What specific action demonstrates an analyst's commitment to data integrity when combining sales data from two different, independently managed databases?

The specific action demonstrating an analyst's commitment to data integrity when combining sales data from two different, independently managed databases is the systematic and iterative process of data validation and reconciliation of key attributes and aggregate metrics across both source databases and the resulting combined dataset, prior to its use. Data integrity refers to maintaining the accuracy, consistency, and reliability of data. When merging sales data from disparate systems, inherent differences in data collection, definitions, and storage formats can lead to inconsistencies. The analyst's commitment is shown by not merely concatenating the data, but by actively ensuring its correctness and coherence. This involves, first, data validation, which means thoroughly examining the quality, completeness, and consistency of the sales data within each individual source system before any combination occurs, identifying issues like missing values, incorrect formats, or logical errors. Second, and crucially, it involves data reconciliation, which is the process of comparing and resolving discrepancies between the two independent datasets and with the newly formed combined dataset. For example, the analyst would verify that the sum of sales transactions or total revenue for a specific period from each original database precisely matches the corresponding sum in the combined dataset, accounting for any known overlaps or exclusions. This also includes meticulous schema mapping verification, ensuring that sales fields with similar meanings but different names or formats in the two databases are correctly aligned and transformed. Furthermore, it involves the proactive identification and resolution of duplicate entries that might emerge when combining data from systems that could have independently recorded the same customer or sales event. Any inconsistencies or variances found during this reconciliation process are investigated, their root cause identified (e.g., differing business rules, data entry errors, or timing differences), and then systematically corrected or accounted for. This rigorous, multi-step checking and balancing of the data content ensures that the final, combined sales data is accurate, consistent, and reliable, thereby exemplifying the analyst's dedication to data integrity.