Data Aggregation System Resolving Format Inconsistencies
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Solution Overview
Problem
Organizations face challenges in aggregating and analyzing data from various sources due to differing formats and locations, making it difficult to extract meaningful insights into employee productivity and operational efficiency.
Innovation Solution
A data analysis system that aggregates data from multiple sources, transforms and cleanses it, and generates statistics by detecting inconsistencies, associating data with unique individuals, and providing efficiency indicators based on comparisons with peers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is aggregated from multiple data sources with different formats, then the quantity and comprehensiveness of data increases, but the complexity of data processing and integration increases
Solution Approach 1:
The patent introduces a data transformation layer that acts as an intermediary between multiple data sources with different formats and the analysis system. This transformation layer standardizes data from various sources (email, system logs, badge swipes, etc.) into a unified format, enabling aggregation without directly exposing the complexity of source format variations to the analysis process.
Solution Approach 2:
The system segments the data aggregation process into distinct modules: data collection from various sources, data transformation and normalization, data association with unique individuals, and analysis. This segmentation allows each component to handle specific format requirements independently, reducing overall processing complexity while maintaining comprehensive data aggregation.
2Reliability
If data from multiple sources is combined to analyze employee productivity, then the accuracy and reliability of analysis improves, but the time required for data processing increases
Solution Approach 1:
The system performs preliminary data transformation and association operations before the actual analysis. By pre-processing data to detect inconsistencies, transform formats, and associate data with unique individuals in advance, the system prepares aggregated data structures that can be quickly analyzed without repeating time-consuming operations during each analysis cycle.
Solution Approach 2:
The system incorporates feedback mechanisms that detect inconsistencies in data formatting and quality during the aggregation process. When inconsistencies are detected, the system can automatically adjust transformation parameters or flag data for manual review, improving analysis reliability while minimizing processing time through automated correction rather than complete reprocessing.
3Ease of operation
If data is transformed into consistent formats for aggregation, then the ease of data combining improves, but the complexity of data transformation increases
Solution Approach 1:
The system manages transformation complexity by dynamically adjusting transformation parameters based on detected data characteristics. Rather than using fixed complex transformation rules, the system analyzes incoming data formats and automatically selects appropriate transformation parameters to achieve consistency, making the combining process easier while managing transformation complexity through adaptability.
Data Source
AI summary
According to certain aspects, a computer system may be configured to aggregate and analyze data from a plurality of data sources. The system may obtain data from a plurality of data sources, each of which can include various types of data, including email data, system logon data, system logoff data, badge swipe data, employee data, job processing data, etc. associated with a plurality of individuals. The system may also transform data from each of the plurality of data sources into a format that is compatible for combining the data from the plurality of data sources. The system can resolve the data from each of the plurality of data sources to unique individuals of the plurality of individuals. The system can also determine an efficiency indicator based at least in part on a comparison of individuals of the unique individuals that have at least one common characteristic.