Master Data Management

Top 7 trends for data annotation market in 2023

Data is the foundation of most businesses, digital or otherwise. However, data must first be sorted, processed and labelled before it can be used as a source of strategic business intelligence. Especially so, if the intent is to harness the power of artificial intelligence (AI) automation. Since AI learns by example, having large quantities of labelled or annotated data to learn from enables AI models to become more and more efficient.

Businesses can gain significant benefits by taking advantage of data annotation solutions available in the market. Research indicates that the global data annotation market will be valued at USD 8.22 billion by 2028. Meanwhile, the data annotation services market, which was worth USD 1.3 billion at the end of 2022, is expected to grow to USD 5.3 billion by 2030.

Here are seven trends that are expected to influence the data annotation market in 2023.

  1. Image, text and video data will rule the roost:

    Image and video annotation are expected to increase by a CAGR of nearly 17 per cent from 2020 to 2030. The image segment will be the bigger driver of the data annotation market this year because of the growing use of computer vision (CV). The industries involved are largely automotive, energy, entertainment, healthcare, manufacturing, media and utilities.

    Growth of text data will follow closely driven by the growth of text annotation in e-commerce, clinical research and social media monitoring. Annotating text data enables the finetuning of AI’s ability to recognise patterns in voice, text and semantic connection of labelled data. Further, text mining applications also depend on pre-annotated text for development.

  2. Impact of technology:

    Technologies such as AI, machine learning (ML), robots and Internet of Things (IoT) all require large volumes of data and the use of these technologies is rising each day. However, without efficient data annotation, the results would be far from perfect. This need is already driving the data annotation market and will continue this year too.

  3. Growth of automated annotation:

    Although manual data annotation holds the greater share of the market, it is more expensive since manually labelled data is prone to inaccuracies and needs time to be identified and corrected. Consequently, the demand for automated annotation, which offers higher accuracy rates, is set to increase. In fact, it is predicted to grow at a CAGR of 18 per cent by 2030.

    AI is gradually becoming a key factor in the data annotation sector since it enables the automated extraction of intricate information from datasets.

  4. Data labelling tools market on the upswing:

    The data annotation tools market worldwide is predicted to grow phenomenally too. The need for tools will continuously rise as developments in the tech sector continue to increase. Such developments trigger as well as require the creation and use of large annotated datasets for learning and gathering insights.

  5. Increased dependence of healthcare on data annotation:

    Data annotation is also expected to trigger the growth of AI applications in the healthcare industry. Patterns and potential injuries can be identified by AI-powered medical imaging data systems that use computer vision thereby enabling healthcare personnel to create reports automatically after a patient has been assessed.   

  6. Predictive annotation will make significant inroads:

    This is expected to make a significant impact in the data annotation market. Tools skilled enough to find and label items after learning from previously completed manual annotation will become a key differentiator for organisations when picking an appropriate data annotation service provider.

  7. Quality control processes:

    Quality assurance (QA) will be big too. Data teams will require data professionals with enough knowledge and experience with large datasets. That implies that data annotation must be strictly checked for quality with significant focus on edge cases. Skilled professionals will then be able to find and fix bugs in large datasets.

Catalysts of the data annotation market

Huge developments in the data annotation market also means great strides in the AI industry in terms of accuracy, quality, security and usability of data across various sectors. For all of it to come together, organisations must ensure that the data annotation trends blend well into the global data ecosystem.

The industries and sectors that are most likely to use the advanced data annotation technologies include:

  • Cloud computing:

    Cloud-based services require labelled data to gather useful information from e-commerce and digital marketing channels, among other platforms. The increased use of cloud-powered AI platforms will lead to improved annotation, which is needed for object recognition, face recognition, landmark detection and many other services.
  • Social media:

    Monitoring of social media platforms, measuring emotional reactions, identifying visual cues, micro-communications and data proliferation all require secure and intelligent data labelling techniques. 
  • Medical AI:

    Data annotation enhances developments in CV, medical imaging technologies and other AI-powered medical applications while also boosting medical research abilities. The need for human input is greatly reduced.
  • Synthesis AI:

    Metaverse is sure to make its presence felt across most large businesses this year. Building it requires large volumes of synthetic data. It is impossible to precisely label such data manually. However, since synthetic data is generated and annotated automatically, it is ready to be used as needed.  
  • Active learning:

    This is an innovative process where training datasets are built and annotated while ML models are trained simultaneously. It requires a very smart approach to data annotation and combines both labelled and unlabelled data.
  • AutoML:

    This process promises to boost the advancement of new data labelling tools. Automating the data annotation process can decrease human errors while building ML models.

At the end of the day, data security, quality and scalability are what will matter most in the data annotation industry. Experts agree that cognitive automation will be driven by efficient and accurate data annotation. And that takes data annotation to the top of the trending list in the global AI economy. In fact 2023 is being labelled as the year of data annotation in some circles!

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