Skip to content
Motives behind Different Types of Data Annotations

Motives behind Different Types of Data Annotations

Posted on : January 16th 2023

Author : Viswanathan Chandrasekharan

The word “algorithm” is ubiquitous today. It is as prevalent as the term Artificial Intelligence (AI). In fact, AI and algorithms are inseparable. Broadly, algorithms instruct a computer on transforming data into insights that inform decision-making. Specifically, it has become fundamental to AI-powered solutions.

With the abundance of data and the diversity of its sources, it has become critical to building Machine-Learning (ML) models that resolve high-dimensional challenges. ML, an AI application, uses algorithms to enable machines to learn continuously from decision-making examples or training data and improve. Consequently, what Gartner believed back in 2016 “that the predominance of algorithms will only increase in the digital age”¹ has come true.

Today, algorithms are helping to distinguish diseases at the molecular level².. They are being developed to mitigate disruptions when critical networks such as power distribution, air traffic control, etc., are under targeted attacks³. Besides, algorithms are playing an intrinsic role in our day-to-day lives. For example, from shopping online to over-the-top platforms and from Alexa to your social media channels, leverage algorithms to provide what you want or like at your fingertips.

The hard fact is that algorithms would only be able to perform with annotated or labeled data. In other words, data annotation or labeling enables algorithms to do the tasks they are designed to do.

A refresher on data annotation

The process of labeling data sets to make them machine-readable is data annotation. Data annotation or labeling is regarded as an indispensable adjutant to machine learning. They help develop and enhance the capability of machines to identify patterns from previous experience or data.

Consequently, if data is incorrectly annotated, the algorithms will deliver results that do not meet the business objective for which they were generated. The algorithms will learn the wrong lessons from the incorrectly annotated data, execute wrong calculations, and deliver misleading results. For specific and complex business objectives, Straive believes it is advisable to engage subject matter experts to ensure the quality and relevance of the annotated data.

Furthermore, industries like financial, information services, banking and insurance, life sciences and healthcare, real estate, and legal have complex data sets that require the services of these subject matter experts to ensure the labeling does not go off the rails.

A peek into the functioning of types of data annotations

Data is humungous in volume and available in numerous formats and types. As a result, there are many data annotation methods. Let us tell you more about data annotation types and their uses.

Text annotations - Today, the challenges of analyzing text data and the mounting realization of its strategic importance drive the need for text annotation. Text annotation allows enterprises to get the full value out of all their text-based data sources by making the important keywords in the texts understandable to machine-learning algorithms.

Text annotation is the process of adding tags or labels to text data to determine its meaning better. Cognitive text data annotations allow enterprises to harness the text's potential information and help machines interpret human language. Natural language and linguistics are leveraged to provide text annotation.

Image annotations – Image annotation or tagging is the first step in making datasets useful for machine learning in image recognition. In computer vision, it performs the vital function of enabling computers to view and interpret visual information from digital images and videos.

Moreover, image annotation helps to assign captions, identifiers, and keywords to images as attributes to help train the ML models. Frequently bounding boxes or semantic segmentation is employed to facilitate a range of AI-based apps such as facial recognition, computer vision, robotic vision, and autonomous vehicles, among others.

Video annotations - Video annotation is the process of labeling or annotating objects in a video clip on a frame-by-frame basis to enable machine learning models to detect or identify objects. It prepares datasets for optimal machine learning functionality. Video annotation calls for a combination of skill and technology, as it is essential for building a comprehensively labeled dataset optimized for a business objective.

Video annotation types and techniques are similar to image annotation, like Bounding boxes, 3D cuboids, semantic segmentation, polygon annotation, etc. In addition, there are key-point annotations, frames classification, and video transcriptions. Processes such as object location and tracking performed by computer vision models require video annotation data, along with a coherent performance of concepts like location, motion blur, object tracking, and more.

Audio annotations – Audio annotation labels audio recordings to develop, train, and improve conversational Artificial Intelligence (AI), chatbots, and speech-recognition AI engines. This technique requires the services of qualified linguists from across the globe. Labeling and transcribing audio files parts to extract meaningful insights from audio formats is also a part of the audio annotation.

Many dynamic concepts in an audio file, such as language, speaker demographics, dialects, mood, intention, emotion, and behavior, make audio annotation a complex task. It requires understanding and identification of all these parameters as they need to be labeled using methods like music tagging, timestamping, acoustic scene classification, and more. Let’s remember the nonverbal cues consisting of sighs, silences, or for that matter, even background noises can be annotated for the machines to achieve a usable understanding of the audio files.

What can Straive do for you?

At Straive, we believe in unlocking the value of data and creating meaningful intelligence. Enterprises can quickly scale up to the demand for data annotations by outsourcing it to experienced vendors like Straive. With our deep roots in content and data business, data annotation tools, and expertise, Straive is equipped to fulfill your annotation requirements. Dedicated teams of skilled, experienced professionals trained to seamlessly adapt to new training data requirements and annotate data for ML modeling purposes back our data annotation expertise. Moreover, our project management team ensures that we have full control over the quality, cost, and schedule and provide visibility to our customers.

Straive offers technically advanced data labeling and annotation solution through a robust platform built on the latest technologies hosted on the cloud. Furthermore, our team is renowned for deploying a client-specific platform in days. Our data annotation solution identifies and understands the sentiment in insurance documents, physician notes, pathology reports, financial statements, and more. To better understand our capabilities, please visit or email

Similar Blogs

The process of data extraction involves identifying and recovering alternative and semi-structured data from various data sources such as files, XMLs, JSON, etc.

Capital markets are an excellent example of a perfect competition. The nature of the market is such the participants have to be competitive and result focussed. For instance, brokerages and investment banks have to deliver passive gains for their clients and, at the same time, earn a margin for themselves.

Today’s ESG analytics require processing data, patterns, and hidden connections to provide insights that investors, asset managers, and companies need. For example, Straive deploys advanced machine learning algorithms to analyze reams of documents to collect evidence across executive statements for signs of vagueness or obfuscation.

Talking about using data to gain insights is easy. But actually doing it will uncover a newer set of challenges, especially when it comes to unstructured data.

Integrating ESG data into commodities trading operations requires structured, easy-to-consume data. By their nature, ESG data resist such integration, and highly scalable data solutions across the data life cycle are needed to allow stakeholders to deploy end-to-end data solutions for a successful data-to-intelligence journey.

We want tohear from you

Leave a message

Our solutioning team is eager to know about your challenge and how we can help.