🗣Communicate openly to understand the client’s data sources and methodology. 🔍Audit both datasets to identify inconsistencies, missing values, or calculation differences. 📊Compare assumptions, definitions, and timeframes to find misalignments. 🛠Use version control and data lineage tracking to trace changes. 🤝Collaborate with the client to establish a single source of truth. 🚀Implement automated data validation checks for future consistency. 🔄Document resolution steps to prevent recurring discrepancies.

When client data doesn’t align with your team’s analysis, it’s crucial to approach the situation with clarity and collaboration. Here’s how I handle it: ✅ Verify the Data 🔄 – Cross-check both sources for accuracy and ensure no miscalculations or outdated data. 🤝 Communicate Transparently 🗣 – Engage with the client and internal teams to understand assumptions and methodologies. 📊 Align on Metrics 📌 – Standardize KPIs to avoid inconsistencies in interpretation. 🔧 Adjust & Improve ⚙ – If needed, refine internal processes to prevent future mismatches. 💡 Final Resolution 📬 – Present findings with a solution-oriented approach to maintain trust and efficiency.

To handle a client data discrepancy:- Investigate the source of both data sets. Clarify with the client to understand their data. Reconcile the data to identify specific discrepancies. Adjust analysis if the client’s data is more accurate. Document assumptions and methods used. Communicate findings and explain how the discrepancy was resolved. This ensures alignment and accurate results.