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What “clean data” means in Protiv and how to get it

Clean data isn’t a nice-to-have. It’s the backbone of your bonus program. Here’s exactly what “clean” means and how to make it happen.

Let’s cut to it: your bonus program is only as good as the data that feeds it.

If time, revenue, or job completion status is wrong (or missing) your bonus payouts will be too. Clean data isn’t just about getting people paid right. It’s about building a system everyone can trust.

So what exactly counts as “clean” in Protiv?

The 3 things you must have for clean bonus data

You don’t need a data science degree. Just focus on getting these three right every week:


1. Completed jobs

This is step one. If a job isn’t marked as Completed, Protiv assumes it’s still in progress, which means no ProPay is created, and no bonus gets calculated.

  • Good data: Jobs are marked as Completed in your time-tracking software or manually updated in Protiv
  • Bad data: Jobs sit in limbo (e.g., “In Progress” or “Scheduled”) with no clear handoff
  • Tip: Set a routine for office or crew leads to mark jobs complete before payroll hits.


2. Logged and approved hours

Bonuses in Protiv are tied to crew hours. No hours? No bonus.

  • Good data: Hours are logged accurately and approved in your time-tracking system
  • Bad data: Missing hours, unapproved logs, or logging time for the wrong job
  • Tip: Make sure your time-tracking integration is active and syncing daily.


3. Verified revenue

If the job shows $0 revenue—or the wrong amount—it throws off your budget and your ProPay calculation.

  • Good data: Revenue matches what was billed/collected or what’s expected for the job
  • Bad data: Placeholder revenue left in, typos, or no revenue entered at all
  • Tip: Set a 2-minute review checkpoint for job revenue before closing Bonus Statements.

Clean data = predictable performance

When this data is clean and consistent, you unlock the real power of Protiv:

  • Accurate ProPays
  • Real visibility into job performance
  • Trust from your crews
  • No surprise gaps in Bonus Statements

If it feels like your bonus program is chaotic, chances are your data’s not clean.

Final takeaway

This isn’t about perfection.
It’s about consistency.

Clean data lets you scale a bonus program that’s fair, accurate, and nearly hands-off. Start with completed jobs, synced hours, and real revenue... and you’ll see results fast.