To keep my data accurate, I focus on: *Reliable Sources: I choose data from trustworthy sources and verify its consistency. *Validation: I perform thorough checks, comparing datasets and ensuring completeness before analysis. *Effective Tools: I use Power BI and Excel for data visualization, as these tools help maintain accuracy and consistency in how data is presented.

When addressing visual discrepancies in data analysis, several critical factors must be considered: 1. Ensure Data Quality: It is essential to apply rigorous data quality checks to the existing dataset. Additionally, the selection of appropriate visualization tools plays a significant role in achieving accurate and meaningful results. 2. Examine Parameters and Transformations: Careful attention should be given to the parameters used in the analysis. Where necessary, apply suitable data transformations. It is equally important to verify visual settings such as axis configuration. Exploring various types of charts and graphs to represent the same data can enhance interpretation and improve the clarity of insights.

To ensure accurate analytical results when facing data visualization discrepancies: - Verify data sources and integrity - Check data processing and transformation steps - Review visualization settings and parameters - Validate calculations and aggregations - Consult with team members and subject matter experts - Use multiple visualization tools and techniques for validation - Document findings and discrepancies - Iterate and refine visualizations and analysis By following these steps, you can identify and resolve discrepancies, ensuring accurate and reliable analytical results.

1. Verify Data Sources: • Ensure all data sources are reliable and consistent. 2. Standardize Data Formats: • Use a consistent format for all data inputs. 3. Validate Data: • Cross-check data points and correct any errors. 4. Review Visualization Methods: • Ensure charts and graphs are created using appropriate and consistent methods. 5. Use Multiple Visuals: • Present data in different formats to cross-verify insights. 6. Get Peer Reviews: • Have colleagues review the visualizations for accuracy.

Ensure that the data comes from an accurate and reliable source. It's important that the data is both real and precise. Additionally, always follow the ALCOA+ strategy.