Navigating Data Interpretation Challenges with Semantic Layers and AI

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The chapter explores the complexities of data interpretation and trust in data across organizational departments, emphasizing the role of semantic layers in providing context and bridging understanding gaps. It discusses the importance of transparent data processing and governance to enhance trust among data consumers and the potential use of AI in automating metric definitions. The conversation also covers the application of AI in data analysis, focusing on building language models for customer data and the need for semantic models to improve AI performance.

Managing data and access to data is one of the biggest challenges that a company can face. It’s common for data to be siloed into independent sources that are difficult to access in a unified and integrated way.

One approach to solving this problem is to build a layer on top of the heterogenous data sources. This layer can serve as an interface for the data and provide governance and access control.

Cube is a semantic layer between the data source and data applications. Artyom Keydunov is the founder of Cube and he joins the show to talk about the approach Cube is taking.

This episode is hosted by Lee Atchison. Lee Atchison is a software architect, author, and thought leader on cloud computing and application modernization. His best-selling book, Architecting for Scale (O’Reilly Media), is an essential resource for technical teams looking to maintain high availability and manage risk in their cloud environments.

Lee is the host of his podcast, Modern Digital Business, an engaging and informative podcast produced for people looking to build and grow their digital business with the help of modern applications and processes developed for today’s fast-moving business environment. Listen at mdb.fm. Follow Lee at softwarearchitectureinsights.com, and see all his content at leeatchison.com.

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