Predicting Disaster Recovery Response Time
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Solution Overview
Problem
Current disaster recovery invocation processes in IT service continuity management are often reactionary and time-consuming, failing to promptly assess the likelihood of disaster recovery invocation and missing potential risks, leading to delayed responses and increased business losses.
Innovation Solution
A method and system that estimate the probability of IT disaster recovery invocation by analyzing incident data, utilizing a knowledge base of past incidents, and incorporating factors like architecture, network configurations, weather, and social media analytics to predict incident risks, thereby enabling proactive notification and preparation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional reactionary disaster recovery processes are used, then system simplicity is maintained, but response time increases and business losses increase
Solution Approach 1:
The system performs preliminary actions by continuously monitoring incident data, weather conditions, social media feeds, and network status before disasters occur. It proactively assesses incident risks and predicts potential disasters, enabling early notification and preparation rather than reacting after disasters strike, thus reducing response time without requiring complex manual intervention systems
Solution Approach 2:
The patent introduces an intermediary predictive assessment system that mediates between raw incident data and disaster recovery activation. This intermediary layer analyzes multiple data sources (incident tickets, weather data, social media, network configurations) and generates risk assessments that trigger appropriate responses, reducing the complexity of direct monitoring while improving response time
2Measurement precision
If comprehensive data analysis is performed to predict incident risks, then measurement precision improves, but device complexity increases
Solution Approach 1:
The system segments the comprehensive data analysis into distinct modular components: incident data collection from multiple sources, weather data acquisition, social media monitoring, network configuration analysis, and predictive assessment processing. Each segment handles specific data types and processing tasks independently, improving measurement precision through specialized analysis while managing complexity through modular architecture
Solution Approach 2:
The patent implements a universal data processing platform that handles multiple data types (structured incident tickets, unstructured social media posts, weather data, network configurations) through a single integrated system. This multi-functional approach improves measurement precision by comprehensively analyzing all relevant data sources while avoiding the complexity of multiple separate specialized systems
3Reliability
If proactive disaster recovery assessment is implemented, then reliability improves, but loss of energy increases
Solution Approach 1:
The system implements continuous monitoring and assessment of incident risks by continuously collecting and analyzing data from incident sources, weather services, social media, and network configurations. This continuous useful action improves service continuity reliability by maintaining constant vigilance for potential disasters while optimizing energy usage through automated processes that operate efficiently without manual intervention
Solution Approach 2:
The predictive assessment system operates autonomously, automatically collecting data, analyzing risks, generating assessments, and triggering notifications without requiring continuous human energy input. The system serves itself by automating the entire disaster prediction and response preparation process, improving reliability through consistent automated monitoring while reducing the energy cost of human intervention
Data Source
AI summary
A method, computer system, and a computer program product for estimating the probability of invoking information technology (IT) disaster recovery at a location based on an incident risk is provided. The present invention may include receiving a piece of data associated with an incident at the location. The present invention may also include estimating a similarity value associated with the incident based on a plurality of past incidents from a knowledge base. The present invention may then include receiving a plurality of mined data based on the location. The present invention may further include predicting the incident risk to the location based on the received plurality of mined data and the estimated similarity value to the plurality of past incidents.