Your modern data infrastructure isn’t delivering even after modernization? The limiting factor could be the processes and operations surrounding the systems. Learn how to improve your operating model to best leverage infrastructure and technology
investments.
Learn how leading healthcare, insurance and life sciences companies can unleash their power in the marketplace by using upgraded cloud backbones, improved processes, tighter value realization loops, best of breed providers and DataOps/MLops
to facilitate platform-based consumption of their data.
Despite growing investments and sustained C-suite interest in advanced analytics, BI and data infrastructure, businesses and their customers often are underwhelmed by the capabilities of their data systems. And the competitive pressure from nimbler competitors and new entrants is on the rise. We also can expect growing regulatory requirements in the data realm – from customer privacy, data sovereignty, compliance, cybersecurity, to fairness and inclusivity.
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Learn MoreNearly every analytics provider has GenAI capabilities that incorporate NLP either in preview or generally available. Enterprises are adopting GenAI as well. In fact, 85% of enterprises believe that investment in GenAI technology in the next 24 months is important or critical (from the ISG 2023 Future Workplace Study). And the recently completed ISG Buyer Behavior research on AI shows enterprises are experiencing positive outcomes from their AI investments.
The year 2022 left a marked impact on the global and U.S. analytics market with Generative AI (GenAI) and large language models (LLMs) getting unprecedented attention and playing a substantial role in driving growth. In 2023, enterprises and providers were making investments and taking initiatives to convert the GenAI hype into reality by generating the necessary momentum. This momentum, with the backdrop of enterprises increasingly relying on data-driven decision-making, further propelled the growth of the analytics market in the U.S. Ongoing economic uncertainty combined with reduced consumer activity has pushed enterprises to seek new ways of generating revenue, based on strong data-led business decisions.
Use data to assess your supplier delivery against regulations and monitor customer-specific requirements. Complexity and regulatory scrutiny of third-party relationships are on the rise. Get a comprehensive and data-driven way to control risk throughout the life of the relationship.
Advanced technologies address some challenges and introduce others. It’s not science fiction to imagine that the technology is based on machine learning, which is based on identifying and repeating certain patterns. Does this mean the software could, therefore, "learn" that most candidates hired come from a specific school or town? Does this encourage bias – or even profiling?
