The semantic layer acts as a crucial link for integrating AI with schema fields, ontologies, and metadata layers. A well-established semantic layer is important for accurately presenting and organizing information, which results in smooth collaboration with AI systems.
Amazon relies on AWS infrastructure and the integration of internal tools - Redshift as their data warehouse with QuickSight & OpenSearch for data reporting and monitoring.
Embracing tool-agnostic practices is pivotal for business success. This approach emphasizes a solid grasp of domain expertise and the adaptability to learn new tools. Businesses need to support employees in acquiring the tools they need, this will foster a culture that values fundamental skills over tool-specific expertise.
Companies need to have human intervention in ensuring AI accurately understands business processes, preventing potential pitfalls associated with over-reliance on automated systems.
“I do see a lot of companies, a lot of people who think we can have AI just build these taxonomies.But is it going to represent your business correctly? How can you trust that? You can train on data, but if your data is not clean, then it's gonna be very confusing for AI”
Jessica Talisman
“The fundamental practice of being an architect, an engineer, an analyst is the capacity to learn the tool.If you're at a point where you're hiring an expert simply for the tool, I hope that person also has the skill set to understand the domain as well. I do believe in tool agnostic practice”
Jessica Talisman
“I appreciate these really focused products where you aren't necessarily on a proprietary cloud because of the tools. But you choose the tools because they are more performant and get the job done. I do think that there's convenience. And there's forward thinking planning where you are choosing the right tools to construct your stack”
Jessica Talisman
Discover how data management is evolving from the Modern Data Stack (MDS) to Intelligent Data Platforms. Our guest, Sanjeev Mohan, sheds light on the reasons behind this shift and its impact on businesses. Tune in to learn about the challenges with MDS, the rise of Intelligent Data Platforms, and what it means for organizations.
In this episode, Sadie, Chief AI Officer at SSL Innovations, talks about how AI has evolved. She covers the move from discriminative models to embracing Gen AI models, the need for a robust data strategy before adopting AI projects, and opportunities for career growth in the AI field. Join us for valuable insights into the changing AI landscape and its strategic aspects.
Join Jessica Talisman, as she sheds light on how Amazon leverages data, how information architecture shapes the company's data landscape, and the pivotal role humans play in AI applications.
Ep 03
Ep 02
Ep 01
Reverse ETL was once an obscure term, now stands as a crucial element in the Modern Data Stack. What sparked this transformation? Join Tarush Aggarwal and Boris Jabes, CEO of Census, as they unravel the evolution of Reverse ETL, share predictions for the data space, and shed light on Census's role in driving business growth.
Join host Tarush Aggarwal with Lightdash Founder Hamzah Chaudhary and Collectors' Director of Analytics, Rohan Thakur. They discuss Lightdash's unique position in BI, catering to data engineers for seamless analytics adoption. The episode delves into Lightdash's disruptive pricing model and explores the shift towards Conversational BI. Insights on AI in data engineering teams provide a snapshot of current advancements. Tune in for a straightforward conversation on Lightdash's impact, practical advantages, and its role in shaping the future of Business Intelligence.
