Best Salary Datasets, Databases & APIs

What is Salary Data?

Salary data, also known as wage data, compensation data, or income data, is a sub-category of Business-to-Business (B2B) data. The data is mainly grouped into ranges or categories relating to earnings, demographics and contracts. Salary data involves the identifiers of job title, full job description, company or employer’s name and location in relation to data about earnings relating to frequency of payroll, average salaries, number of employees earning each salary level.

How is Salary Data collected?

Salary data is can come from many different sources. It is most often collected from online salary surveys, payroll reports, or full job postings sourced directly from employer websites using code specific to salary data. Salary data is also collected from and made up of statistics and analysis of the employer website’s job postings.

What are the attributes of Salary Data?

Salary data is made up of various different data points. Company or employer’s name, job title, job description and location will be simple word data - with the possibility of being grouped in relation to earnings. For example, it is common to find salary data which shows how much a specific type of employee or group of employees earn. Salary data relating to earnings will often be presented in ranges e.g. from $100K to $200K. Salary data concering timeframes of payroll deposits as well as payroll periodicity and frequency will be displayed using dates and timespans. Occupation codes and sector codes present further examples of salary data which can be group into categories.

What is Salary Data used for?

Salary data is made up of various different data points. Company or employer’s name, job title, job description and location will be simple word data - with the possibility of being grouped in relation to earnings. For example, it is common to find salary data which shows how much a specific type of employee or group of employees earn. Salary data relating to earnings will often be presented in ranges e.g. from $100K to $200K. Salary data concering timeframes of payroll deposits as well as payroll periodicity and frequency will be displayed using dates and timespans. Occupation codes and sector codes present further examples of salary data which can be group into categories.

How can a user assess the quality of Salary Data?

The best salary data providers will have certifications to show that their data is high-quality, and they will be able to explain the verification procedures they carry out to ensure data accuracy. They will also be updating their datasets regularly, and you should be able to find good reviews from clients of the best salary data providers. Lastly, ask for a data sample before you buy from a salary data provider to ensure that their salary data is of the quality that your business needs.

Where can I buy Salary Data?

Data providers and vendors listed on Datarade sell Salary Data products and samples. Popular Salary Data products and datasets available on our platform are Global Jobs Feed (company, description, salary, location, applicants etc), 250M records, with 30M+ added monthly by Mattermark, ISS ESG Executive Compensation Data & Profiles (32k executives) by ISS ESG, and Indeed Job Postings Data|Grepsr by Grepsr.

How can I get Salary Data?

You can get Salary Data via a range of delivery methods - the right one for you depends on your use case. For example, historical Salary Data is usually available to download in bulk and delivered using an S3 bucket. On the other hand, if your use case is time-critical, you can buy real-time Salary Data APIs, feeds and streams to download the most up-to-date intelligence.

What are similar data types to Salary Data?

Salary Data is similar to B2B Leads Data, Firmographic Data, Technographic Data, Business Ownership Data, and B2B Intent Data. These data categories are commonly used for Salary Benchmarking and Compensation Planning.

What are the most common use cases for Salary Data?

The top use cases for Salary Data are Salary Benchmarking, Compensation Planning, and Job Pricing.