Performance Optimization for Anaplan Models
Anaplan Development and Consulting Services
Identifying Optimization Opportunities
Our approach to performance optimization starts with a thorough assessment of your existing Anaplan models. We delve into every aspect of the model to identify bottlenecks and inefficient processes that could be slowing down performance. We analyze data flows, calculation processes, Data Hub models and Data Orchestrator pipelines, and integration points to pinpoint exactly where improvements can be made.
Hyperblock vs. Polaris: Choosing and Tuning the Right Engine
Anaplan runs two calculation engines, and the first performance question in 2026 is whether your model is on the right one.
Hyperblock is the original in-memory engine. It allocates every cell of every dimensional combination, so performance and workspace size are governed by sparsity management: DISCO module structure, line item subsets, sensible summary settings, avoiding calculated dimensions you never need, and ruthless control of list sizes. Most production models today are Hyperblock models, and most of them have headroom we can recover.
Polaris was built for very large, naturally sparse models such as retail assortments or SKU-by-customer demand. It stores and calculates only populated cells, so the sparsity engineering Hyperblock demands is unnecessary, and models that were split into many pieces to fit memory can often be reunited. The trade-offs are real: formula behavior and the supported function set differ, some calculation patterns are slower, and a Hyperblock model cannot simply be switched over.
We profile your workload (cell counts, populated density, calculation chains, concurrency) and recommend one of three paths: tune in place on Hyperblock, migrate to Polaris, or split the estate so each engine handles what it does best. Our model builder's guide to Hyperblock and Polaris explains the decision in detail.
Streamlining Model Architecture
Efficient model architecture is critical for optimal performance. We focus on restructuring models to follow best practices, such as breaking down large models into functional modules and, on Hyperblock, utilizing sparsity to reduce unnecessary calculations. We also review the usage of lists, dimensions, and hierarchies to ensure they are configured to maximize speed and efficiency. This restructuring helps in reducing model complexity, which in turn enhances performance.
Enhancing Data Processing
We employ advanced techniques to improve data processing speeds and reduce latency. Our efforts include refining data import/export processes, optimizing data mappings, moving transformation out of the model and into Anaplan Data Orchestrator or CloudWorks where it belongs, and leveraging incremental data loads where appropriate. This makes sure that your Anaplan models handle large volumes of data seamlessly and perform calculations in real-time, enhancing overall responsiveness.
Implementing Best Practices
We bring a wealth of knowledge in Anaplan best practices to ensure your models are not just fast, but also robust and scalable. This involves establishing standardized procedures for data management, creating efficient calculation logic, and employing best practices for user inputs and reporting. We also provide training and documentation to enable your team to maintain and further optimize models independently.
Continuous Monitoring and Improvement
Performance optimization is not a one-off task, but an ongoing process. We set up monitoring tools and processes to continuously track the performance of your Anaplan models. Regular performance audits and health checks ensure that the models remain efficient as your business grows and changes. We also offer ongoing support services to address any issues that may arise and to implement further optimizations as needed.
By focusing on these areas, we ensure that your Anaplan models run as efficiently as possible, providing you with the performance you need to make timely and effective business decisions.