Hi. My name is Chris Camillo, and I am an auditor and consultant on international child labor and education issues. As part of my auditing work, I visit rural development projects in Africa and Latin America to assess the quality of their GPRA performance data, their compliance with program requirements and their learning environments for beneficiaries.
My Hot Tips are recommendations for improving monitoring systems from an auditor’s perspective.
Hot Tip 1: When designing a project for a rural environment, thoroughly assess potential barriers to efficient monitoring. In many countries that I’ve visited, heavy seasonal rains, rugged terrain, unpaved roads, strikes and inadequate transportation result in significant delays in data collection and reporting from target communities. A monitoring plan that relies on volunteer data collectors making frequent visits on foot to sites that are located many miles apart would be too challenging to implement under these circumstances.
Hot Tip 2: Make certain that the monitoring system is robust by requiring thorough documentation of all data collected and by requiring periodic data audits to validate the accuracy and reliability of performance numbers against the source documentation. Use automated controls whenever possible to help prevent errors in data collection, data entry, and reporting.
Hot Tip 3: In addition to training, consider providing performance-based compensation or incentives to employees and volunteers to ensure the accuracy and timeliness of data collection, transmission and reporting.
Do you have questions, concerns, kudos, or content to extend this aea365 contribution? Please add them in the comments section for this post on the aea365 webpage so that we may enrich our community of practice. Would you like submit a Tip? Send it to aea365@eval.org aea365 is sponsored by the American Evaluation Association and provides a Tip-a-Day by and for evaluators.
These are helpful.
I’d just add that, as evaluators, we need to be careful not to require too much data to be collected. Systems fall apart and data integrity suffers when we require those collecting it to have to spend time collecting and thoroughly documenting a lot of information that, in actuality, doesn’t add much value – or even get used.