A simple explanation of the Monte Carlo Data Set and Data Compose Smart Shape in PEGA

Sandeep Pamidamarri
5 min readMar 5, 2023

In this post, let’s understand the usage of the Monte Carlo data set and the Data Compose smart shape in the data flow. A customer entity record in an organization will have many sub-entities like accounts, products, incomes, expenses, etc... There will be compliance-related concerns around customer data usage in lower environments. The Monte Carlo data set usage addresses this problem and generates sample data records with the providers like Customer First Name, Last Name, ID, etc…

To display the customer information in a case/ to refer to the customer record as part of processing — we need to combine the data from multiple data sources. The data compose smart shape in a data flow helps to combine the data from various sources to populate the embedded pages during runtime in the customer's primary page.

Let's take a simple example with a scenario, we have the following tables:

  • Customer main table — CustID, First Name, Last Name
  • Customer employment table — EmpID, CustID, EmployerName

It is a one-to-many relationship. One customer can have many employment records. Let’s say in production, these tables get the data feed from an external system. For lower environments, the organization recommended populating the sample records to test the functionalities. In this scenario, let’s populate these tables using the Monte Carlo data sets.

Step 1: Configure the customer and its employment data tables

Note: It is out of scope from this article's perspective — to create a sample application and the respective data classes.

The two data table classes as below

Customer Table

  • Required properties in the data model
  • Database table source with the Cust ID as the primary key



Sandeep Pamidamarri

Digital Transformation Leader | Pega Lead Solution Architect | Pega Certified Data Scientist | Pega Customer Service | Pega Sales Automation | AWS Cloud