GenAI takes the spotlight, but what about data modeling and governance?
The (Present) Future of GenAI and Data Analytics
The integration of GenAI is undoubtedly transforming the landscape of automated data analysis and SQL query optimization. In the near future, it may even extend to tasks such as constructing dashboards and calculating metrics upon request.
But in a context where business analysts are increasingly embracing self-serve analytics through their preferred BI tools, the inclusion of AI demands a careful approach from data practitioners.
The Challenge of AI-Driven Data Integrity
Questioning the reliability of data produced with the assistance of AI becomes relevant when considering the possibility of inconsistencies in the models it was trained on. If the models of AI contain discrepancies, it raises concerns about the trustworthiness of the generated data.
Complicating matters further is the evolving role of analysts, who are not merely consumers of data but also contributors, shaping new business logic continuously.
This evolving logic must be consistently coded into the data layer through a governed process to keep data models consistent. It is our best guarantee for the production of quality data through AI-based analytics.
Data Modeling and Governance at The Forefront
With AI becoming a part of analytics, data modeling and governance return to center stage. The space for innovation lies in developing data modeling practices that offer a more comprehensive approach to semantic alignment and capturing business logic effectively across different layers.
Where Do You Stand?
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Euno it! Euno is officially on the dbt Cloud integrations list. Check us out under Data Catalogs.
