Krishna Roy is a senior research analyst in the 451 Research technology research group within S&P Global Market Intelligence. She leads the data science and analytics coverage areas for the Data, AI & Analytics channel. Prior to this, Krishna was a research analyst in the Data, AI & Analytics channel covering business intelligence and data management, including data integration and data warehousing. Krishna arrived at S&P Global Market Intelligence through its 2019 acquisition of 451 Research, where she was a senior research analyst. During her 20-year tenure with 451 Research, she was responsible for many coverage areas, including business applications and business intelligence. Prior to joining 451 Research, Krishna held several journalist roles in London, New York and San Francisco. Krishna’s recent areas of concentration include data-driven decision-making, AI governance and compliance, and generative AI’s role in data science and analytics. Krishna holds a Bachelor of Arts, First Class Honors Degree, in English literature and American studies from Brunel University, London.
The provision of data and analysis within business applications has existed since the term "embedded analytics" was coined almost 20 years ago, before artificial intelligence was as prevalent as it is today. How is generative AI changing this approach, and what are the key challenges to effective embedded analysis?
The company is entering 2025 with a revamped multi-tenant cloud service that has business intelligence and artificial intelligence modules, as well as a data platform module optimized for analytical use cases, called an Analytics Lake, as it eyes profitable growth by courting the more sophisticated end of the embedded analytics spectrum.
The vendor now positions its offering as a no-code AI analytics platform for its existing target market of business individuals to ask questions of data they already have access to. DataChat came out of the gate with a heavy emphasis on generative AI. How is its new messaging reflected in product improvements and go-to-market strategy?
Generative AI technology is moving fast, but are we ready? IT professionals discuss how their organizations are handling GenAI. Take five minutes to read five Voice of the Customer qualitative interview comments and what they mean.
This report highlights the key topics we expect to be prominent in 2025 in the data, AI and analytics space. While generative AI remains a predominant theme, for the technology to succeed, preexisting issues must be resolved around data management, data operationalization, machine learning operationalization and other infrastructure-related tasks.
As generative AI moves from experimentation to deployment, the discipline of large language model operationalization (LLMOps) becomes critical to project success because LLMs are the first type of GenAI to be adopted by enterprises. However, LLMOps is still nascent; this report explores what it is, why it matters, and adoption challenges it faces.
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