With over 5 billion users connected to the internet, a massive amount of unstructured data is flooding organizational systems, giving rise to the big data phenomenon. Modern enterprise data consists of around 80 to 90% unstructured datasets, with the volume growing three times faster than structured data. Unstructured data encompasses information that does not adhere to a predefined data model or organizational structure, including text documents, audio clips, images, and videos. It holds immense value, offering rich insights across various domains, from social media sentiment analysis to medical imaging. To unlock this potential, specialized database systems and advanced data management architectures are needed. Processing unstructured data often involves converting it into a format that machines can understand, such as transforming text into vector embeddings for computational analysis. Understanding and managing unstructured data is crucial for utilizing its depth of information, driving insights, and informing decision-making. With an average of 400 data sources, organizations must have efficient processing pipelines to quickly extract valuable insights from their data assets. Effective management of unstructured data can allow organizations to analyze the data objects to reveal valuable insights for decision-making, while challenges such as scalability issues, data mobility concerns, complex processing requirements, and redundancy need to be addressed. Observing best practices, such as defining requirements and use cases, establishing a robust data governance framework, creating metadata management systems, implementing informational retrieval systems, and using data management tools, can help enterprises leverage their full potential efficiently.