FEATURE : ANALYTICS

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 .

As AI models converge in capabilities , having highquality , 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

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 .

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

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