How to Achieve in pre-built Anaplan Eco-System , Hierarchy into to flat list & Replacement

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snigdhakapila
snigdhakapila
in Modeling

Hi All,
Currently the Anaplan eco-systems has multiple workspaces and multiple models[8 workspaces ~55 GB], now there is a proposed hierarchy change(as some models are quite complex and have Dependency on that flat list to create further Folded Lists for Reporting)

1. The List to be eliminated is a Flat List
2.Against that Flat list a Hierarchy Role up is required.

Questions:
1. How to implement over such huge dataset? How to analyze how much space required for Parallel development?
2.How to Integrate Budget/Forecast Data?
3. How to Achieve this with ~ 100% accuracy?

Answers

  • Dikshant
    Dikshant
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    1. Get the raw data on the hierarchy and see how many levels and new members will be added to the hierarchy list.
    2. First, identify the reason. what are the benefits you will get from folding lists?

    If you have solid reasons to make this change? Or there is a priority from business? Try to think of alternative ways of reporting. Play around with filters and have multiple grids aggregating data into multiple levels? Kindly provide more inputs and yours thoughts on the same.

  • Angellian
    Angellian
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    1. Implementing a huge dataset requires careful planning and consideration of various factors such as data storage, processing power, and parallel development. To analyze quordle the space required for parallel development, you can start by estimating the size of the dataset, the processing requirements for individual tasks, and the potential for parallelizing these tasks.
    2. Integrating budget/forecast data involves understanding the data sources, formats, and structures. You can start by identifying the systems or sources where budget and forecast data reside, and then develop a strategy to extract, transform, and load (ETL) this data into your target system or application.
    3. Achieving close to 100% accuracy in data analysis and processing is a significant challenge. It often involves a combination of rigorous data validation, quality assurance processes, and the use of advanced algorithms and models. Employing techniques such as data cleaning, outlier detection, and statistical validation can help improve accuracy.

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