As a data analyst, you'll often manage thousands, hundreds of thousands, or even millions of records. But how can you generate insights from all this raw data? You can convert it into data models. In this video, you'll explore the basics of data models and learn how to create them. Over at Adventure Works, the company needs to generate insights and increase sales from different data sources. These data sources include customer sales and marketing data. But these data sources are all in separate locations, and the only way to generate insights is to combine them. That's where the data model comes in. Adventure Works can integrate its data sources as a data model in Microsoft Power BI, then generate insights in the form of visualizations. Let's find out more about data modeling and learn how Adventure Works can make use of it. At its core, data modeling is creating a structured representation of data. This representation can then be used to support different business aims. In other words, a data model shows how different data elements interact, and it also outlines the rules that influence these interactions. Data models can be built in Microsoft Power BI. Microsoft Power BI is software that provides data analysts with a user friendly interface for building data models. Other benefits of a Power BI data model are that it can be used to define relationships between tables and assign data types. You can also create calculated columns and measures, and update your model as your business requirements change. In Power BI, the foundation of creating reports and dashboards lies within the data model. It's important to understand how to design a data model that effectively aligns with the visual elements within your reports and dashboards. There are several steps involved in building a data model in Power BI. Connect to your data sources, prepare and transform your data, and configure table and column properties, then create model relationships. And finally, create measures and calculated columns using DAX or data analysis expressions. Once your data model is in place, you can analyze the data to generate insights to help you achieve your business objectives. Let's explore some examples of how data models can be applied to business data. By optimizing the data model, you can significantly improve the performance of your Power BI reports and dashboards. It's also easier to aggregate structured data in a data model, thanks to the clear relationships and hierarchies. With an effective data model, you can perform more advanced analytical capabilities like complex measures and predictive analysis. When your underlying data is structured, organized, and aligned, your insights and reports are more likely to be accurate and reliable. Now that you understand more about data models, let's briefly explore how Adventure Works can build one with Power BI to generate the sales insights they need. First, Adventure Works needs to connect to its data sources by executing a query in Power Query Editor. The result is then loaded into the Power BI data model as a table. Using Power Query in Power BI, Adventure Works can finish importing and cleaning their data sources. This creates a data model that contains cleaned customer, date, employee, and marketing data as separate tables. Each table in the model represents a specific business entity, and each table also has its own related attributes. The next step is to define the relationships between the tables in Power BI's model view. The company can link its customers and sales tables using the Customer ID column, which is common to both tables. With this relationship, the company can now view each customer's transactions. Adventure Works could also link its sales and marketing tables to understand which campaigns were most effective for boosting sales. Finally, the company needs to create measures and calculated columns using DAX, our data analysis expressions. DAX is a syntax used in Power BI to analyze data. You learn more about it later in the course. For now, just know that Adventure Works can use DAX to create aggregations and custom calculations to generate insights on important aspects of their data, like sales totals. A strong understanding of data models will help you maximize your data's full potential. Building sophisticated data models creates a robust foundation for data analysis and generating insights. Remember that your data model is the foundation of everything else.