Polaris Modeling and Optimization — Community Q&A Challenge

Think you’ve mastered the Polaris calculation engine? It's time to put your skills to the test!

For our next Community Challenge, we're moving into hands-on problem-solving. We have put together several real-world modeling and optimization problems that will require you to apply your Anaplan expertise.

This is your chance to demonstrate how you would tackle complex module rebuilds and tricky optimization scenarios in Polaris. We’re not just asking for best practices — we want to see you apply them. Are you up for the challenge?

Polaris Community Challenges

Below is a series of challenges developed by our experts. To participate, please choose at least one challenge to respond to!

Module Challenge

Module rebuild: The below module exists in a spoke model in Classic. How would you recommend building it differently in Polaris?
1. Additional questions:
  1. Where should a module like this sit?
  2. What questions would you ask before building it?
  3. Why did you land on the solution you’ve suggested?

Optimization Challenges

Optimization of High Calculation Effort: review the below line items and their blueprint data. Choose one and recommend modeling techniques to bring Calculation Effort down.
1. Option 1:
2. Option 2:
Optimization of High Calculation Complexity: review the below line items and their blueprint data. Choose one and recommend modeling techniques to bring Calculation Complexity down.
1. Option 1:
2. Option 2:

Bonus questions

What other optimization techniques have you used in Polaris that you didn’t mention above?
If you had one tip for new Polaris model builders, what would it be?

How to participate

The Polaris Modeling and Optimization Challenge kicks off today, April 22, and concludes May 13.
Please select at least one of the Challenge prompts above to answer. Share a detailed write-up, screenshots, or short video walk-through of your response.

What's in it for you?

Recognition: Gain recognition as a modeling pro in the Community.
Learn from your peers’ creative approaches and best practices.
Earn a badge: As a thank you for your participation, everyone who participates in one of the Polaris challenges in this post will receive an exclusive Community Challenge badge. It’s a fun way to show off your contribution!
Earn a shoutout in our upcoming event: on June 9, we will be hosting an upcoming ACE Community Challenge recap event. Participants’ response will be highlighted at this event.

Join the Challenge today!

We are excited to see your solutions and best practices in action. Jump in, share your knowledge and expertise, and let’s all learn from each other!

Comments

namdevgurme

Tips and optimization tech From myside to new Polaris model builders.

Optimize for populated space
-Focus on measuring populated cells (cells which have values) vs total potential cells. Many operations depend on populated count.
Guarded Formulas - Instead of broad calculations across all intersections, use guards (conditional logic) so Polaris only calculates where values are non-default. This reduces unnecessary effort
Inline Conditions - Scope formulas tightly to relevant dimensions. For example, calculate only for active products or valid time periods rather than the entire dimension.
Blueprint - Use the Populated Cell Count and Calculation Effort metrics to identify which line items are heavy and restructure them for efficiency.
Hierarchy and time management - Deep hierarchies can increase sparsity; manage them carefully. also Longer time ranges (future, historical) increase dimensionality, ensure that unused time periods are handled efficiently.
Migration from Classic to Polaris - Evaluate existing models for pain points, where complexity, flattening, or slow performance are present, Re-architect models rather than simply lifting into Polaris.

Humay

Module Challenge:

Where should a module like this sit?
—> Ideally, a module containing this level of transactional detail should sit in a Data Hub. You’d keep the raw transaction details in the Hub and only move summarized data or specific subsets needed for calculations into the Spoke.

What questions would you ask before building it?
—>Who is the end-user? What is the data volume? Is it read-only? What is the "So What?" (What action does a user take after looking at this list? Should there be a "Delete" or "Approve" action button?)

Why did you land on the solution you’ve suggested?
Performance: Keeping it in a Data Hub prevents the functional model from becoming bloated with "flat" data that isn't being used for complex multidimensional calculations.

Optimization Challenges:

Option 1:

Convert to Boolean —> New Formula: ITEM(Account) = 'DAT.Data'.Account
Anaplan processes Booleans significantly faster. Also, I can eliminate the "IF THEN ELSE" logic, which is not necessary for a simple comparison.
Check "Applies To" —> if this calculation needs to happen at the level it's currently at.

Summary Settings
—> the Summary for this line item is set to None.

One tip for new Polaris model builders: Do not use 2 LOOKUPs in the same formula. Make sure to create 2 separate line items, bring first lookup on the 2nd lookup's dimension then combine it with the 2nd lookup.

Wombowser

Optimization challenge :

Optimization of High Calculation Effort

Option 1

Early exit is not important in Polaris so it is best to simplify the formula :

IF
  Item(Account)='DAT.Data'.Account
THEN
  0
ELSE 
  1

Optimization of High Calculation Complexity

Option 2

Create a new line item "Parent for child account" in the sys by account flat module with the following formula :

IF 
  'is child account?' 
THEN
  parent 
ELSE
  blank

2. Reference this new line item in the formula :

 Region [lookup:'sys by account flat'.Parent for child account]

Bonus tip

Polaris only consider a value when it is not equal to 0 so any formula with a condition looking like "if x<>0 and y? then " can be optimized to "if y? then"

SIVAPRASADPERAM

—>Optimization Challenge: ←

Optimization of High Calculation Effort:

No hardcoding is involved, and I recommend Option 2, as it utilizes native time functions (Previous), minimizes item-based cross-dimensional calculations, and restricts the calculation scope, resulting in improved performance.

Optimization of High Calculation Complexity:

I recommend options 2 because the Model using hierarchy‑driven logic, conditional LOOKUPs, and SYS modules at native grain, while avoiding SYS Boolean broadcasts into large data spaces.

—> Benefits of the Polaris engine include: ←

Sparsity handling: Polaris naturally ignores zero and null values, significantly reducing the number of cells evaluated during calculations and improving performance in large, sparse data spaces.

Reduced memory consumption: Since zero-valued data has not materialized or processed, overall model memory usage is lower compared to classical engines.

umangsinghal

Instead of keeping everything in a single wide transactional module, I would recommend structuring it into three logical modules.

1. Transaction Module - This module would store the raw transactional data coming from upstream systems (via the Data Hub) with minimal formulas.

2. Attribute Mapping Module - This module would store list-formatted references and mappings, such as Account, Employee, Region, Opportunity, etc. Separating these mappings allows them to be reused across calculation and reporting modules while keeping the transactional module lean.

3. Reporting Module - This module would contain the dimensions required for reporting and user visibility, such as time, geography, sales hierarchy, etc. Aggregations and calculations should happen here so that they are performed only when needed for reporting or downstream exports.

Q1: Where should a module like this sit?

This module should sit in the data layer of the spoke model, receiving transactional data from the Data Hub.

Q2: What questions would you ask before building it?

What is the expected volume of the transactional data?
How frequently will the data be loaded or refreshed (batch, daily, or real-time)?
What aggregation levels are required for reporting (region, rep, product)?
Will there be a need for historical restatement or recalculation?

Q3: Why did you land on this solution?

Separating the modules helps avoid dimensional explosion.
Polaris performs well with sparse structures, so avoiding unnecessary dimensional intersections keeps the model efficient.
Keeping attributes separate from transactional metrics makes the model easier to maintain and reuse.
Performing aggregations in dedicated reporting modules ensures calculations happen only where needed.

Optimization Challenges

Optimization of High Calculation Effort:

Option 1:

Red Flags

ITEM function
Huge popiulated cell count

Solution

Instead of using the ITEM() function repeatedly across modules, it is better to reference it through a SYS module. A SYS module for the Account list can store commonly used attributes such as ITEM(Account) and Code(Item). These values are calculated once and then reused across the model wherever needed. This approach reduces unnecessary repeated calculations and improves overall model performance.

Another important consideration in Polaris is how sparsity is handled. Polaris does not count sparse cells toward the overall model size, so allowing cells to remain NULL / 0 is generally more efficient than forcing values into them.

So, I would create a new boolean formatted lineitem and formula will be:

Account Exist? = NOT ('SYS: Account Attributes'.Item = 'DAT.Data'.Account)

This formula would return lower populated cell counts.

Then, use this boolean linitem in reverse in Revenue of CAL P&L module.

2. Optimization of High Calculation Complexity:

This logic belongs in a SYS module dimensioned only by Account.

Create this lineitem in SYS module.

Assigned Region =

Is Child Account?

THEN

Parent Region

ELSE

BLANK

Then simply reference it here: SYS Account.Assigned Region

Bonus questions

What other optimization techniques have you used in Polaris that you didn’t mention above?

There are following best practices:

i. Used single filter condition when creating Saved view.

ii. Calculate one, reference multiple times

iii. Avoid using Text formatted lineitems

iv. Avoid using Summary levels, opt for Just a Dash instead.

v. Avoid SUM and LOOKUP in a single formula

vi. Avoid Finditem and SELECT, opt for LOOKUP instead.

vii. Avoid writing complex formulas

viii. Follow DISCO methodology

ix. Avoid direct references of list items.

x. Avoid Hard coding

xi. Avoid List properties

xii. Avoid importing from lists, opt for saved view instead

xiii. Subsidiary views: Break the module if there is a need of less/more dimensions

xiv. Avoid using User dimension. If it is necessary to use, keep it with trhe subset.

xv. Always add action into process.

2. If you had one tip for new Polaris model builders, what would it be?

Design your models with sparsity in mind from the start. Polaris performs best when calculations happen only where data exists, so focus on minimizing unnecessary dimensional intersections and avoid replicating dense design patterns from Classic models. Before building, take time to understand the data structure, transaction volume, and reporting requirements. A well-designed model architecture upfront can prevent major performance issues later.

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