Dashboard Guide: Blending Data
Business Case: Sales Performance Dashboard
Imagine you are working with a retail company, and they would like to create a sales performance dashboard. They have two primary datasets: one for sales transactions and another for customer information. We will blend these datasets to analyze key performance indicators (KPIs) like revenue, average transaction value, and customer lifetime value.
Step 1: Load the datasets
Assuming you have already imported and cleaned your datasets, load them into your dashboard tool. For this tutorial, let’s call them SalesData and CustomerData.
Step 2: Identify the common field
To blend datasets, you’ll need to find a common field between them that can serve as a linking point. In our case, we can use the CustomerID field in both SalesData and CustomerData.
Step 3: Blend the data
Now, let’s blend the data using the dashboard tool’s built-in data blending features. Different dashboard tools may have slightly different ways to do this, but the general process should be similar:
- Locate the data blending option in your dashboard tool. This is often called “Data Blending” or “Combine Data.”
- Select your primary dataset (e.g., SalesData) and your secondary dataset (e.g., CustomerData).
- Specify the common field (CustomerID) to create a relationship between the datasets.
- Choose the fields you want to include in the blended dataset. For our example, we might want fields like Revenue, Date, CustomerID, and CustomerSegment.
- Confirm your selections and your tool should create a new blended dataset.
Step 4: Visualize the data
With your newly blended dataset, you can now start creating visualizations to analyze your KPIs. For example:
- Revenue: Create a bar chart to show the total revenue by month.
- Average Transaction Value: Calculate the average transaction value by dividing the total revenue by the total number of transactions. Display this as a line chart over time.
- Customer Lifetime Value: Calculate the average revenue per customer and
It’s essential to note that the specific steps and interface might differ depending on your dashboard tool. However, the overall process of blending data remains the same: identify a common field, create a relationship between datasets using that field, and select the fields you want to include in the blended dataset.