When working with data, there's often a strong emphasis on using the latest and most advanced models. However, even the most sophisticated model can’t work magic if the data itself is flawed. Poor-quality data—whether incomplete, inconsistent, or full of errors—leads to unreliable outcomes.

Common issues include missing values, duplicates, outliers, corrupted records, or simply having the wrong type of data altogether. No matter how powerful the model, if the input data isn’t clean and accurate, the results won’t be meaningful. High-quality analysis starts with high-quality data.

Can you share any examples where bad data led to unexpected or misleading results?