Create Your First GoodData Insights

In this article, you will learn how to create simple yet informative insights using data from the sample CSV file that you uploaded to your workspace in one of the following tutorials:

The goal of this tutorial is to break down raw sales figures by order category and status, and examine the pricing structure of your sales.


After you load your data, the Analytical Designer screen displays. This will be your analytical playground where you create and modify your insights.

As you can see, the columns of the original csv sample file appear in the catalog panel on the left. This is possible thanks to the GoodData’s ability to work directly with human readable data modeling metadata (facts, attributes, column names).

Exercise 1 - Order Category and Order Status

To create your first insight:

  1. Drag and drop Order ID onto the Measures panel. This automatically creates a Count of unique Order IDs.
    Analytical Designer applied Count because the Order ID column was annotated as an attribute instead of as a numerical fact.
  2. Insert the Compare helper, select Category from the drop-down menu and click Apply.
    The number of orders is now split by the product category.
  3. Drag and drop Order Status to the Stack By panel to look into the data in more detail..
    The columns are now further split by the status of the orders.
  4. Click the Save button in the top right corner of the screen to save the insight and name the insight it Orders by Status and Category.
    You have just created your first insight! 

Exercise 2 - Sales Pricing Structure

In the following example, your insights will analyze the pricing structure of your sales - the highest priced items and the price range.

Follow these steps:

  1. Click the Clear button in the toolbar to clear the insight.
  2. Drag and drop Price onto the Measures panel.
    This displays the Sum of all prices on all order lines but it does not consider how many times the products were sold at their price.
    Note: You can apply different mathematical functions to this particular column, because the Price column was annotated as a numerical fact.
  3. In the Measures panel, click the arrow to the left of Sum of Price item, and from the drop-down menu select Average to display the average product price.
  4. Drag Category to the View By panel.

You see that the Outdoor category contains the highest priced items. But what is the range of prices?

  1. In the Measures panel, click the arrow to the left of Avg of Price and change Average to Minimum.
  2. Drag and drop Price to the Measures panel again.
    A new Sum of Price item appears.
  3. Click the arrow to the left of Sum of Price, and from the drop-down menu, change Sum to Maximum. You can now see the range of prices for each category.

You can easily handle many analytical queries without needing to write SQL for individual variations.

Next Steps

Now that you created your first insights using our sample data, you can either: