In today’s internet age, most companies deal with large datasets in one way or another. Data are a vital tool that help companies optimize their operations, and depending on the industry, their organization can make or break a company. In some cases, organizing data requires the use of a data annotation platform for swift and easy-to-manage organization.
In concept, data labelling seems pretty straightforward. Every piece of datum needs to be labeled for organization and swift recall. Consider when you have a document on your computer. You label it with a name and organize it into a folder. A document is just a collection of data anyway, and labeling was not a challenge at all.
But now consider a scenario where you have thousands of similar images that need labeling and organizing. Rather than toiling through individually labeling each picture, a data annotation platform can do it for you with exceptionally high accuracy.
How Does Data Annotation Platforms Work?
Data annotation platforms use a complex combination of AI and machine learning to process and label data in large numbers. Artificial intelligence identifies key attributes in a string of data labeled by a human and then looks for similarities in other strings. They are given a baseline from which to work from before applying what they have learned to larger datasets.
For accuracy in data annotation platforms, machine learning needs to be highly functional; else, there is a lot of room for error. By teaching the machine to identify patterns and learn from its mistakes, you can be more confident that your data annotation platform is labeling data accurately.
Once your data has been labeled and sorted, you have more ease of access to find what you are looking for. Similarly, properly sorted data will lead to faster recall from other machines in your system.
Where is Data Labeling Useful
Any company that deals with large datasets can benefit from a data annotation platform that has advanced machine learning and artificial intelligence capabilities. But, websites and companies that deal with a great number of images may find the most practical use.
Images are entirely complex and contain countless bits of information that can be labeled. Consider a still image of a western-style neighborhood. You could easily manually assign labels for things like “car”, “house”, or “mailbox”, but there are likely tons of smaller identifying features that would be laborious to label by just using your eye.
In addition, you wouldn’t be able to accurately label things like shapes, depth, or color. A data annotation platform with advanced machine learning and artificial intelligence can do that instantaneously.
With all of these data labels attached to an image, image searching will produce many more results and provide more comprehensive options for your users or your internal departments.
With data being such a critical tool these days, having them be easily sortable and accessible has never been more necessary.
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