Out-of-the-Box Features: Profitability and Cost Management Cloud Services (PCMCS) - System Reports: Statistics
If you have kept an eye on my latest blog posts regarding PCM Out of the box functionality, then you should already be familiar with the Program Documentation and Execution Statistics Reports, available in PCM.
But wait, there is more!
PCM comes packed with even more Statistics Reports such as Dimension Statistics and Point Of View (POV) Statistics, which will be covered in detail in this blog. Here you will find out how to access these reports and the value they bring to your implementation of Profitability and Cost Management Cloud.
Disclaimer: the PCM menu options and display can evolve over time. As such, screenshots contained in this blog may not be 100% aligned with your current version of Profitability and Cost Management. All images were taken from the 20.09 version of the Cloud software.
Dimension Statistics Report
Where to access the Dimension Statistics Report: System Reports menu
Available formats: PDF, EXCEL, WORD, XML, HTML
The Dimension Statistics report includes key statistical data such as:
- the list of dimensions for your current application
- total number of members in each dimension, as well as level 0 members
- hierarchy depth (more details on this topic below)
- storage type and dimension type
- associated attributes
- last updates for each dimension
When is the Dimension Statistics Report Useful?
Either during development activities or during Business as Usual, it is always a good idea to check the master data overall changes. The Dimension Statistics Report can provide meaningful information when trying to anticipate or troubleshoot performance impact, especially when this is used in combination with the Program Documentation and Execution Statistics reports.
All columns included in the Dimension Statistics Report provide basic information, however, there is one column that has a direct impact on the number of automated calculations generated by PCM in the background, and that is “Hierarchy Depth”.
Why does “Hierarchy Depth” matter?
This question takes us back to how PCM works.
PCM Rules will be dynamically read during calculation execution jobs, and a series of calculation scripts will be generated in the background based on their configuration.
What can impact the volume of calculations generated in the background?
- Rule complexity and references (ex.: increased number of filters based on attributes or member names)
- Number of members selected for each dimension (selecting many base level children vs. a Parent node)
- Hierarchy depth
Experience tells us that the larger the volume of calcs, the slower the execution time.
While there are many reasons why calculations could be slow in a PCM application, hierarchy depth is certainly one of the reasons. A record of hierarchy depth and its evolution over time is useful when performing system performance degradation analysis, and if reducing hierarchy depth is an option, then it should be considered as part of the optimization effort.
Check your hierarchy depth and try to limit the number as much as possible. Using a Master Data Management tool such as EDM would prove beneficial in these circumstances, as you could dynamically pick and choose which parents would be subscribed to your application, and users would not have to worry about manual intervention or adhering to standards when creating hierarchies.
Point of View (POV) Statistics Report
A point of view represents a slice or portion of the application’s data. For example, one POV could be FY20, Sept, Actual Scenario, Final Version. Users of PCM will create POVs, load data to POVs, create Rulesets and Rules against a POV, and, ultimately, launch allocations for a given POV.
Where to access the POV Statistics Report: System Reports menu
Available formats: PDF, EXCEL, WORD, XML, HTML
The POV Statistics report includes key statistical data such as:
- Data Point of View (represents a combination of dimension members that form a Data POV)
- Model POV Name (reference to the Model / Rulesets and Rules referenced during calculation)
- Unique Job ID when this POV was last calculated
- Calculation Parameters, indicating what the user selected during the last run time of the POV
- Cells updated – a total value of all data cells that were updated as part of a particular POV calculation
Here is a short explanation of Data POV vs Model POV:
Model POV – POV where we store Rulesets and Rules
Data POV – POV where we store our expense, revenue, and driver data
A Data POV may contain Model information as well, however, it is not mandatory to be able to launch allocations.
When is the POV Statistics Report useful?
The POV Statistics Report is useful when monitoring the progress of that POV calculation in time, both in terms of run time duration but also in terms of the number of cells updated.
Execution Statistics Report, discussed in a prior blog post, will include the number of cells updated by each rule for a given POV. However, the POV Statistics Report will show you the progress over time for a given POV – how much longer did a POV processing take and how many more data cells were updated with each run. This gives you a quick reference that can be generated in seconds.
One other potential use case for the POV Statistics Report is in situations when we do not store Model Data along with the Data POV. The POV Statistics Report will showcase if a Data POV was calculated using one or more Model POVs, therefore clarifying any potential discrepancies between run times, cells generated, and ultimately, calculation results.
Monitor the POV Statistics Report information and identify performance degradation trends early on as part of your monthly forecasting or close cycle.
The POV Statistics Report is a valuable tool to use when analyzing changes in allocation logic, enabling you to compare execution results for the same data set calculated with different Model POVs. By leveraging the POV Statistics report data, you can draw meaningful conclusions as to which version of the Rulesets and Rules performs better for your PCM model.
This post is of importance because it showcases performance analysis capabilities for the Profitability model, enabling easy troubleshooting and evolution tracking of a model over time. PCM administrators can leverage the System Reports contents to be proactive and anticipate potential performance degradation issues by analyzing POV Statistics or Dimension Statistics reports over time.
There are many variables that define how a PCM model will perform, and while some fine-tuning relies heavily on implementation experience, business users can gain valuable knowledge by analyzing their model via the System Reports provided out of the box with PCM.
If you want to hear more about how to use these reports to their full capability or if you would like to identify performance improvement opportunities for your PCM model, reach out to us at firstname.lastname@example.org.
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