Predictive Analysis Framework for Proactive Incident Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Enterprise support management applications typically operate reactively, leading to delays in incident prevention and challenges in proactively addressing issues before they occur.
Innovation Solution
A predictive analysis framework that retrieves raw data from in-memory databases, uses configurable data points, and applies predictive algorithms to generate insights, enabling proactive incident management by integrating with various applications and providing insights through user interfaces and APIs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If enterprise support management applications operate reactively by logging incidents after they occur, then incident management functionality is provided, but there is a delay in assigning incidents to support technicians and proactively preventing incidents before they occur
Solution Approach 1:
The system performs preliminary actions by analyzing historical incident data and identifying patterns before actual incidents occur. The predictive analytics framework processes historical data to generate predictions about future incidents, enabling support technicians to take preventive actions before problems manifest, thereby eliminating the reactive delay inherent in traditional incident management
2Reliability
If predictive analytics framework processes historical data to generate predictions, then proactive incident management is enabled, but data processing complexity and computational requirements increase
Solution Approach 1:
The system segments the data processing workload by dividing historical data into manageable datasets and processing them in stages. The predictive analytics framework processes data in batches, analyzing specific patterns and generating predictions separately, which reduces the complexity of processing the entire historical dataset at once while maintaining prediction accuracy
3Adaptability or versatility
If the system integrates with multiple applications and provides insights through multiple interfaces, then user accessibility and versatility improve, but system integration complexity increases
Solution Approach 1:
The predictive analytics framework is designed with multi-functionality, providing a unified interface that serves multiple purposes: it processes data from various applications, generates predictions, and delivers insights through different channels. This universal approach allows the system to integrate with multiple applications without requiring separate complex integration mechanisms for each, as the framework handles all interactions through a single cohesive architecture
Data Source
AI summary
To generate insights in a predictive analysis framework for support management, raw data is received as input from an in-memory database. A predictive analysis library is integrated in the predictive analysis framework. The predictive analysis framework is generated as a configurable application-programming interface (API). Predictive analysis is performed based on the raw data and the configurable data points. The predictive analysis library functions are invoked from the in-memory database to perform predictive analysis. Predictive data model is generated based on computation performed using a prediction algorithm. Predictive insights are generated based on the predictive data model. The predictive insights are displayed in a user interface associated with a device.