5 Main Questions You Have To Ask Before Starting A Data Annotation Project

Any AI or machine learning project requires a lot of data. The more data, the more accuracy. More information means better, more informed decisions. Anyone creating or funding an exciting, innovative AI also wants to make informed decisions. Before an AI or machine learning algorithm can learn, it needs the data it is learning from to be manually annotated.

For the AI to have a high degree of accuracy, a high volume of data must be accurately annotated. Put simply, more information needs more layers of it added. Datasets get even more metadata through data annotation. This sometimes incredible depth of layered information allows deep learning to work so well.

Different types of data take different data annotation types. Different types of data also often need expert service for expert accuracy. For example, anyone with eyes, hands, and basic intelligence might label a face and tag their Facebook friend. Not everyone can handle medical data annotation. You also can’t trust just anyone with medical data, which may contain protected patient information.

Before trusting a company by outsourcing your next big data annotation project, you should know something about it. You should try to know what you don’t know. Even if you think you know, it helps to ask questions. Getting the right answers helps to know what questions to ask. Even if the question and answer seem obvious, these are complex technical questions, and you may get surprising answers.

So, without any more to do, here are the 5 top questions and simple answers to help you make an accurate, informed decision.

The 5 Main Questions and Answers:

1. What is the purpose of your data annotation project? As in what is your goal and desired outcome? You need to ask yourself that and clearly understand what you want to achieve.
2. How much data and what kind of data do you have in your dataset? Do you need more data collection or data creation? Usually, more data in a larger dataset is better and can provide better results. In case you don’t already have a ready-made dataset or you don’t have enough data to meet your goal, we offer data collection and data creation services.
3. What kind of data annotation do you need to achieve your goal? Video annotation, image annotation or something else? Different projects and datasets can have very different data annotation needs. For example, if the dataset includes many videos, you’ll need video annotation.
4. How much precision do you need in your data annotation? Precision is critical. Having the highest degree of precision, with less uncertainty, is normally for the best.
5. Should you outsource or annotate in-house? You should use outsource data annotation services, especially if you are a start-up. This will save you time and money and increase accuracy. Trying to DIY manual data annotation will take a long time and only give you a better appreciation of data annotation services.