Companies Are Drowning in Visual Data. Here’s How to Make Sense of It

Following the unprecedented advances in artificial intelligence (AI) in 2023, large language models (LLMs) and similar AI-run deep learning platforms have gone from a “nice-to-have” to a “must-have” for businesses in 2024. Data remains at the center of this shift, as it is critical for these platforms to run and deliver desirable results. With the U.S. data annotation tools market projected to grow by 23 percent from 2024 to 2030—fueled by the rise of mobile computing, AI in digital shopping, and e-commerce—the demand for image and video data annotation is surging. Last year, this segment alone accounted for more than 30 percent of the market's revenue share.

Accurate data annotation is essential, as the massive amount of data generated is vital for training AI models, IoT devices, and other machine learning algorithms. To keep pace, U.S. businesses must prioritize structuring their data effectively, or risk producing subpar results.

Visual Data Is Increasingly Important for Customer Insights

Visual data, such as images and videos from social media posts, emerges as a gold mine of insights into consumer behavior, preferences, and sentiments. By analyzing and providing qualitative feedback on such data, businesses can uncover nuanced patterns and trends that traditional data analytics might miss. This deep understanding of customer insights can drive more targeted marketing strategies, personalized product recommendations, and improved customer experiences, ultimately leading to increased customer satisfaction and loyalty.

But challenges related to data quality and the availability of adequate training are among the top concerns for 72 percent of organizations aiming to scale AI models. Businesses must evaluate their data management approaches in order to ensure accurate insights are churned out.

As AI models converge in capabilities, having high-quality, unique, and updated datasets enables companies to delve more deeply into their customers’ choices and preferences and tailor strategies. This can help businesses create an edge which can empower them to stand out against competitors in an increasingly challenging business landscape.

Data Labeling and Annotation a Key Foundational Step

With the growing importance of visual data in gaining deep customer insights and driving business success, companies are increasingly investing in the labeling and annotation of their image and video data. This process involves assigning relevant tags, categories, and metadata to visual data, enabling AI models to accurately interpret and analyze the data. This is an important step to ensure that their AI models are trained on high-quality and relevant data; and, in turn, this paves the way for more accurate and effective AI applications that can deliver tangible business value.

Companies are adopting various approaches to labeling their visual data. Some are leveraging in-house teams of data annotators who are trained to accurately categorize and tag visual data. Others are partnering with third-party data annotation services that specialize in visual data labeling, providing them with access to a global pool of skilled annotators and advanced annotation tools.

Additionally, some companies are exploring the use of semi-automated and automated data labeling solutions that leverage AI algorithms to speed up the data labeling process. These solutions can significantly reduce the time and resources required for data labeling, allowing companies to scale their AI initiatives more efficiently.

The Value of Human Expertise

A recent study indicated that 60 percent of all American firms are looking to adopt labor-replacing automation over the next 12 months. In the case of data labeling, while labeled data is usually equated with ground truth, datasets can—and do—contain errors. The advantage is also lost when AI systems lack contextual understanding, and this is when the “humans-in-the-loop” approach can play an integral role in driving AI excellence. Humans have a clear grasp on context, nuances, and subtleties, and can fill analytical gaps that AI cannot.

Recommendations from the President’s Council of Advisors on Science and Technology echo this—current AI tools are still quite weak at demonstrating true creativity, analysis, or high-level strategic thinking. As AI assistance becomes more commonplace, and additional AI technologies go beyond the current state-of-the-art machine learning models (such as LLMs), scientific research should continue to be directed by human scientists.

In interpreting visual data, the need for context becomes even greater. AI algorithms can identify patterns and features within images or videos, but they often struggle to understand the broader context in which these visual elements exist. For instance, an emoji that accompanies a customer review of nachos at a Mexican restaurant that says, “The nachos here are the bomb,” may be inaccurately classified as negative. However, once the AI has been adequately trained by humans, the AI soon learns that the word “bomb” may not always have negative connotations. Similarly, for a fashion company, analyzing images of fashion enthusiasts on social media can help predict future fashion trends and meet customer preferences quicker.

Navigating the Visual Data Deluge with Strategic Data Labeling

With data volumes seeing significant expansion, and AI becoming ubiquitous, organizations that accurately make sense of their data are the ones most likely to see success with their AI strategies. The key to success lies in adopting a strategic approach to visual data labeling, leveraging both in-house teams and third-party data annotation services, as well as exploring semi-automated and automated data labeling solutions.

When tapping third-party data annotation services, it is also important to ensure that the partner’s approach is in line with the company’s goals. Some considerations include the provider’s ability to annotate the data in line with different cultural contexts, sublanguages, and influences. We have seen several such instances where the data is labeled correctly but lacks the refinements that make it a truly powerful source of insights for the business. This is also why TDCX recently collaborated with SUPA, a generative AI-powered data labeling company, to help companies around the world in this aspect.

The visual data revolution presents both challenges and opportunities for businesses. By strategically investing in data labeling and annotation, companies can navigate the visual data deluge and harness the power of AI to gain deep customer insights, improve decision making, and ultimately drive business success.

Lianne Dehaye is a senior director at TDCX AI with more than 15 years of experience in digital marketing, strategy, and operations within the technology industry. In her current role, she focuses on revenue generation and business expansion. She also lends valuable support to teams in implementation, ensuring that sold solutions are effectively brought to fruition.