With this feature, organizations can enhance their data analysis capabilities and accommodate evolving business needs by incorporating additional data fields post-implementation. This means users can add new dimensions for data classification, reporting, planning, and analysis even after the system is fully deployed.
Scenario: A global retail chain uses planning software each month to re-forecast. In the middle of the fiscal year, the company decides to sell an exclusive line of products through Walmart.com. To forecast and track its performance, they need to include a new dimension “channel” and new product categories under the existing product dimension in the system.
Solution: They add a new dimension named Channel with “Direct”for existing and new direct sales from their website, and Walmart.com for sales via that channel. The also add a new item to the product member called “Walmart.com Exclusive” without disrupting the existing system or data, enabling them to forecast and analyze sales and performance data for the new product / channel combination.
This is an important requirement, especially for growing companies. We say this because of the frequency it comes up in retail, SaaS, biotech and so on. The businesses grow in unexpected ways, new requirements come up, and the user is helpless to do anything about it. They end up back in Excel because adding a new dimension is kryptonite to the planning tool's database.
Make sure to ask the questions here and ask to see it live. If upon adding the dimension it requires a million additional inputs, a refresh, some edits to code, it's not going to work for you as you grow unless consulting hours are no big deal to you.
As always, changing the architecture usually necessitates changing how source data is mapped and imported. Make sure your source data is formatted to handle this new dimension after it is added.
Better yet, have the vendor add dimensions that you “might” need at the beginning of setup and save yourself the pain of adding them in the future.