AI Powered Analytics for SAP Data with Qlik Cloud and Amazon Bedrock | S02E10 Lets talk about Data Show
In this show, John Park and Sathisan Vannadil will discuss how Qlik and Amazon Bedrock enable customers to leverage large language models (LLMs) and generative AI capabilities and enhance their ability to generate insights and build AI applications.
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In this episode of the "Let's Talk About Data" we focused on using AI-powered analytics for SAP data with Qlik Cloud and Amazon Bedrock. Host Prasad Matkar is joined by John Park from Qlik and Satish from AWS who provide an overview of how AI can enhance business intelligence and analytics for complex SAP enterprise data.

Amazon Bedrock provides access to large foundation AI models that can understand natural language and generate insights. Qlik Cloud offers an integrated platform for data integration, reporting, and analytics now augmented with AI capabilities like autoML. Together, they enable more robust analytics beyond just descriptive for SAP data. John demonstrates prompting an AI model with a sample business question on SAP data and getting a contextual response. He highlights best practices like high-quality data and metadata, testing for hallucinations, and building capabilities incrementally. Satish elaborates how retrieval augmented generation (RAG) models can continuously integrate external knowledge to provide updated, relevant insights from SAP data. The guests emphasize how AI-powered analytics helps organizations extract more value from their SAP data.

Key Highlights:

AI-powered analytics provides predictive insights and recommended actions, beyond just descriptive analytics.

SAP data is highly complex, AI can help analyze it more easily.

Amazon Bedrock provides access to large foundation AI models.

Qlik Cloud offers an integrated platform from data integration to reporting, now with AI like autoML.

Demo shows prompting an AI model with a sample business question on SAP data.

AI assists with natural language interfaces, automated data insights, data classification.

Best practices include quality data/metadata, testing for hallucinations, incremental build-up of capabilities.

RAG models integrate external knowledge to generate updated, relevant insights from SAP data.

AI-powered analytics helps organizations extract more value from their SAP data.

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Prasad Matkar - Database Specialist Solutions Architect @ AWS

John Park - Solution Architect @ Qlik

Sathisan Vannadil - Sr. Partner Solutions Architect @ AWS