Which function helps ensure the data is in a suitable shape for Tableau analysis?

The choice that helps ensure the data is in a suitable shape for Tableau analysis is data shaping and join operations. This aspect is critical during the data preparation phase, as it involves structuring and manipulating the data to align with the analytical requirements of the project.

Data shaping includes various tasks such as pivoting, un-pivoting, filtering, aggregating, and joining datasets. Joins are specifically used to combine multiple tables based on related fields, which enables users to harness a more comprehensive view of their data from different sources. By appropriately shaping the data and establishing the right joins, you can eliminate unnecessary complexity and create a dataset that is optimized for analysis, ensuring that visualizations are accurate and insightful.

The other options focus on different areas: field synonyms help in improving user experience by allowing alternative names for fields, logical hierarchies aid in organizing data for drill-down analysis but do not alter data shape, and calculated fields create new data based on existing fields, which may enhance insights but do not contribute to the structural integrity of the data itself. Thus, while all these elements are valuable in their own contexts, it is the data shaping and join operations that fundamentally prepare the data for effective analysis in Tableau.