Accidental Misrepresentation of Data - Real World Use Cases

In the world of data , not all deception is deliberate. Sometimes, our dashboards, visuals, or analyses mislead without meaning to , a result of misunderstood metrics, missing context, or over-polished narratives. These are the accidental data lies that quietly distort decisions and erode trust.

This session uncovers how good teams can unintentionally create misleading insights and how to prevent it. Through real-world examples, we’ll explore how chart design, sampling bias, time windows, and aggregation choices can turn truth into illusion. More importantly, you’ll learn practical frameworks to communicate data responsibly keeping both integrity and influence in your storytelling.

Key Takeaways:

Recognize common sources of unintentional data distortion from incomplete data to misapplied visual emphasis.

Learn to spot “truth gaps” caused by missing context, selective metrics, or incorrect comparisons.

Discover ethical storytelling principles that balance persuasion with accuracy.

Understand how to build data review loops to validate insights before they go live.

Gain a checklist to ensure your dashboards inform

Attendees will learn: - Recognize common sources of unintentional data distortion - Learn to spot “truth gaps” caused by missing context - Understand how to build data review loops to validate insights before they go live.