EnFuse Solutions Blogs

The popular adage, "Garbage in, garbage out" is perfectly applicable to the field of data annotation. There is a growing emphasis on high-quality data for accurate annotations. As mentioned by our co-founder Kamran Shaikh , “no matter how good the AI model is, the investment is wasted if the data is low-quality.” The best AI and machine learning models emerge only from high-quality datasets with complete labels. In the words of Wilson Pang of Appen, “using poor-quality data to train your machine learning system is like preparing for a physics test by studying geometry.” Effectively speaking, this means that without feeding it with the right data, no AI model will deliver accurate output. To make data-driven decisions, business leaders need to understand the importance of ensuring data quality for any form of data labeling and annotations. Be it for text, video, or image annotations, data-dependent enterprises need to be able to define and measure data quality. How can this be