Data Lake System for Objective Intervention Impact Assessment
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Solution Overview
Problem
Conventional processes for assessing the impact and effectiveness of training programs and interventions in enterprises are flawed due to faulty assumptions, subjective biases, and lack of objective metrics, failing to provide actionable insights for improving employee and stakeholder performance.
Innovation Solution
A computerized system that collects and analyzes data from various sources, including IoT devices and enterprise systems, to objectively characterize the impact of interventions on behavior by storing and querying data in a data lake, using machine-learning algorithms to identify trends and propose interventions, and providing a graphical user interface for users to visualize the results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional assessment processes are used to evaluate training program impact, then the process is simple and quick to implement, but the measurement precision and objectivity of the assessment results deteriorate due to subjective biases and faulty assumptions
Solution Approach 1:
The system segments the assessment process into multiple independent components: data collection from diverse sources (IoT devices, enterprise systems, surveys), data storage in data lakes, machine learning model processing, and result visualization. This segmentation allows each component to be optimized independently while maintaining overall system precision without excessive complexity.
Solution Approach 2:
The patent introduces machine learning models as intermediary components that objectively process raw data from multiple sources and transform it into actionable insights. These ML intermediaries eliminate subjective biases by using algorithmic processing rather than human judgment, thereby improving measurement precision while managing complexity through automated mediation.
2Measurement precision
If comprehensive data from multiple sources is collected to improve assessment objectivity, then the measurement precision improves, but the device complexity and data processing requirements worsen
Solution Approach 1:
The system employs universal data processing architectures that can handle multiple data types (structured and unstructured) from various sources through a common pipeline. The data lake infrastructure and ML processing framework serve multiple functions: storing diverse data, preprocessing information, training models, and generating insights, thereby managing complexity through multi-functional design.
Solution Approach 2:
The system implements self-service mechanisms where machine learning models automatically process and analyze the comprehensive data collected from multiple sources without requiring manual intervention for each data point. The automated processing pipelines and self-training algorithms reduce the operational complexity of handling extensive multi-source data while maintaining high objectivity.
3Productivity
If machine learning algorithms are used to identify trends and propose interventions, then the productivity and actionable insight generation improve, but the device complexity and computational requirements worsen
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing data in structured formats within data lakes before analysis is needed. Data is cleaned, tagged, and organized in advance, so when ML algorithms need to identify trends and generate insights, the computational work is reduced to pattern recognition rather than raw data processing, thereby improving productivity while managing complexity.
Solution Approach 2:
The patent replaces manual analysis mechanisms with automated machine learning algorithms. Instead of human experts manually processing data to identify trends and propose interventions, ML models automatically perform these functions through computational patterns recognition, significantly improving insight generation efficiency while the system complexity is managed through automated substitution rather than manual processes.
Data Source
AI summary
The disclosed embodiments include computerized methods and systems, including computer programs encoded on a computer storage medium, for objectively characterizing an impact of an intervention on individuals or groups. For example, a computing system may obtain, and store in a data lake, intervention data identifying interventions, and objective data characterizing an impact of these interventions on at least one individual. The computer system may also populate a queryable interface with query parameters (e.g., at least one of the interventions and at least a portion of the objective data), which may be presented to a user via a device. The computer system may obtain stored intervention and objective data consistent with a received query, which may be provided to the device for presentation to the user. The disclosed embodiments also provide a visual representation of trends in the stored data and candidate interventions the address the identified trends without user input.