Predictive Analysis Aggregating Corporate and Financial Data
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Solution Overview
Problem
Existing data aggregation techniques fail to provide accurate analytical forecasting based on geographically diverse data from various sources such as companies, stocks, educational institutions, and job markets.
Innovation Solution
A system and method that aggregate data from multiple geographically diverse sources to generate predictive analysis for corporate and investment outcomes, allowing users to search and filter data by various parameters and generate custom queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is aggregated from multiple geographically diverse sources, then measurement precision and reliability of analytical forecasting are improved, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The patent segments the data aggregation system into specialized modules including a data collection module that gathers information from multiple geographically diverse sources, a data processing module that cleans and standardizes the data, and a predictive analysis module that generates forecasts. This segmentation allows each module to handle specific tasks efficiently, improving forecasting accuracy while managing system complexity through functional decomposition.
Solution Approach 2:
The patent introduces intermediary components including a standardized data schema that acts as a mediator between diverse data sources and the analysis engine, and an API layer that mediates between external data sources and the internal processing system. These intermediaries enable integration of geographically diverse data without proportionally increasing system complexity.
2Reliability
If comprehensive data from multiple sources is aggregated, then reliability of predictive analysis is improved, but loss of time for data collection and processing increases
Solution Approach 1:
The patent implements preliminary action through pre-processing steps including data validation rules applied at collection, pre-computed aggregates stored in the data warehouse, and pre-established relationships between data entities. This preliminary processing ensures data quality and reduces the time required for actual predictive analysis while maintaining reliability.
Solution Approach 2:
The patent enables continuous data collection and processing through automated ETL (Extract, Transform, Load) operations that run continuously or on scheduled intervals. The system maintains continuous connections to data sources, continuously validates incoming data, and continuously updates the predictive models, eliminating idle time and ensuring reliable forecasts are always available.
Data Source
AI summary
According to various embodiments described herein, mechanisms are provided for providing a single source for a wide range of data and predictive analyses, integrating data describing corporations, finances, stock performance, competition, educational institutions, and/or job markets, in any suitable combination. Various embodiments provide mechanisms for integrating any or all of such data to generate predictive analysis yield expected corporate and/or investment outcomes. In addition, the system and method described herein are able to create and answer questions in an outcome format that can be used to make financial decisions for investors, business executives, boards of directors, and/or the like. In particular, according to various embodiments, the system and method described herein are able to aggregate data from many different sources, such as for example nationwide jobs data, company information, stock portfolios, and/or educational data, and to provide accurate analytical forecasting based on such aggregated data.