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HollyRieke
Did You Know: Code for Line Item Subsets
Author: Jonathan Cushing is a Certified Master Anaplanner and Senior Consultant at Vuealta Consulting.
The video gives the details of a little-known function in Anaplan whereby you can add codes to line item subsets (LiSS). The video shows:
- Where to input the LiSS code
- How it can be used to map a line item subset to a list based general ledger.
- This allows for an Income Statement forecast to be mapped back on to an Income Statement presented at general ledger level.
- Shows how to use the CODE(ITEM(Line item subset formula))
Clearly there are many more applications of this functionality, but I found it particularly useful for list based financial reporting.
Questions? Leave a comment!
JonCushing
Manual Input for Recipients of Notifications - CW
At the time of writing of this post, it's only possible to choose from a limited list of recipients who receive the Notifications of CloudWorks integrations.
However, it would be very convienent to be able to manually input email adresses of the recipients. This would allow to send the notifications directly to Team Inboxes or even whole mailing lists for information purposes.
Please let me know what you think or if this was already proposed as an idea (couldn't find it in the forum).
kpasko
PlanIQ Error Messages and Warnings
Users may encounter error and warning messages in PlanIQ. The table below includes descriptions of the messages and details what should be done in order to address them, where applicable.
Message types:
- Errors will fail the relevant PlanIQ step. Users will have to follow the instructions in order to avoid the errors.
- Warnings will not fail the relevant PlanIQ step. Users will be able to proceed as planned.
|
Message |
Type |
Description |
Comments |
|
{Dataset Name} headers contain the following reserved words: {Used Reserved Words}. Please change module line item names. |
Error |
Some field names are reserved by Amazon Forecast and therefore not allowed (see the full list of reserved words here). Please change the field names accordingly. |
{Dataset Name} refers to the name of the dataset.
{Used Reserved Words} refers to a list of words reserved by Amazon Forecast that were used by the user. |
|
Data type of historical / related time series column {Attribute Name} has changed from {Expected Data Type} and now includes {Value}. Please revert data type change in the source module. |
Error |
One of the data columns contains a data type (e.g. Number) that was later changed to a different data type. Please revert to the original data type.
|
{Attribute Name} refers to one of the data columns in the dataset.
{Expected Data Type} refers to the expected data type.
{Value} refers to a sample value from the data column that doesn’t align with the expected data type. |
|
The data has less than 300 historical value records and cannot be used by advanced forecast algorithms such as DeepAR+, CNN-QR, Anaplan AutoML . Please add more historical data. |
Warning |
PlanIQ will not be able to use advanced algorithms (DeepAR+, CNN-QR, Anaplan AutoML) if less than 300 historical values were provided across all time series. |
|
|
Historical data contains records that cannot be converted to numerical values. Please check the data. |
Error |
The prediction target column in the historical data contains non-numeric values (e.g. String or Date). Please check the historical data and export action for invalid values.
|
|
|
Unable to identify the time format in the historical data. |
Error |
The historical data includes a date format that PlanIQ cannot identify. Please check the date format and verify it is PlanIQ-compatible.
|
|
|
The historical data does not contain all the required columns. |
Error |
The historical data must contain three columns: item unique identifier, time dimension and prediction target. If some of those columns are missing, it will cause an issue with the export action. Please examine the historical data module and export action to verify that the three columns are available. |
|
|
Maximum number of rows in the data collection must be less than 1,000,000. |
Error |
The maximum number of records in the historical data is 1,000,000 records. Please reduce the number of records that the export action generates. If possible, a view with filter should be utilized in order to achieve higher level of control. |
|
|
The data collection contains more than 100,000 individual items. Please reduce number of individual items to 100,000. |
Error |
The historical data module may contain up to 100,000 distinct unique item identifiers. Please consider breaking down the dataset and training less unique items per forecast model.
|
|
|
Maximum number of columns in the historical data is 3. Please reduce number of columns to 3 or less. |
Error |
The historical data contains more than 3 columns. Required columns are: item unique identifier, time dimension and prediction target.
|
|
|
The historical data contains too many missing values, more than 50%. Please check if you can fill missing values. |
Error |
More than 50% of the values in the historical data are missing. Please review the data and consider filling the missing values or forecasting at a different level of granularity. |
|
|
The historical data time scale could not be found. |
Warning |
This warning is presented if any of the following conditions in the historical data exist · Some dates are missing · There are duplicate dates · Some dates not aligned with the time scale of the historical data set (for example, if weekly scale is specified and all weeks are represented by values for Mondays, and occasionally there is a record with date on Sunday)
Please examine the date format in the historical data module and export action. |
|
|
The prediction target column is empty. Please check the source module and export action. |
Error |
The prediction target column in the historical data module has no data. Please examine the module and export action.
|
|
|
The related data does not include all the required columns. |
Error |
The related data must have a minimum of two columns: item unique identifier and time dimension. If at least one of those columns is missing, it indicates that there is an issue with the configuration of the export action, or that previously-available columns in the related data export action are now missing. Please examine the related data module and export action carefully. |
|
|
The related data includes the prediction value column. Please remove the {Prediction Value} column. |
Error |
The related data contains the prediction value column. This might indicate that there is an issue with the export action. For instance, you may have erroneously used your historical data export action for the related data. Please examine the related data export action file format accordingly. |
{Prediction Value} refers to the prediction value column. |
|
Unable to identify the time format in related data. |
Error |
The related data includes a date format that PlanIQ cannot identify. Please check the date format and verify it is PlanIQ-compatible. |
|
|
Maximum number of columns in the related data is 13. Please reduce number of columns to 13 or less. |
Error |
The related data contains more than 13 columns (not including the item unique identifier and time dimension). Please reduce the number of columns.
|
|
|
All the numeric and boolean columns in the related data are empty. Please check the source module and export action. |
Error |
All the numeric and Boolean columns in the related data set (apart from item unique identifier and time dimension column) are empty. Please configure the export action properly. |
|
|
Columns {Empty Columns} in the related data are empty. Please check the source module and export action. |
Error |
The related data columns listed in the error message are empty. Please validate that the export action is properly configured.
|
{Empty Columns} refers to columns in the related data set that are empty. |
|
Related data time scale could not be found. |
Warning |
This warning is presented if any of the following conditions in the related data exist · Some dates are missing · There are duplicate dates · Some dates not aligned with the time scale of the historical data set (for example, if weekly scale is specified and all weeks are represented by values for Mondays, and occasionally there is a record with date on Sunday)
Please examine the date format in the related data module and export action. |
|
|
There are more than 10 columns in the attributes dataset. Please reduce number of columns to 10. |
Error |
The attributes data contains more than 10 columns (not including the item unique identifier). Please reduce the number of columns.
|
|
|
The attributes don't include the item id: {Item ID column} |
Error |
The attributes data does not include item unique identifier column. Please validate that the export action is properly configured. |
{Item ID} refers to the item unique identifier column. |
|
The attributes don't include all items in the historical data. Missing item ids: {Missing Items} |
Error |
Some items that appear in historical data are not present in the attributes data. All listed items must be present in the attributes data. |
{Missing Items} refers to items that are present in the historical data but are missing in the attributes data. |
|
Timeline of historical and related data does not match for items: {Invalid Items}. Related data timeline should start on or before historical data timeline. |
Error |
The related data must start on or before the start date of the historical data. This error message is displayed if at least one item’s start date in the historical data precedes its first appearance in the related data. Please adjust the data of the problematic items.
|
{Invalid Items} refers to items for which the timeline of the historical and related data doesn’t match. |
|
The related and historical data do not contain the same item ids. |
Warning |
There is at least one item that exists in the historical data set but does not exist in the related data set, or vice versa. |
|
|
The related data does not include forward looking information. Add forward looking data to improve forecast accuracy. |
Warning |
The related data does not include forward-looking information. Even though some algorithms can operate without forward-looking related data, it is advised to provide this data to improve forecast accuracy.
|
|
|
The time scale of related and historical data is incompatible. |
Error |
There are certain restrictions concerning the time scale of the related and historical data. Please refer to the relevant article for more details. |
|
|
PlanIQ has been unable to check the model prediction quality based on the actuals that have been provided.
|
Warning |
The model prediction quality comparison process cannot run because unified actuals timeline cannot be constructed. Forecast action execution and generation of forecasts is not affected by this warning. |
|
|
PlanIQ has been unable to check the model prediction quality based on the previous forecasts.
|
Warning |
The model prediction quality comparison process cannot run because unified historic forecast timeline cannot be constructed. Forecast action execution and generation of forecasts is not affected by this warning. |
|
|
PlanIQ has been unable to check the model prediction quality.
|
Warning |
The model prediction quality comparison process cannot run due to some internal error. Forecast action execution and generation of forecasts is not affected by this warning. |
|
|
Your prediction quality has reduced from {Original Predictor} to {Current Predictor}.
|
Warning |
Model quality has deteriorated from either high → medium or low, or from medium → low.
Forecast action execution and generation of forecasts is not affected by this warning. |
{Original Predictor} refers to the previous model quality.
{Current Predictor} refers to the current model quality. |
JakeIRL
Re: Share your boards, reports, and worksheets best practices — May 2025 Community Challenge
General UX Principles
When designing NUX, always aim for a clean, minimal UI that reduces clicks and streamlines user actions. For example, if a user needs to approve employee forecasts, design it so they scroll less, check fewer boxes, and click fewer buttons to complete the task.
Good UI isn’t just about aesthetics—it's about delivering an efficient, intuitive experience tailored for end users. It goes beyond Anaplan best practices and focuses on making the system user-friendly, flexible, and process-optimized.
Here are some key principles I follow to achieve that in UX design.
Grid & Layout Design
- Add titles to every grid.
- Use solid header colors for better readability.
- Apply conditional formatting to editable fields to guide users. As shown in the Example (#fffacd) color code for editable fields.
- Keep label height to 2 lines if headers are short.
- Maintain consistent column widths.
Formatting & Usability
- Adjust font size and row height based on data volume.
- Use the "Format" option to visually separate line item or time periods, like quarters or fiscal years.
- Disable sync to page/hierarchy filters if unnecessary to avoid cluttering contexts.
- Create landing pages to organize related pages for smoother navigation.
Page Types & Usage
- Boards: Best for input dashboards and functionality-heavy pages — flexible layout.
- Worksheets: Ideal for reviewing data with minimal interaction — utilize Additional Insights for KPIs, Filters and Charts.
- Reports: Use for leadership-facing decks — polished, presentation-style summaries.
Dikshant
Re: Share your boards, reports, and worksheets best practices — May 2025 Community Challenge
Here are a few tips for navigation and user guidance / instruction:
- Use the pre-existing navigation and filtering capabilities in the UX. Trying to create custom navigation links or custom filters adds clutter in a page and can be time-consuming to maintain.
- Only show filters that are required, and show them in multiple areas to be flexible for how users like to filter (i.e. in card vs. up top right).
- Add simple instructions for users when needed. If users need detailed instructions, set it up in elsewhere and add a link for users to access as needed.
- Many users find it useful to see the module name in the card description.
- Consider having both a number and title for sections and pages, and order them in a way that makes sense and gives an efficient user experience. Numbering them makes it super easy to reference in conversations/meetings/emails/chats.
- Example:
- 1 Reporting
- 1.1 Report A
- 1.2 Report B
- 2 Analysis
- 2.1 Analysis A
- 2.2 Analysis B
- 1 Reporting
- Example:
- Only show what is relevant to each role by applying Restrict Access in the Page Settings.
iWonder
Used in Pages (Apps) column
On the Actions and Modules tab, similar to the 'Used in Dashboard' column it would be useful to have a 'Used in Pages' column which shows where action or module in used.
Re: How I Built It: YTD values using a fake calendar
Hello Anaplan Champions! Just wanted to mention if you're a video-type-learner you'll notice I don't walk you through the formulas. Deliberate - wanted to keep the video as short as possible. But there's good news! Below the video is the documentation which I show you all the formulas.
I use this strategy for nearly all my retail clients. For example, hourly planning, limited time ranges that don’t require a whole year, promotional planning, and non-conforming 454 calendars.
Enjoy! Keep on, keepin' on. Thank you @GingerAnderson and @becky.leung for considering this post for publication!
Replace type link not working in ADO
Hey,
I'm trying to run a replace type of link to a model hierarchy from a transformation view. I'm able to setup the link correctly (choose desired destination, source dataset, and do the correct mapping).
But when I try to push the data using "replace" update type I'm stuck at Generate differences screen for hours. It stops loading after few seconds, but the screen just gets stuck after that (screenshot attached).
The same link I'm able to run using "Upsert" (without changing anything else in the link). The data also updates in the model.
Please help. If it's a bug in ADO, would appreciate if this can be looked into.
Unlocking the power of Polaris: A guide to efficient model building
This article is part of a series on Polaris best practices. Click here for more Community content or visit Anapedia for detailed technical guidance.
Polaris is Anaplan's next-generation calculation engine, designed for highly dimensioned calculation at scale. It represents a paradigm shift in the ability to model business planning challenges without compromise.
This deck and the accompanying videos delve into best practices for modeling in Polaris to help you build more efficiently and effectively.
Here are some of the key takeaways:
- Natural dimensionality: Most business problems are inherently multi-dimensional, but the resulting datasets are often largely unpopulated. Polaris is designed to handle this natural dimensionality by focusing on populated space rather than empty space. For more on this topic, check out this article on Natural Dimensionality.
- Populated vs. empty space: In Polaris, only the populated space in a module consumes memory and affects calculation effort. This means that increasing the size of a dimension does not necessarily increase the amount of memory used or the calculation effort required.
- Blueprint insights: Polaris provides insights to help optimize your models, including cell count, populated cell count, calculation complexity, and calculation effort. Understanding these metrics can help you identify opportunities to improve efficiency.
- Optimization strategies: To optimize your models in Polaris, you should focus on writing efficient formulas and only doing math when you need to. This includes scoping your formulas to relevant intersections and taking advantage of Polaris's populated cell count calculation.
Polaris enables you to build less complex models that more naturally fit the shape of your data. By understanding the concepts of natural dimensionality, populated space, and blueprint insights, and by following the optimization strategies outlined here, you can start to unlock the full potential of Polaris.
Download the presentation and watch the videos to learn more.
Presentation:
Additional resources
For more detailed information about each of these concepts, please see the following articles. Each article includes a video overview also.
- Anaplan Polaris – Natural dimensionality
- Anaplan Polaris – Populated space
- Anaplan Polaris – Blueprint insights
- Anaplan Polaris – Understanding Blueprint Insights and Optimizing for Populated Space
…………..
Authors:
Anaplan’s Theresa Reid (@TheresaR), Architecture and Performance Director; Stephen Rituper (@Stephen), Sr. Director, OEG; and Terry Archsmith (@TerryA), Sr. Director, Platform Training.
Special thanks to Mike Henderson (@hendersonmj) for his contributions.
