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HyperModel: 5 Tips to Improve Your Experience

Are you considering HyperModel for your ecosystem, or have you recently deployed it? Fantastic! We've compiled a shortlist of the top five tips to help make kicking off your HyperModel experience as smooth as possible.

Check out these top 5 tips to improving your experience:

1. Recognize That the Hyperblock Has Not Changed

  • With HyperModels, only model/workspace sizes and the available memory are increased.
  • The Hyperblock engine remains the same, meaning:
    • Calculation performance is the same. HyperModel performance is dependent on modeling best practices and how the model is constructed.
    • Hyperblock dictates the maximum block cell count.
  • The following functions maintain the same limit set of 50 million cells, which may be easier to reach with HyperModels:
    • ISFIRSTOCCURRENCE
    • RANK
    • RANKCUMULATE

2. Embrace Sparsity

  • Sparse modules are not inefficient when it comes to calculations. The Anaplan Hyperblock engine is designed to work with multi-dimensional structures. At its heart, the Directed Acyclic Graph (D.A.G.) indexes data to calculate only what is needed when upstream data points have changed.
  • Model size can often be reduced with increased multi-dimensionality by reducing list sizes. For example, this could apply to transactional lists that include dates. For more information, see Data Hubs: Purpose and Peak Performance.

Read The Truth About Sparsity: Part 1  for additional information about sparsity.

3. Understand the Effects of Text

  • Text can quickly consume memory and potentially slow calculations. Scaling any text formatted line items or text-based calculations in a HyperModel could cause memory-related issues.

  • Avoid importing data as text where possible to maintain memory and calculation speed. Instead, import as List items.
  • Create new lists to allow for more list-formatted items in data modules. We recommend this method rather than importing lists as text and conducting a FINDITEM search.

For more information, see Data Hubs: Purpose and Peak Performance - Anaplan Community.

4. Understand Top-Level Summary

  • Try to reduce using summaries and top-level summaries to maintain optimal HyperModel performance. The increased scale with HyperModels means there's more data to aggregate, which may result in slower summary calculations.
  • If a summary is only needed on one or two dimensions in a multi-dimensional line item, we recommend turning off the summary and creating a new line item for that specific summary.

5. Test and Optimize

  • Do it yourself: Optimize models with best practices by leveraging the Planual, PLANS, and D.I.S.C.O.
  • Leverage our packaged services and reach out to your account team for more information:
    • Model optimization.
    • Model concurrency testing.

Are you ready to scale up on HyperModel? Or, are you already leveraging it?

Let us know your thoughts in the comments below!

The content in this article has not been evaluated for all Anaplan implementations and may not be recommended for your specific situation.
Please consult your internal administrators prior to applying any of the ideas or steps in this article.
Comments

Great Tips @annejulie @MarkWarren @MelanieMartinez and @dafinkapancheva 

I posted this link to the HyperModel Group forum. Well done!

These are all great tips - thank you for sharing. I especially like the tip on sparsity; definitely a top for discussion in the future.

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