Conflicting data from multiple sources can severely impact decision-making. A study by KPMG found that 60% of executives distrust their analytics. Here's how to ensure accurate insights and maintain confidence in business analysis: -Cross-Reference Sources: According to Deloitte, businesses that triangulate data from at least three independent sources reduce decision-making errors by 26%. -Validate Data Quality: Poor data quality costs businesses an average of $15 million annually, as Gartner. -Consult Experts: Organizations that include domain experts in their data review process report 40% more accurate insights (McKinsey). -IBM reports that businesses with robust data governance frameworks experience 2x the analytics adoption rate.

When I face conflicting data, my first step is to double-check the sources and understand the context—like methodology and timeframes—behind the numbers. I also reach out to my team for their perspectives, as they can help clarify things. If needed, I’ll dig deeper or seek third-party validation to ensure I’m working with the most accurate insights. It’s all about getting to the truth and making decisions based on reliable data.

When faced with conflicting data, I follow a structured approach to ensure accuracy. First, I assess the credibility of each source based on its reliability and data collection methods. Next, I cross-verify the data with independent, reputable sources to identify consistent patterns. I analyze the context, such as methodologies or timing, to understand variations. If ambiguity remains, I consult experts or stakeholders for clarity. I also use analytical tools to spot trends, anomalies, and outliers. Finally, I would document findings and facilitate team discussions to establish a consensus if I am not able to arrive at a decision. This ensures insights are reliable, actionable, and grounded in evidence.

Understand the context the data is meant to address. Analyze the sources of data(data points, collection method, purpose, timeframe), Assess data quality (patterns, anomalies, alignment with historical trends or expected values, completeness, creditability, check metadata), Engage stakeholders to discuss discrepancies in data, potential reasons for the differences, confirm business rules ( assumptions, definitions & interpretations), perform RCA of data discrepancies source, reconcile data using standardize metrics, cross validation & prioritize reliable sources, use leverage analytical tools to identify outliners or confirm trends. Document & communicate findings. Monitor &update processes or systems to prevent similar conflicts in future.

in one project, I was analyzing market share data from two different research firms. The data showed significantly different market share figures for our company. After investigating the data collection methodologies, I discovered that one firm used a larger sample size and more comprehensive data sources. Based on this analysis, I determined that the data from the larger firm was more reliable and used it for my analysis.