Data is all around us-it can be found in almost every form imaginable. Even now, while you read this, information is being collected. With the arrival of the internet, it’s now even easier to gather information via emails, texts, photos, and videos. To put it into perspective, there will be 175 zettabytes worth of data in existence by 2025. In simple terms, it comes to up to a whopping 175 trillion GB. That is far more than any of us can ever grasp.
It’s no surprise how substantial data has become, now that it is effectively used in various applications ranging from forecasting to risk analysis to budgeting. How do we maximise the value of the information we have, given the amount of data generated and stored? Herein lies the significance of Data Annotation-it categorises and structures data so that it can be used for additional research and analysis.
What is Data Annotation?
In the new era of Web3, Machine Learning and Artificial Intelligence are becoming increasingly popular topics. Studies suggest that AI and ML will drive innovation and development for many years to come in nearly every industry.
It’s no secret that these two components will steer business operations, research, production, sales, marketing and almost every other component in companies, but the importance of data annotation and structured data in piloting good AI/ML algorithms seems to be lost in the shuffle.
This takes us to the central question of the article: What does it mean to annotate data?
Data Annotation is the process of transforming unstructured data into structured data for a variety of applications, including the training of artificial intelligence programmes, data management, data interpretation, data analysis, and data augmentation. It consists mostly of labelling and categorising data items into several categories so that machines can interpret the data that is being provided to them.
Types of Data Annotation
Clearly, multiple forms of data are available; consequently, labelling and categorising each type would entail the application of distinct procedures and techniques. Text annotation, image annotation, and video annotation are the three types of data annotation based on the data type.
- Systematic categorization of text by linking it to a collection of concepts or meta-data in order to determine the many features of the text is known as Text Annotation. There are further different types of text annotation which include phrase chunking and text categorization.
- Tagging and mapping features of an image to relevant concepts or metadata is image annotation. Semantic annotation, entity linking, classification, and other types of picture annotation, based on intent, are all types of services that fall into this category.
- In video annotations, features in the video are identified and associated with essential attributes or metadata in order to aid in the identification of various items throughout all data in the form of videos.
Relevance & Paradigm
It is easy to assume that Data Annotation is solely relevant to the tech business because it has a lot to do with the AI/ML industry, but this couldn’t be further from the reality. The riveting truth is that data annotation, if correctly accomplished, can be applied in several sectors, with its impact reaching beyond our expectations. It can be used in a variety of disciplines, including banking, healthcare, agriculture, and industry.
Data Annotation accuracy and labelling quality have more consequences than you might think. If a dog in an image is incorrectly tagged as a cat, then the dataset containing photographs of dogs to train the machine learning software to detect dogs will not be accurate; it will treat dogs and cats alike, but in reality, they do not even come close to being similar enough.
This was a very simple example; this misclassification can happen on any scale in any field if the data is not annotated by professionals with significant expertise. Imagine if a diagnosis of kidney stones is labelled as a diagnosis of cancer, it could cause a series of profound implications.
Skilled practitioners who have a knack in this field are the best consultants in the field of Data Annotation. While choosing to outsource this function to train your dataset for an algorithm, a firm should look at factors like experience, accuracy and speed to avail the best services of the lot! This is why you should choose us; at ActiveLoc, we have a carefully curated team of experts with an excellent track record and an eye for detail, reducing errors to none. Besides data annotation, we also have 8+ years of experience in providing other services such as localization, translation and other consulting services.