Why is data normalization important in Relativity?

Data normalization is essential in Relativity because it ensures consistent formatting for analysis. Normalization involves standardizing data across a dataset, which is critical when working with diverse document types and sources. This consistency allows for more accurate and efficient analysis of information because analysts can rely on uniform data structures and formats.

In a legal context, where the integrity of data is paramount, normalization helps streamline the review process. For example, if documents are in varying formats or contain inconsistent metadata, it can lead to misinterpretation of data, errors in review, or challenges in data comparison. By normalizing data, you create an environment conducive to comprehensive analysis, making it easier for teams to draw insights and conclusions from the information at hand.

While other options point to valuable processes within Relativity, they do not directly address the foundational importance of consistency that normalization provides. This consistency underpins effective searching, filtering, and ultimately, the reliability of the legal analyses performed using the data.