PerformancePoint gives you the flexibility to have multiple assumption models to use as you please in your main models. Although this is great, I’ve found a problem when the two assumption models have different member sets for the same dimension, and so in an extension to my last assumption model post, this post provides a workaround for the issue.
Consider the following example. I’ve got a main model where I want to use 2 assumption models, namely:
- HR Assumptions – Uses the HR member set from the BusinessDriver dimension;
- Strategic Assumptions – Uses the Strategic member set from the BusinessDriver dimension.
If you go and add the two assumption models to the main model at the same time, then everything looks normal, as shown in the screen shot below:
Once you deploy successfully, you will of course want to write a business rule to pick up the assumption model data. However, when writing the rule and trying to pick from the BusinessDriver member selector, you will see that you can unfortunately only select from one member set, as shown below:
If you need to write rules that reference specific members in both member sets, then you will be out of luck. It’s not even possible in any kind of native MDX rule, as the main model cube that gets created in Analysis Services only contains the dimension that has been created from the ‘HR’ member set. It would seem that PerformancePoint just picks the member set that is first alphabetically.
The workaround for this issue is simply to create a single member set that combines the two original member sets. Therefore, each assumption model will contain more members than required, but that’s far better than not being able to write the rules that you need.
So just something to be aware of, and catch, at design time – rather than in the middle of your build.
How Artificial Intelligence and Data Add Value to Businesses
Knowledge is power. And the data that you collect in the course of your business
May
Databricks Vs Synapse Spark Pools – What, When and Where?
Databricks or Synapse seems to be the question on everyone’s lips, whether its people asking
1 Comment
May
Power BI to Power AI – Part 2
This post is the second part of a blog series on the AI features of
Apr
Geospatial Sample architecture overview
The first blog ‘Part 1 – Introduction to Geospatial data’ gave an overview into geospatial
Apr
Data Lakehouses for Dummies
When we are thinking about data platforms, there are many different services and architectures that
Apr
Enable Smart Facility Management with Azure Digital Twins
Before I started writing this blog, I went to Google and searched for the keywords
Apr
Migrating On-Prem SSIS workload to Azure
Goal of this blog There can be scenario where organization wants to migrate there existing
Mar
Send B2B data with Azure Logic Apps and Enterprise Integration Pack
After creating an integration account that has partners and agreements, we are ready to create
Mar