What is Corporate Data? Uses, Examples & Database Sources
On This Page:
- Overview
- Datasets
- Providers
- Attributes
- Guide
- FAQ
On This Page:
- Overview
- Datasets
- Providers
- Attributes
- Guide
- FAQ
What is Corporate Data?
Corporate data is information generated and collected by a company during its operations. It includes various types of data such as financial records, customer information, sales figures, employee details, and market research. Examples of corporate data include financial statements, sales reports, customer databases, employee records, and market research data.
Best Corporate Databases & Datasets
Here is our curated selection of top Corporate Data sources. We focus on key factors such as data reliability, accuracy, and flexibility to meet diverse use-case requirements. These datasets are provided by trusted providers known for delivering high-quality, up-to-date information.
Corporate Data | Corporate Actions Data | Merger & Acquisition Data | Entity Resolution & Disambiguation
Global Corporate Data | Merger & Acquisition Data | Customizable Corporate Data: Custom Engineering
USA Fixed Income Data | US Corporate Bond data | Reference and Corporate actions Data | 150K issues
Success.ai | Corporate Data from LinkedIn | 700M Public Profiles & 70M Companies – Best Price Guarantee
Corporate Data – 250K+ Stocks/ISINs on Corporate Actions Data, Merger & Acquisition Data, and Dividend Data for 15+ History and Refreshes Globally
DEI (Diversity, Equity and Inclusion) Data | Corporate Data | 14k+ Companies | Workplace Diversity
Coresignal | Corporate Data | Company Data | AI-Enriched Datasets | Global / 35M+ Records / Updated Weekly
Global Corporate Actions Data | Listed Securities incl. Dividends, Splits, Spin-offs, Mergers
Global Corporate Data |Â Corporate Credit Rating Data | 300M Records, 24-Hour Research Cycle, Company Credit/ Investigation Reports
Corporate Intellectual Property (IP) Data, B2B dataset, active Patents & Trademarks, global, +10M records, updated weekly, full history
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Top Corporate Data Providers & Companies
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Canaria Inc.
CoverageUSAUnited Kingdom+248VAs an AI-focused nonprofit, we've found Canaria Inc. to be an invaluable partner in understanding job market trends. Their comprehensive datasets, sourced responsibly from platforms like Indeed and LinkedIn, coupled with their advanced AI and NLP processing (this enables us to get additional insights which are not natively available in the source such as predicted salary, seniority level etc.), provide unmatched insights into the labor market. Their high deduplication rates ensure the accuracy and utility of the data, essential for our strategic planning and analysis. Canaria's tailored pricing and client-centric approach make them a top recommendation for any organization needing detailed labor market analytics.
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Predik Data-driven
CoverageUSAUnited Kingdom+247We are experts in the collection, extraction, management and analysis of large volumes of all types of data, from alternative and complex sources...Updated & consistentLatam, US, Asia MarketsStrict & precise controls -
Reomnify
CoverageUSAUnited Kingdom+247Power your analysis with the bespoke and off-the-shelf datasets created by our powerful data platform. We work across all markets and supply some... -
InfoTrie
CoverageUSAUnited Kingdom+248InfoTrie: Global Data Intelligence Leader since 2012. Unleash insights from unstructured data. Alternative datasets on E-commerce, SEC Filings,...Asset ClassesLanguagesActive Users -
Coresignal
CoverageUSAUnited Kingdom+247VCoresignal has strong demographic and firmographic datasets both on quality and volume while keeping the data as fresh as it can be. We've been using Coresignal for years and we can only speak highly about the product and team behind it. Highly recommended.
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NewsCatcher API
CoverageUSAUnited Kingdom+248VWe use Newscatcher in various parts of our business. We feel the depth, breadth and quality of their product is top-notch. The customer service and the helpful nature of their staff are part of our success.
Main Attributes of Corporate Data
Below, we outline the most popular attributes associated with this type of data—features that data buyers are actively seeking to meet their needs.
| Attribute | Type | Description | Action |
|---|---|---|---|
| String | The name of a company or business, might be the legal or brand name. | View 84 datasets | |
| String | The name of a country. | View 65 datasets | |
| String | The industry classification of a company. | View 63 datasets | |
| String | The approx. number of employees working for a company. | View 54 datasets | |
| String | The last name (surname) of a contact. | View 50 datasets | |
| String | The first name of a contact. | View 47 datasets |
Corporate Data Explained
Use Cases
Use Case 1: Business Intelligence and Analytics
One of the main use cases of corporate data is to derive business intelligence and perform analytics. By analyzing the data collected from various sources within the organization, businesses can gain valuable insights into their operations, customer behavior, market trends, and more. This data-driven approach helps in making informed decisions, identifying opportunities for growth, optimizing processes, and improving overall business performance.
Use Case 2: Risk Management and Compliance
Corporate data is also utilized for risk management and compliance purposes. Organizations need to ensure that they adhere to legal and regulatory requirements specific to their industry. By analyzing and monitoring data related to financial transactions, customer interactions, and internal processes, businesses can identify potential risks, detect fraudulent activities, and ensure compliance with relevant laws and regulations. This use case helps in mitigating risks, protecting the organization’s reputation, and avoiding legal consequences.
Use Case 3: Customer Relationship Management
Another significant use case of corporate data is customer relationship management (CRM). By collecting and analyzing customer data, businesses can gain a deeper understanding of their customers’ preferences, behaviors, and needs. This information enables organizations to personalize their marketing efforts, improve customer service, and build stronger relationships with their clients. CRM systems utilize corporate data to track customer interactions, manage sales pipelines, and enhance customer satisfaction and loyalty.
These three use cases highlight the importance of corporate data in driving business growth, managing risks, and improving customer relationships.
Main Attributes of Corporate Data
Corporate data refers to the information that is generated and collected by a company during its operations. It encompasses a wide range of attributes that provide insights into various aspects of the organization. These attributes can include financial data such as revenue, expenses, and profit margins, as well as operational data like production metrics, inventory levels, and supply chain information. Additionally, corporate data may include customer data such as demographics, purchase history, and feedback, enabling companies to better understand their target market. Other attributes of corporate data can involve employee data, such as payroll information, performance metrics, and training records, which help in managing and optimizing workforce productivity. Furthermore, corporate data may also encompass legal and regulatory information, market research data, and strategic planning data. Here’s a table of the main attributes you might find on Corporate Datasets:
| Attribute | Description |
|---|---|
| Accuracy | Corporate data should be accurate and free from errors or inconsistencies. It should reflect the true state of the organization’s operations and activities. |
| Completeness | Corporate data should be complete, meaning it includes all relevant information required for decision-making and analysis. It should not have any significant gaps or missing data. |
| Consistency | Corporate data should be consistent across different sources and systems. It should use standardized formats, definitions, and units of measurement to ensure compatibility and comparability. |
| Timeliness | Corporate data should be up-to-date and reflect the most recent information available. It should be collected, processed, and made available in a timely manner to support real-time decision-making. |
| Relevance | Corporate data should be relevant to the organization’s goals, objectives, and information needs. It should provide meaningful insights and support decision-making processes. |
| Security | Corporate data should be protected from unauthorized access, alteration, or loss. It should adhere to data privacy regulations and be stored and transmitted securely to maintain confidentiality and integrity. |
| Accessibility | Corporate data should be easily accessible to authorized users who need it for their roles and responsibilities. It should be organized and stored in a way that allows efficient retrieval and analysis. |
| Scalability | Corporate data should be scalable to accommodate the organization’s growing data needs. It should be able to handle increasing volumes of data without significant performance degradation. |
| Interoperability | Corporate data should be able to seamlessly integrate and interact with other systems, applications, and data sources within the organization’s technology landscape. |
| Auditability | Corporate data should be auditable, meaning it can be traced back to its source and the processes used to collect, transform, and store it. This attribute ensures data quality and accountability. |
How are Corporate Data products priced?
Corporate datasets are typically priced based on various factors such as the size and complexity of the dataset, the level of exclusivity or uniqueness it offers, and the potential value it can bring to businesses. Pricing models for corporate datasets can vary, but they often involve a combination of one-time fees, subscription-based models, or tiered pricing structures. The cost may also depend on the industry or sector the dataset caters to, with datasets in highly specialized or niche areas commanding higher prices. Additionally, factors like data quality, accuracy, and the level of support provided by the data provider can also influence the pricing. Overall, the pricing of corporate datasets is determined by the value they can provide to businesses and the resources required to collect, maintain, and deliver the data.
Frequently Asked Questions
What is Corporate Data?
Corporate data refers to information related to businesses, including financial data, operational metrics, market trends, customer behavior, and other relevant data points that provide insights into the performance, operations, and activities of companies.
How is Corporate Data collected?
Corporate data can be collected from various sources such as regulatory filings, financial reports, market research, surveys, customer databases, web scraping, social media monitoring, and partnerships with data providers. It can also be obtained through APIs, data feeds, and data integration with external systems.
What is Corporate Data used for?
Corporate data is used for a range of purposes, including market analysis, competitive intelligence, risk assessment, investment decisions, strategic planning, product development, customer segmentation, compliance, financial forecasting, and performance evaluation. It helps businesses gain insights, make informed decisions, and drive growth.
What’s a quality checklist for Corporate Data?
A quality checklist for corporate data includes factors such as data accuracy, reliability, completeness, timeliness, relevance to the business needs, data source credibility, consistency, data security and privacy measures, adherence to data standards, proper documentation, and compatibility with data analysis tools.
How is Corporate Data priced?
The pricing of corporate data varies depending on factors such as the data source, data quality, data coverage, level of customization, licensing terms, data provider reputation, data frequency, and the complexity of data processing required. Pricing models can include one-time purchases, subscription-based models, data volume tiers, or customized pricing based on specific requirements.
