Which method aims to standardize data values and formats for better integrity?

The chosen method, which focuses on standardizing data values and formats for enhanced integrity, is data normalization. This process involves adjusting the values in a dataset to a common scale or format without distorting differences in the ranges of values, which ultimately enhances the accuracy and utility of the data. By doing this, data normalization ensures that various datasets can be compared and combined effectively, minimizing errors and inconsistencies that might arise from differing data formats or scales.

In addition to improving data integrity, normalization is crucial for ensuring that analytical processes, machine learning models, and databases can handle the data more efficiently, leading to more reliable and precise outcomes.

While data mining involves discovering patterns in large datasets, it does not specifically focus on standardizing data values. The concept of a data pipeline relates to the flow and processing of data from one system to another, but it does not inherently involve standardization either. Data science encompasses a broader set of techniques used to extract insights from data, which may include normalization as one of its practices, but it is not solely defined by that goal. Therefore, data normalization stands out as the most appropriate method for the aim of standardizing data values and formats for better integrity.