Final answer:
In Power BI, creating relationships between tables is vital for comprehensive data analysis without redundancy. Grouping data in different ways can reveal various insights, and relationships aid in switching between tables to analyze different data dimensions.
Step-by-step explanation:
Creating relationships in Power BI is essential when dealing with multiple tables containing related data. Relationships allow you to integrate and link the tables so that you can analyze comprehensive data across them. This prevents data redundancy and maintains data integrity.
In the context of having three tables with sales information, no single table is more 'correct' than the other; rather, they each serve different aspects of the dataset. When you create relationships, you ensure that analysis can be done seamlessly across these aspects. For instance, you might have a table for sales by state, another by product, and a third with sales dates or customer demographics. Linking these allows for a robust and multidimensional analysis.
Regarding how the data could be grouped, there are many possible ways based on analysis needs. You could group by geography, time period, product category, customer demographics, or any other dimension present in your data. Grouping data differently can provide varying insights, and the advantage of one method over another depends on the questions being asked of the data.
If you switch between tables in Power BI, it's usually to utilize different dimensions or granularities of the data that are relevant to different types of queries or reports. Relationships in Power BI make switching between the perspectives held in each table a seamless process, thus enabling richer, more comprehensive analysis.