Dimensional Data Explorer for Enterprise Reporting
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Solution Overview
Problem
Human capital management systems lack the ability to efficiently assist managers in day-to-day decision-making due to the time-consuming and expertise-requiring process of extracting aggregate information, which often becomes outdated during offline data analysis, and manual searching across tables for related data.
Innovation Solution
A dimensional data explorer system that enables aggregation and analysis of business data by designating class methods as report fields, measures, and dimensions, allowing for the creation and exploration of reports that can be interactively disaggregated and reaggregated along various dimensions, facilitating easier data exploration and management within an enterprise system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If managers use traditional extensive reports and offline data analysis programs, then comprehensive data can be obtained, but the process becomes time-consuming and requires expertise
Solution Approach 1:
The patent introduces an intermediary component (data exploration interface and processing system) between the manager and the extensive report data. This intermediary automatically processes, aggregates, and presents relevant information without requiring the manager to manually extract data from offline programs, thus reducing time loss while maintaining comprehensive data access.
Solution Approach 2:
The system enables self-service by allowing managers to directly access and explore aggregated data through an intuitive interface without needing expertise in offline data analysis programs. The system automatically performs data aggregation and presentation, making the data extraction process self-serve and eliminating the need for specialized skills.
2Loss of information
If offline data analysis is performed, then data can be extracted, but the data may become out of date during the analysis process
Solution Approach 1:
The system performs preliminary actions by pre-aggregating and pre-processing data before it becomes stale. Data is aggregated and made available through the interface in advance, ensuring that when managers access it, the information is already prepared and current, eliminating the delay and potential obsolescence associated with offline analysis.
Solution Approach 2:
The system maintains continuity of useful action by providing continuous access to aggregated data through an always-available interface. Unlike offline batch processing, the system keeps data continuously updated and accessible, ensuring that the data remains current throughout the entire analysis period rather than becoming stale between offline processing cycles.
3Loss of information
If manual searching across tables is performed, then related data can be found, but the process requires manual effort and expertise
Solution Approach 1:
The patent implements a universal data exploration interface that can handle multiple types of data relationships and queries through a single unified system. Instead of requiring separate manual searches across different tables, the interface provides multi-functional access to related data through consistent aggregation and exploration mechanisms, greatly simplifying the operation.
Solution Approach 2:
The system introduces an intermediary layer that automatically identifies and connects related data across tables. Instead of requiring managers to manually search through multiple tables, the intermediary system understands data relationships and automatically presents related information through the exploration interface, eliminating manual search effort while maintaining complete data discovery capability.
4Loss of information
If extensive reports are generated, then complete information is available, but the reports are not useful for day-to-day decision-making
Solution Approach 1:
The patent applies segmentation by dividing the extensive report data into manageable, relevant segments through automatic aggregation. Instead of presenting complete but overwhelming extensive reports, the system segments data by relevant dimensions and criteria, presenting only the portions most useful for day-to-day decisions while maintaining access to complete information when needed.
Solution Approach 2:
The system applies local quality by providing different levels of data aggregation and detail at different locations in the interface. Managers can access highly aggregated summaries for quick decisions at one level, while being able to drill down to detailed complete information at other levels, optimizing the utility for different decision-making contexts within the same system.
Data Source
AI summary
A dimensional data explorer for an enterprise system is disclosed. Dimensional data exploration includes providing a list of dimensions by which aggregated data in a report can be disaggregated and reaggregated. Aggregated data comprises a set of measures that have been aggregated for each object of a set of objects. Dimensional data exploration further includes providing the aggregated data disaggregated and reaggregated using a dimension from the list of dimensions.