Buying data means acquiring external datasets that help organizations make better decisions, uncover opportunities, and build more effective products and workflows. Companies that buy B2B data often use it to support sales prospecting, market research, recruitment platforms, investment analysis, and increasingly, AI-driven applications.
Before buying data, it's important to understand exactly what information you need and how you plan to use it. The right dataset for lead generation may look very different from one designed for company enrichment or analytics initiatives. In this guide, you'll learn what B2B data is, how businesses approach buying data in 2026, and what to consider when selecting a data provider.
Statistics about buying data
The market is huge for high-quality data, and the statistics are here to back it up:
- 98.4% of organizations increased investment in Data & AI initiatives in 2025, up from 82.2% in 2024;
- 88% of organizations now regularly use AI in at least one business function, up from 78% the prior year;
- Only 37% of companies report that efforts to improve data quality have been successful, even as AI investment accelerates – a figure that’s likely more reflective of the appropriate use of data for AI than the actual effectiveness of AI implementations.
The use of artificial intelligence (AI) and machine learning (ML) is becoming increasingly prevalent in data buying and analysis. If appropriately utilized, these technologies enable businesses to analyze large amounts of data and identify patterns and insights that would be difficult or impossible to uncover using traditional methods.
Why businesses buy B2B data in 2026
Organizations buy B2B data to gain access to insights they cannot generate from internal data alone. As markets become more competitive and AI adoption continues to grow, external business data helps companies identify opportunities, reduce uncertainty, and make more informed decisions across different functions.
- Better market visibility: B2B data provides a clearer view of companies, industries, hiring trends, and competitive landscapes. This enables organizations to monitor market developments, identify emerging opportunities, and better understand their target audiences.
- Faster decision-making: access to up-to-date business information reduces the time spent gathering and validating data manually. Teams can act on current insights rather than relying solely on assumptions or outdated information.
- More accurate sales, HR, and investment intelligence: sales teams use B2B data to identify and prioritize prospects, HR technology platforms leverage it to support talent-related initiatives, and investment firms rely on business intelligence to evaluate companies and market trends more effectively.
- Stronger AI and automation workflows: high-quality, structured datasets are becoming essential for powering analytics, enrichment workflows, AI agents, and LLM-based applications. Organizations increasingly seek AI-ready data that can integrate seamlessly into automated systems and support reliable outputs.
What types of B2B data can you buy?
Business data breaks down into many different categories, some of which are:
- Firmographic data. Consists exclusively of company-related data, such as its name, size, website, location, industry, revenue, and more. You can buy information about companies to generate investment signals and conduct market research.
- Employee data. Consists of employee-related data, such as name, location, connections, job title, company name, education, and more. You can use it to source talent more efficiently and find the best-fit talent.
- Job posting data. Consists of job ad data, such as job title, location, employment type, job description, skills required, and more. You can use it to see where certain companies intend to expand, conduct labor market research, and more.
- Sentiment data. Consists of company employee reviews and tech product reviews. You can see how employees rate the company they work in and how the market responds to a certain tech product. You can use it to enhance investment and product intelligence, respectively.
- Community data. Consists of community-related data. You can use it to find the best professionals and also research state-of-the-art products.
- Funding data. Consists of funding-related data, such as funding rounds, amounts, last funding date, acquisition data, and more. You can use it to find a company that is hungry for the next investment.
- Technographic data. Consists of technographic-related data, such as stack lists, tool features, pros, cons, and more. You can use it to find businesses that might want to buy your product or for market research.
You can select and buy database that reflects your needs the best. Or you can buy several and generate actionable insights from multiple data sources and categories.
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What are the main data formats?
There are several main data formats: SQL, JSON and JSONL, CSV, Parquet, XML, and HTML. The main difference between them is their structure.
- Structured data is SQL. This data format is usually sourced from online relational and tabular forms and stored in data warehouses. It's not flexible but demands less storage than unstructured data.
- Unstructured data includes formats such as JPEG, DOC, PDF, MOV, and more. This type of data is undefined and non-relational. It's used for natural language processing, text mining, and can be sourced from video files, emails, online documents, or social media platforms, among others. Unstructured data is flexible, requires a lot of storage space, and is stored in data lakes.
- Semi-structured data includes formats such as JSON, JSONL, CSV, XML, HTML, and more. This type of data is semi-defined, tagged, and semi-relational. Just like unstructured data, it's used for natural language processing and text mining. It's usually sourced from online documents, JSON files, and XML files. Semi-structured data can be stored in both data warehouses and data lakes. It's semi-flexible and demands a medium amount of storage space.
How to identify your data needs
Before purchasing data, it’s crucial to clearly define your intended use case. Whether you’re building a product, enriching your analytics, or expanding into new markets, having a focused objective will help you select the right datasets and avoid unnecessary spending.
Start by asking: What level of insight or context do I need? Do you want to analyze the market in its raw form, or are you aiming for refined, insight-ready data?
For example, if you're building a data product, say, a recruiting platform, you'll likely need multi-source employee data to ensure comprehensive coverage. Later, job posting data could add functionality that differentiates your product, such as real-time labor market trends or targeted recommendations.
If your goal is investment analysis, firmographic data provides a foundational layer. With it, you can surface companies based on specific attributes, such as location, employee count, or founding year.
Looking for sentiment or employee experience insights? You’ll benefit from filtered, high-quality review data that excludes low-value profiles and spam. The same applies when analyzing workforce dynamics or tech adoption trends. In these cases, it's not just about having more data, but having better, cleaner data aligned with your goals.
Ultimately, choosing the data is also about understanding whether you’re equipped to work with datasets or if you need refined, insight-ready information that lets your team focus on analysis rather than preparation.
Delivery methods: datasets vs. data APIs
When choosing how to receive data, it’s important to consider your technical setup, use case, and scalability needs.
When to buy datasets
Buying datasets is a good option when you need access to large volumes of information for analysis, research, enrichment, or AI initiatives. Unlike APIs, datasets provide bulk access to data at a specific point in time, making them suitable for projects that do not require real-time updates.
Datasets are particularly useful for market research and investment intelligence teams, organizations training AI and machine learning models, and businesses with data engineering resources that can integrate and manage large-scale data internally. They can also support periodic database enrichment and historical trend analysis.
When to use data APIs
Data APIs are the preferred option when you need continuous access to up-to-date information and want to integrate external data directly into your products or workflows. Unlike static datasets, APIs enable automated data retrieval and support applications that depend on regularly refreshed data.
Data APIs are well suited for sales and HR technology platforms, AI-powered applications, and organizations building internal tools that require real-time or near real-time business intelligence. They are particularly valuable for teams looking to scale enrichment processes and reduce manual data management.
Below is a quick comparison of two delivery methods: datasets and data APIs.
How to buy data? Best practices for buying data
While it might seem convenient to purchase a static list of companies, these one-off datasets quickly become outdated. Business landscapes shift rapidly: companies grow, pivot, or shut down altogether. Relying on stale data can lead to poor decisions and missed opportunities.
Instead, opt for a data provider that delivers continuously refreshed datasets or APIs. This ensures you’re always working with accurate, up-to-date information, which is crucial for market research, lead generation, investment analysis, and beyond.
Before you make your first purchase, follow these best practices to get the most value from your data:
- Define your business goals – be clear on how the data will support your objectives.
- Identify the type of data you need – align your requirements with your use case.
- Set a realistic budget – data pricing varies by volume, delivery method, and update frequency.
- Research providers thoroughly – look for companies with strong reputations and transparent sourcing practices.
- Request data samples – preview the structure, freshness, and relevance of the data before committing.
- Talk to the sales or data team – collaborate to find the right package for your needs and avoid overbuying.
- Choose providers focused on data quality – low-cost, aggregated, or infrequently updated sources rarely hold up under scrutiny.
- Maintain communication post-purchase – ensure ongoing support, updates, and feedback loops.
Investing in high-quality, ethically sourced, and regularly updated data ensures your business can move faster, analyze better, and stay ahead of the competition.
Where to buy data: reliable sources
To buy data, you can either:
- Browse a data marketplace
- Utilize web data collection services
- Find a data provider directly
Here is how each of these methods differs from the others:
Browse a data marketplace
Data marketplaces such as Datarade usually advertise many different data providers or datasets. You can find a list of options there and select the one that fits your needs the most. Usually, the platform provides information about data features, freshness, a description of the company, and more that allows you to buy data for lead generation and other purposes.
Once you pick a dataset that meets your needs, you can purchase it directly from the marketplace or contact the vendor for more information.
Utilize web data collection services
If you don’t need loads of data and you don’t want to pay for an entire dataset, you can opt for APIs instead. With Coresignal’s Company API, Employee API, and Jobs API, your spend is based on the number of credits you use.
Web data collection is a difficult and resource-intensive process that requires a dedicated experienced team. Also, it should be mentioned that the most popular sources of business data are notoriously difficult to collect from.
Find a data provider directly
Another option is finding a data provider directly. One public web data provider that can help you build your data product or extract deep insights into investment opportunities is Coresignal. We can offer you broad coverage of over 4.5 billion public web data records from multiple sources, such as Indeed, Owler, and more. Talking about more, the data is always fresh so you won't have to worry about inaccuracies.
Also, we believe in data collection ethics and collect only publicly available data and only personal data that is related to businesses. We also do not gather contact data to prevent spam emails to the data subjects in our databases
One of the main issues with buying raw data is that you need to have an in-house data team to process and analyze it properly. In case you'd like to learn more, we have prepared a guide explaining how to prepare for working with public web data.
Buying data from a reliable data provider: how to choose one?
There are many companies selling data nowadays and it might be challenging to select one. Each of them differs in data quality, delivery format, delivery frequency, price, service, and more. Some might offer a single dataset, while others could be providing multiple databases.
In short, the best data provider is the one that offers exceptional data quality, freshness, convenient delivery, and finds new ways to overcome web data challenges. These qualities will define the value of the data. For example, if the data is not fresh, it will most likely be inaccurate and might lead to bad business decisions.
Here’s what to consider when evaluating vendors to ensure you’re buying high-quality, reliable, and ethically sourced data:
- Determine business goals. Define how the data would help you achieve your target goals, put a data strategy in place and identify high-impact areas that require data.
- Check competition. Research the tools and solutions that your competitors are using. It could help you better navigate the proverbial data waters and create new opportunities.
- Identify the end-user. Make sure that the data is relevant to the people in your business who will end up using it for analysis.
- Consider the costs. Data solutions vary greatly in price, quality, deliverability, and other aspects. Make sure that the data you get is the data you need.
All in all, choosing the right data solution for your business shouldn't be taken lightly. Not only is it going to cost you some money, but it will also affect your future business decisions. Therefore, take your time and analyze all your data needs and business goals related to them.
Top big data sellers
Coresignal stands out due to data freshness, broad coverage, and flexible access options designed for investment intelligence, HR tech, sales tech, and AI-driven use cases. The company offers billions of parsed, ready-to-use records that are regularly updated to help organizations work with relevant and timely information. Clients also benefit from dedicated account management to support onboarding, implementation, and issue resolution.
For organizations that prefer programmatic access, Coresignal provides several APIs, including Company API, Employee API, Jobs API, Historical Headcount API, and Agentic Search API. Rather than requiring businesses to purchase entire datasets, Coresignal's credit-based model allows users to spend only when collecting the data they need. This flexibility makes it easier to support both targeted enrichment workflows and large-scale data initiatives.
Other providers in the market, including People Data Labs, Thinknum, and Explorium, offer distinct advantages depending on the use case. People Data Labs is often recognized for its developer-friendly APIs and identity-focused datasets, while Thinknum emphasizes alternative data and market intelligence capabilities commonly used by investment professionals. Explorium differentiates itself through its external data platform, which focuses on data enrichment and feature discovery for analytics and machine learning applications.
However, differences in coverage, update frequency, delivery methods, and specialization mean that the best provider ultimately depends on an organization's specific requirements. For businesses prioritizing regularly refreshed business data at scale, Coresignal offers a strong combination of data freshness, broad coverage, and flexible access through datasets and APIs.
How to evaluate a B2B data provider: a checklist
Not all B2B data providers offer the same level of quality, coverage, or flexibility. Before buying data, evaluate potential vendors against the following criteria to ensure the data aligns with your business needs and long-term objectives.
- Data freshness. Determine how frequently records are updated. Regular updates help ensure you're working with current and relevant information.
- Data coverage. Assess whether the provider offers sufficient geographic, industry, and company coverage for your specific use case.
- Data accuracy. Look for evidence of quality assurance processes that help maintain reliable and consistent data records.
- Deduplication and normalization. Verify that the data is cleaned, standardized, and deduplicated to improve usability and reduce manual processing.
- Historical depth. Consider whether access to historical data is important for trend analysis, investment research, or forecasting initiatives.
- Delivery flexibility. Evaluate whether the provider supports the delivery methods you need, such as APIs, bulk datasets, or self-service platforms.
- Documentation and integration support. Review the quality of API documentation, onboarding resources, and available technical support to simplify implementation.
- Ethical sourcing and compliance. Ensure the provider maintains transparent data collection practices and is in line with applicable regulations and legal requirements.
- Pricing transparency. Understand the pricing structure, including usage limits, subscription terms, and any additional costs that may affect your budget.
Advantages and disadvantages of data-buying
Scraping the data and buying it have their strengths and weaknesses. Overall, it all comes down to your project. Generally, scraping data in-house provides more flexibility but will require a lot of resources upfront.
While buying data might look like a larger investment, it ensures you will receive a high-quality dataset collected following all the regulatory guidelines.
Here is a quick comparison:
Why is external data so important for every business
There are a lot of ways that companies can use purchased data. For example, some industries that benefit the most from buying external data are investing, HR tech, and lead generation companies.
- Investors use external data to identify new investment opportunities, track company performance, identify expansion opportunities, and more.
- HR tech companies use external data to improve talent sourcing, enhance talent intelligence, build recruitment platforms, conduct labor market research, and more.
- Lead generation companies use external data to discover potential customers and personalize sales and marketing messages.
Regardless of the industry or use case, data freshness is one of the most important factors when buying B2B data. Companies constantly change through new hires, leadership transitions, expansion efforts, and other business developments. Outdated information can lead to missed opportunities, inaccurate insights, and inefficient workflows. Regularly updated data helps organizations make decisions based on the most current view of the market.
These are just a few examples of how companies in different industries can leverage external data for their benefit. Yours might be different, depending on your business goals.
How AI is changing the way companies buy and use data
AI is changing what organizations expect from external data providers. Beyond traditional analytics and enrichment use cases, businesses increasingly need AI-ready datasets that can support LLM applications, AI agents, and automated workflows. This shift places greater emphasis on data quality, freshness, and accessibility.
At the same time, natural language search is making business data easier to explore, allowing users to retrieve information through conversational queries rather than complex filters. Solutions such as Agentic Search APIs and Model Context Protocol (MCP) connectivity are also helping organizations integrate external data directly into AI systems, enabling agents to access relevant information when and where it's needed.
Interested in finding out more?
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