To analyze customer churn using a large dataset in a relational database, I would start by ensuring that the dataset is well-structured and indexed properly to improve query performance. I would use SQL to write efficient queries, focusing on selecting only the necessary columns and using WHERE clauses to filter data effectively. To optimize performance, I would leverage indexing on columns that are frequently used in JOINs and WHERE clauses. Additionally, I would consider using Common Table Expressions (CTEs) to break down complex queries into more manageable parts, which can also improve readability and maintainability. I would avoid using subqueries where possible, as they can be less efficient, and instead use JOINs or CTEs. To ensure accuracy, I would perform data validation checks and use aggregate functions to summarize data accurately. Finally, I would integrate the results with Power BI to create visualizations that help identify trends and patterns in customer churn, providing actionable insights for decision-making.