Pigment has two types of lists: Dimension Lists and Transaction Lists. Each one has its own unique characteristics and uses, yet both contain Items and have Properties. This article explores both of these Lists. 

Table of Contents

What are Dimension Lists?

Dimension Lists are the axes used to analyze your data. They provide structure for Metrics by grouping similar Items, such as employees, products, or countries. Each Item in a Dimension List must be unique to ensure that Pigment can accurately reference it across multiple Metrics. These lists are reusable, making them efficient for various Metrics.

Examples of Dimension Lists

Here are some different examples of Dimension Lists you might have, organized by different areas. Each of these Lists contain individual Items. For example, in a Dimensions List called Products, this would contain an Item for each Product in your company. 

1. Calendar - months, quarters, and years.
2. Geography  - country, state, or region.
3. Product  - product or product category.
4. Customer  -  customer or customer segment.
5. Employee  - employee or job role.
6. Currency  - currencies used in the application.
7. Cost center  - cost center or expense category.
8. Sales channel  -  sales channel or distribution channel.
9. Account  -  accounts that are sold into
10. Asset class  -  asset class or investment category.
11. Versions - Actual, Budget, Forecast.

What are Transactions Lists? 

A Transactions List contains a list of Items that represents events or transactions. Often, these are transformed or grouped for analysis. For example, you might have a Transaction List showing individual bookings, which you might group by Account, Month, or Product. In this example, you use a Transaction List for the bookings, and use Dimension lists for Accounts, Products, and Months.

The image below shows a list of orders or accounting booking entries.

Examples of Transaction Lists

Here are some different examples of Transaction Lists you might have, organized by business function. 

More information: 

• Understand Pigment Blocks
• Measure Data with Metrics