Organizations with data-specific dependencies need to follow a step-pronged approach to data processing. For instance, a company planning to develop an intelligent machine-learning model will need access to feed its algorithms with tagged, labeled, or market data. Going blind hardly helps! In this discussion, we will touch upon the very aspect of data annotation and how companies looking to get the data labeled should proceed.
Here are the three key takeaways:
- Data annotation— a process of labeling or tagging data— makes it easier for AI and ML algorithms to process audio, text, images, and even video. Most people miss that annotation requires prioritizing, as machines can only work on labeled data.
- Companies can handle data annotation in-house or even consider outsourcing. The latter often results in better labeling quality, minimized internal bias, the ability to work with datasets in bulk, and the flexibility to dedicate the in-house teams to the more pressing and time-intensive jobs.
- In-house data annotation has its place. It makes sense when the company needs to work with fewer data sets or is on a budget. Also, if confidentiality is a concern, it is advisable to go completely in-house or make the outsourced firms sign confidentiality agreements.
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https://www.analyticsinsight.net/data-annotation-outsourcing-v-s-in-house-roi-and-benefits/
