Describe data visualization in Microsoft Power BI Flashcards
Identify capabilities of Power BI
Microsoft Power BI is a suite of tools and services that data analysts can use to build interactive data visualizations for business users to consume. Users can consume reports, dashboards, and apps in the Power BI service through a web browser, or on mobile devices by using the Power BI phone app.
-Data Connectivity: Connects to diverse data sources.
-Data Transformation and Cleaning: Uses Power Query for data preparation.
-Data Modeling: Supports relationship building and uses DAX language.
-Interactive Reports and Dashboards: Enables easy creation and interaction.
-Visualization: Offers a variety of visualizations and custom visuals.
-Natural Language Query (Q&A): Allows for querying in natural language.
-Sharing and Collaboration: Facilitates sharing and collaborative features.
-Data Gateway: Facilitates secure data transfer from on-premises sources.
-Row-Level Security (RLS): Enables data access restrictions based on roles.
-Power BI Desktop: A way to design and ingest reports
-Power BI Mobile: View reports on the go
-Power BI Service: Cloud-based platform where users view and interact with reports and they can create Dashboards.
-Power BI Embedded: Allows embedding reports into custom applications.
Describe features of data models in Power BI
Analytical models enable you to structure data to support analysis. Models are based on related tables of data and define the numeric values that you want to analyze or report (known as measures) and the entities by which you want to aggregate them (known as dimensions).
For example, a model might include a table containing numeric measures for sales (such as revenue or quantity) and dimensions for products, customers, and time. This would enable you aggregate sale measures across one or more dimensions (for example, to identify total revenue by customer, or total items sold by product per month).
Conceptually, the model forms a multidimensional structure, which is commonly referred to as a cube, in which any point where the dimensions intersect represents an aggregated measure for those dimensions.)
Tables and schema
Dimension tables represent the entities by which you want to aggregate numeric measures – for example product or customer. Each entity is represented by a row with a unique key value. The remaining columns represent attributes of an entity – for example, products have names and categories, and customers have addresses and cities.
The numeric measures that will be aggregated by the various dimensions in the model are stored in Fact tables.
-This type of schema, where a fact table is related to one or more dimension tables, is referred to as a star schema
The schema of fact and dimension tables is used to create an analytical model, in which measure aggregations across all dimensions are pre-calculated; making performance of analysis and reporting activities much faster than calculating the aggregations each time.)
Attribute hierarchies
One final thing worth considering about analytical models is the creation of attribute hierarchies that enable you to quickly drill-up or drill-down to find aggregated values at different levels in a hierarchical dimension.
For example, consider the attributes in the dimension tables we’ve discussed so far. In the Product table, you can form a hierarchy in which each category might include multiple named products. Similarly, in the Customer table, a hierarchy could be formed to represent multiple named customers in each city. Finally, in the Time table, you can form a hierarchy of year, month, and day.
The model can be built with pre-aggregated values for each level of a hierarchy, enabling you to quickly change the scope of your analysis
Analytical modeling in Microsoft Power BI
You can use Power BI to define an analytical model from tables of data, which can be imported from one or more data source. You can then use the data modeling interface on the Model tab of Power BI Desktop to define your analytical model by creating relationships between fact and dimension tables, defining hierarchies, setting data types and display formats for fields in the tables, and managing other properties of your data that help define a rich model for analysis.
Identify appropriate visualizations for data
After you’ve created a model, you can use it to generate data visualizations that can be included in a report.
There are many kinds of data visualization, some commonly used and some more specialized. Power BI includes an extensive set of built-in visualizations, which can be extended with custom and third-party visualizations. The rest of this unit discusses some common data visualizations but is by no means a complete list.
-Tables and text = Tables are useful when numerous related values must be displayed, and individual text values in cards can be a useful way to show important figures or metrics.
-Bar and column charts are a good way to visually compare numeric values for discrete categories.
-Line charts can be used to compare categorized values and are useful when you need to examine trends, often over time.
-Pie charts are often used in business reports to visually compare categorized values as proportions of a total.
-Scatter plots are useful when you want to compare two numeric measures and identify a relationship or correlation between them.
-Maps are a great way to visually compare values for different geographic areas or locations.
-Matrix = Tabular structure that summarizes data
-Key influencers = The major contributors to a selected result of value
