Data Enthalpy Metric for KPI Recommendation
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Solution Overview
Problem
Organizations face the challenge of underutilizing data in their data lakes and warehouses, leading to untapped potential for generating insights, as most data remains unexplored and only limited key performance indicators (KPIs) are utilized.
Innovation Solution
A method and system that calculate a data enthalpy metric to identify unutilized data by comparing available and in-use KPIs, using an insight recommendation model to generate recommendations for underutilized KPIs, thereby increasing data enthalpy realization and generating additional insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If organizations build dashboards and KPIs based on limited data, then the complexity of data processing is reduced, but the productivity of data utilization decreases
Solution Approach 1:
The system automatically calculates data enthalpy metrics and generates KPI recommendations without requiring manual analysis. The insight recommendation model autonomously processes available KPIs, identifies unutilized data, and provides actionable recommendations, allowing the system to serve itself in optimizing data utilization.
Solution Approach 2:
The patent replaces manual data analysis and KPI selection processes with an automated computational system. The data enthalpy calculation and machine learning-based recommendation model substitute human analytical efforts, transforming the mechanical process of data exploration into an automated intelligent system.
2Quantity of substance
If organizations explore more data potential in data lakes, then the quantity of insights generated increases, but the difficulty of detecting and measuring useful insights increases
Solution Approach 1:
The data enthalpy metric serves as an intermediary that bridges the gap between raw data and useful insights. It provides a measurable indicator of unutilized data potential, making it easier to identify which data deserves further exploration. The insight recommendation model acts as another intermediary that translates complex data relationships into actionable KPI recommendations.
Solution Approach 2:
The system introduces new parameters (data enthalpy metric, KPI significance scores) to quantify and measure data utility. By changing the measurement parameters from traditional manual assessment to calculated metrics, the system makes it easier to detect and evaluate useful insights among vast amounts of data.
3Measurement precision
If organizations calculate data enthalpy metrics for all available KPIs, then the precision of identifying unutilized data improves, but the loss of time for processing increases
Solution Approach 1:
The system calculates data enthalpy metrics selectively rather than exhaustively for all possible KPIs. The insight recommendation model prioritizes KPIs based on initial assessments and data enthalpy thresholds, performing detailed calculations only on the most promising candidates. This partial action approach maintains precision for critical insights while reducing overall processing time.
Data Source
AI summary
A method and system for generating recommended data insights are disclosed. The method may include obtaining available Key Performance Indicators (KPIs) associated with an operation process from a KPI repository. The available KPIs may represent metrics that can be calculated based on data for the operation process stored in a data repository. The method may include identifying in-use KPIs comprising a subset of the available KPIs and calculating a data enthalpy metric for the operation process based on a number of the available KPIs and a number of the in-use KPIs. The method may further include obtaining an insight recommendation model trained to predict a significance of an available KPI not in use, executing the insight recommendation model to generate a KPI recommendation for the operation process based on the data enthalpy metric, and outputting the KPI recommendation via the user interface.