What are the ramifications of not normalizing data?

Not normalizing data can lead to skewed analysis and inaccurate insights, as raw data may have varying scales and distributions that obscure meaningful patterns. This can result in poor model performance in machine learning, where algorithms may be biased towards features with larger values. Additionally, unnormalized data can complicate data visualization and interpretation, making it difficult to draw reliable conclusions. Overall, failing to normalize data undermines the integrity of data-driven decision-making processes.