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Watch On-Demand: Delivering on the promise of AI: Increasing Accuracy with a Semantic Layer

Learn about what happens when there is a semantic layer vs. none when using LLMs for a chatbot

  • Cube

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In a recent webinar, David Jayatillake, Cube's VP of AI, presented ideas on improving accuracy in AI using semantic layers. Here are the top five takeaways for data engineers from the session:

1. Embracing a Universal Semantic Layer David spoke about the concept of a universal semantic layer and its significant impact on data modeling. The semantic layer aids in bypassing the model chaos and duplicate efforts often experienced in data modeling. It emphasizes a single source of truth and the capability to update the data model across all data experiences, thereby streamlining the process for engineers.

2. The Four Pillars of a Semantic Layer The semantic layer is built on four key pillars – data modeling, access control, caching, and APIs. Understanding these pillars can help data engineers effectively make use of semantic layers to enhance their data operations.

3. Role of AI in Cube The AI features in Cube augment the experience for data engineers, app developers, and analysts. Tools such as chatbots and the navigation interface enable users to delve into their data assets and dependencies. Moreover, the AI can even generate syntax for SQL statements, access control functions, and pre-aggregations, making Cube a powerhouse tool for enhancing productivity.

4. Importance of Text to SQL Jumping further into AI applications in data analytics, David explored the concept of Text to SQL. This ingenious use of generative AI technologies creates SQL queries from supplied database schema information. This function, considerably different from a copilot use case, allows non-technical users to make data-based decisions – a technology revolutionizing the world of data analytics.

5. Comparing Copilot and Text to SL Chatbot The webinar analyzed the pros and cons of generative AI tools such as Copilot for data analytics. David emphasized the significance of context and knowledge graphs for generating consistent results and introduced the concept of text to semantic layer queries. By comparing Copilot (text to SQL) and a Text to Semantic Layer bot, it is clear that the semantic layer provided more reliable and controlled data analysis for complex queries, proving the role of a semantic layer in data analytics.

From the four pillars of a semantic layer to the application of AI features within Cube, David Jayatillake's insights on AI tools and their capacity to increase accuracy in data modeling offers data engineers a clearer picture of how they can integrate these strategies into their work to streamline processes, utilize text to SQL capabilities, and ensure more effective and reliable data analysis. Overall, as the data environment continues to evolve, fundamental to this evolution is understanding how tools like a semantic layer can play a crucial role in delivering on the promise of AI.