Data from Statista shows that there were almost 7 billion smartphone mobile network subscriptions in 2023. Most people on this planet have access to the internet right now. In a world where massive amounts of data are being created daily, it's only natural that businesses want to utilize it to improve their operations and chances of success.
However, in order to start data acquisition, you first need to determine what kind of information you need. For instance, you might need to buy marketing data for improving your lead generation or company data to enrich your contacts.
In this article, you will learn all there is to know about B2B data and how to select and buy it.
Statistics about buying data
The market is huge for high-quality data, and the statistics are here to back it up:
- 79% of businesses that surpass their revenue goals use customer data for enhanced marketing strategies;
- 91.6% of executives are investing in both big data and artificial intelligence to improve decision-making;
- 97.2% of companies invest in big data and AI, but only 24% use the data to inform decision-making.
The use of artificial intelligence (AI) and machine learning (ML) is becoming increasingly prevalent in data buying and analysis. 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.
Types of data you can 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 developer community-related data. You can use it to find the best developers and also research state-of-the-art software 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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Data processing levels
You can choose between different processing levels depending on how much effort you're willing to invest in making the data work for you:
- Base data – Delivered in its unprocessed form. This type of data provides full flexibility but requires technical expertise to parse, clean, and format. You'll need internal resources to filter out duplicates, correct inconsistencies, and structure it for analysis.
- Clean data – Parsed and deduplicated, ready for analysis. Clean datasets are ideal for businesses that want to reduce time-to-insight and start using the data immediately. They’re typically filtered for relevance and standardized into consistent schemas.
- Multi-source data – Aggregated and enriched from various public web sources to offer broader context and better coverage. This type is ideal if you're looking to enhance decision-making with deeper insights and contextual layers, but don’t want to build the enrichment pipelines yourself.
Choosing the right level depends on your internal capabilities. If you have a strong data engineering team, raw data may suffice. If your team is more focused on analytics or product development, clean or multi-source data will help you get started quickly.
High-quality data is clean, fresh, accurate, deduplicated, and easily integrated. It should require minimal transformation and be immediately actionable for your specific use case.
What are the main data formats?
There are several main data formats: SQL, JSON, CSV, 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, social media platforms, and more. Unstructured data is flexible, requires a lot of storage space, and is stored in data lakes.
- Semi-structured data includes formats such as JSON, 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 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.
Below is a quick comparison of two delivery methods: datasets and data APIs. Hopefully, it helps you decide which is the best fit for your workflow:
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 – Avoid vendors offering outdated or non-validated lists.
- Maintain communication post-purchase – Ensure ongoing support, updates, and feedback loops.
Investing in high-quality, 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 3B 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 is the leader of the market in terms of data freshness. We also have a large coverage of billions of records. Our parsed and ready-to-use data is tailored for investors, HR tech, and sales tech clients. Each of our clients gets a dedicated account manager for quick communication and problem resolution.
Coresignal also offers 4 different APIs - Company API, Employee API, Jobs API, and Historical headcount API, which you can use to extract records without paying for the entire dataset. Our credit-based system allows you to search for free and use credits only to collect the data.
Some other big players in the web data industry, such as People Data Labs, Thinknum, and Operia, also offer similar datasets and services.
Compared to Coresignal, the scope of their coverage and focus areas varies. However, in terms of data freshness and updates, which is one of the most important data qualities, Coresignal provides an advantage with regular and large-scale record updates, refreshing millions of company and employee records monthly, focusing on the most relevant profiles.
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.
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.
Interested in finding out more?
Stay ahead of the game with fresh web data
Coresignal's data helps companies achieve their goals
