Industry leaders share their insights into the future of GenAI applications. Here is a look at some of the opportunities they find that organizations are investing in now.
RTInsights attended the Gartner Data & Analytics Summit 2024 to hear about the most recent research the firm had done, the insights and recommendations it had distilled from hundreds of inquiries with the companies across industries and technology maturity levels, and both adoption and innovation trends.
We also had an investigative agenda–find examples of real-time data serving to advance AI, especially GenAI use cases, and take the pulse of the rate of adoption, especially given the many discussions around the risk and cost involved. Were companies actually implementing GenAI solutions? And what were they using them for?
Interviewing industry-leading software providers about what they were seeing their customers do resulted in an interesting aggregation of use cases–interesting especially because these software providers viewed the AI and GenAI use cases through the lens of their products’ capabilities and the business problem they focus on solving. These different perspectives result in a composite view of the opportunities organizations see in AI, the ones they are investing in first, and the technology strategies they rely on.
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Unifying analytics and AI for a 360-view
Customer 360 and customer experience use cases still reign as the primary objective of enterprise data initiatives. However, the goal of having a comprehensive view of a customer, let alone a customer base, is still beyond our reach. The more the technology for this use case develops, the more data and analytics methods we uncover, AI and GenAI being the most recent advances that will help us fill in the gaps.
Nik Acheson, Dremio’s Field CTO, identifies supply-chain analytics as another significant area where companies are investing heavily to bring together disparate types of data, historical and real-time, structured and unstructured, to build a holistic view of a complex process that can predict events and recommend options based on current inventory or suppliers’ logistics. What underpins these use cases is query acceleration and unified analytics. The latter is defined as a comprehensive view of data offered by a semantic layer over a data lakehouse that gives users and applications access to clearly identifiable and well-governed data.
Regeneron, a pharmaceutical company, adopted Dremio’s unified analytics, which changed the way research and pre-manufacturing teams interacted with data. One immediate outcome was to save money and align on standardized data when the company realized they were purchasing the same third-party data sets multiple times and stopped the practice. Overall, having a consistent view of, and accelerated access to, their data has sped up their innovation cycles as they can bring products to market faster, continue to test them, and more quickly make improvements based on analyzing previous product introductions.
Read the full story, via RT Insights.
