Start by checking data sources for errors, inconsistencies, or missing values. Compare methodologies—are metrics, time frames, or filters misaligned? Visualize data trends for anomalies. Validate assumptions and rerun tests. If issues persist, seek peer review for fresh insights. Triangulate findings with external data to ensure accuracy.

I had similar issues in my past projects. The balance was not equal with the sum of transaction fields. Furthermore, it happened for some particular warehouses not for all entities. The reason was one of my junior engineers had a mistake in the database transaction. I analyzed all the logs and found the timestamp where the balance had diff, and carefully reviewed the code. Finally I found the mistake and was able to fix it.

We can examine the underlying assumptions behind each analysis to identify potential biases or misinterpretations. We could also assess external factors such as seasonality, anomalies, or unexpected events that may be influencing the results. Additionally, we can collaborate with domain experts to gain contextual insights that might explain the discrepancies.

One approach to employ is a backward data audit or reverse data tracing, whereby the whole data analysis process is reviewed in a backward manner to understand the cause and effect. Quite useful and saves time for instances where the discrepancies might have been added at later stage of the data analysis process.

1. Verify data sources for inconsistencies. 2. Check data quality (missing, duplicate, incorrect values). 3. Examine assumptions behind analyses. 4. Review methodologies and processing differences. 5. Trace data transformations for errors. 6. Reproduce results with controlled data. 7. Compare timeframes and external factors. 8. Check for bias or human error. 9. Consult stakeholders and domain experts. 10. Perform sensitivity analysis on key parameters.

"Data doesn’t lie, but it can get misunderstood." When your analysis throws up conflicting results, don’t panic — dig deeper. The story’s still there, you just need to clean the lens. Here’s how we tackle it: 1. Start with the source: Double-check where the data is coming from. Inconsistent inputs = unreliable outputs. 2. Look at the cleaning process: Small preprocessing mistakes can lead to big analysis gaps. 3. Audit your methods: Different techniques can lead to different conclusions — compare approaches and validate logic.

To uncover the root cause of conflicting data analysis results, I'd review data sources and collection methods, examine data processing and cleaning, analyze data models and assumptions, investigate statistical methods and parameters, visualize and explore data, and consult with stakeholders and domain experts. By systematically addressing these areas, I can identify and resolve the inconsistencies, ensuring accurate and reliable results.

When data analysis yields conflicting results, the root cause often lies in one of three areas: flawed data integrity, methodological inconsistencies, or unexamined assumptions. A rigorous diagnostic approach should systematically examine each layer—from source validation and preprocessing to algorithmic choices and interpretation frameworks. Implementing traceability throughout the analytical pipeline enables precise identification of divergence points, while sensitivity analysis helps quantify the impact of methodological variations. The most robust solutions emerge from combining technical audits with domain expertise to distinguish true anomalies from procedural artifacts.

When I get conflicting results in data analysis, I start by checking data consistency across different sources. Then, I review the data preprocessing steps to catch any errors in handling missing values or incorrect formats. If everything looks fine, I run a small sample with different analysis methods to compare outcomes. This helps me identify where the conflict is coming from and take corrective action.