As a data analyst, unlocking fresh insights requires exploring data from multiple angles. With role playing dimensions, you can explore your data from different perspectives and eliminate the need for redundant data structures through active and inactive relationships. In this video, you'll explore the concept of role playing dimensions and active and inactive relationships. Adventure Works receives thousands of orders from all over the world. It's important that the company continually analyzes its orders to avoid delayed or mistaken deliveries. It can use multiple dimensions to explore its order related data from multiple angles. Let's find out more about role playing dimensions by exploring how Adventure Works makes use of it. In the context of Power BI dimensions represent the various attributes or business entities used to organize data. Role playing dimensions are instances of the same dimension used multiple times in a data model. Each instance plays a unique role by representing different aspects of the data. This provides the flexibility to analyze data from different viewpoints without duplicating data tables. Let's demonstrate this with an example from the Adventure Works database. Adventure Works' sales and shipping departments operate in sequence. First, new sales are recorded in the sales dataset as order date. Then the orders shipping date is recorded in the sales dataset. Finally, the system automatically generates a delivery date when the customer receives the product. In Adventure Works sales data set, the date dimension is used three times for new sales, shipping dates, and receipt dates. Adventure Works can analyze sales performance by order and shipping date without needing separate tables, optimizing delivery time by delivery date analysis. This helps the business to analyze sales performance based on order date and shipping date without creating separate tables for each date type. When Adventure Works queries its data, the role of the date dimension is based on the fact column used to join the tables. For example, the table join relates to the sales order date column when analyzing sales by order date. An important part of role playing dimensions are active and inactive relationships. An active relationship is a relationship between two tables used for analysis, reporting, and visualization. An inactive relationship is a valid relationship not being actively used in the current analysis. To differentiate between active and inactive relationships, Power BI marks active relationships with a solid line and inactive relationships with a dotted line. Let's examine an example from Adventure Works. In the Adventure Works table, the date and the sales tables have three relationships. However, there can only be one active relationship between two Power BI model tables. All remaining relationships must be set to inactive. A single active relationship means there is a default filter propagation from the date to the sales table. The active relationship is set to the most common filter used by the company's reports, which is the order date relationship. You can utilize the inactive relationship for specific analytical needs using the DAX USERELATIONSHIP formula. How do active, inactive relationships relate to role playing dimensions? Here's a quick demonstration of how these concepts function in the Adventure Works database. Let's begin with creating a role playing dimension. After importing sales and date tables, you can create two relationships between them. One for order date and another for shipping date. By default, the first relationship is active and the second is inactive. The date table serves as a role playing dimension for both order and shipping date. Any analysis, reporting, and visualization you require can make use of this active relationship. Occasionally, you'll need to analyze data from a unique perspective. For example, Adventure Works needs to calculate its total sales based on the shipping date. However, the shipping date is an inactive relationship, so using this calculation requires a measure. To create such a measure, an inactive relationship needs to be employed. This is where the DAX function USERELATIONSHIP comes in. To use the shipping date, the inactive relationship, create a measure using USERELATIONSHIP. For instance, to calculate the total sales based on the shipping date, you can create a DAX formula. CALCULATE is used here to alter the filter context of the entire measure. SUM is summing up the sales amount column of the sales table. As the sales table is connected to the date table via order date column by default, each DAX calculation is based on the relationship between the tables. USERELATIONSHIP function in DAX overrides the relationship and establishes a temporary relationship based on the shipping date column of the sales table or inactive relationship. The relationship becomes active only for the current calculation. This formula forces Power BI to use the inactive shipping date relationship for the calculation. Role playing dimensions and active, inactive relationships in Power BI create an efficient data model for comprehensive analysis. Although it might take some time to get used to these concepts, they will prove invaluable as you navigate your Power BI journey.