Let’s say your task is to regularly update information about local businesses in order to keep an up-to-date list. You give performers an offline task to find a particular business, check the address and opening hours, and provide a photo. After the task is complete, you find out that some answers are only partly correct. Some performers didn’t provide a quality photo, while others got the opening times wrong. How can you clean up this data? Do you need to pay the performers who were only partly correct? Where do you get an extra budget to re-label the objects with missing data? This task can be decomposed by splitting it into three independent projects where one simple piece of information is collected, and performers don’t get confused with multi-tasking:
- An entrance photo
- Opening hours
This allows you to use simple quality control mechanisms, choose performers who are better at each individual task, and save money on relabeling incorrect data.