Master Data Management
How data annotation is changing the future of businesses
As we approach a future in which AI-based systems will dominate, the accurate training of machine learning (ML) algorithms will become increasingly important. In this context, data annotation has emerged as a crucial element for effectively training ML algorithms.
The What & Why of Data Annotation
Data has become a vital asset for businesses because it enables them to optimise their workflows and build robust strategies. Data is essentially of two types, structured data that follows a specific pattern, and unstructured data that does not have any pattern or logic.
Data can be effectively harnessed by AI-based automation that can recognise patterns and make predictions. ML algorithms can be trained with the help of existing data to forecast the future. The only hurdle in this process is that ML cannot comprehend unstructured data in its original form. And, unstructured data in the form of social media posts, emails, etc., is growing rapidly. According to statistics, 80% of the data generated is unstructured. This massive quantity of data is of little use if ML algorithms cannot understand it. Hence, it is necessary to provide this data in a readable form to ML algorithms.
This is where data annotation steps in!
Data annotation is the process of labelling data to help ML algorithms understand the content, context, and significance of the text, audio, image, or video data. Human data annotators label different formats of data. ML algorithms are trained through supervised learning with the help of annotated data sets. The performance of ML models depends on the quality and quantity of annotated data.
Data annotation can be of different types based on the format of data:
- Text annotation
- Image annotation
- Video annotation
- Audio annotation
Text annotation helps machines understand text data.
Text annotation can be:
Semantic: Text documents are tagged with relevant concepts to make it easier to locate text.
Intent: In this type of annotation the text is analysed and categorised according to the need/intention behind the text.
Sentiment annotation:It helps machines understand human emotions by tagging emotions within the text.
Each element in the image is labelled so that ML algorithms can comprehend and interpret them just like a human would.
It helps ML algorithms to recognise objects from videos. Image and video annotations train Computer Vision (a subset of AI) systems.
It classifies components of audio data to train Natural Language Processing (NLP) algorithms.
While these are the building blocks of data annotation, each industry can customise it according to its specific needs.
The demand for data annotation services is growing at a CAGR of 26.6% and is expected to reach $ 5.3 billion by 2030. This growth is fueled by factors such as increased investments in technologies such as driverless vehicles, and in the widespread adoption of AI-based automation in commercial applications and research, etc. Besides, the volume of digital content is surging and if businesses want to leverage this data, data annotation is imperative. Technologies like IoT, AI, ML, and RPA are also producing large volumes of data every day making data annotation services a must-have for businesses. The importance of data quality cannot be stressed enough for accurately training ML models.
Let’s peek into what the future of data annotation looks like!
Annotation of text data Is expected to increase because it helps AI comprehend patterns in text, voice, and semantic connection of labelled data. Also, text mining applications depend on pre-annotated text.
Currently, manual data annotation is dominant, but there are predictions that automated annotation will become more prevalent in the future. According to statistics, the market for automated data annotation is forecasted to grow at a CAGR of 18% by 2030.
There is an increasing need for data annotation tools, resulting in a projection for the market for these tools to expand at a CAGR of 27.1% from 2021 to 2028, which is not unexpected.
The next disruptive technology to watch out for is cognitive automation. It is expected that cognitive automation will be deployed by most businesses. Accurate data annotation is the foundation to build cognitive automation.
As businesses accelerate their digital transformation journeys, AI-based automation is expected to become mainstream. Effective training of ML algorithms is the cornerstone of successful automation. And, high-quality data annotation is essential for training ML. Hence, we can conclude that data annotation is enabling businesses to adopt digitalisation and laying the foundation for a smart business based on intelligent automation solutions.
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