuk Gully Sündiken Mountain Chain Area Upper Sakarya Area Konya - Eregli Vicinity Middle Kizilirmak Section Upper Kizilirmak Section

Eastern Anatolia Region Eastern Anatolia Region Upper Euphrates Section Erzurum - Kars Section Upper Murat - Van Section Upper Murat Area Van Area Hakkari Section

Marmara Region Marmara Region Çatalca - Kocaeli Section Adapazari Area Istanbul Area Ergene Section Southern Marmara Section Biga - Gallipoli Area Bursa Area Karesi Area Samanli Area Yildiz Section

Mediterranean Region Mediterranean Region Adana Section Çukurova - Taurus Mountains Area Antakya - Kahramanmaras Area Antalya Section Antalya Area Göller Area Taseli - Mut Area Teke Area

Southeastern Anatolia Region Southeastern Anatolia Region Middle Euphrates Section Gaziantep Area Sanliurfa Area Tigris Section Diyarbakir Area Mardin - Midyat Area

Context

The Customer Shopping Preferences Dataset offers valuable insights into consumer behavior and purchasing patterns. Understanding customer preferences and trends is critical for businesses to tailor their products, marketing strategies, and overall customer experience. This dataset captures a wide range of customer attributes including age, gender, purchase history, preferred payment methods, frequency of purchases, and more. Analyzing this data can help businesses make informed decisions, optimize product offerings, and enhance customer satisfaction. The dataset stands as a valuable resource for businesses aiming to align their strategies with customer needs and preferences. It's important to note that this dataset is a Synthetic Dataset Created for Beginners to learn more about Data Analysis and Machine Learning.

Content

This dataset encompasses various features related to customer shopping preferences, gathering essential information for businesses seeking to enhance their understanding of their customer base. The features include customer age, gender, purchase amount, preferred payment methods, frequency of purchases, and feedback ratings. Additionally, data on the type of items purchased, shopping frequency, preferred shopping seasons, and interactions with promotional offers is included. With a collection of 3900 records, this dataset serves as a foundation for businesses looking to apply data-driven insights for better decision-making and customer-centric strategies.

Dataset Glossary (Column-wise)

• Customer ID - Unique identifier for each customer
• Age - Age of the customer
• Gender - Gender of the customer (Male/Female)
• Item Purchased - The item purchased by the customer
• Category - Category of the item purchased
• Purchase Amount (USD) - The amount of the purchase in USD
• Location - Location where the purchase was made
• Size - Size of the purchased item
• Color - Color of the purchased item
• Season - Season during which the purchase was made
• Review Rating - Rating given by the customer for the purchased item
• Subscription Status - Indicates if the customer has a subscription (Yes/No)
• Shipping Type - Type of shipping chosen by the customer
• Discount Applied - Indicates if a discount was applied to the purchase (Yes/No)
• Promo Code Used - Indicates if a promo code was used for the purchase (Yes/No)
• Previous Purchases - The total count of transactions concluded by the customer at the store, excluding the ongoing transaction
• Payment Method - Customer's most preferred payment method
• Frequency of Purchases - Frequency at which the customer makes purchases (e.g., Weekly, Fortnightly, Monthly)

Structure of the Dataset

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Acknowledgement

This dataset is a synthetic creation generated using ChatGPT to simulate a realistic customer shopping experience. Its purpose is to provide a platform for beginners and data enthusiasts, allowing them to create, enjoy, practice, and learn from a dataset that mirrors real-world customer shopping behavior. The aim is to foster learning and experimentation in a simulated environment, encouraging a deeper understanding of data analysis and interpretation in the context of consumer preferences and retail scenarios.

Cover Photo by: Freepik

Thumbnail by: Clothing icons created by Flat Icons - Flaticon

With 1.7 Million Businesses in Mexico , Techsalerator has access to the highest B2B count of Data/Business Data in the country.

Thanks to our unique tools and large data specialist team, we can select the ideal targeted dataset based on the unique elements such as sales volume of a company, the company's location, no. of employees etc...

Whether you are looking for an entire fill install, access to our API's or if you are just looking for a one-time targeted purchase, get in touch with our company and we will fulfill your international data need.

We cover all regions and cities in the country: Aguascalientes Aguascalientes Baja California Ensenada Mexicali Tijuana Baja California Sur La Paz Campeche Campeche Chiapas Comitán San Cristóbal de Las Casas Tapachula Tuxtla Chihuahua Casas Grandes Chihuahua Ciudad Delicias Hidalgo del Parral Juárez Nuevo Casas Grandes Coahuila Ciudad Acuña Monclova Múzquiz Nueva Rosita Piedras Negras Sabinas Saltillo San Pedro Torreón Villa Frontera Colima Colima Manzanillo Tecomán Durango Durango Gómez Palacio Federal District (administrative district) Atzcapotzalco (delegación) Churubusco (neighbourhood) Coyoacán (delegación) Magdalena (delegación) Mexico City Tlalpan (delegación) Villa Obregón (delegación) Xochimilco (delegación) Guanajuato Acámbaro Celaya Cortazar Guanajuato Irapuato León Moroleón Salamanca San Francisco del Rincón San Miguel de Allende Silao Valle de Santiago Guerrero Acapulco Chilpancingo Iguala Taxco Hidalgo Pachuca Tulancingo Jalisco Ameca Arandas Autlán Ciudad Guzmán Guadalajara La Barca Ocotlán Puerto Vallarta Tepatitlán Tlaquepaque Zapopan México Nezahualcóyotl Tlalnepantla Toluca Michoacán Apatzingán Ciudad Hidalgo La Piedad Cavadas Morelia Sahuayo Uruapan Zacapú Zamora Zitácuaro Morelos Cuernavaca Xochicalco Nayarit Tepic Nuevo León Guadalupe Monterrey Oaxaca Juchitán Oaxaca Puebla Atlixco Cholula Matamoros Puebla Teziutlán Querétaro Querétaro Quintana Roo Cancún Chetumal San Luis Potosí Matehuala San Luis Potosí Valles Sinaloa Culiacán Los Mochis Mazatlán Sonora Ciudad Obregón Guaymas Hermosillo Navojoa Nogales San Luis Tabasco Villahermosa Tamaulipas Ciudad Mante Ciudad Victoria Matamoros Nuevo Laredo Reynosa Tampico Tlaxcala Apizaco Tlaxcala Veracruz Ciudad Mendoza Coatzacoalcos Córdoba Cosamaloapan Minatitlán Orizaba Papantla Poza Rica San Andrés Tuxtla Tierra Blanca Tuxpan Veracruz Xalapa Yucatán Mérida Progreso Zacatecas Fresnillo Jerez de García Salinas Zacatecas

Context

E-commerce has become a new channel to support businesses development. Through e-commerce, businesses can get access and establish a wider market presence by providing cheaper and more efficient distribution channels for their products or services. E-commerce has also changed the way people shop and consume products and services. Many people are turning to their computers or smart devices to order goods, which can easily be delivered to their homes.

Content

This is a sales transaction data set of UK-based e-commerce (online retail) for one year. This London-based shop has been selling gifts and homewares for adults and children through the website since 2007. Their customers come from all over the world and usually make direct purchases for themselves. There are also small businesses that buy in bulk and sell to other customers through retail outlet channels.

The data set contains 500K rows and 8 columns. The following is the description of each column. 1. TransactionNo (categorical): a six-digit unique number that defines each transaction. The letter “C” in the code indicates a cancellation. 2. Date (numeric): the date when each transaction was generated. 3. ProductNo (categorical): a five or six-digit unique character used to identify a specific product. 4. Product (categorical): product/item name. 5. Price (numeric): the price of each product per unit in pound sterling (£). 6. Quantity (numeric): the quantity of each product per transaction. Negative values related to cancelled transactions. 7. CustomerNo (categorical): a five-digit unique number that defines each customer. 8. Country (categorical): name of the country where the customer resides.

There is a small percentage of order cancellation in the data set. Most of these cancellations were due to out-of-stock conditions on some products. Under this situation, customers tend to cancel an order as they want all products delivered all at once.

Inspiration

Information is a main asset of businesses nowadays. The success of a business in a competitive environment depends on its ability to acquire, store, and utilize information. Data is one of the main sources of information. Therefore, data analysis is an important activity for acquiring new and useful information. Analyze this dataset and try to answer the following questions. 1. How was the sales trend over the months? 2. What are the most frequently purchased products? 3. How many products does the customer purchase in each transaction? 4. What are the most profitable segment customers? 5. Based on your findings, what strategy could you recommend to the business to gain more profit?

Photo by CardMapr on Unsplash

Use our Owler companies dataset, a sales intelligence and business information research company, to map your ecosystem, and find market trends and investment opportunities. Access a database of competitors, revenue, employees, and funding for any company. Depending on your needs, you may purchase the entire dataset or a customized subset. The Owler companies information dataset offers public information on all companies listed in Owler. The dataset includes all major data points: Company size Revenue News Key executives Location Website and more. Freshness configuration: monthly refreshes refresh rate of up to 8 million records a month

This table contains figures on the use of information and communication technology (ICT) by companies, specifically the extent to which they use external networks, incl. the internet, for e-commerce (purchasing and sales). The table shows the percentages of the total purchasing value and the total turnover realised via external networks. The figures refer to companies with 10 and more employed persons. The figures are broken down by sector of industry (SIC 2008) and size of company. Data available from: 2008 - 2009 Status of the figures: The figures in this table are final. Changes as of 11 January 2019: None, this table is discontinued. When will new figures be published? Not applicable anymore.

Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features

Profiles: Dive into detailed public profiles featuring names, titles, positions, experience, education, skills, and more. Utilize this data for talent sourcing, lead generation, and investment signaling, with a refresh rate ensuring up to 30 million records per month. Companies: Access comprehensive company data including ID, country, industry, size, number of followers, website details, subsidiaries, and posts. Tailored subsets by industry or region provide invaluable insights for CRM enrichment, competitive intelligence, and understanding the startup ecosystem, updated monthly with up to 40 million records. Job Listings: Explore current job opportunities detailed with job titles, company names, locations, and employment specifics such as seniority levels and employment functions. This dataset includes direct application links and real-time application numbers, serving as a crucial tool for job seekers and analysts looking to understand industry trends and the job market dynamics.

Customizable Subsets for Specific Needs Our LinkedIn dataset offers the flexibility to tailor the dataset according to your specific business requirements. Whether you need comprehensive insights across all data points or are focused on specific segments like job listings, company profiles, or individual professional details, we can customize the dataset to match your needs. This modular approach ensures that you get only the data that is most relevant to your objectives, maximizing efficiency and relevance in your strategic applications. Popular Use Cases

Strategic Hiring and Recruiting: Track talent movement, identify growth opportunities, and enhance your recruiting efforts with targeted data. Market Analysis and Competitive Intelligence: Gain a competitive edge by analyzing company growth, industry trends, and strategic opportunities. Lead Generation and CRM Enrichment: Enrich your database with up-to-date company and professional data for targeted marketing and sales strategies. Job Market Insights and Trends: Leverage detailed job listings for a nuanced understanding of employment trends and opportunities, facilitating effective job matching and market analysis. AI-Driven Predictive Analytics: Utilize AI algorithms to analyze large datasets for predicting industry shifts, optimizing business operations, and enhancing decision-making processes based on actionable data insights.

Whether you are mapping out competitive landscapes, sourcing new talent, or analyzing job market trends, our LinkedIn dataset provides the tools you need to succeed. Customize your access to fit specific needs, ensuring that you have the most relevant and timely data at your fingertips.

Online Retail Transaction Data

UK Online Retail Sales and Customer Transaction Data

By UCI [source]

About this dataset

Comprehensive Dataset on Online Retail Sales and Customer Data

Welcome to this comprehensive dataset offering a wide array of information related to online retail sales. This data set provides an in-depth look at transactions, product details, and customer information documented by an online retail company based in the UK. The scope of the data spans vastly, from granular details about each product sold to extensive customer data sets from different countries.

This transnational data set is a treasure trove of vital business insights as it meticulously catalogues all the transactions that happened during its span. It houses rich transactional records curated by a renowned non-store online retail company based in the UK known for selling unique all-occasion gifts. A considerable portion of its clientele includes wholesalers; ergo, this dataset can prove instrumental for companies looking for patterns or studying purchasing trends among such businesses.

The available attributes within this dataset offer valuable pieces of information:

• InvoiceNo: This attribute refers to invoice numbers that are six-digit integral numbers uniquely assigned to every transaction logged in this system. Transactions marked with 'c' at the beginning signify cancellations - adding yet another dimension for purchase pattern analysis.

• StockCode: Stock Code corresponds with specific items as they're represented within the inventory system via 5-digit integral numbers; these allow easy identification and distinction between products.

• Description: This refers to product names, giving users qualitative knowledge about what kind of items are being bought and sold frequently.

• Quantity: These figures ascertain the volume of each product per transaction – important figures that can help understand buying trends better.

• InvoiceDate: Invoice Dates detail when each transaction was generated down to precise timestamps – invaluable when conducting time-based trend analysis or segmentation studies.

• UnitPrice: Unit prices represent how much each unit retails at — crucial for revenue calculations or cost-related analyses.

Finally,

• Country: This locational attribute shows where each customer hails from, adding geographical segmentation to your data investigation toolkit.

This dataset was originally collated by Dr Daqing Chen, Director of the Public Analytics group based at the School of Engineering, London South Bank University. His research studies and business cases with this dataset have been published in various papers contributing to establishing a solid theoretical basis for direct, data and digital marketing strategies.

Access to such records can ensure enriching explorations or formulating insightful hypotheses about consumer behavior patterns among wholesalers. Whether it's managing inventory or studying transactional trends over time or spotting cancellation patterns - this dataset is apt for multiple forms of retail analysis

How to use the dataset

1. Sales Analysis:

Sales data forms the backbone of this dataset, and it allows users to delve into various aspects of sales performance. You can use the Quantity and UnitPrice fields to calculate metrics like revenue, and further combine it with InvoiceNo information to understand sales over individual transactions.

2. Product Analysis:

Each product in this dataset comes with its unique identifier (StockCode) and its name (Description). You could analyse which products are most popular based on Quantity sold or look at popularity per transaction by considering both Quantity and InvoiceNo.

3. Customer Segmentation:

If you associated specific business logic onto the transactions (such as calculating total amounts), then you could use standard machine learning methods or even RFM (Recency, Frequency, Monetary) segmentation techniques combining it with 'CustomerID' for your customer base to understand customer behavior better. Concatenating invoice numbers (which stand for separate transactions) per client will give insights about your clients as well.

4. Geographical Analysis:

The Country column enables analysts to study purchase patterns across different geographical locations.

Practical applications

Understand what products sell best where - It can help drive tailored marketing strategies. Anomalies detection – Identify unusual behaviors that might lead frau...

The annual Small Business Procurement Scorecard is an assessment tool to (1) measure how well federal agencies reach their small business and socio-economic prime contracting and subcontracting goals, (2) provide accurate and transparent contracting data and (3) report agency-specific progress. The prime and subcontracting component goals include goals for small businesses, small businesses owned by women (WOSB), small disadvantaged businesses (SDB), service-disabled veteran-owned small businesses (SDVOSB), and small businesses located in Historically Underutilized Business Zones (HUBZones). Each federal agency has a different small business contracting goal, negotiated annually in consultation with SBA. SBA ensures that the sum total of all of the goals meets the 23 percent target established by law.

Content

The San Francisco Controller's Office maintains a database of purchasing activity from fiscal year 2007 forward. This data is presented on the Purchasing Commodity Data report in CSV format, and represents detailed commodity-level data by purchase order. Additional lines have been added to this dataset to reconcile some document totals from the City's purchasing system to the totals from the City's accounting system in cases when the two amounts are different, which sometimes occurs due to adjustments entered into the accounting system but not the purchasing system. We have removed sensitive information from this data – this is intended to show payments made to entities providing goods and services to the City and County and to protect individuals. For example, we have removed payments to employees (reimbursements, garnishments) and jury members, revenue refunds, payments for judgments and claims, witnesses, relocation and rehousing, and a variety of human services payments. New data is added on a weekly basis.

Context

This is a dataset hosted by the city of San Francisco. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore San Francisco's Data using Kaggle and all of the data sources available through the San Francisco organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Kari Shea on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

With 501,000 Businesses in the United Arab Emirates, Techsalerator has access to the highest B2B count of Data in the country.

Thanks to our unique tools and large data specialist team, we are able to select the ideal targeted dataset based on the unique elements such as sales volume of a company, the company's location, no. of employees etc...

Whether you are looking for an entire fill install, access to our API's or if you are just looking for a one-time targeted purchase, get in touch with our company and we will fulfill your international data need.

Data Broker Service Market Outlook

The global data broker service market size is projected to grow from USD 250 billion in 2023 to an estimated USD 450 billion by 2032, reflecting a compound annual growth rate (CAGR) of 6.7%. This substantial growth can be attributed to increasing digitalization, the exponential rise of data-driven decision-making across industries, and the growing realization of the value derived from data analytics. As businesses continue to recognize the potential of leveraging consumer, business, financial, and health data, the demand for data brokerage services is poised to expand significantly.

One of the primary growth factors for the data broker service market is the increasing importance of data in driving business strategies and operations. Companies are increasingly relying on consumer and market data to gain insights into market trends, consumer behavior, and competitive landscapes. This surge in data utilization across sectors such as retail, healthcare, and finance is propelling the demand for data brokerage services that can provide accurate and comprehensive data sets. The proliferation of digital platforms and the Internet of Things (IoT) has further amplified the volume of data generated, thus boosting the need for efficient data brokerage services.

Moreover, advancements in artificial intelligence (AI) and machine learning (ML) technologies are significantly contributing to the market's growth. These technologies enable enhanced data analysis, predictive analytics, and real-time decision-making, making data brokerage services more valuable. Businesses are increasingly investing in AI and ML to analyze large datasets more efficiently and extract actionable insights. Data brokers, in turn, are leveraging these technologies to offer more sophisticated and tailored data solutions, thus attracting a broader customer base.

Privacy regulations and data protection laws are also playing a crucial role in shaping the data broker service market. While these regulations pose challenges, they also create opportunities for compliant data brokers to differentiate themselves in the market. Companies are more inclined to partner with data brokers that demonstrate robust data governance practices and adhere to regulatory requirements. This trend is driving the market towards more ethical and transparent data brokerage practices, increasing the trust and credibility of data brokers among businesses and consumers alike.

The regional outlook for the data broker service market highlights North America as a dominant player, primarily due to the high adoption of data-driven strategies among businesses and the presence of major data brokerage firms. Europe follows closely, driven by stringent data protection regulations like GDPR, which necessitate secure and compliant data handling. The Asia Pacific region is expected to witness the fastest growth, fueled by the rapid digital transformation in countries like China and India and the increasing use of data analytics in various industries. Latin America and the Middle East & Africa regions are also showing promising growth, supported by the rising awareness of data's strategic value and increasing investments in data analytics infrastructure.

Data Type Analysis

The data broker service market by data type comprises consumer data, business data, financial data, health data, and other categories. Consumer data is one of the most significant segments within this market. This type of data includes information on consumer behavior, preferences, purchasing patterns, and demographics. Businesses leverage consumer data to tailor their marketing strategies, enhance customer experiences, and drive sales growth. The increasing use of digital platforms for shopping, social interaction, and information consumption is continually generating vast amounts of consumer data, thereby fueling the demand for consumer data brokerage services.

Business data, encompassing company profiles, industry trends, and competitive intelligence, is another vital segment. Organizations require business data to strategize market entry, expansion, and competitive positioning. Data brokers play a crucial role in aggregating and providing actionable business insights that help companies navigate complex market dynamics. The rise of global trade, the need for cross-border business intelligence, and the growing importance of data-driven decision-making in corporate strategies are driving the demand for business data brokerage services.

Financial data is crucial for sectors like banking, fina

Unlock the door to business expansion by investing in our real-time eCommerce leads list. Gain direct access to store owners and make informed decisions with data fields including 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.

Ensure a lifetime of access for continuous growth and tailor your campaigns with accurate and reliable information, initiating targeted efforts that align with your marketing goals. Whether you're targeting specific industries or global locations, our database provides up-to-date and valuable insights to support your business journey.

• 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

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When examining the intricate relationship between economic conditions and purchasing decisions, the utilization of practice datasets can offer invaluable insights. This particular artificial dataset comprises three main components: a dimension table of ten companies, a fact table documenting purchases from these companies, and a set of data points regarding economic conditions. These elements are meticulously designed to mimic real-world scenarios, enabling analysts to dissect and understand how fluctuations in the economy can influence the purchasing behavior of different types of companies.

The dimension table serves as the foundation, listing ten distinct companies, each potentially operating in varied sectors. This diversity allows for a comprehensive analysis across a spectrum of industries, highlighting sector-specific sensitivities to economic changes. The fact table of purchases acts as a historical record, offering detailed insights into the buying patterns of these companies over time. Analysts can observe trends, frequencies, and the magnitude of purchases, correlating them with the economic conditions presented in the third component of the dataset.

The economic conditions data is pivotal, as it encompasses a variety of indicators that can affect purchasing decisions. These may include inflation rates, interest rates, GDP growth, unemployment rates, and consumer confidence indices, among others. By examining the interplay between these economic indicators and the purchasing data, analysts can identify patterns and causations. For instance, an increase in interest rates might lead to a decrease in capital-intensive purchases by companies wary of higher borrowing costs.

Through this dataset, researchers can employ statistical models and data analysis techniques to uncover how economic fluctuations impact corporate purchasing decisions. The findings can offer valuable lessons for businesses in terms of budgeting, financial planning, and risk management. Companies can use these insights to make informed decisions, adjusting their purchasing strategies in anticipation of or in response to economic conditions. This proactive approach can help businesses maintain stability during economic downturns and capitalize on opportunities during favorable economic times.

Ultimately, this practice dataset not only aids in academic and educational pursuits but also serves as a practical tool for business analysts, economists, and corporate strategists seeking to better navigate the complex dynamics of the economy and its effects on corporate purchasing behaviors.

Korea Credit Card Business: Year to Date: ED: Purchase data was reported at 269,463.200 KRW bn in Jun 2018. This records an increase from the previous number of 130,999.000 KRW bn for Mar 2018. Korea Credit Card Business: Year to Date: ED: Purchase data is updated quarterly, averaging 264,638.750 KRW bn from Mar 2016 (Median) to Jun 2018, with 10 observations. The data reached an all-time high of 521,692.300 KRW bn in Dec 2017 and a record low of 113,009.000 KRW bn in Mar 2016. Korea Credit Card Business: Year to Date: ED: Purchase data remains active status in CEIC and is reported by Financial Supervisory Service. The data is categorized under Global Database’s South Korea – Table KR.KA013: Credit Card Statistics: Financial Supervisory Service. The data is derived from the Credit Card specialized companies only.

CE Scanner US provides financial services investors with point-of-sale transaction data. Proprietary M&A attribution and volume equivalency offer rollup views to ticker and brand level with comparative detailed category/subcategory views into retail sales, volumes, distribution, and trends.

Business process event data modeled as labeled property graphs

Data Format
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The dataset comprises one labeled property graph in two different file formats.

#1) Neo4j .dump format

A neo4j (https://neo4j.com) database dump that contains the entire graph and can be imported into a fresh neo4j database instance using the following command, see also the neo4j documentation: https://neo4j.com/docs/

/bin/neo4j-admin.(bat|sh) load --database=graph.db --from=

The .dump was created with Neo4j v3.5.

#2) .graphml format

A .zip file containing a .graphml file of the entire graph


Data Schema
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The graph is a labeled property graph over business process event data. Each graph uses the following concepts

:Event nodes - each event node describes a discrete event, i.e., an atomic observation described by attribute "Activity" that occurred at the given "timestamp"

:Entity nodes - each entity node describes an entity (e.g., an object or a user), it has an EntityType and an identifier (attribute "ID")

:Log nodes - describes a collection of events that were recorded together, most graphs only contain one log node

:Class nodes - each class node describes a type of observation that has been recorded, e.g., the different types of activities that can be observed, :Class nodes group events into sets of identical observations

:CORR relationships - from :Event to :Entity nodes, describes whether an event is correlated to a specific entity; an event can be correlated to multiple entities

:DF relationships - "directly-followed by" between two :Event nodes describes which event is directly-followed by which other event; both events in a :DF relationship must be correlated to the same entity node. All :DF relationships form a directed acyclic graph.

:HAS relationship - from a :Log to an :Event node, describes which events had been recorded in which event log

:OBSERVES relationship - from an :Event to a :Class node, describes to which event class an event belongs, i.e., which activity was observed in the graph

:REL relationship - placeholder for any structural relationship between two :Entity nodes

The concepts a further defined in Stefan Esser, Dirk Fahland: Multi-Dimensional Event Data in Graph Databases. CoRR abs/2005.14552 (2020) https://arxiv.org/abs/2005.14552


Data Contents
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neo4j-bpic19-2021-02-17 (.dump|.graphml.zip)

An integrated graph describing the raw event data of the entire BPI Challenge 2019 dataset.
van Dongen, B.F. (Boudewijn) (2019): BPI Challenge 2019. 4TU.ResearchData. Collection. https://doi.org/10.4121/uuid:d06aff4b-79f0-45e6-8ec8-e19730c248f1

This data originated from a large multinational company operating from The Netherlands in the area of coatings and paints and we ask participants to investigate the purchase order handling process for some of its 60 subsidiaries. In particular, the process owner has compliance questions. In the data, each purchase order (or purchase document) contains one or more line items. For each line item, there are roughly four types of flows in the data: (1) 3-way matching, invoice after goods receipt: For these items, the value of the goods receipt message should be matched against the value of an invoice receipt message and the value put during creation of the item (indicated by both the GR-based flag and the Goods Receipt flags set to true). (2) 3-way matching, invoice before goods receipt: Purchase Items that do require a goods receipt message, while they do not require GR-based invoicing (indicated by the GR-based IV flag set to false and the Goods Receipt flags set to true). For such purchase items, invoices can be entered before the goods are receipt, but they are blocked until goods are received. This unblocking can be done by a user, or by a batch process at regular intervals. Invoices should only be cleared if goods are received and the value matches with the invoice and the value at creation of the item. (3) 2-way matching (no goods receipt needed): For these items, the value of the invoice should match the value at creation (in full or partially until PO value is consumed), but there is no separate goods receipt message required (indicated by both the GR-based flag and the Goods Receipt flags set to false). (4)Consignment: For these items, there are no invoices on PO level as this is handled fully in a separate process. Here we see GR indicator is set to true but the GR IV flag is set to false and also we know by item type (consignment) that we do not expect an invoice against this item. Unfortunately, the complexity of the data goes further than just this division in four categories. For each purchase item, there can be many goods receipt messages and corresponding invoices which are subsequently paid. Consider for example the process of paying rent. There is a Purchase Document with one item for paying rent, but a total of 12 goods receipt messages with (cleared) invoices with a value equal to 1/12 of the total amount. For logistical services, there may even be hundreds of goods receipt messages for one line item. Overall, for each line item, the amounts of the line item, the goods receipt messages (if applicable) and the invoices have to match for the process to be compliant. Of course, the log is anonymized, but some semantics are left in the data, for example: The resources are split between batch users and normal users indicated by their name. The batch users are automated processes executed by different systems. The normal users refer to human actors in the process. The monetary values of each event are anonymized from the original data using a linear translation respecting 0, i.e. addition of multiple invoices for a single item should still lead to the original item worth (although there may be small rounding errors for numerical reasons). Company, vendor, system and document names and IDs are anonymized in a consistent way throughout the log. The company has the key, so any result can be translated by them to business insights about real customers and real purchase documents.

The case ID is a combination of the purchase document and the purchase item. There is a total of 76,349 purchase documents containing in total 251,734 items, i.e. there are 251,734 cases. In these cases, there are 1,595,923 events relating to 42 activities performed by 627 users (607 human users and 20 batch users). Sometimes the user field is empty, or NONE, which indicates no user was recorded in the source system. For each purchase item (or case) the following attributes are recorded: concept:name: A combination of the purchase document id and the item id, Purchasing Document: The purchasing document ID, Item: The item ID, Item Type: The type of the item, GR-Based Inv. Verif.: Flag indicating if GR-based invoicing is required (see above), Goods Receipt: Flag indicating if 3-way matching is required (see above), Source: The source system of this item, Doc. Category name: The name of the category of the purchasing document, Company: The subsidiary of the company from where the purchase originated, Spend classification text: A text explaining the class of purchase item, Spend area text: A text explaining the area for the purchase item, Sub spend area text: Another text explaining the area for the purchase item, Vendor: The vendor to which the purchase document was sent, Name: The name of the vendor, Document Type: The document type, Item Category: The category as explained above (3-way with GR-based invoicing, 3-way without, 2-way, consignment).

The data contains the following entities and their events

- PO - Purchase Order documents handled at a large multinational company operating from The Netherlands
- POItem - an item in a Purchase Order document describing a specific item to be purchased
- Resource - the user or worker handling the document or a specific item
- Vendor - the external organization from which an item is to be purchased

Data Size
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BPIC19, nodes: 1926651, relationships: 15082099