One of the biggest challenges in big data is creating labels for images, groups of ideas, and unstructured data fields. A data annotation platform can help you quickly add the necessary labels for building machine learning models, search engines, and similar systems.

Before you adopt a data labeling platform, it's wise to learn what to expect. Organizations can expect the following four things.

Annotators

Foremost, you can expect to deal with annotators. These are human workers, and they can work either on-site or remotely. Each annotator is someone added to the system to ensure that a human provides an understanding of what each labeled data point is.

If you want to develop a handwriting analysis database, for example, you might have annotators classify millions of written letters and words. Multiple annotators can weigh in on each entry to provide something of a majority opinion regarding which scans of writings deserve specific classifications.

Dashboard

Most platforms will use a dashboard to help you manage your projects. You will need a way to upload spreadsheets, images, and other files. The dashboard will then allow you to assign annotators to jobs and monitor their projects. If you need 10,000 image labels to train a machine vision system, for example, the dashboard will tell you how many annotators have accepted and completed assignments. Likewise, it will tell you how many completed assignments have passed approval. You can then see how far along each project is.

Qualifications

A good data annotation platform should ensure that all workers assigned are qualified. Especially if you're working with a third-party provider to dole out the assignments as microtasks, you'll want the system to pass along qualifications. If the workers operate on your system, then they can qualify through a series of tests. You might have a set of known classifications, and you can have annotators work through these to verify their ability. Depending on the nature of the task, you could then accept only the top 25 percent of test performers.

Output

Ultimately, the goal is to generate machine-readable and clean output data. The data can serve as the core of your database, or you can use it for further training of machines. Ideally, the output will match the necessary input formats for your computational activities. You can then feed the data into the system and expect it to be machineable. As necessary, you can also send buggy classifications back into the system for further examination by annotators.

For more information about using a data annotation platform, contact a local company.