How would you handle conflicting data sets from different sources during your analysis process?

When handling conflicting datasets from different sources, first investigate the origin, collection methods, and reliability of each source. Identify and understand the reasons behind the discrepancies. Give more weight to data from trusted, consistent, and authoritative sources. Where possible, reconcile differences through cross-verification, and document any assumptions or adjustments made. Always communicate any uncertainties or limitations clearly in your final analysis.

When handling conflicting data, it's crucial to first understand the root cause—whether it's source reliability or data inconsistencies. Cleaning the data, analyzing patterns, and comparing key metrics helps reconcile discrepancies. The key is finding clarity amidst the chaos and. documenting the process for transparency and future reference.

First, I will define them into different categories based on source, format, and reliability. Then I check for consistency, overlaps, and gaps. If there are conflicts, I trace back to the origin, validate with the most trusted source, and document any assumptions. Clear categorization helps make sense of the data and guides accurate decisions.

Handling conflicting data sets requires a careful and methodical approach. It involves understanding the sources, investigating the discrepancies, developing logical reconciliation strategies, documenting everything thoroughly, and assessing the potential impact on your analysis. The goal is to arrive at the most reliable and well-supported conclusions possible, given the data challenges.

🧹 Data Cleaning: Next, I run integrity checks — deduplicating, normalizing formats, removing outliers. AI-assisted data profiling tools can help identify anomalies and missing values faster than manual inspection.

I apply consistent cleaning steps across all datasets, such as standardizing formats, resolving missing values, and removing duplicates or outliers. This ensures that any conflicts aren’t simply due to formatting issues or data quality problems.

How I start data cleansing is by focusing on the basics: • Identifying common errors like missing values or inconsistent naming • Standardizing formats: dates, units, naming conventions to ensure compatibility across sources Then, I move toward business-specific validation rules. For example, in supply planning, all internally manufactured products should have production data. If not, that’s a red flag. I apply these cleaning steps uniformly across all datasets. This not only improves consistency but also helps uncover deeper, more complex errors that might otherwise go unnoticed. Because at the end of the day, clean data isn’t just about accuracy, it’s about enabling fair comparisons and building trust in the insights we deliver.

Totally agree! In my work, I pull data from a data warehouse into Power BI, and keeping things consistent can be tricky — especially with different tables and historical data. I’ve found that using Power Query and keeping the cleaning steps well-documented really helps make things smoother and more reliable.

3. Compare Metrics: When metrics conflict (e.g., different revenue figures), I line up each source’s methodology and timeframe. Often, differences stem from definitions (e.g., gross vs. net revenue). A side-by-side comparison helps identify logical mismatches.

I take time to understand how key metrics are defined and calculated across sources. Even something as simple as “user activity” can vary significantly depending on the context, so aligning definitions is critical before drawing any conclusions

This step often reveals why datasets seem to conflict in the first place. Different sources might use the same terms but define or calculate them differently. For example, “inventory” might mean on-hand stock in one system and include in-transit goods in another. So I take time to: • Align definitions and formulas across sources • Transform metrics where needed to ensure consistency • Validate that we’re truly comparing apples to apples This alignment helps surface real discrepancies, rather than perceived ones caused by inconsistent logic.

Analyze Patterns: With cleaned and contextualized data, I look for underlying patterns. Are discrepancies consistent across time or categories? Do they follow predictable variances? Pattern recognition reveals systematic gaps.

As a data engineer, when I encounter conflicting datasets from different sources, I first assess the quality and reliability of each source to understand which one is more trustworthy. I check for issues like missing values, outdated records, or formatting inconsistencies. Then, I work closely with data owners or domain experts to understand the business rules and identify the root cause of the conflict. Based on that, I apply a resolution strategy—such as prioritizing one source, merging values logically, or flagging discrepancies for manual review. I also make sure to document the decisions made and, if needed, build automated checks to catch similar issues in the future.

I use data profiling, statistical techniques, and visualizations to explore trends and detect anomalies. Often, these patterns point to the root of the discrepancies or reveal valuable context that wasn’t obvious at first glance

When handling conflicting data sets from different sources, I follow a structured reconciliation process. First, I identify the source of each data set and assess their reliability. Then I align them based on key identifiers like timestamps, IDs, or categories. Discrepancies are flagged, and I investigate the root causes, whether due to data entry errors, timing issues, or differing definitions. I also consult relevant stakeholders for context. Finally, I document the reconciliation steps taken and create a unified, validated version of the data for analysis. Transparency and traceability are key throughout the process.

Reconcile Data: I create a single, unified view by either prioritizing the most reliable source or building weighted models that account for credibility and variance. AI models can even assign confidence scores to competing data points.

I weigh the reliability and context of each dataset, look for opportunities to cross-validate, and document any assumptions made when reconciling conflicting figures. If conflicts can’t be resolved definitively, I make sure to highlight those uncertainties in my analysis

Transparency is key. I maintain clear documentation of how the data was handled, what decisions were made, and why—so that others can trust the process and easily replicate it if needed.

Document Process: Finally, I document every step — what was cleaned, assumptions made, how conflicts were resolved. This transparency builds trust and enables reproducibility across teams and tools.