Auto-Granularity for Multi-Dimensional Data Planning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Managing data interactions in multi-dimensional data models is challenging due to the need for different granularities for various data elements and scenarios, leading to complex intersections and difficulties in planning, analytics, and processing.
Innovation Solution
A system and method for configuring data granularities in multi-dimensional data models, allowing for automatic adjustment of planning granularities based on comparisons between planned and actual data, using user interfaces to select and display data at various granularities, and generating rules for valid data combinations to filter and manage mixed granularities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If different granularities are used for various data elements in multi-dimensional data models, then planning accuracy and scenario-specific performance are improved, but data interaction management complexity increases
Solution Approach 1:
The patent applies local quality by allowing different granularities to be assigned to different data elements based on their specific planning and analytics requirements. Each data element can have its granularity independently configured, enabling precise local optimization without forcing a uniform granularity across the entire data model, thus improving planning accuracy while managing complexity through localized control.
Solution Approach 2:
The system dynamically adjusts and manages granularities for different data elements based on planning needs and analytics requirements. The granularity configuration is not static but can be modified and optimized over time, allowing the system to adapt to changing planning accuracy requirements while maintaining manageable data interactions through automated granularity management.
2Measurement precision
If automatic granularity adjustment is implemented based on planned vs actual data comparison, then planning accuracy improves, but system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms by automatically comparing planned data with actual data and using this comparison to adjust granularities. The system continuously monitors planning accuracy and dynamically modifies granularity configurations based on the feedback from performance measurements, enabling automatic optimization of planning accuracy while managing system complexity through rule-based automated adjustments.
Solution Approach 2:
The system performs self-service by automatically adjusting granularities based on planned vs actual data comparisons without requiring manual intervention for each adjustment. The automated granularity management system independently optimizes planning accuracy by comparing performance metrics and making necessary granularity changes, reducing the burden on users while improving planning precision.
3Adaptability or versatility
If mixed granularities are supported for different dimensions, then adaptability to different use cases improves, but data processing complexity increases
Solution Approach 1:
The patent applies universality by creating a unified data model framework that can handle multiple granularities across different dimensions simultaneously. The system provides a universal interface and processing mechanism that works consistently regardless of the specific granularity configuration, enabling adaptability to various use cases while managing data processing complexity through standardized multi-functional processing routines.
Data Source
AI summary
Planning granularities can be stored for data elements including a first granularity for a first data combination, the first data combination including a combination of multiple dimensions of data. A comparison metric can be calculated between planned data at the first granularity for the first data combination and actual data observed for the first data combination, wherein the observed data is stored at a different granularity than the planned data. Based on the calculated comparison, the planning granularity for the first data combination can be adjusted from the first granularity to a second granularity. A selection of a cross-section of data spanning multiple dimensions can be received, the selection including the first data combination, wherein a user interface is configured to display data for the first data element at the second granularity based on the adjusting.