HRIS data integrations for highest level of accuracy
Ravio's HRIS data integrations result in more accurate data compared to traditional uploads:
- Data from the source: Information comes directly from company HRIS systems, hence, are 100% the accurate information of what someone is being paid. The Automated integrations minimise the risk of human error in data entry.
- Comprehensive view: The integration further guarantees that full picture of company compensation philosophy is captured, not just partial data. The latter puts the benchmarks at risk of just telling ‘part of the story’, whereas the HRIS integration ensures we capture salaries at all departments, countries and levels.
- Real-time updates: Compensation data is automatically refreshed as changes occur in the HRIS. This allows us to monitor changes, such as promotions, new joiners, and vertical job changes.
Ravio in-house levelling services for consistency and verification
Ravio's levelling approach provides greater accuracy in compensation benchmarking for several reasons:
- Standardization across companies: Ravio maps all companies to a consistent level framework, ensuring that comparisons are truly like-for-like. This eliminates discrepancies caused by different companies using varying job titles and level structures.
- Clear differentiation between levels: The Ravio framework uses objective criteria such as leadership, impact, scope, autonomy, expertise, and complexity to define each level. This precise definition helps avoid ambiguity and ensures accurate placement of roles within the framework.
- Flexibility and automation for company growth: The framework is designed to accommodate both small startups and large enterprises, allowing companies to add levels as they grow without needing to overhaul their entire structure. This ensures consistent benchmarking and automation throughout a company's growth journey. (For example new hires are automatically levelled to the right benchmark, ensuring avoiding human error and manual work)
https://ravio.com/blog/mapping-employees-to-the-ravio-level-framework
Advanced statistical validation
When assessing the data submissions and creating our benchmarks, our in-house data team applies advanced statistical validation to evaluate the robustness and credibility of the data.
Some of those techniques are, but aren’t limited to:
- Outlier identification: Our team detects and excludes extreme outliers to maintain data integrity.
- Cross-validation: We randomly split the Ravio data set into 2 groups and evaluate if each group generates approximately the same benchmark.
- Data Smoothing: This step helps reduce noise and random variations in compensation datasets, which allows our analysts to identify underlying market trends and highlight patterns more accurately. This process enables more reliable compensation insights by filtering out short-term fluctuations and providing a clearer view of true market compensation trends.
- Market validation: Comparing datasets from different sources (e.g. inflation trends) to ensure consistency.
Market trend analysis
On a regular basis, our team monitors real-time changes based on the HRIS-integrations and dataflow.
When a company increases salaries, Ravio's advanced statistical validation ensure the impact on benchmarks is proportional to the actual market change.
The Ravio app also highlights market trends, providing users with insights into compensation shifts on a 90d basis, that might reflect a market trends, however, have not impacted the overall salary benchmarks shown in the app.
Qualitative assessments prior to release
Our team does an additional manual qualitative assessments prior to release in collaboration with customers, reward consultants and local partners.
Note: Benchmarks are only displayed when there are enough underlying data points to ensure reliability and compliance.
Data reliability and transparency within Ravio
We want to provide users with as much transparency as user-friendly in regards to the data quality and confidence we have in them. On each benchmark you will find indicators on:
- Confidence scores: Each data point is assigned a confidence score based on our statistical confidence and monitoring real-time changes.
- Sample size indicators: Users can see the number of data points contributing to each benchmark.
By addressing these key aspects, Ravio provides a more accurate, timely, and transparent solution for compensation benchmarking compared to traditional providers.
Still need a hand?
If you have followed these steps and are having trouble building your scenario, we want to hear about it! Our team is ready to dive deeper.
Reach out to us: Click the Contact us button above or email customersupport@ravio.com
Pro tip: If you can, send us a screenshot of the specific setting you are trying to adjust – it helps us solve the mystery much faster!