Step 1: Baseline Data Collection

The first critical step is gathering the foundational data through Bindbee's endpoints. Your HR tech platform needs to understand not just what the policies are, but how they're being used in practice.

Start by implementing regular data pulls from the hris_time_off endpoint. This will give you the ground truth about leave usage - when people are taking time off, how much notice they're giving, and whether their requests are being approved. You'll want to track both the request_type and status fields to understand different leave categories and their approval rates.

Next, pull data from hris_time_off_balance to understand how employees are managing their leave allowances. The key fields here are balance and used, which tell you whether people are actually using their allocated time off or if it's accumulating unused.

For context, you'll need the hris_employee endpoint data to segment your analysis by department and employment status. This helps identify whether policy effectiveness varies across different parts of the organization.

Step 2: Building the Analysis Framework

Now that you have your data sources, build an analysis framework that can identify patterns and issues. This isn't just about counting days - it's about understanding how policies are working in practice.

Create a system that tracks:

• Time from request to approval (using hris_time_off.created_at and status change timestamps)
• Patterns in request denials (looking at status transitions)
• Usage rates by policy type (comparing hris_time_off_balance.used against total allowances)
• Department-level variations (segmented by hris_employee.department)

The key here is to build trending capabilities. Don't just look at current numbers - track how these metrics change over time to identify emerging patterns or issues.

Step 3: Implementing Usage Monitoring

This is where you start actively tracking how policies are being used day-to-day. Build a monitoring system that watches for specific patterns indicating policy success or failure.

For example, if you see a pattern of last-minute leave requests (detected through hris_time_off.start_time proximity to request time), this might indicate that your advance notice policy isn't working well. Or if particular departments consistently show low utilization rates in hris_time_off_balance.used, it could signal that the policy doesn't meet their needs.

Key metrics to monitor include:

• Utilization rates by policy type
• Approval rates and timeframes
• Patterns in request timing
• Departmental variations

Step 4: Impact Assessment

Here's where you connect leave policy effectiveness to actual business impact. Use the hris_timesheet_entry endpoint to understand how leave-taking affects work patterns.

Create correlation analyses between leave patterns and:

• Team coverage (using hris_timesheet_entry.hours_worked)
• Work distribution (analyzing patterns in hris_timesheet_entry data)
• Policy compliance (comparing actual usage against policy rules)

This step should produce clear metrics that show whether your policies are supporting or hindering business operations.

Step 5: Automated Response System

Finally, build an automated system that can respond to what you're learning. This should include:

Alert Generation:
When your analysis detects potential issues (like unusually high rejection rates or department-level underutilization), automatically generate alerts for HR managers. Base these alerts on deviations from historical patterns in your hris_time_off and hris_time_off_balance data.