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Avoiding CSV import errors: Three practical tips for Excel uploads across regions
Author: Ayesha Zoha, Certified Master Anaplanner and Manager at Kearney.
One of the most common issues I have found during end user training or UAT is the difficulty in uploading data from an Excel csv. This usually arises because users need to upload small but detailed datasets from existing systems. Even with multiple integrations, data hubs and connectors set up, we still need the ability to upload smaller data sets through an Excel upload.
The issue comes up when users sitting in various geographical locations have different Excel settings (number formats, decimal formats, thousand formats, column separators etc). Because of these regional differences, CSV import actions must be configured correctly to avoid data loss or incorrect mappings.
Here are my top three tips for smooth, consistent uploads across regions, that architects and model builders can follow:
* Structure of the file: It’s very important that before you create an action, the csv file needs to be in the right structure and format.* Create an Excel file with Column headers and format the Columns if needed (in final format that’s needed).
* Save this as xlsx on a SharePoint or local system. This will be the export template which the users can download/refer for creating their file in the csv format.
* Importing data (based on column separator)* CSV file with comma (,) as column separator (Usually followed in India, UK, USA etc)* Save the above file as csv with comma Delimiter, with data. Check the system settings of your desktop to confirm this. (Refer SS1)
If you want to change the Excel format - Use Control Panel from the Start - Search for region - Additional settings - check the List operator with; or,
* Create an Import action to upload this data into the corresponding module using this file. Anaplan shows the screen below with the headers and column separators matched to the right format. (Refer SS2)
* If the column separator is incorrect (e.g., semicolon instead of comma), all data may appear in one column. This indicates a separator mismatch. (Refer SS3)
* CSV file with semicolon (; ) as column separator (Usually followed in EU)* Save the above file as csv with semicolon delimiter, with data. Check the system settings of your desktop to confirm this. (Refer SS4)
* Create an Import action to upload this data into the corresponding module using this file. Anaplan shows the screen below with the headers and column separators matched to the right format. (Refer SS5)
* If data appears incorrectly mapped, it usually means the separator is set to comma instead of semicolon. (Refer SS6)
* UX and end user training* The above steps help you differentiate when building the imports for various formats. In case of multiple regions using the same data imports, create multiple actions and corresponding processes for the same and publish the multiple processes on the board with appropriate instructions.
* Have a training session for users from various regions to explain how the two processes work and what the implication is.
* The same format changes can be done for the decimal format, and multiple actions can be created
CSV imports may look simple, but regional Excel settings can easily lead to data issues if they’re not handled correctly. By standardizing file structures, configuring imports based on column separators, and creating region-specific actions where needed, model builders can ensure accurate and consistent uploads. Clear user instructions and targeted training complete the picture and help avoid errors at scale.
Questions? Leave a comment!
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How I Built It: Period-driven variance reporting
Author: Nizar Cherkaoui is a Certified Master Anaplanner and EPM Manager and Solution Architect at VISEO.
In this How I Built It video, I demonstrate how to design period-driven variance reporting in Anaplan when comparison logic is unknown upfront and flexibility is required.
Instead of embedding period choices directly into calculations that often rely on user-dimensioned logic for concurrency, the model is built on multi-dimensional modules that enable a fully context-driven user experience. Users select a base period and a current period, and the model dynamically aligns volumes, prices, and revenues to those selections, without duplicating modules or rewriting formulas.
The main advantage of this approach is scalability and performance. By clearly externalizing context selection through multi-dimensionality and mapping, the design stays stable, explainable, and easy to maintain as business evolve.
https://play.vidyard.com/FtkP6in9x8oyT1cAbwvFdF
Questions? Leave a comment!
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Techniques for Anaplan model optimization (performance + sizing)
Author: Bhanu Chalotra is a Certified Master Anaplanner working with Anaplan community and designing different solutions around FP&A, headcount planning, and ICM.
Anaplan is not just about how a model is built — it’s also about how efficiently it runs. This article shares proven optimization techniques to improve model performance and right-size your solution, with practical examples to help you design an optimized model.
* Design at the lowest necessary grain and use subsets
Practice: Build modules at the minimum granularity the process truly needs.
Example: If finance plans headcount monthly by department, don’t store it at employee level for detail. Keep the planning module at department × month and only add employee-level modeling in a separate, purpose-built module if there’s a real use case.
* Avoid using whole model calendar
Practice: Build modules with the time ranges wherever applicable.
* Avoid using Multiple IF – ELSE Condition
Practice: Build the formula with lesser if else condition. If more than 3 if else condition, then try to use mapping module.
* Avoid combining high cardinality lists
Practice: Don’t multiply large lists together unless required.
* Reduce sparsity by splitting modules
Practice: If most cells are blank, redesign to reduce sparsity.
Example: One module holds marketing spend for 12 channels, but only 3 channels apply to each brand. Split into separate modules by channel group (or use a mapping module + allocation approach) so you aren’t storing empty intersections for non-applicable combinations.
* Separate Input / Calc / Reporting modules
Practice: Keep input simple, calculations controlled, and reporting shaped for UX.
Example:* Input module: planners enter “Units” and “Price” only (Product × Month)
* Calc module: margin, FX conversion, allocations, phasing logic
* Reporting module: adds executive-friendly line items (YoY, variance, KPI flags) without burdening the input layer
* Minimize formulas in the largest modules
Practice: Large modules should be mostly data + simple references, not heavy logic.
Example: Instead of calculating complex pricing logic inside a large SKU × Customer × Month module, compute “Net Price” in a smaller SKU × Price Listmodule, then reference it into the detailed module.
* Be intentional with summaries (rollups)
Practice: Turn on rollups only where they’re needed.
Example: If users only report totals at Region and Country, don’t enable summaries for every intermediate hierarchy level (e.g., district/store). Keep rollups selective to avoid extra compute/storage.
* Centralize shared logic to prevent duplication
Practice: One source of truth for common calculations.
Example: If “Active Product” logic (based on launch date, status, end-of-life) is repeated in 6 modules, create one Product Attributes / Flags module and reference the Boolean flag everywhere. This improves consistency and reduces maintenance.
* Optimize UX pages for rendering speed
Practice: Design pages to load quickly and show only what users need.
Example: Instead of a single page showing 40-line items across multiple large grids, create two pages:* “Plan inputs” (small grid + key KPIs)
* “Plan review” (aggregated reporting view + variances)
This improves initial load and reduces unnecessary rendering.
* Use lean, dedicated integration views
Practice: Build saved views specifically for imports/exports — minimal dimensions/line items.
Example: For an actuals load, create a view with only: entity, account, period, amount (and the exact line item needed). Don’t export an entire reporting view that includes variances, KPIs, and extra line items.
* Continuously monitor and govern model growth
Practice: Make optimization a recurring activity, not a one-time cleanup.
Example: Add a monthly “model maintenance” routine: review top largest modules, identify any new sparse combinations, and retire unused line items or old versions/scenarios from prior planning cycles.
Thoughts or questions? Leave a comment!
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Community Manager note: Optimization tactics may differ for Polaris models. Both Polaris and Classic will leverage core model building best practices but will differ when optimizing around sparsity as this is not a key consideration for Polaris.
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Anaplan implementation: A game of snakes, ladders, and smart moves
Author: Pradip Kakade is a Certified Master Anaplanner and Sr. Principal Consultant of Enterprise Solutions at Genpact.
Ask anyone who has been part of a Major Anaplan E2E implementation, and they will tell you: the journey is never a straight line. You build momentum, you feel confident, and then — snap. A hierarchy shifts. Data volume explodes. A user asks for a small tweak that unravels the logic.
It is not just a project; it is a game of Snakes and Ladders. The difference between a smooth delivery and a painful one isn't about avoiding the game, it's about anticipating the snakes and knowing exactly where to place your ladders.
The snakes you should expect
Some of these pitfalls are so common that experienced architects can practically feel them coming.
Take hierarchy changes, for example. They usually hit during the build phase. What looked fine on a whiteboard suddenly falls apart when you try to layer real-world calculations on top of it. It forces you back into dimensional modeling, undoing days of work you thought were done.
Then there’s the silent killer: sparsity and size explosion. A model might fly when you test it with sample data, but once you load actual historical volumes, it crawls. This isn't usually a design error, it's a stress-test failure that drags you back into redesign discussions just when you want to move forward.
Code quality is another trap (spaghetti code). long, winding formulas, excessive concatenation, or hard-coded rules. It works for a week, but it’s brittle. The moment business logic shifts, the model breaks, forcing a refactor to restore maintainability.
And of course, the classic: The Excel Replica. Users feel safe with what they know, so they push for Anaplan to behave exactly like their old spreadsheets. If you give in, you lose the benefits of scalable, flexible, and intuitive modelling/ planning and end up with a complex, unmanageable system.
Why ladders matter more than speed
You can’t realistically eliminate every snake on the board. The smarter play is to position your ladders so you can skip the hardest parts of the climb.
The most obvious way to do this is to stop starting from scratch. Prebuilt Anaplan apps let you bypass the blank-page anxiety and land straight on a working foundation. Prebuilt Apps from Anaplan are built on industry best practices. Partner accelerators take this even further. Instead of debating architecture or UX for weeks, you’re pulling in proven solution patterns. It cuts out the uncertainty that usually leads to design flaws later on.
Then there is the methodology itself. PLANS-based modeling isn’t just a best practice; it’s practically insurance. If you apply it rigorously from day one, you effectively immunize the model against the performance issues that tend to crop up when data volumes grow. You spend less time fixing logic and more time refining value.
We also need to look at how we handle data and quality checks. Manual integration is almost always a trap — it invites reconciliation errors and technical expert team is needed. Using an Enterprise Data Fabric standardizes that movement, stabilizing the system before you even get to testing. It is no code connector which can be configured easily by end user. Similarly, waiting until UAT to find structural faults is too risky. Automated blueprint reviews act as an early warning system, catching risks while they are still cheap to fix.
The ultimate ladder: decision clarity
But if I had to pick the single most effective ladder, it isn’t a piece of tool. It’s Decision-first MVP definition.
When you force the team to align on exactly what decisions the model needs to support before you build a single module, the fog clears. Requirements get sharper. "Scope creep" loses its momentum because you have a clear boundary.
This doesn't mean you lower your ambition. It just means you’re smart about sequencing the value.
The endgame
Timing is everything. A snake hitting you in the design phase is annoying; a snake hitting you during UAT is expensive. Late-stage scope creep or last-minute requests to "just change this one thing" can **** confidence faster than a calculation error. Sometimes, the smartest move on the board is knowing when to say "no" or "not yet."
And remember, go-live isn’t the finish line, it’s just a transition. This is where modern tools really shine. Using GenAI-driven accelerators to speed up adoption essentially gives users a co-pilot for insights and communicate with planning data in natural language. It turns the platform from a project into a sustained capability.
Final thought
Every project has snakes. That’s just the reality of enterprise planning.
The teams that win aren't the ones who avoid them entirely, that’s impossible. The winners are the ones who spot them early, build ladders intentionally, and keep their eyes on the value, not just the mechanics. In the end, winning the game is not about avoiding every snake . It is about knowing the board and playing it wisely.
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How to approach a user story?
Author: Devrath Ahuja is a Certified Master Anaplanner with over six years of experience in implementing and delivering global Anaplan solutions.
As someone starting out on their Anaplan journey, sometimes user stories and the build can feel overwhelming. Have you ever looked at a user story and wondered, “Where should I even begin?” Do I create a new module, a new list, a subset, or do I already have everything I need? Don’t worry, every seasoned Anaplanner has gone through the same questions and worries as you at some point.
So how do we overcome this challenge? Well, one of the biggest learnings in my journey came from a piece of advice I got from my senior back then which was:
“Break down the task into multiple smaller sub-tasks.”
Because when you try to build everything all at once there are chances you might end up being confused and not even know where to start.
Let’s take an example of a user story from Anaplan’s L3 certification and try to break it down:
As a Regional Sales Executive, I need to be able to add an annual percentage increase “stretch goal” to the Baseline Financial Forecast to establish the Initial Country Sales Target for each country that I manage.
The percentage increase in each country’s revenue projections should be reflected across all product families and quarters of the fiscal year. For each country that I supervise, I want to be able to see the Baseline Financial Forecast values, calculated Initial Country Sales Targets, and the difference between the two values for each product family and the total of all products.
I’ll know this is complete when I can input an annual percentage increase for a country and see the Initial Country Sales Target that results and the difference between the Baseline Financial Forecast and the Initial Country Sales Target.
How do we break this down? Start by answering these 2 simple questions:
* What is the output required?* Initial Country Sales Target
* Difference between Baseline forecast and Initial Country Sales Target
* What are the inputs?* Annual percentage increase
In case something is unclear at this stage, revisit the user story and seek clarification, since it’s always better and worthwhile to spend some time understanding the requirement than to directly jump into build mode to avoid re-work at later stages.
Once we have the inputs & outputs clearly defined we need to answer the question:
“How are these connected?”
Remember, only think about the process and the business at this stage. Don’t go straight into solution mode!
What usually helps here is to create a process flow diagram. It could be a rough one in your mind or drawn with a pen/paper.
Once you have an answer to the above 3 questions, the flow is clear, and you can now jump into solution mode.
Remember: You do not need to replicate an excel as-is into Anaplan! You will use your understanding of Anaplan and leverage its capabilities and functionalities to provide the best solution to meet the requirements.
Elements to be considered at this stage
* Dimensions involved — Think of lists, subsets, line-item subsets
In this user story, we know the input is required Annually (Time – Year) and by Country and the output is by Quarters (Time – Quarter) and by Product Family and Country.
* Source data
The % increase is on the baseline financial forecast as well as the difference is against the baseline forecast, so that becomes 1 of our data points.
Check if you already have that data somewhere in the model and if not, create a new module to import data into it.
* Build
Start creating input modules, output modules, calculation modules. Add the appropriate dimension with the right level of granularity in each of them and start connecting them.
You may notice a difference in the granularity between inputs and outputs, consider if you want to aggregate the data or disaggregate it and use either native summary or Anaplan formulas accordingly.
Refer to the Planual and Anapedia at this stage to get the most appropriate and optimal formulas for your use case. In some cases, if the user story feels too complex, break individual line items into further 5-6 line items and take it step-by-step. Once you have the output, optimize it where possible.
* Validate
Always test out your build. Put yourself in the shoes of the planner and follow the process flow you made earlier. Compare the output you get with the desired output. Fix any issues you encounter on the way. Compare the output at different level of summaries to ensure the calculation works (Especially useful for calculated metrics. E.g – Selling price).
Last but not the least, if you’re still unclear, ask for help. 😄 From your project team, organization CoE, or the Anaplan community!
Note: The above example considers a simple isolated user story. Building models in real-world scenarios will be much more complex and you would also need to consider connection points (to other models, modules), the UX build (this is what the end user will interact with), Security and access setup (DCA/Selective Access/Roles and access setup) and other things. As you advanceor as an experienced model builder or a solution architect, you should also spend a considerable amount of time on the scalability of the current build. Consider if you can scale this to include different versions/dimensions if required in future. Consider if the current user story has any linkages to some other user story and hence maybe needs to already be built with different considerations in mind than it would if looked at in isolation? And, all of this, will come to you as well in due time.
For now, to summarize:
* Identify the inputs and outputs from the user story
* Create a rough process flow diagram
* Identify the dimensions involved
* Identify or Import the source data
* Build empty modules with the right level of dimensionality
* Get into the world of Anaplan formulas and logics!
Bonus: Common mistakes to avoid while building a user story
* Jumping straight into building modules without understanding user story
* Ignoring subsets
* Ignoring summary settings
* Hardcoding logic instead of using mappings
* Ignoring time ranges where entire model calendar is not required
* Not following naming conventions for modules, lists, imports
* Not making use of the notes column to capture key details about the line item
What tips would you add? Leave a comment!
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End user engagement & communication: Getting people to actually use Anaplan
Author: Mikhil Agarwal, 7x Certified Master Anaplanner and Delivery Manager at VISEO USA Inc., New York.
Anaplan has a unique ability to bring teams together around a single, connected view of the business. When implemented well, it becomes more than a planning tool and becomes part of how decisions are made. However, long-term success depends on how comfortable users feel with the platform, how well they understand its value, and whether they see it as their system rather than just another corporate tool.
End user engagement is not something that happens at go-live or during initial training. It develops over time through consistent communication, thoughtful enablement, and a clear understanding of how different users interact with Anaplan in their day-to-day roles.
Start by recognizing that users interact with Anaplan differently
One of the strengths of Anaplan is its ability to serve a wide range of users from hands-on planners to senior leaders. That also means engagement looks different for each group:
* Some users work in Anaplan daily.
* Some interact with it during planning or forecast cycles.
* Some only engage when numbers look “off”.
A single engagement approach rarely works for all of them.
Segment users before deciding how to communicate
A simple but effective way to improve engagement is to group users based on how they use the platform:
* Key users: These users care about speed, clarity, and knowing exactly what has changed.
* Reviewers: Managers and controllers want confidence in the numbers and clarity on actions.
* Executives: They engage with outcomes, not technicalities.
Choose the right medium (this matters more than people think)
The medium you choose often determines whether your message is absorbed or ignored.
* For key users* Short zoom / Teams’ videos
* Live working sessions during close or forecast cycles
* Dedicated Slack / Teams channel for quick questions
* For reviewers* Clean emails with:* what changed
* what action is needed
* by when
* One-page PDFs or screenshots showing “before vs after”
* For executives* No training. Ever.
* One slide or dashboard visual explaining:* what decision this supports
* what changed since last time
* what risk/opportunity exists
The goal is not to communicate more; it is to communicate in the way users naturally consume information.
Communicate early and consistently
Proactive communication builds confidence in the platform. Simple updates such as upcoming changes, expected data movements, or temporary performance impacts help users stay aligned and avoid confusion. Over time, this consistency reinforces Anaplan as a reliable and well-managed system.
Create visible feedback loops
Engagement improves significantly when users feel their input matters.
What works well:
* Maintain a visible backlog (even a simple SharePoint list)
* Categorize requests as:* Planned
* Under review
* Not prioritized (with reason)
Even when requests are not implemented, transparency builds trust.
Look beyond logins to measure engagement
True engagement is reflected in behavior, not access metrics.
Better indicators:
* Are users submitting inputs on time?
* Are offline files reducing?
* Are follow-up emails decreasing each cycle?
These provide a much more accurate picture of how embedded Anaplan has become in the business.
Closing thought
Strong end user engagement turns Anaplan from a planning system into a shared decision-making platform. With the right communication strategy, tailored enablement, and consistent follow-through, organizations can ensure Anaplan continues to deliver value well beyond go-live.
Thank you for taking the time to read and for being part of such a collaborative and forward-thinking Anaplan community. The real strength of this ecosystem lies in people sharing ideas, experiences, and lessons learned. Here’s to better planning, smarter decisions, and people helping people. Until we meet again, happy Anaplanning!
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Other articles by Mikhil:
* Planning, pie, and people: What I’m thankful for in the Anaplan Community
* Curiosity, Collaboration, and Connected Planning: Inside a 3-Day workshop
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How I Built It: Refreshing current date in an Anaplan model using Cloudworks
Author: Sai Bandaru is a Certified Master Anaplanner with six years of experience architecting Anaplan models.
Hello Anaplanners! In today's fast-paced business environment, having accurate and up-to-date information is crucial for making informed decisions. In this ‘How I Built It’ tutorial, I'll explore a practical and efficient way to refresh the current date in your Anaplan model using Cloudworks.
How to refresh the current date with Cloudworks
Follow along in the video as we walk through the step-by-step process of using Cloudworks to refresh the current date in your Anaplan model. Whether you're a seasoned Anaplan user or just getting started, this tutorial will provide valuable insights into streamlining your workflow and keeping your models dynamically linked to real-time information.
Key benefits
* Real-time decision making: keeping the current date updated ensures that your models reflect the most recent business context, allowing for more accurate and timely decision-making.
* Automation for efficiency: Cloudworks simplifies the process, automating the refresh of the current date, saving you time and reducing manual effort.
* Improved model accuracy: by regularly updating the current date, you can enhance the accuracy of your forecasts, budgets, and plans, leading to more reliable insights.
https://play.vidyard.com/4nJKBPtUY38r3kwVVHKrS3
Conclusion
In conclusion, mastering the art of refreshing the current date in your Anaplan model with Cloudworks is a valuable skill that can significantly impact the effectiveness of your planning and analysis processes.
Questions? Leave a comment!
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All the 'How I Built It' tutorials can be found here.
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Evolving business requirements over time: Retroactive changes and effective dating
Author: Marc Weinzimmer, Certified Master Anaplanner and Senior Project Manager at Alpha.
The day a new Anaplan model goes live is always exciting. The model is clean, fast, and aligned with current business needs; admins are trained, users are engaged, and everything appears to be running smoothly. However, as time passes, business requirements inevitably evolve. How a model is designed to handle these changes will directly affect user trust, historical accuracy, and the long-term effort required to maintain and enhance the solution.
When time and structure change
One of the first major tests a model faces occurs when time moves forward. Rolling the calendar ahead often requires adding new years or removing old ones, and Anaplan handles this type of change well through its dynamic time settings. More challenging, however, are structural changes such as organizational or regional reorganizations. While hierarchy relationships can be easily updated, doing so raises an important question: should historical data reflect the new structure, or should it preserve how the organization looked at the time? This decision fundamentally shapes how the model must be designed.
Retroactive changes vs. effective dating
If a change should apply to both current and historical data, it is considered retroactive. Retroactive changes simplify modeling but permanently alter historical context. When historical relationships must be preserved, effective dating becomes necessary. Effective dating introduces a time component to attributes like region assignments, manager relationships, or pricing, allowing multiple versions of a relationship to exist while ensuring only the correct one applies at any given point in time. Although effective dating provides accuracy and flexibility, it increases model complexity and can impact usability due to additional lists and similar-looking records.
Models as living systems
Over the years, models continue to evolve as new functionality is added, user experiences shift, and business processes grow more complex. What began as a simple solution often becomes a deeply embedded system that supports critical planning and reporting. At some point, teams must evaluate whether ongoing enhancements are sustainable or if a model refresh is needed. This decision involves balancing improved design and performance against the cost and risk of rebuilding, while also considering how much historical data truly needs to remain active in the model.
Conclusion
Successful Anaplan models are designed with change in mind. Understanding when to apply retroactive changes, when to use effective dating, and how to plan for a model’s lifecycle can mean the difference between a model that scales gracefully and one that becomes difficult to manage. By making thoughtful design decisions early and revisiting them as the organization evolves, model builders can ensure their solutions remain accurate, performant, and trusted over time.
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More from Marc: A practical guide: Customer tenant split
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