Analyzing how a financial system is used provides several benefits for individuals, businesses, and regulatory bodies, and IBM Planning Analytics (TM1) systems are no different by any means.

Understanding how the user base interacts with your TM1 model offers several benefits:

1. Increased User Satisfaction:

By comprehending how users navigate and utilize TM1, improvements can be made to address pain points and enhance user satisfaction, i.e. the dimensionality of a cube and/or the measures that a user wants to investigate or derive. A satisfied user base is more likely to engage with your TM1 system consistently.

2. Efficient Problem Resolution:

Insight into user interactions aids in identifying and resolving issues promptly. Support and troubleshooting efforts can be more effective when aligned with the specific challenges faced by users during interactions with the TM1 model.

3. Optimized User Training:

Understanding how users interact with the system informs the design of training programs. Training materials can be targeted to address areas where users commonly face challenges, leading to quicker onboarding and increased proficiency, happy user, happy days!

4. Data-Driven Decision-Making:

Data on user interactions serves as a foundation for data-driven decision-making. Strategic decisions regarding system updates, improvements, licensing and future development can be based on empirical evidence rather than assumptions.

5. User-Centric Innovation:

A deep understanding of user interactions encourages a culture of innovation centered around user needs. Innovations are more likely to align with user expectations, driving the continuous improvement of the TM1 system. Feedback loops can be established to collect user input, enabling you to adapt and evolve your TM1 systems based on changing user needs rather incorrect assumptions.

In essence, understanding how the user base interacts with your TM1 system empowers developers and stakeholders to create a more user-friendly, efficient, and engaging experience, resulting in higher satisfaction and sustained user adoption.

Typically, a company’s TM1 applications are crafted to support effective financial resource management, budget forecasting, and strategic decision-making. These applications function as robust tools for corporate financial planning and analysis.

In this instance, we’d like to demonstrate the process of extracting Pulse data from its database and integrating it into a TM1 cube for further analysis. By clicking the link below, you can effortlessly set this up:

• Loading Pulse data into TM1

Subsequently, by leveraging the capabilities of Slice, MDX, and Excel, we’ve simplified the access, retrieval, reporting, and analysis of your TM1 usage data, allowing you to accomplish these tasks in just a few minutes.

Run the TI process so all the TM1 usage data by user is updated in the Pulse Session cube from the Pulse database (select the parameter pDataLoadAll to 1 to load all history):

Then open the Pulse Session cube and export the default view to Excel as an MDX form.

Once you are in Excel, we’ll break down the query into 3 lines as shown below, this is mainly due to a limitation on Excel that can only accept 255 text characters in a formula, so we’ll sue the concatenate function for this situation.

Last but not least, we will make some adjustments to the report format and include a couple of other relevant information such as Unique Sessions by period as well as TM1 usage by UI. Again, don’t worry we got your back and you can download the example so you can see what has been done.

You will note that the Measure “Avrg Session Time” Was created on the spot with the following simple TM1 Rule:

[‘Pulse Session M’:’Avrg Session Time’]= [‘Pulse Session M’:’Session Time’] \ [‘Pulse Session M’:’Login Count’] ;

This is to show you is that you can do anything you want with your Pulse TM1 usage data. So, unlock the power of your Pulse data! With this simple approach in an environment that you know, TM1!