Juan Sequeda

Data and AI engineers are flooded with technology, tools, blogs, and quick fixes to get their systems production ready. But to build systems that are truly AI-ready, we must return to the fundamentals: what does the data actually mean.

Learn how to move just a few floors up the "data elevator" from the engine room of technical pipelines toward the strategic layer where business questions live. Through a structured "pay-as-you-go" methodology, you'll learn how to capture, implement, and validate the knowledge that matters most.

This talk unpacks what "semantics", "semantic layers" and "knowledge graphs" really mean, why they matter now more than ever, and how data engineers can start applying them today. We'll walk through concrete, tactical ways to apply them in your daily work.

Learn how to move just a few floors up the "data elevator" from the engine room of technical pipelines toward the strategic layer where business questions live. Through a structured "pay-as-you-go" methodology, you'll learn how to capture, implement, and validate the knowledge that matters most.

This talk unpacks what "semantics", "semantic layers" and "knowledge graphs" really mean, why they matter now more than ever, and how data engineers can start applying them today. We'll walk through concrete, tactical ways to apply them in your daily work.

What do enterprises lose by not investing in semantics and knowledge? The ability to reuse data effectively due to the lack of context and understanding of what the data means.

In this talk, we will make the case that the success of enterprise AI depends on an investment in semantics and knowledge, not just data. Our LLM Accuracy benchmark research provided evidence that by layering semantic layers/knowledge graphs on enterprise SQL databases increases the accuracy of LLMs at least 4X for question answering.

A data catalog is a tool used to organize, manage, and discover data assets within an organization. The modern data stack boosted the popularity of data catalogs to the point that they are now an essential part of the stack in addition to ETL tools, Data Lakes/House/Cloud and Analytics/ML tools.

A popular application of LLMs for the enterprise is question answering systems, aka chat with the data. Being able to chat with data residing in enterprise SQL databases provides tremendous potential for transforming the way data-driven decision making is executed.

If you are being asked to provide ROI and you are not able to provide it, you should be concerned. The irony is that if you are a data person, but don’t have the data to measure your own impact, how can you conceivably be doing that for other parts of the organization?

Knowledge Graphs can be considered to be fulfilling an early vision in Computer Science of creating intelligent systems that integrate knowledge and data at large scale.

Knowledge Graphs are fulfilling the vision of creating intelligent systems that integrate knowledge and data at large scale. The technology has matured: graph databases, information extraction techniques, ontology languages and tools.

Every large company has accumulated tens, hundreds or even thousands of data sources, these days mainly relational databases. Business users do not understand these complex data sources.