A dashboard includes various interactive visualizations that help gain information and make important business intelligence decision. This article discusses various concepts for creating Interactive Dashboards in Power BI. The topics include:
- Filled Maps
- Pie Charts
- Bin Distribution
The dataset used is of BankCustomers. Upload the dataset in Power BI and refer to the dataset to follow along with the below given sections of the article. You can download the dataset from here : Dataset
A filled map uses shading or patterns to display how a value differs in proportion across a geography or region. When you create a map visualization in Power BI Desktop, the data in the Location, Latitude, and Longitude (that is being used to create that visualization) is sent to Bing.
Steps Involved: Step 1 - Select Filled Map from Visualization panel. Step 2 - Select Region from BankCustomers dataset and drag and drop it into Location. Step 3 - Select Region from BankCustomers dataset and drag and drop it into Legend. (As shown in Fig 1)
A pie chart is used to illustrate the contribution of different values to a total. For example, number of male and female customers involved with a bank.
Steps Involved: Step 0 - Select the '+' symbol on bottom left to create a new page and rename it as Gender. Step 1 - Select Pie Chart from Visualization panel. Step 2 - Select Customer ID from BankCustomers dataset and drag and drop it into Values. Step 3 - Select Gender from BankCustomers dataset and drag and drop it into Legend. (As shown in Fig 2)
Grouping and Bin Distributions:
Grouping is a method of creating a new measure by manually setting all its parameters such as type, size, range etc. For example, create a bin of a particular data column such as balance or age. Bins in Power BI are useful to create a range of data. It helps consolidate data into a more presentable or meaningful visualization. For example, the range of ages of customers of a bank.
Steps Involved: Step 0 - Select the '+' symbol on bottom left to create a new page and rename it as Age. Step 1 - Select Age from Feilds. Right click on it and select New group. This will pop up a new window named Groups. Step 2 - In Groups window, set the bin size to 5. Step 3 - Press OK. This will create a new column of Age(bins) in BankCustomers dataset. (As shown in Fig 3)
Step 4 - Select Age(bins) from BankCustomers dataset and drag and drop it into the empty workspace. Step 5 - Add Customer ID from BankCustomers dataset and drag and drop it into values. This will create a bin distribution of customers in a bank as per their ages. (As shown in Fig 4)
Treemaps display hierarchical data as a set of nested rectangles. Each level of the hierarchy is represented by a colored rectangle (branch) containing smaller rectangles (leaves). For example, we can use to segregate customers of a bank on the basis of job type.
Steps Involved: Step 0 - Select the '+' symbol on bottom left to create a new page and rename it as Job type. Step 1 - Select Treemap from Visualization panel. Step 2 - Select the following from BankCustomers dataset and drag and drop it into the empty visualization. 2.a - Customer ID 2.b - Gender 2.c - Job Classification (As shown in Fig 5)
Interactive dashboards are important when making a Business Intelligence report. To make an informed decision, your dashboards should contain precise and contain informative visualizations in it. Now we will use all the above created visualizations to create our dashboard.
Steps Involved: Step 1 - Select the '+' symbol on bottom left to create a new page and rename it as Dashboard. Step 2 - Copy and paste visuals from all your pages into this new page. Step 3 - Add slicers (discussed in previous articles) to your dashboard to make it more interactive and visually appealing. Then drag and drop the following from BankCustomers dataset: 3.a - Date Joined 3.b - Region This will create an interactive dashboard for your BI Report in Power BI. (As shown in Fig 6)
Dashboards are used by many businessmen to analyze the data in order to make informed business intelligent decisions. You can now look around the various visualizations and incorporate them with filters and slicers in order to make your own dashboard in Power BI. For any doubt/queries, comment below.
- Power BI - Maps, Scatterplots and Interactive BI Reports
- Converting Power Law Distribution to a Linear graph
- Power BI - Tools and Functionalities
- Power BI - Timeseries, Aggregation, and Filters
- Power BI - Drilling Down and Up in Hierarchies
- Facebook Transcoder
- FOCL Algorithm
- Hyperparameter tuning using GridSearchCV and KerasClassifier
- Image GPT
- Hebbian Learning Rule with Implementation of AND Gate
- ALBERT - A Light BERT for Supervised Learning
- How L1 Regularization brings Sparsity`
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