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If you were AI, how would you reach your full potential? Check out this episode of Six Five On the Road at AWS re:Invent, where host Keith Townsend is joined by Qlik's Brendan Grady, General Manager, Analytics Business Unit, and Sam Pierson, SVP of Data Business Unit R&D Organization. Learn about the pivotal role of data quality and open architectures in realizing the full potential of AI technologies within organizations.
Deep dive into this ⤵️
The essentiality of a robust data foundation as a precursor to AI adoption and the potential pitfalls in the absence of a solid data groundwork.
Identifying the hallmarks of a dataset that is genuinely prepared for AI utilization.
The significance of open architectures for AI implementations, including the risks of siloed systems in the evolving landscape of AI technology.
Insight into how Qlik is assisting its clients to establish strong data foundations and spearhead successful AI projects, complemented by illustrative examples.
Reflections on instances where AI initiatives have faltered and the strategies that could have steered these projects back on course.
By Patrick Moorhead and Daniel Newman3.9
1111 ratings
If you were AI, how would you reach your full potential? Check out this episode of Six Five On the Road at AWS re:Invent, where host Keith Townsend is joined by Qlik's Brendan Grady, General Manager, Analytics Business Unit, and Sam Pierson, SVP of Data Business Unit R&D Organization. Learn about the pivotal role of data quality and open architectures in realizing the full potential of AI technologies within organizations.
Deep dive into this ⤵️
The essentiality of a robust data foundation as a precursor to AI adoption and the potential pitfalls in the absence of a solid data groundwork.
Identifying the hallmarks of a dataset that is genuinely prepared for AI utilization.
The significance of open architectures for AI implementations, including the risks of siloed systems in the evolving landscape of AI technology.
Insight into how Qlik is assisting its clients to establish strong data foundations and spearhead successful AI projects, complemented by illustrative examples.
Reflections on instances where AI initiatives have faltered and the strategies that could have steered these projects back on course.

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