Understanding how TalentUp’s data is built helps you use it with confidence. This section explains where the salary data comes from, how it is processed, and what the Confidence Ratio means when you are reading a benchmark result.
Where Does the Data Come From?
TalentUp collects compensation data from three complementary sources, giving the platform a broad and representative view of the job market:
The majority of TalentUp’s data comes from hundreds of public job portals, scraped daily to capture job descriptions, salary offers, location, benefits, and company metadata. This provides a real-time pulse on what companies are actively offering in the market.
Employees voluntarily submit their compensation details through the TalentUp platform. Each submission is validated by comparing it against similar roles, companies, and locations to ensure accuracy and relevance.
TalentUp clients can upload anonymized internal compensation data via Excel templates or HRIS integrations. This data is included only if it was collected in the current year, keeping the dataset fresh and reliable.
4-Step Data Process
Every data point goes through a four-step pipeline before it is used in a benchmark: Collection, Normalization, Deduplication, and Validation.
1. Collection
TalentUp collects over 20,000 new salary data points per day from 300+ sources across 70+ countries and 600+ job roles. In addition to salaries, the platform captures bonuses, job descriptions, company information, and benefits, giving each benchmark a richer context.
2. Normalization
Raw data from different geographies and sources is standardized so it can be compared meaningfully. This step covers three areas:
This makes it possible to compare a Software Engineer in Sao Paulo with the same role in Berlin or Toronto on a like-for-like basis.
3. Deduplication
Duplicate listings and repeated job offers are filtered out using natural language processing (NLP) algorithms that detect semantic similarities in job descriptions and employer metadata. This ensures every data point is unique and avoids inflated or skewed values in the final benchmark.
4. Validation
Both automated checks and manual reviews are applied before a benchmark is published:
All benchmarks are refreshed every 1 to 2 months, so the data you see reflects current market conditions.
Predictive Modeling
For roles, locations, or seniority levels where collected data is sparse, TalentUp uses predictive analytics to fill gaps and maintain continuous coverage without sacrificing reliability.
Salary trends are estimated based on seniority and experience using regression models tuned to reflect realistic career compensation growth.
If direct data is unavailable for a given city, TalentUp estimates benchmarks by drawing on correlations with similar locations, industries, or company sizes, enabling reliable coverage even in less data-rich regions.
Predictive modeling allows TalentUp to provide complete salary ranges, from base pay to bonuses, even when only partial source data exists, ensuring a consistent experience across all roles and locations.
Confidence Ratio
Every benchmark in TalentUp includes a Confidence Ratio, a score from 0 to 1 that tells you how reliable that specific data point is. It is calculated from three factors:
Use this score to calibrate how much weight to put on a benchmark when making compensation decisions:
The maps below show the Confidence Ratio across regions, so you can quickly see where TalentUp’s data is strongest for the markets you operate in.
Europe Confidence Ratio:
window.addEventListener("message",function(a){if(void 0!==a.data["datawrapper-height"]){var e=document.querySelectorAll("iframe");for(var t in a.data["datawrapper-height"])for(var r,i=0;r=e[i];i++)if(r.contentWindow===a.source){var d=a.data["datawrapper-height"][t]+"px";r.style.height=d}}});
