The exponential growth of unstructured data presents both an opportunity and a
challenge for organizations seeking to leverage generative AI (GenAI). While this
data—which includes text, images, video, and audio—holds immense potential for
training sophisticated AI models, effectively harnessing it requires overcoming
significant hurdles. By 2025, global data creation is projected to grow to more
than 180 zettabytes.

And about 80-90% of this is unstructured data.2
GenAI models thrive on structured data with clearly defined features and labels.
However, the inherent complexity and heterogeneity of unstructured data
requires specialized techniques for efficient processing and analysis. Traditional
data management tools and ETL processes often prove inadequate for handling
the nuances of multi-modal data—such as images, audio, videos, and natural
language text—which can contain crucial contextual information for model training.
Furthermore, the absence of standardized schemas for unstructured data can
introduce inconsistencies and ambiguities that impede the development of robust
GenAI models.