As business analytics coaches, it is our passion to discuss topics that make your job easier. Today we are going to talk about a subject that TM1 clients have recently mentioned in conversations.  The subject is about TM1 Architect. Many of our clients have models that have been built utilizing TM1 Architect.  Some of these clients are having difficulty either understanding the model logic or troubleshooting when issues arise. The question then becomes, how many models are being built with Cognos TM1 Performance Modeler? There is no data on the ratio of models built in PM vs Architect but it seems like most complex models are still being built utilizing TM1 Architect.  Even though these models are being built with the intent of being turnkey and maintenance free, the fact is that issues still come up. This requires you to have specific knowledge of how TM1 really works under the covers in order to fix these issues. Gaining a little bit of this knowledge goes a long way in troubleshooting minor issues. Especially if your models is legacy and pre PM days.

Here at Lodestar Solutions, we are working on developing a training class that will do just that. It will focus on everything related to TM1 Architect and enable you to effectively manage your TM1 Architect specific models. The curriculum of the class will include the following:

• Basic and brief explanation of in-memory multidimensional modelling (You should know most of this from your PM training)
• The anatomy of TM1
• Elements – Figure out your Metadata
• Dimensions – Create dimensions from your Metadata
• Subsets – Create Subsets of Elements
• Attributes – Create Attributes for the Dimensions
• Cubes – Create Cubes from the Dimensions
• Rules – Create Rules
• Views – Create Views
• TI processes – Use them in TI processes and create ETL
• Chores – Schedule TI Processes

The underlying purpose of this class is that you will take away these items:

• You can understand what your consulting partner is building and how to troubleshoot if any issues arise
• You can build better models in PM because you get a deeper understand of the inner workings of the OLAP engine and its capabilities