Business Intelligence has had an impactful history as traditional BI originally appeared in the 1960s as a system of sharing information across organizations. In the 1980s, it further developed alongside computer models for decision-making and turning data into insights before becoming a specific offering from BI teams with IT-reliant service solutions. In today’s vast data producing environment, modern BI solutions prioritize flexible self-service analysis, governed data on trusted platforms, empowered business users, and speed to insight.
Business intelligence software are rapidly developing as them becomes compulsive for many organizations. Currently, a number of leading organizations are leveraging GPU parallel processing technology to infuse AI into their BI applications, and this strategy will quickly define the next generation of business analytics. Adding AI into BI is the most impactful way to speed up data insight. Establishing an integrated AI+BI database, an insight engine means an organization can shift from an analytics position that looks back to the one that looks forward.
There are several use cases where businesses combine AI and BI for next-gen analytical insight engines that utilize both in-memory storage and GPU processing. For instance, retailers are transforming supply chain management as they can now feed and assess streaming data from suppliers and shippers against real-time inventory data from retail operations.
