With unstructured data volumes set to double for businesses this year, making sense of data, especially visual data, becomes key to maximizing the potential of AI applications, says Lianne Dehaye, Senior Director, TDCX AI.
Following the unprecedented advances in AI in 2023, LLMs and similar AI-run deep learning platforms have gone from a ‘nice-to-have’ to ‘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. Image and video data are leading the pack here, with an Infosys report predicting their annotation will grow by a CAGR of nearly 17% by 2030. With unstructured data volumes set to double for businesses this year, making sense of data, especially visual data, becomes key to maximizing the potential of AI applications.
Visual data an increasingly important area for customer insights
Visual data, such as customer photos, videos and social media posts, offers a rich vein of insights into consumer behavior, preferences and sentiments.
By analyzing 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.
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, pave the way for more accurate and effective AI applications that can deliver tangible business value.
Companies are adopting various approaches to label 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
The use of AI in data labeling initiatives certainly accelerates go-to-market timelines for enterprise AI applications. However, it is important to note that there are benefits to taking a ‘humans-in-the-loop’ approach. Humans have a clear grasp on context, nuances, and subtleties, and can fill analytical gaps that AI cannot. A human-AI collaboration ensures that the data used for training AI models is not only vast but also accurate and relevant, leading to more effective and reliable AI systems.
In interpreting visual data, the need for context is 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 be negative in common parlance. Similarly, for a fashion company, analyzing images of fashion enthusiasts on social media can help predict future fashion trends and meet customer preferences quicker.
This combined approach not only enhances the accuracy of AI in visual analysis but also opens new possibilities for innovation and insight in fields such as healthcare, surveillance and environmental monitoring.
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 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.
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