Client Benchmark Data Dimension Mapping
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Comparing multidimensional performance metrics between enterprise data and external benchmarks is computationally complex due to differences in data dimensions and structures, making it difficult to provide meaningful comparisons.
Innovation Solution
A system and method that maps client data dimensions to benchmark data dimensions using mapping tables, allowing for the retrieval and analysis of client and benchmark data to enable rapid comparison of performance metrics, including the use of a server to compute and visualize these comparisons in a graphical user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multidimensional performance metrics are compared between enterprise data and external benchmarks, then meaningful performance evaluation is achieved, but computational complexity increases due to differences in data dimensions and structures
Solution Approach 1:
The patent introduces mapping tables as intermediary structures that bridge client data dimensions with benchmark data dimensions. These mapping tables translate and align different dimensional representations, enabling accurate comparisons without directly processing the full complexity of both data systems simultaneously. The mapping tables serve as a mediator that simplifies the comparison process while maintaining measurement precision.
Solution Approach 2:
The patent segments the data comparison process into distinct phases: retrieving client data, retrieving benchmark data, mapping dimensions between the two data systems, and performing comparisons. This segmentation allows each phase to be optimized independently and reduces the computational burden by processing data in manageable chunks rather than attempting to compare entire multidimensional datasets at once.
2Adaptability or versatility
If client data is compared to external benchmarks with different dimension names and members, then comprehensive benchmarking is enabled, but data mapping complexity increases
Solution Approach 1:
Mapping tables function as intermediary structures that store pre-defined relationships between client data dimensions and benchmark data dimensions. These tables translate dimension names and members from the client system to the benchmark system, enabling comprehensive benchmarking across different data structures without requiring complex real-time mapping algorithms. The mapping tables pre-resolve the complexity of dimensional translation.
Solution Approach 2:
The patent performs data mapping operations in advance by pre-establishing mapping tables that define relationships between dimensions before the actual comparison process. This preliminary action of creating and storing mapping relationships eliminates the need for complex on-the-fly dimension translation during benchmarking, thereby reducing processing complexity while maintaining broad benchmarking coverage.
3Speed
If rapid query resolution is achieved through on-the-fly data retrieval and analysis, then user responsiveness improves, but system processing load increases
Solution Approach 1:
The patent retrieves and processes data in advance by pre-fetching client data and benchmark data into memory structures before comparison queries are executed. This preliminary data retrieval and preparation reduces the processing load during actual query resolution, as the system only needs to perform mapping and comparison operations on already-loaded data rather than retrieving and processing everything from scratch in real-time.
Solution Approach 2:
The patent creates copies of data in memory structures that can be rapidly accessed during query processing. By maintaining cached copies of client and benchmark data in appropriate data structures, the system enables rapid query resolution through simple lookup and comparison operations rather than performing heavy data retrieval and processing operations during each query, thereby reducing instantaneous processing load while maintaining high response speed.
Data Source
AI summary
Systems and methods of performing comparisons of client data and benchmark data are described. When client data is compared to internal benchmark data, comparative result values may be computed by performing a multidimensional operation with respect to client data for a particular dimension member and client data for additional dimension members that are related to the particular dimension member. When client data is compared to external benchmark data, a client result value may be computed based on a multidimensional operation and a benchmark result value may be determined based on a mapping of client dimension members to benchmark dimension members. The result values may be provided by a server to a client, which may generate a graphical user interface (GUI) based on the result values.