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Data confidence and reliability 

Ravio provides real-time market data that is more accurate and timely than traditional annual surveys. This guide explains the systems and processes we use – from HRIS integrations to statistical validation – to ensure our benchmarks are the most reliable in the European market.

Overview

Traditional compensation benchmarking often relies on manual data entry and outdated reports. Ravio solves this by pulling data directly from source systems and applying rigorous statistical checks. This ensures your pay decisions are based on what is happening in the market today, not six months ago.

Prerequisites

  • Integration: To benefit from the highest level of accuracy for your own internal data, an active HRIS integration is recommended.

  • Access: Benchmarking data is available to all users with access to the Benchmarks or Analyse your company modules.

The pillars of Ravio data reliability

1. HRIS data integrations

The foundation of our accuracy is the direct connection to company HRIS (Human Resources Information System) platforms.

  • Source of truth: Information comes directly from the system of record, ensuring 100% accuracy of pay data and eliminating human entry errors.

  • Comprehensive data: Integration ensures we capture the full picture across all departments, countries, and levels, rather than just a partial snapshot.

  • Real-time updates: Data is automatically refreshed to reflect promotions, new joiners, and salary changes as they happen.

2. In-house levelling services

To ensure apples-to-apples comparisons, Ravio maps all company data to a consistent, proprietary framework.

  • Standardisation: We eliminate discrepancies caused by varying job titles across different companies by mapping everyone to a universal taxonomy.

  • Objective criteria: Roles are levelled based on leadership, impact, scope, autonomy, expertise, and complexity.

  • Automation: Once your framework is set, new hires are automatically mapped to the correct benchmark, reducing manual work for your team.

3. Advanced statistical validation

Our in-house data team applies complex statistical techniques to maintain data integrity:

  • Outlier identification: Extreme data points are detected and excluded to prevent them from skewing the benchmarks.

  • Cross-validation: We split data sets into groups to ensure they generate consistent results.

  • Data smoothing: This reduces ‘noise’ and random fluctuations, providing a clearer view of long-term market trends rather than short-term volatility.

  • Market validation: We compare our datasets against external factors, such as inflation trends, to ensure consistency.

4. Human qualitative assessments

Technology is supported by human expertise. Before any data release, our team performs manual qualitative assessments in collaboration with:

  • Reward consultants.

  • Local partners.

  • Customer feedback loops.

Transparency within the app

We provide indicators on every benchmark so you can assess the strength of the data yourself:

  • Confidence scores: Each data point is assigned a score based on statistical confidence and real-time monitoring.

  • Sample size indicators: You can see exactly how many data points are contributing to a specific benchmark.

  • Reliability thresholds: Benchmarks are only displayed when there are enough underlying data points to ensure both reliability and compliance.

Why benchmarks change

Ravio data is live, so benchmarks move. These are the usual reasons for a change:

  • Benchmark refreshes: We refresh benchmarks country by country. A refresh brings in new data and often adds new roles and filters. Changes after a refresh can be bigger than the usual week-to-week movement.
  • Methodology improvements: We keep refining how benchmarks are built. Where we have lots of data, benchmarks follow it closely. Where we have less, our model does more of the work.
  • Market movement: Pay for some roles, such as AI engineering, moves faster than the rest of the market.
  • Changes on your side: Changing an employee's role, level or location mapping changes their benchmark. So does switching the market filter you compare against.

Note: Variable pay benchmarks are a percentage of base salary. If a base salary benchmark changes, the variable pay amount moves with it.

Why a benchmark might be missing

  • There isn't enough data. If a role and location don't meet our publishing thresholds, you'll see a "no benchmark" message. To compare against a role with more data, open the employee, go to Market Comparison > Ravio Mapping and click the edit icon.
  • A role was retired in a refresh. Occasionally a role no longer meets our thresholds after a refresh and is removed.
  • The location isn't mapped. Employees need a work location that maps to a Ravio benchmark location. Read more in Understanding Ravio's level mapping.
  • Your own data is incomplete. Ravio can't compare an employee with the market without a salary, level and location. Read more in How to troubleshoot salary and data issues in Ravio.

Still need a hand?

If you require more technical details regarding our data methodology or have specific questions about your benchmarks, please get in touch.

  • Reach out to us: Email support@ravio.com or contact your dedicated Success Manager.

  • Pro tip: When reviewing a benchmark, hover over the confidence score for a breakdown of why that specific data point is rated as it is.

Want to know more about Ravio's data confidence and reliability? Check out our blog post here.