With the fast-paced development of the Artificial Intelligence (AI) and Machine Learning (ML) domain of technologies, the field of data annotation is gaining more importance. The global market for AI- and ML-relevant data preparation solutions is expected to grow to $3.5 billion by the end of 2024.
77% of the devices that are in use presently utilize ML in some form or other. From virtual personal assistants like Apple Siri, Amazon Alexa, & Google to social media platforms like Facebook, the use of AI and ML technologies is projected to increase over the coming years. From healthcare and automotive to the IT and retail sectors, these technologies are being used across sectors. Data annotation and data labeling play a critical role in preparing the data to train the AI/ ML models.
To keep up with this growing demand, business enterprises across industry domains are looking for data annotation experts or providers who can think strategically and help reap the benefits of AI and ML initiatives.
The Need For Data Annotators
Data is now emerging as the backbone of modern customer experiences. As enterprises gather more insights into their customers, AI is making the collected data actionable. To deliver actionable insights, the smart algorithms need to be trained on data. This is where data annotators (or labelers) can help. For instance, even the most advanced computer is unable to differentiate a “man” from a “woman” using a picture.
It requires the right algorithm along with supervised training to execute tasks that are deemed ‘easy’ for the human brain. Data annotators make it easy by labeling content such as text, images, audio, and videos so that the machine learning models can recognize those and use them to make useful predictions. However, data annotation is not as easy as it sounds. It requires several skills, domain expertise, and patience to be an excellent data annotator.
We will discuss the eight most important skills that data annotators need to possess:
1. An Eye For Detail
Data annotators must pay attention to the finest details. Incorrect annotation can reduce the data quality and jeopardize the entire ML algorithm. Be it text or images, annotators must highlight specific data ‘pieces’ that can be interpreted easily by machine algorithms. For example, annotating the specific legal clauses and context in a court ruling statement.
Further, data labeling for image recognition also takes observation skills and attention to detail. For example, a data labeler must know where to draw the bounding box around only the part of the image that has the characteristics described in the label (for example, exact facial features for a face recognition model). Including too much (or too little) of the image could result in inaccurate data model outputs.
2. Expertise In Working With Large Volumes Of Data
Unstructured data makes up more than 80% of enterprise data, and it is growing at the rate of 55-65% each year. In the absence of tools to analyze these massive data volumes, organizations are just left with vast amounts of valuable data on the business intelligence table.
Further, to be accurate, AI and ML models also need large volumes of training data. On their part, data annotators must have the skills to handle and process massive volumes of structured and unstructured data without compromising its quality. With a massive amount of unlabeled data, data labeling is a high-volume task and goes a long way in data preparation and preprocessing for building AI models.
3. Ability To Deliver High-Quality And Consistent Data Output
Skilled data annotators who can deliver high-quality training data can help in developing accurate AI and ML algorithms. Be it an image or text annotation, high-quality data is an absolute must for accurate model outputs. Essentially, the quality of data is determined by the accuracy, consistency, and integrity of data annotation experts.
For example, a computer vision system trained for autonomous vehicles using poor-quality images of mislabeled road lanes can lead to devastating results. Hence, the ability to deliver accurate and consistent output is critical for data annotators.
4. Managing Data Complexity
According to TechJury, data creation is projected to grow to over 180 zettabytes by the year 2025. This means more data types and sources are being introduced. The complexity of data indicates the level of difficulty enterprises face when trying to translate them into business value. Data annotators must be able to handle complex data-related operations as well as work with more data types.
For example, image recognition systems often require bounding boxes drawn around specific objects, while product recommendation and sentiment analysis systems require natural language processing skills along with a cultural context. Essentially, data annotators should be skilled enough to take into account the complexity of the task and the size of the project.
5. Strict Adherence To Project Timelines
Data annotation is a collaborative effort that includes multiple stakeholders. Non-adherence to project timelines can delay the overall project and increase costs. On the other hand, a limited timeline may impact the output quality of the labeled data. Project managers in charge of the data annotation effort need to carefully assess the timelines based on the involved datasets, available workforce, and the overall complexity.
6. Domain Expertise
Ontologies (or the understanding of the entities that exist for a particular industry domain) are a crucial part of any ML project. Do business enterprises need to have subject matter experts when it comes to efficient annotation work? That is determined by the complexity of the data project.
Data annotators can deliver better data quality with proper domain expertise. This includes high-demand industry domains such as security, defense-related satellite image analysis, and medical diagnosis (that include potentially life-threatening conditions).
7. Technology Knowledge
Essentially, this means how oriented are data annotation professionals at learning new technologies and software tools. Computer programming skills aren’t “mandatory,” although it would be a “nice-to-have” skill in any data annotation project. Data annotators also need to be adept at learning about machine learning models, to deliver model-ready data that can be processed without any delay.
8. Perseverance
As data labeling is a time-consuming process, it requires data annotators to have perseverance in data iteration and features as they train and tune the models to improve data quality and model performance. With growing data complexity and volume, data labeling is likely to become more labor-intensive.
For example, video annotation is especially labor-intensive, with each hour of video data collected consuming about 800 human hours to annotate. Effectively, a data annotator should be able to sit for long hours and pay attention to what’s happening on the screen, without being easily distracted and making mistakes.
Conclusion
The number of data annotators is expected to increase in the upcoming years with the rise of AI and machine learning. Several large corporations like IBM, Google, and Facebook are already recruiting new people for data labeling.
It’s time you also hop on to it and look for someone who enjoys technology and is eager to learn new tools and techniques of data labeling. At EnFuse Solutions, our team of data annotation experts adheres to the best data security standards and timelines to guarantee speed, high quality, and security for your data projects.
Want to reap maximum gains on AI initiatives? Get in touch with us now.
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