Power BI Data Modeling and Design is the process of using data discovery, and data profiling to produce a data model aligned to business requirements. A data model ultimately represents an organization’s data assets. Leveraging an iterative approach within the Power BI tool allows a modeler to quickly capture information from subject matter expects. The modeler can then supply a conformed foundation about organization’s data source systems, and data assets. Types of data commonly modeled…
Categorical data: Data often used to group other data together. A common example would be customer demographic data like age, gender, occupation, marital status, etc. Organizations will often have regions to group sales data, and product categories by usage type, or color etc.
Reference data: Data often used to identify an entity. Customer address, product code, account number are common example of reference data. A lookup table is key to adding conformity within a data model. This allows the data to be used again, and again across the model without having to store the values in multiple places. It reduces the risk of data entry typos from the source systems.
Process data: Data often used to understand the stages a product or service go through until it is solid. Process data will typically have a starting date and ending date, but not always. IoT data can extend the life cycle of gathering Process Data by providing insights on how the customer is using a product, or consuming a service. Another example of Process Data would be tracking a customer’s interaction on a website.
Transnational data: Data often used to capture point-in-time transactions like purchases, returns, reservations, payroll, interest, signups, etc. A common example would be a sale transaction at a store or online. Analyzing large volumes of transaction data will help organization discover hidden patterns, and correlations. This allows organizations an opportunity to grow by incorporating the patterns into their business process.