A retail company is experiencing discrepancies in its quarterly profitability reports due to inconsistent data integration from multiple sales channels. How would you design an ETL process to ensure accurate, timely, and consistent data flow for reliable business metric calculations and market analysis?

Apply the MECE (Mutually Exclusive, Collectively Exhaustive) framework to structure the ETL process. First, define data sources (sales channels) and extract data using APIs or connectors. Next, standardize formats (dates, currencies) and resolve inconsistencies via transformation rules. Finally, load data into a centralized warehouse with validation checks. Ensure error logging and reconciliation mechanisms to address discrepancies.

To resolve discrepancies in profitability reports, I would design an ETL process using the MECE framework. First, extract data from all sales channels (e.g., e-commerce platforms, POS systems) via APIs or file imports, ensuring all data sources are covered without overlap. During transformation, standardize date formats, currency codes, and product IDs, while applying business rules (e.g., discount calculations, tax adjustments). Use data quality checks to flag missing or inconsistent values. Load the cleaned data into a centralized data warehouse, ensuring schema alignment. Implement automated reconciliation workflows to compare aggregated channel data against source systems. For example, calculate total quarterly revenue by summing all channel-specific revenue fields after transformation. This ensures consistency, reduces manual intervention, and enables accurate profitability analysis.