Oct 1, 2024

Code Validation in IBM Planning Analytics

In the world of IBM Planning Analytics (TM1), code validation plays a crucial role in ensuring the accuracy, efficiency, and reliability of your TM1 models. Whether you’re a seasoned developer or just starting with TM1, validating your code can help you avoid common pitfalls, ensure your rules and processes are optimized, and make troubleshooting much easier.

In this blog, we’ll dive into why code validation matters and show you how you can streamline this process.

Why Code Validation Matters in TM1

TM1 is a robust platform designed to handle financial and operational data models. Because of this flexibility and adaptability to any business model, it’s essential to ensure that the code you write—whether it’s TurboIntegrator processes and/or rules—is error-free and efficient. Here’s why code validation is crucial:

  • Error Prevention: Incorrect code can cause issues that affect performance, data integrity, and user experience. Validating your code helps identify and correct errors before they affect your TM1 model.
  • Performance Optimization: Validation can highlight inefficiencies in your code that might slow down your model. It’s much easier to optimize code at an early stage than to deal with performance issues later on.
  • Consistency & Reliability: Consistent validation ensures that your models are always functioning as expected, making your work more reliable. It builds trust with users who rely on your models for accurate and timely data.
  • Maintainability: Well-validated code is easier to maintain and update. If you’ve ensured that your code is clean and efficient, future changes will be less prone to introducing new issues.

Arc has embedded best practices with a wider range of features designed to help TM1 developers write and validate code better. Simple yet very handy things such as syntax highlighting can make a big difference.

There are a set of validation rules in Pulse that come out of the box, these rules are based on best practices so you can simply run a validation report to get a complete analysis of all your cube rules and TI processes.

Furthermore, With Arc+ you can now execute Pulse best practice validations rules on all processes and rules and order the list of processes and rules by the number of failed rules making it easy to quickly find some code that needs to be fixed.

3 Tips for Code Validation with Arc +

Comment Your Code

Clear comments make your code easier to read and maintain. While they don’t directly affect code validation, well-commented code helps future developers (or yourself!) understand the purpose behind certain logic or calculations, making troubleshooting simpler.

Validate Data References and Dependencies

Ensure that all data references, such as dimensions, cubes, and elements, exist and are correctly defined. Inaccurate references can cause data inconsistencies or even process failures.

Check for Hard-Coded Values

Hard-coded values in rules and TI processes can make your models inflexible and prone to errors when data changes. Use variables and parameters whenever possible to create adaptable code that’s easier to validate and maintain.

Automatic peer-reviews

Code validation is an essential part of maintaining and developing efficient, accurate, and high-performing IBM Planning Analytics models. By following best practices and leveraging the right tools, you can ensure that your models remain robust and your code error-free. As you develop more complex models, these validation techniques will help you maintain clarity, prevent issues, and deliver reliable results to your users.

If you don’t have the right tools, applying these best practices is time consuming. It is to rely on someone going through each code line. With Arc+, you can check your code by clicking one button!

Happy coding, and remember:
The more you validate now, the less you’ll have to debug later!

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