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When modelling data in Power BI and the entire Power Platform, it’s best to use the Kimball Schema, also known as the Star Schema. This method of organizing data is considered the most effective for data analytics because it separates data into Facts and Dimensions, offering efficiency. Many people find it challenging when they first start modelling data this way. In our first discussion, we’ll focus on dimensions, and next week we’ll dive into facts.

Dimensions play a pivotal role in the Kimball Schema, empowering users to conduct multidimensional analysis and reporting on the data housed in the data model. They facilitate data manipulation, allowing users to slice and dice the data, filter, group, and aggregate it based on various criteria. Moreover, dimensions enable users to navigate the data, moving from a higher level of detail to a lower level or vice versa.

Simply put, a dimension is a set of attributes that describe a business entity or a process. For instance, a customer dimension might encompass “attributes” like name, address, phone number, and loyalty status. Other dimensions could include product and Location. In essence, dimensions provide the context for the data.

Dimensions are usually organised in a hierarchical structure, where each attribute can be aggregated or drilled down to a lower level of detail. For example, a date dimension might have a year, quarter, month and day hierarchy. A geographic dimension might have a country, region, state, city, and postcode hierarchy. A hierarchy allows users to analyse data at different granularity levels and compare data across dimensions.

For example, suppose a user wants to analyse the sales performance of a retail company. The user can use the dimensions of product, customer, time, and store to answer various business questions, such as:

·       What are the total and average sales per product category, subcategory, and product?

·       What are the sales trends over time, by year, quarter, month, week, and day?

·       Which customers are the most loyal, profitable, and frequent buyers?

·       Which stores are the most successful, profitable, and popular?

By using dimensions, Analysts can gain insights and discover patterns and trends in the data, which can help them make better business decisions and improve performance.

Next week, we will look at the next piece of the puzzle – Facts.

Geordie Consulting is a business intelligence and analytics company that helps clients across various industries leverage their data and gain insights. It offers services such as data integration, data visualisation and dashboard development, reporting and predictive analytics. Our team of experienced and certified consultants uses the latest tools and technologies to deliver solutions that meet the client’s needs and expectations. Geordie Consulting is your trusted partner for help achieving your goals and objectives through data-driven decisions.