Executive Summary
A strong data stack is the foundation for successful AI and analytics. Here's how to integrate, manage, and use data at scale.
Key Components
- Data Integration: Use ETL/ELT pipelines to bring together data from different sources.
- Data Quality: Set up validation, cleaning, and enrichment processes.
- Analytics Enablement: Give business users self-service tools and governed access.
How to Build It
- Assess Data Needs: Figure out what data you need and what analytics you want to support.
- Modernize Infrastructure: Move to cloud-native data warehouses and lakehouses.
- Automate Pipelines: Use orchestration tools for reliable, repeatable data flows.
- Monitor and Govern: Set up data stewardship and keep an eye on quality.
Conclusion
A modern data stack helps organizations get the most from AI and analytics. Invest in integration, quality, and governance for data-driven success.