What methodology is used for 'data preparation' in Oracle Analytics?

Data preparation in Oracle Analytics primarily involves data cleansing and transformation, making it the most comprehensive methodology for preparing data for analysis. This process is vital because raw data often contains inaccuracies, inconsistencies, and formats that need to be standardized before it can be effectively analyzed.

Cleansing involves identifying and correcting (or removing) errors and inconsistencies in the data to improve its quality. This can include tasks such as handling missing values, correcting typos, and ensuring data types are consistent across the dataset.

Transformation refers to the process of converting data from one format or structure into another. This can include aggregating data, changing data types, or even enriching the dataset with additional relevant information. Transforming the data ensures that it fits the analytical methods to be used and aligns with business questions or data visualization needs.

While other options mention aspects of data handling, they do not encompass the full scope that data cleansing and transformation cover, which are essential for creating a reliable and actionable dataset in Oracle Analytics.