Tech Talks Daily21 Dec 2023

2616: Demystifying AI in Data Analytics With Qlik

In today's episode of Tech Talks Daily, we delve into AI's intricacies in data analytics and business intelligence with James Fisher, Chief Strategy Officer at Qlik. Qlik, a leader in the field, has recently unveiled its ambitious AI strategy, marking a significant commitment to advancing AI applications in data handling. This move is especially noteworthy following Qlik's acquisition of Talend, strengthening its capabilities in managing data quality, governance, and lineage in the cloud.

Fisher offers a comprehensive insight into why data stands at the core of any AI development. He emphasizes the importance of generating trustworthy and actionable data outputs for enhanced decision-making processes. This topic aligns seamlessly with Qlik's long-standing integration of AI into its data analytics products and its recent focus on GenAI, which is crucial for understanding and improving data quality, especially for large language models (LLMs).

We'll explore how Qlik, in collaboration with partners like Snowflake and Databricks, is pioneering the delivery of LLMs in the cloud. Fisher will share success stories from early adopters such as Harman and ChatGPT, illustrating these technologies' practical applications and benefits in business environments.

Throughout the conversation, Fisher will shed light on the role of AI and GenAI in transforming data analytics and business intelligence. He'll discuss the challenges and opportunities in creating a data infrastructure supporting enterprise-scale generative AI. This includes a deep dive into the necessity of data quality and governance for the success of generative AI and how Qlik's holistic approach is shaping the future of AI in the workplace.

Listeners will gain insights into how AI democratizes data beyond expert circles, lowers barriers to insight generation, and evolves workplace roles. We'll also explore Qlik's cloud partnerships, emphasizing how hybrid cloud access and scalability are vital for training domain-specific models.

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