"Behind every messy report is a broken data process waiting to be fixed." Here’s how I get to the root: Trace the Source: Follow the data back to where it originated. Check Data Pipelines: Look for broken ETL steps or sync delays. Review Logic Rules: Spot errors in formulas, joins, or filters. Validate Inputs: Ensure consistency in how data is entered or imported. Collaborate Across Teams: Get input from data owners for deeper insight. Document Findings: Track patterns to prevent future issues.

🔍Start with schema validation to detect structural mismatches in source data. 🧪Trace lineage by tracking data flow from source to report to find transformation issues. 🧠Apply the 5 Whys method to dig deep into the origin of inconsistency. ⚙️Compare source and destination tables using checksum or row counts. 🗂Use data profiling tools to identify nulls, duplicates, and anomalies. 💬Involve both data engineers and analysts for multi-angle investigation. 🛠Document findings and automate quality checks to prevent recurrence.

I’d say to start relying on self-generated data. Quality > Quantity and that goes for SEO/Websites/Artificial Intelligence, and life itself!

It's good to have backuup data always To avoid alot of problem like this, not only this one exactly. You can buy space on google drive.. etc

The most effective approach combines systematic investigation with collaboration between data and business teams to ensure both technical accuracy and business alignment. More specifically, to find the root cause of data inconsistencies in reports would check all of the following: Document all inconsistencies with specific examples Trace data lineage from source to output Check ETL processes and data transformations Verify data collection methods and timing Review business rule applications Examine system integrations and API connections Test for data type mismatches or formatting issues Inspect aggregation methods and calculations Analyze query logic and filtering criteria Implement data validation checks at critical points

First, I identify patterns and validate data sources. Then, I review data capture and transformation processes to detect issues. I use data lineage and quality tools to trace the root cause and correct it at the source to prevent recurrence