Conflicting sales data in a data warehouse demands a thorough, technical approach to reconciliation. Start by validating the data ingestion pipelines, ensuring all source systems are consistently capturing and transferring data without loss. Implement automated data quality checks at key points, such as validating aggregates or applying rule-based data validations. Analyze data lineage to trace discrepancies back to the source, identifying misaligned joins or duplicate entries in the ETL processes. Use incremental loads to correct specific discrepancies, or perform a full reload for larger issues. Regular auditing and monitoring can preempt future conflicts, ensuring the data remains accurate and reliable.

Conflict in data occurs due to many reasons. Common reasons can be source system may not be capturing the data correctly, some days the data did not flow. Error in data model due to duplicate either in source itself or the join did not work correctly. Some of the ways to handle is - Identify KPIs for data validation and then setup an alert mechanism to shoot an email to concerned team if the numbers are way higher or lower than benchmark numbers. - if the source data is correct, you can then identify and fix the issue in your data model and then full reload data since inception again or identify dates where data did not flow and reload only those as incremental data. - if the source system itself has issues, inform concerned team to fix

To reconcile conflicting sales data, first identify source systems and review data consistency across sources. Check ETL processes for errors, manage duplicates, standardize timestamps, and generate reconciliation reports. Finally, conduct data quality audits regularly to ensure ongoing accuracy.

To reconcile conflicting sales data in the data warehouse, I would first verify data consistency across the source systems and thoroughly examine the ETL processes for any anomalies. Implementing a robust auditing and reconciliation framework is essential to efficiently trace and resolve such discrepancies. From experience, these inconsistencies often stem from misaligned joins or unintended data filtering.

Identify the sources of the discrepancies, such as different data formats, missing records, or timing issues in data updates. Conduct a thorough audit to trace the inconsistencies back to their origins. Standardize the data integration process by applying consistent rules for data entry, transformation, and validation across all sources. Implement data cleansing techniques to remove duplicates and correct errors. Finally, work with stakeholders to agree on a single version of the truth by aligning metrics and definitions, ensuring all data is accurate and consistent moving forward.