Understanding how to redefine our perception of data is crucial. Thus far, we’ve only scratched the surface of the potential of data analytics, primarily concentrating on the largest segment of enterprise data. Our comprehension of data typically falls into two categories: structured and unstructured. Predominantly, leading analytics tools have been tailored and optimized for structured data, residing in standardized formats within traditional or cloud warehouses, such as SQL databases. However, unstructured data, encompassing documents, images, videos, and complex architectural, engineering, and manufacturing models, poses challenges when attempting to fit them neatly into tables.

The pivotal question lies in deciphering how to extract insights and intelligence from vast volumes of data and translate them into tangible value for your business. This endeavor necessitates four fundamental steps:

• Grasp: Seek a solution that aids in comprehending the nature of the data at your disposal.
• Organize: Employ an automated tool or service capable of tagging the data for indexing and organizing based on its type, content, or format.
• Liberate: If a portion of the newly organized and labeled data is confined to traditional storage infrastructure or another medium restricting accessibility, migrate this dataset to the cloud.
• Leverage: Derive value from this freshly tagged, accessible, and discoverable data. Leading cloud platforms offer machine learning (ML), artificial intelligence (AI), and deep learning (DL) services geared towards image recognition, pattern matching, content search, compliance discovery, and a plethora of other functionalities.

We stand at the threshold of a burgeoning era of analytics, where the possibilities are limitless now that data emerges as its own entity. While the era of big data may span over a decade, this novel epoch of insights and intelligence delivered by data could potentially prove to be even more transformative.