Big Data

The advent of generative AI technologies marks a significant milestone in business intelligence and operations. However, the efficacy of these AI systems heavily hinges on the quality of data they are fed. This article delves into why data quality is paramount for generative AI's success in

The vast landscape of artificial intelligence has seen rapid advancements, particularly with the rise of generative AI technologies. Among these technologies, Retrieval Augmented Generation (RAG) systems stand out for their ability to leverage external knowledge to better inform AI outputs.

Metadata, often dubbed "data about data," plays a crucial role in today’s data-driven world by providing context and essential details about the data without revealing its content. Traditional metadata, however, can be passive and prone to human error, making it less reliable in c

In the ever-evolving landscape of business intelligence, data analytics has consistently pushed the boundaries of what companies can achieve. Over the past decade, we have witnessed transformative shifts in how data is collected, processed, and utilized. As we look ahead, the interplay between

Generative AI is revolutionizing the field of business intelligence (BI), transforming how companies analyze data, make informed decisions, and strategize for the future. As this technology continues to advance, it becomes integral to modern data analytics, revolutionizing traditional processes and

Engine Data Science has recently unveiled a strategic partnership with Databricks as part of the Databricks Built-on Partner Program. This collaboration is a milestone intended to drive innovation and efficiency within the Retail and Consumer Packaged Goods (CPG) industry. By leveraging advanced