I’m working on a UiPath automation that reads customer data from a CSV file and inputs it into a CRM system. While the workflow runs successfully for a small batch (about 100 rows), I’m unsure about the best way to verify the automation results.
How do you ensure that the data was entered correctly? Should I consider making the automation attended for monitoring? Or is it better to build a secondary automation to validate the entries? Is manual verification still needed, or do you just trust the process until it fails?
I’d love to hear how others approach result validation in similar scenarios.
Having worked on several data-entry automation projects, I’ve learned that the key to validation lies in integrating checks directly into the workflow.
For example, on a project where we automated data entry from Excel to a legacy CRM system, we built post-entry validation right into the same process.
After each data entry, we’d trigger a lookup in the CRM and compare fields with the source CSV. If something didn’t match, we’d log it with a timestamp and row ID. This method allowed us to validate in real-time, avoiding the need for a separate audit bot or time-consuming manual checks.
That approach definitely makes sense, @ian-partridge.
I’ve taken a slightly different direction though. I usually split it into two separate automations. The first one handles the data entry, while the second is purely focused on validation. For this, we pull the data back from the CRM, either via screen scraping or API, and compare it with the source file.
This separation keeps the main workflow streamlined and fast, which is especially useful when dealing with larger volumes. Plus, it gives us flexibility to schedule the validation during off-peak hours. We also log any mismatches into an error report, which the QA team can review later. It’s a little extra effort upfront, but it keeps everything organized.
Great point, @miro.vasil
I used to depend on manual spot-checks for validation, but I quickly realized that small UI glitches or inconsistencies could go unnoticed unless I added more comprehensive checks. So now, every automation I build generates a validation report after completion. This includes a status column (Success/Fail) next to each row in the source CSV, updated in real-time as the bot processes each entry. If the bot encounters any discrepancies or can’t get confirmation from the CRM, it flags that row for a manual recheck. While it’s not fully automated, it significantly reduces the QA overhead and gives the team better visibility without too much complexity.