Share your data integration best practices — September 2025 Community Challenge

We’re excited to launch our September 2025 Community Challenge, focusing on one of the most critical aspects of Anaplan success: data integrations. Whether you’re just getting started with CloudWorks or Anaplan Connect, or you have built advanced orchestrations using APIs and Anaplan Data Orchestrator, integrations are at the heart of connecting Anaplan to the rest of your ecosystem.

This month’s Challenge is your chance to showcase how you move data in and out of Anaplan, share tips that have made your integrations run smoothly, and inspire others in the Community.

Challenge prompts

To help spark your ideas, here are some questions you might explore in your response.

Note: Please avoid using AI/ChatGPT when writing your Challenge response — it should come from your own expertise so the Community can learn from you!

Please select one to two prompts from the Beginner or Advanced categories below to answer!

Beginner focus

How do you set up a simple import or export between Anaplan and a source system?
What are your go-to tips for mapping flat files or leveraging saved views?
Do you prefer CloudWorks or Anaplan Connect for your first integrations — and why?

Stretch goals (advanced concepts)

How do you orchestrate multiple integrations together using Anaplan Data Orchestrator or scheduling tools?
Have you used APIs to automate your flows? If so, what benefits did you gain?
What error handling, monitoring, or logging practices keep your integrations reliable?
Any advice for working with large files or optimizing performance?

Learnings

In addition to sharing your tips, tell us about mistakes you’ve made and what you have learned from them — your experiences with data integrations can help fellow Community members succeed.

Bonus

Share a diagram, workflow screenshot, or snippet of your integration script to illustrate your process.

How to participate

The data integrations Best Practices Challenge kicks off today, September 9, and concludes on September 26.
Post your response directly in this thread — whether it’s a detailed write-up, a quick list of tips, screenshots, or even a short video walkthrough. Pick one or two prompts from the beginner or advanced categories and let us know your responses! No matter your style, your perspective will help the Community grow.
Check out tips shared by your fellow Community members.

What's in it for you?

Recognition: Gain recognition as a data integration 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 shares their best practices will receive an exclusive Community Challenge badge. It’s a fun way to show off your contribution!
Earn a shout-out in our upcoming event: on October 28, we’ll be hosting an event discussing data integration best practices in an upcoming ACE Spotlight event. Participants' responses will be highlighted at this event.

Ready to get started?

Share your data integration best practices below — whether you’re a beginner experimenting with your first CloudWorks schedule or an advanced builder orchestrating complex API workflows, your learnings and ideas will spark inspiration across the Community!

Comments

andrewtye

To kick-off and initially for imports and exports

Get used to loading the data manually (ie CSVs). You'll soon clean up the file format and other issues before using a more automated tool. One file for properties, one file for data - leverage best practices (Tips & Tricks in OEG Best Practice: Data Hubs: Purpose and peak performance - Anaplan Community)

If you can use Cloudworks, it's so much easier to leverage cloud storage areas and your CISO function is more likely to sign-off on it's usage vs use of Connect. Oh and it's no code which makes it a lot easier to pick on

And always create a saved view for exports - yes if you need to amend the export you need to re-save it but at least you've got the latest export to go from

matthewreed

Quick tips that have served me well when leveraging saved views:

Use detail in your saved view name. Make it clear what this view is for and how it's different from others in this module, and adding the destination model can help save time in future at well (eg. Export L1 Item List & Details |[Destination Model Name])

As is best practice, create export filter line items for your saved views. We create a filter line item for each saved view, even if it's a duplicate, to ensure we have a matching export filter line item for each saved view. Because filters used in a saved view do not trigger a dependency flag in the 'referenced to' column, having a export filter line item for each saved view ensures you don't remove boolean line items that drive your filters by accident.

SunnyNafraz

Simple Integrations:

To move data in or out of Anaplan, you use import and export actions. You'll find these under the Actions tab in your model.

Import: Brings data into Anaplan from a file (like a CSV) or a saved view from another Anaplan model.
Export: Sends data out of Anaplan to a file.

Tips for Mapping Data:

Flat Files: Make sure your column names in the file match the names in Anaplan. This makes mapping easy and automatic.
Saved Views: Use these to move data between Anaplan models. Create a view in one model with only the data you need, then use it as the source for an import in another.

CloudWorks vs. Anaplan Connect:

I recommend starting with CloudWorks. It's built into Anaplan and is easy to use for scheduling imports and exports from cloud storage. Anaplan Connect is a command-line tool. It's more powerful but harder to learn, so save it for when you need to do something very complex.

Advanced Tips:

Orchestration: Use ADO to set up a series of actions that run in a specific order (e.g., import master data, then transactional data).
APIs: Use APIs to automatically trigger your imports and exports from other systems. This is great for real-time automation.
Error Handling: Always check the log files after an import. Set up CloudWorks notifications to get an email if something fails. This helps you fix problems quickly.
Big Files: Compress large files or break them into smaller ones to improve performance.

My biggest mistake was not checking the import logs. When an import failed, I didn't find out until much later. Now, I always check the logs and have email alerts set up so I can fix any issues right away.

Prajjwal88

A quick workaround that helped me with transactional datasets with no unique key:

While working with Anaplan Connect to upload transactional data, I often faced the issue of files not having a unique key (Even after considering combination of properties). Instead of asking stakeholders to provide the file with a predefined unique key, one quick workaround that has served me well is to use a simple batch script to add a serial number column.

This serial number acts as a unique key, which allows the data to be uploaded as a full load into a staging list. From there, we can easily filter and transform the data as needed inside Anaplan.

Here’s the script I used:

set "inputfile=C:\Users\Documents\Scripts\Test_Pre.csv"

set "delimiter=;" set "outputfile=C:\Users\Documents\Scripts\Test_After.csv"

if not exist "%inputfile%" ( echo Input file not found. exit /b )

setlocal enabledelayedexpansion

set "linenum=0"

"%outputfile%" ( for /f "usebackq delims=" %%a in ("%inputfile%") do (
if !linenum! EQU 0 (
echo %%a!delimiter!_lineno_ )
else (echo %%a!delimiter!!linenum! )
set /a linenum+=1 ))

echo Processing complete. Output saved to "%outputfile%".

Here's the before and after view of csv file ( the additional _lineno_ column acts as a unique identifier for each record, applying this made my integrations much smoother) :

SIVAPRASADPERAM

Sample script followed by a detailed explanation:

*Anaplan integration will support for both inbound (data loading) and outbound (data extraction):

*ADO is used to create and manage automated data workflows between Anaplan and external systems.

*comparison between CloudWorks and Anaplan Connect:

Feature	CloudWorks	Anaplan Connect
Interface	Web-based platform, no coding required	Command-line tool, requires scripting
Ease of Use	User-friendly, ideal for beginners	Complex, suited for advanced users
Scheduling	Built-in scheduling for imports/exports	Requires external schedulers (e.g., informatica)
Integration	Connects with cloud storage (e.g., AWS, Azure)	Can integrate with various systems via scripts (eg.informatica)
Automation	Limited to scheduled tasks	Highly customizable automation (Require external schedulers)
Error Handling	Email notifications for failures	Manual log review required

I've implemented a batch scripting workaround to streamline the file import process. Here's a brief overview of how it works:

Overview of the Implementation:

A new file is imported from Informatica to Anaplan using batch scripting via Anaplan Connect.
Once above step done, the file is automatically moved to the Archive folder.
The archived file is renamed with today’s timestamp appended to ensure traceability and prevent overwriting.
Additionally, a output log file is generated with the current date's timestamp in the Anaplan Connect directory.

ImportBatchScript:

@Echo off

setProxyUrl=URL Link

setProxyUser=UserName:Password

for/f"tokens=2delims=="%%ain('wmicOSGetlocaldatetime/value')doset"dt=%%a"

setYYYY=%dt:~0,4%

setMM=%dt:~4,2% setDD=%dt:~6,2% setHH=%dt:~8,2%

if“%HH:~0,1%”==””set HH=0%HH:~1,1%

setMin=%dt:~10,2% setSec=%dt:~12,2%

setsubfolstamp=%HH%_%Min%_%Sec%

setfilstamp=%YYYY%%MM%%DD%%HH%%Min%%Sec% setfolstamp=LOG_%YYYY%%MM%%DD%

setAnaplanUser="UserName:Password"

setWorkspaceId="Workspace UD"

setModelId="Model ID"

setServiceUrl="https://api.anaplan.com" setAuthUrl="https://auth.anaplan.com"

setFileName1="BU.csv"

SetFilePath1="C:\AnaplanConnectDemo-TEST\BUImport" SetImportName1="I1.ImportBU"

SetDumpPath1="C:\AnaplanConnectDemo-TEST\BUImport\DUMP"

setOperation=-maxretrycount15-httptimeout60-retrytimeout60-debug-service%ServiceUrl%-auth%AuthUrl%- workspace%WorkspaceId%-model%ModelId%

setOperation=%Operation%-file%FileName1%-put%FilePath1%-process%ImportName1%-execute-output

%DumpPath1%

rem***Endofsettings-Donoteditbelowthisline***

setlocalenableextensionsenabledelayedexpansion||exit/b1 cd%~dp0

ifnot%AnaplanUser%==""setCredentials=-user%AnaplanUser% setCommand=.\AnaplanClient.bat%Credentials%%Operation% @Echo %Command%

cmd/c%Command%>>BUOutput%filstamp%.txt

pushd "Informatica path\Imp ortFiles"

rename"BU.csv""Test_BU%filstamp%.csv"

move/-y"Test_BU*.csv" " Informatica path \Archive\BU"

pause

Snehitha Adicherla

👉️My Beginner Approach to Anaplan Data Integrations
When I first practiced imports during Level 1 and 2 training, I started with simple CSV files as my source. I made sure the column headers matched the list or module names in Anaplan, which made mapping straightforward and avoided errors during the import. For exports, I created dedicated saved views in the modules, so I could pull exactly the data I needed without including extra fields.

⚡️I prefer using CloudWorks for first integrations because it’s built into Anaplan, doesn’t require scripting, and allows me to schedule imports and exports easily. Anaplan Connect seems more powerful, but since it involves command-line scripts, it’s something I plan to explore after I’m comfortable with basic imports and saved views.

💡A key learning so far is to always check import logs. Even with small test files, I’ve seen rows fail to load because of tiny mapping mistakes reviewing the logs helps me fix issues quickly and ensures the data is accurate.

Dikshant

Few points that comes on top of my mind in terms of Data Integration in Anaplan:

Recon reports: In tems of Integration, Recon models plays crutial role ensuring that there is no mismatch b/w source and target objects. Let's say users are forecasting Employees in Anaplan, and that needs to be sent to MS SQL for downstream systems to consume for further reporting. In order to ensure that there is no data mismatch b/w the two systems, we can create a Recon model, and import Employee Forecast from both Anaplan and MS SQL and create a report for Dev/Support teams to review the data discrepency and export manually to ensure Anaplan Data matches MS SQL.

Delimeter Issue: Ensure that data that we're sending or importing does not contain a comma, as CSV file consider it as a delimiter and split the data incorectly into multiple columns. If data contains comma, then we need to change the delimiter to a pipe, or something which is not expected to come in your data.

Exports: While creating export action in Anaplan, make sure to create an export view with the same name as action name, as it will give a visibility to a model builder to backtrack what filters and columns are being displayed in the export action.

Imports: While importing data into Anaplan make sure that the code/ unique identifier does not exceed 60 character and does not contains a special character, anaplan action will not be able to import that record.

Cheers! 😊

namdevgurme

Structural Best Practices

1.Use Staging Modules

Always route incoming data through staging modules before pushing it into calculation modules.
This allows for validation, transformation, and rollback without disrupting core logic.

2. Design for Scalability

Avoid hardcoding list items or dimensions.
Use dynamic naming conventions and modular actions that can scale across models and workspaces.

3. Minimize Model Saves

Large imports can trigger frequent model saves, which block other actions.
Reduce save frequency by optimizing list sizes and using efficient import structures.

Integration Process Best Practices

1. Automate with Tools

Use Anaplan Connect, REST APIs, or ETL platforms like Informatica, MuleSoft, ADO for automation.
Ensure secure authentication and encrypted data transfers.

2. Validate Before Import

Perform data quality checks outside Anaplan to avoid importing corrupt or incomplete data.
Use flags and error logs to catch anomalies early.

3. Monitor and Audit

Set up logging and monitoring for all automated jobs.
Use centralized dashboards or external tools to track job status and failures.

Best Practices for ADO Data Integration

1.Centralize Your Data Flow

Use ADO as the single orchestration layer to push validated data into Anaplan.
Avoid duplicating staging models—ADO allows centralized storage and transformation.

2. Design Reusable Pipelines

Externalize transformation logic from models into ADO pipelines.
Build modular, reusable workflows that can be triggered across multiple models.

3. Push-Based Scheduling

Shift from pull-based (Anaplan-triggered) to push-based orchestration.
Trigger data flows from external systems when data is ready, improving reliability and timing.

4. Use Built-In Transformations

Leverage ADO’s UI-driven transformation tools—filters, joins, formulas—without writing custom scripts.
Automate repeatable transformations to reduce manual effort and errors.

KARTHIKNAGURU

How do you set up a simple import or export between Anaplan and a source system?

Import: Extract the required data from csv files.Upload the file into anaplan, map the fields to list or line items once and run it once.
save this as import action so it can be re used
Export: Create a saved view in anaplan select the csv form and export the data save this as an export action
Automation:Both imports and exports can be scheduled or automated using anaplan connect

jstockwell

As an end user and during my time at Bedford Consulting, I’ve built both simple and complex integrations across Anaplan Connect, Cloudworks, ADO, 3rd Party ETL tools, and custom API scripts written in various programming languages. These integrations connect to a multitude of systems.

Choosing which tool to use is important and driven by many factors, including budget, technical expertise, and the available IT landscape.

With the flexibility that API scripting has, it also brings complexity and security considerations. Large files must be split into chunks. Success and error logging can be done on the server, but sharing logs to end users so the right people can react can be challenging.

The biggest learning is having good control of your codebase. Invest time in managing your code using version control tools, such as GitHub, so you can build and release changes in a structured way (similar to using ALM). The main benefit is being able to see all changes and get back to snapshot points in time (commits) so breaking changes can be reverted. Multiple developers can collaborate, and requests can be approved or rejected by GitHub workflow. This is great for Anaplan Connect scripts, API scripts, even the JSON output of the lineitems endpoint, which can track changes to formulae!

Having well-documented processes is important across any tool, and sharing knowledge in an accessible place where other users can access and easily understand is crucial to support.

A good workflow we've discovered is to have integrated data scheduled to come into a Datahub model every 15 mins, then automate it into the Spoke model daily, but users can pull the latest dataset into the Spoke model on demand for the greatest level of flexibility.

Pradyumna

Setting Up a Simple Import or Export in Anaplan

1. Import Tips

Start with a flat file (.csv) or a saved view from another model.
Choose the right source and carefully map dimensions, especially time, if the format doesn’t match, use “Matching Names”.
Match column headers in your file with the target module’s line items to reduce mapping errors.
Keep codes simple. Avoid special characters and stay within the 60-character limit.
Save and rename your import action clearly for reuse and easy identification.

2. Export Tips

Use saved views with filters and sorting to keep data clean and relevant.
Choose your export format (CSV, Excel, TXT).
Save the export as an Action for automation or scheduling.

Lesson Learned

I once used a long code for a parent list and then included it in the child list’s code. This made the child code exceed Anaplan’s 60-character limit and caused import errors. I fixed it by shortening the parent codes — a simple change that made a big difference!

DelahayeOlivier

Tips on what NOT to do with Anaplan Data Orchestrator (ADO) and what you should do instead (based on my personal experience)

DON'T ❌	WHY ❓️	DO INSTEAD 👍️
Import your entire database	Impact on performance + storage allowance	Define relevant data scope
Create a new source data for each new file	Redo mappings manually + dataset allowance	Use sync data to refresh your existing source data
Map a link directly to a source data	No transformation possible	Always use a transformation view
Keep all fields in string type	Possible format error	Use the relevant format type for each field in the transformation view
Concatenate multiple fields to define the ID key of a transactional list	60 character limit	Use the HACH function
Run links one by one in ADO	Time-consuming + run in the wrong order	Create workflows to run links in the right order automatically
Duplicate links for each models	Time-consuming + links daily usage allowance	Use a Data Hub to centralize and redistribute data to spoke models

Poojalakshmi

Imports and Exports Integration Essentials between Anaplan and Source systems

Import:

When integrating external data into Anaplan, the goal is to bring in clean, structured data that aligns with your model’s dimensions and logic.

Ensure the external system (e.g. Azure, AWS S3 or flat file) outputs data in a consistent format - ideally .csv with clear headers.
Keep dimension names and line items consistent with your Anaplan model to make mapping easier.
Use the import to map columns to dimensions and line items. This is where naming consistency matters most.
Setting up a CloudWorks integration to schedule and automate imports from cloud storage. This removes manual steps and ensures timely updates.
Always check the import log for mismatches or unmapped items or formatting issues. Fix and re-import if needed.

Export:

Exporting is about sending Anaplan data to downstream systems for reporting or analytics

Create a saved view with filters and sorting to export only the data you need — this keeps files clean and relevant.
Export as .csv for flat files or .xlsx if formatting is needed .csv is best for automation.
Schedule exports to cloud destinations (like Azure) using CloudWorks. This is ideal for automated pipelines.
Use meaningful names for exported files so they're easy to identify in downstream systems.
Check exported files for completeness and formatting. This helps avoid issues in the receiving system.

CloudWorks:

I’ve recently been working with CloudWorks in Anaplan, and it has made a real difference in how data flows between systems. What I like most is how it:

Simplifies integration without requiring deep technical skills
Automates data imports and exports seamlessly
Reduces dependency on manual processes or complex scripts
Saves time and minimizes errors

Anaplan Data Orchestrator (ADO) – Integration Essentials

No-Code Integration: Easily connect Anaplan to external data sources (like SAP, AWS, Azure etc.) without writing code.
Visual Data Pipelines: Build, monitor, and reuse data flows using a drag-and-drop interface for clear data movement and transformation.
Automated Scheduling: Schedule and automate data imports and exports, reducing manual work and ensuring timely updates.
Centralized Monitoring: Track all integration jobs, view logs, and quickly troubleshoot issues from a single dashboard.
Data Transformation: Clean, map, and transform data within ADO before loading it into Anaplan, ensuring data quality and consistency.

lokesh1156

Here are a few key thoughts that immediately come to mind regarding best Practices for robust Anaplan Data Integration.

1.Establish a Unified Data Integration Model

Create a centralized model to manage all inbound and outbound data flows, enhancing consistency, scalability, and governance across systems.

2.Implement Seamless Data Automation

Utilize integration tools like Anaplan Connect, Informatica, or MuleSoft to automate recurring data tasks, minimizing manual intervention and boosting efficiency.

3.Maintain High Data Integrity Standards

Apply rigorous data cleansing and validation protocols before import to ensure accuracy, prevent errors, and maintain model reliability.

4.Adopt Standardized Naming Practices

Use intuitive and consistent naming conventions for files, actions, and processes to simplify navigation, collaboration, and troubleshooting.

5.Enable Proactive Integration Monitoring

Set up real-time monitoring and alert mechanisms to track data movements and quickly respond to failures or anomalies.

6.Integrate Systems with Real-Time APIs

Leverage Anaplan’s REST APIs to enable dynamic, real-time data exchange with external platforms, supporting agile decision-making.

7.Establish a Change Management Framework

Develop a structured approach for managing updates to integration processes, including version control, stakeholder communication, and impact analysis to ensure stability and transparency.

pyrypeura

I agree a lot with @jstockwell on realising the complexity using custom integration with Anaplan API brings together with flexibility compared to other integration options. As rule of thumb when building new integration I'd inspect Cloudworks/ADO as first option thanks to built-in error handling and notifications. As one of biggest learnings I have had is pushing business to discuss how failures are handled when discussing new integration. It's a fact that any integration fails sometimes and it's important to have plan on what to do in case of failure. When building integration at least following points should be answered regarding error handling.

1. Who get's notified in case of failure and how
2. Where does error dump file get saved in case it exists
3. Should integrated data set be logged somewhere or is it easy to reproduce afterward
4. How to fix state of model after failed run

Answering these questions before starting a build of integration ensures that you will have sufficient budget and resources to build a integration that can handle also errors.

kamalakar1144

Integration Best Practice. Here we can understand what it is exactly.

INTEGRATION:

It refers to the process of connecting Anaplan with other systems to exchange data automatically either importing data into Anaplan or exporting it out to external platforms. This is crucial for keeping models up-to-date and aligned with other business systems like ERPs, CRMs, data warehouses, or reporting tools.

Key Concepts of Integration in Anaplan:

Data Import/Export:
- You can manually or automatically import data from files (CSV, Excel) or external systems.
- Exporting data from Anaplan can be done similarly, either manually or via automated processes.
Anaplan Connect:
- A command-line utility that allows you to automate data integration using scripts.
- Commonly used for scheduled jobs via tools like Windows Task Scheduler or cron.
Anaplan API:
- A RESTful API that enables programmatic access to Anaplan models.
- Useful for building custom integrations or connecting with middleware platforms.
ETL Tools & Middleware:
- Tools like Informatica, Dell Boomi, MuleSoft, or Snap Logic can be used to integrate Anaplan with other enterprise systems.
- These tools often provide pre-built connectors for Anaplan.
Cloud Works:
- Anaplan’s native integration platform.
- Allows users to schedule and automate data imports/exports without needing external tools or scripting.
Third-party Connectors:
- Some platforms offer plug-and-play connectors for Anaplan (Salesforce, Workday, SAP).

Why Integration Important:

Ensures data consistency across systems.
Reduces manual effort and errors.
Enables real-time planning and decision-making.
Supports scalability and automation in enterprise planning processes.

Quick Links

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