Data Annotation Market in 2024: Current Trends and Future Demand

2023 has been the “year of generative AI,” shaking up the global tech industry. Initially unfamiliar with this technology, we now rely on generative AI solutions in both work and daily lives. As we enter 2024, we eagerly anticipate further advancements in AI. Despite varying emerging tech trends this year, the foundation for each is one: data.

Many business endeavors today rest on data, including GenAI that everyone’s talking about. Data applies to everything from business transactions and consumer purchases to watching Netflix movies. Moreover, companies can optimize their workflows and develop better strategies for the future with this single most important asset.

Yet, in an effort to harness the power of automation, businesses struggle to process large volumes of raw and unstructured data. For AI to recognize patterns or make accurate predictions, such data must be annotated to build well-performing models for different sectors, from healthcare to the automotive industry. By adopting the emerging tech and data annotation trends, businesses can gain a competitive edge for long-term success.

So, let’s see what the latest trends of data annotation in 2024 will be!

The Status Quo of Data Annotation Market in 2024

The importance of labeled data is only increasing as we enter the new year. There are numerous fascinating things to look forward to in 2024 (and beyond) as data annotation for machine learning has gone from a narrow niche to a big industry.

According to Grand View Research, the global data annotation market is projected to be worth USD 8.22 billion by 2028. Besides, through 2030, it’s expected that the global data annotation services market will grow at a CAGR of 26.6%. By 2030, the market is projected to be worth US$ 5.3 billion. However, we can see the industry thriving already today.

Such impressive stats are mainly caused by the rapid growth of data, necessitating businesses to learn how to deal with large training datasets. Consequently, one of the most influential trends, big data, has emerged. Big data has a direct impact on the development of the data annotation industry, along with recent advances in AI and other digital solutions created to handle mass data.

In 2024, data annotation becomes even more integrated into the modern digital landscape due to the rise of digital image processing and mobile computing platforms. What’s the purpose of data annotation in these areas, and where does it come into play?

• Digital commerce: improving customer experience.
• Banking, finance, and insurance: document verification, customer interaction in real-time.
• Research: parsing scores of accumulated and unstructured datasets as part of data labeling services for universities.
• Social media: content monitoring and curation, inappropriate content identification.
• Agricultural sector: crop monitoring, soil assessment, etc.

Factors Driving Annotated Data Demand in 2024

1. More complex datasets

   High-level ML requires more intricate work on data annotation to provide datasets for efficient model training. This also implies that the need for expert data labeling services will keep growing.

   Our annotators are specifically trained for each project to get a comprehensive understanding of the nuances within the data they handle. Contact our team to find out more!

2. Real-time data annotation

   Data annotation will become essential during the collection phase, with a growing demand for real-time annotation. For annotators, this means operating with increased efficiency and precision. Mistakes at this stage could significantly influence the model training process results.

3. Automated data labeling

   The trend for automation is growing. Using algorithms for automatic data annotation is great for many cases in machine learning, especially when dealing with large datasets. However, not without the shortcomings. Automation is not always a reliable solution, which means that human supervision is required for such cases to ensure accuracy and precision in the annotation process. Thus, automated labeling is expected to complement, not replace, traditional human-based labeling in 2024 and beyond.

   Yet, this is not an exhaustive list of factors shaping the current data annotation industry trends. Another aspect to consider is the phenomenal growth of digital content across all business platforms. This implies dealing with mass user data via a plethora of digital channels. Here, data annotation helps businesses fully exploit the benefits of online content, add value, and attain new customers.

   Finally, innovations in generative AI will significantly influence the data labeling industry. This impact is twofold: GenAI will increase the demand for labeled data and serve as a supplementary tool for automating or semi-automating the data labeling process.

Generative AI’s Impact on Data Labeling in 2024

As you’ve likely experienced, today’s generative AI can create a variety of content, spanning written, audio, and visual formats. McKinsey predicts that by the end of this decade, GenAI will match the average person’s proficiency in these tasks and even compete with the top 25% of people by 2040 — much sooner than experts previously estimated, possibly by up to 40 years.

In terms of data annotation, GenAI is wearing two hats in 2024. Beyond leveraging mass annotated data for training purposes, GenAI technology will actively contribute to the annotation process itself. This marks a shift towards a collaborative approach, in which human expertise and generative AI capabilities converge to streamline and enhance the data annotation workflow.

Here’s the summary of the generative AI in data labeling market research:

• Generative AI will cut down manual work for annotating extensive datasets.

  Models like GANs can create masks or bounding boxes around objects, streamlining tasks like image segmentation. OpenAI’s DALL-E can generate images from text, aiding data labeling by minimizing manual efforts.

• GenAI will automate dataset labeling through algorithms.

  Case in point, Snorkel Flow, launched by Snorkel AI, uses generative AI to automate data labeling. Custom labeling functions created by users speed up and improve the accuracy of large datasets’ annotation.

• Companies will employ generative AI for high-quality data annotation.

  GPT-3 and similar models already excel in NLP tasks like named entity recognition (NER), text classification, and language translation. For instance, Scale AI introduced an NLP pipeline in May 2021, using generative AI models such as GPT-3 and BERT for text data labeling with improved accuracy.

Top 6 Trends for Data Annotation Market in 2024: Industry Dynamics & Future Outlook

Let’s start with the main factors shaping this year’s trends for data annotation markets:

• Massive generation of data daily and, thus, increasing reliance on it.
• Rising popularity of facial recognition technology.
• Increasing demand for autonomous driving solutions.
• Current and emerging AI & ML trends shaping the course of the industry.
• Increasing demand for secure and high-quality annotations.
• The need for specialized annotation services tailored to specific domains.

The increasing demand for data labeling up to 2030 is primarily due to the growth of machine learning tools and algorithms in commercial applications and research. Besides, data annotation is soon to become imperative for national security and surveillance purposes, as it’s already successfully used in MilTech projects.

Not to mention the fact that AI is employed in the operations of nearly 40% of organizations globally. The technology may now be said to be one step closer to human intelligence by relying less on humans and more on itself, thanks to the latest trends of data annotation:

#1: Unstructured data is booming

Each day, over 4 billion people use the internet, generating about 3 quintillion bytes of data. Yet, most of the data we store is raw and unstructured. Such data is hard to manage, and so one of the trends this year is taking measures to handle unstructured data for enhanced intelligent capabilities of AI.

Forrester predicts that enterprises will witness a twofold increase in managed unstructured data by 2024, creating promising opportunities for AI. Despite having less than half of their data unstructured, companies embracing generative AI will witness a twofold increase in this proportion as they implement more conversational interactions for customers and employees.

In 2024, 80% of newly established data pipelines will be designed to handle the ingestion, processing, annotation, and storage of unstructured data.

#2: Large Language Models (LLMs) are on the rise

Natural Language Processing (NLP) is a valuable technology that allows human-machine communication through well-annotated text and audio data. Text data is used by almost 70% of businesses, driven by the rise of chatbots and other NLP innovations. Annotation of text data helps fine-tune the AI’s capacity to recognize patterns in text, voice, and semantic connection of data. Plus, the development of text mining applications depends largely on pre-annotated text.

Grand View Research projects a 40.4% CAGR for the NLP market, expecting it to reach $439.85 billion by 2030. In particular, LLMs contributed to the advancement of NLP last year by providing advanced solutions for processing and generating human language. Since GPT-3’s introduction in 2023, LLMs have seen substantial growth, ranking among the top 14% of emerging global technologies. Regarding audio data, there will be more AI voice assistants (8.4 billion) than people on Earth by 2024.

#3: Visual data does not lag behind

With a CAGR of nearly 17% between 2020 and 2030, both imageandvideo annotation will keep leading the data labeling industry. The expansion of the data annotation market in 2024 will be primarily driven by the image segment as a result of the increased use of computer vision that is estimated to reach a value of $48.6 billion. The sectors concerned are automotive, healthcare, manufacturing, energy and utilities, and media and entertainment.

For instance, by 2024, software-based facial recognition solutions will be integrated into approximately 1 billion devices globally. Another fact to note is that 2.7 million industrial robots are in operation, necessitating top-notch annotations for developing and testing CV models in robotic navigation systems.

#4: GenAI impacts the data labeling market growth

Among the prominent trends for data annotation market in 2024 will be the widespread adoption of GenAI for enhanced efficiency and accuracy in labeling datasets. As we’ve already mentioned, generative models like GANs are leveraged to autonomously generate masks or bounding boxes around objects in images, significantly reducing manual annotation efforts for tasks such as image segmentation.

Generative AI will change the way we work. Nearly 80% of workers expect that GenAI tools will affect around 20 hours or half of their workweek. However, a majority (63%) recognize the need for acquiring new skills or an entirely fresh skill set by the end of 2024 to fully leverage the benefits of this technology.

Moreover, generative AI will be increasingly employed to augment human-labeled datasets, where algorithms automatically label portions of the data while humans handle the rest. This trend will accelerate the annotation process, improving accuracy, and lowering the overall cost of dataset creation.

#5: Automation is changing the labeling workflow

Automation is shifting the industry dynamics, demanding annotators to move from basic manual labor roles to more productive and niche requests, like geospatial annotation services for example. Automated annotation is predicted to grow at 18% CAGR through 2030. The use of data annotation tools keeps rapidly expanding due to the developments in GenAI, research, IoT, and ML products. By 2028, the global data annotation tools market is estimated to increase at a CAGR of 27.1 %.

Regardless, manual data annotation will remain the most popular approach in the field, holding the greatest share of over 76% of the total market revenue. However, the cost of the entire procedure is much higher since manually labeled data may occasionally contain inaccuracies, and the time required to identify them may vary.

#6: More stringent data requirements for AI

AI needs data. And not any data, but high-quality, annotated data used for training advanced ML models. Even ChatGPT has been through intricate text data collection and annotation to serve as a valuable tool for around 180.5 million users. Besides, certain projects will demand more precise data, which means that data annotators will be more involved in industry-specific projects, like data annotation services for aviation.

Also, there’s the overall lack of confidence in ML models, which is due to limited resources and a lack of thorough quality assurance (QA). Data teams will be required to deal with large datasets. And so the main focus should be on edge cases and quality control of the labeling process.

Major Technological Trends to Watch in the Next Decade

Regardless of your AI mission, both data and technology are crucial. However, you must be aware of the potential effects of rising tech developments and be cognizant of their timing. Then, consider which technologies and trends will be most beneficial, while keeping in mind that not every one of them needs to be embraced immediately.

In 2024, we will see the data annotation industry facing major growth opportunities and fresh technological trends (based on Gartner’s research) shaping its current outlook in the global AI ecosystem:

• Next Gen GenAI

  The next generation of generative AI is set to revolutionize content creation. It will craft intricate narratives, compose music, and potentially collaborate on bestselling novels. A major leap forward is the emergence of multi-modal generative AI, seamlessly blending text, voice, melodies, and visuals to produce diverse content and immersive experiences in various languages. As we approach 2024, the distinction between human and AI creations becomes increasingly indistinguishable.

• AI Governance

  AI is advancing rapidly, and it needs proper governance now. In 2024, global leaders are planning to pay more attention to detailed AI policies, including countries like China, the EU, the U.S., and India. The goal of this trend is to boost new technology, attract investments from all over the world, and make sure the society is safe from any unintended AI problems. Tech experts are also discussing the possibility of countries working together on AI legislation rules and standards globally.

• Edge AI

  Edge AI is on the rise, enabling real-time data processing at the source for businesses to gain insights, identify patterns, and adhere to data privacy regulations. It also streamlines AI development, integration, and deployment. Gartner predicts that by 2025, over 55% of deep neural network data analysis will happen at the point of capture in edge systems. Organizations should focus on specific applications and requirements for a seamless transition to edge environments near IoT endpoints.

• Cloud Data Ecosystems

  Organizations would be able to tackle the most pressing issues and cases in their own industries with the help of industry clouds. More than half of modern organizations will adopt sector-specific cloud platforms by 2027 to speed up their business activities.

  Besides, the data ecosystem landscape is evolving to fully cloud-native solutions. Projections for 2024 suggest that half of new cloud system deployments will opt for unified cloud data ecosystems over manually integrated solutions. Gartner recommends companies assess their data systems' ability to handle dispersed data challenges and seamlessly connect with external data sources beyond their usual setup.

• Platform Engineering

  Pioneering companies have started to create operating platforms that are between users and the supporting services they depend on. It’s estimated that by 2026, 80% of software engineering firms will create platform teams to supply reusable services, components, and tools for application delivery internally.

• Data-Centric AI

  Both a mindset and a technical architecture, data centricity puts data as the most valuable resource to deploy and maintain an effective enterprise architecture. This means a more targeted focus on data over just models and code. AI-specific data management, synthetic data generation, and data labeling will tackle challenges like accessibility, volume, privacy, security, complexity, and scope. An upward trend involves using generative AI for synthetic data, reducing reliance on real-world data for more efficient ML model training. It’ anticipated that by 2024, about 60% of data for AI will be synthetic, simulating reality and future scenarios to reduce risks.

• Conscientious AI

  As we approach 2024, there's a growing focus on teaching AI ethics. To use AI responsibly, businesses should weigh factors like risk, trust, transparency, and accountability. Gartner warns that by 2025, excessive reliance on pre-trained AI models by 1% of vendors could pose societal issues associated withresponsible AI. Organizations are advised to adopt a risk-proportional approach and seek assurances from AI vendors to manage potential financial, legal, and reputational risks.

Each of these recent industry trends presents companies with both an opportunity and a risk. To observe a real impact on reaching various strategic goals with your AI initiative, you should build a robust technology roadmap. And don’t forget about the importance of a well-annotated dataset for your project. → Contact our team

Starting 2024 Off Right

As data annotation shoots up, companies specializing in data labeling services become the hottest projects and the main targets for businesses following the current AI boom. And the key to success is to keep up with current data annotation and labeling trends to find what works best for your AI project.

The data annotation market will expand tremendously this year, providing additional opportunities for AI to permeate the entire business sector and our personal lives. We’ve outlined the key trends in data annotation to keep an eye on in 2024 and the years ahead. And we hope now you can better set your business goals during this period.

Eager to conquer AI in 2024? Make sure you have the right data to get started →Contact our team

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Written by

Yuliia Kniazieva Editor-at-Large

One of the technical writers at Label Your Data, Yuliia has been gradually delving into the intricate aspects of AI. With her strong passion for the written word and technical expertise, Yuliia has developed a keen interest in the evolving field of data annotation and the power of machine learning in today's tech-savvy world. Check out her articles to learn more about the complex world of technology and find the solutions that work best for your AI project!