The elasticity of the cloud supports the swift deployment of complex, resource-intensive AI-ML solutions, leading to deeper and real-time insights.
This is an exclusive article series conducted by Santosh Vaswani, Journalist & Editor at CIO News with Mr. Anurag Sanghai, Principal Solutions Architect at Intellicus Technologies and Mr. Kesava Reddy, Chief Revenue Officer at E2E Networks Ltd.
The convergence of artificial intelligence (AI) and cloud-based analytics is transforming how businesses derive value from their data, enabling rapid conversion of data into actionable outcomes. Industry veterans emphasize the profound impact this technological synergy has on modern business intelligence (BI).
Anurag Sanghai, Principal Solutions Architect at Intellicus Technologies, highlights the pivotal role AI-driven cloud analytics plays in today’s BI landscape. “The convergence of AI and cloud-based analytics is revolutionizing BI,” he notes. “It helps enterprises in getting quicker actionable insights, self-service, and real-time decision-making.” A significant advantage of this approach is the expedited time to value from data-driven insights. The cloud’s infrastructure-free environment facilitates rapid deployment, enhanced by pre-built AI models that streamline data preparation and complex analysis, significantly reducing development time.
Moreover, the elasticity of the cloud supports the swift deployment of complex, resource-intensive AI-ML solutions, leading to deeper and real-time insights. This capability allows organizations to make better-informed decisions as AI algorithms can analyze real-time data to identify trends, predict outcomes, and trigger alerts for proactive decision-making—a necessity in the face of growing data volumes. “The convergence truly democratizes analytics, fostering a data-driven culture,” Sanghai adds. GenAI assistants provide a user-friendly, natural language interface, empowering business users to explore intricate datasets independently. This democratization ensures that even non-technical users can gain valuable insights quickly and efficiently.
Echoing these sentiments, Kesava Reddy, Chief Revenue Officer at E2E Networks Ltd., emphasizes the transformative potential of integrating cloud analytics with large language models (LLMs). “The integration of cloud analytics and LLMs promises to revolutionize data analytics in the near future,” he asserts. The open-source nature of many cutting-edge generative AI models, such as LLMs like Llama3 and Mistral, alongside powerful vector database technologies, enables developers to harness both structured and unstructured data. This capability facilitates the development of RAG-powered analytics systems, distilling key insights from complex datasets.
Reddy further explains that future cloud analytics systems will allow users to explore data not just through traditional reports but also via natural language queries. This democratization of data analytics will make it more accessible to non-technical users, providing personalized insights tailored to individual needs. Generative AI models, capable of producing realistic synthetic data, will be extensively used to test and refine analytics systems across a wider range of use cases, even those not yet encountered in real-world scenarios.
Ensuring data accuracy during model training and providing reliable, up-to-date data sources is crucial for the effectiveness of any generative AI system. Reddy concludes, “The future of data analytics lies in Generative AI making analytics more accessible and transformative, empowering businesses to make more informed decisions and drive innovation.”
In summary, AI-driven cloud analytics is emerging as a future-proof technology delivering unparalleled value to organizations. By enabling faster, smarter decisions and achieving significant business outcomes, it is reshaping the landscape of business intelligence and driving a new era of data-driven success.
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