If there is something permanent in data collection and annotation projects is that requirements are continuously changing. Data needs change constantly, whether the changes affect the type of data to be collected, the way of collecting data, the data source, the kind of information that will be annotated, or the data annotation criteria. Every process and methodology has to be designed with this changing reality in mind to be able to adapt to new conditions quickly.

Because artificial intelligence is being applied to new areas that are being digitally transformed, data collection can be challenging. For example, data may have to be gathered from less frequent situations, that even occur randomly; the data may be challenging to access to because it contains confidential or personal information and requires compliance with regulations that are becoming more and more complex, etc.

As artificial intelligence evolves, more detailed, complex, and even specialized annotations are needed. Therefore, data annotation professionals have to be flexible and continuously adapt. Training programs have to be comprehensive, so annotators understand and correctly follow the annotation guidelines’ nuances. The training programs not only have to include an explanation of the procedures and tools, but they also have to prepare candidates to be an active part of this constantly evolving ecosystem. This strategy provides Sigma with the capability to adapt to new conditions quickly.