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There are five distinct types of causal analysis that make availability and performance data actionable. While each brings a valid perspective, change-based causal analysis — particularly when combined with Bayesian causal network analysis — holds the most promise for IT operations leaders.
Table of Contents
Add Causal Analysis to Pattern Discovery and Anomaly Detection to Make the Data Stored in ITOA Platforms Truly Actionable
- Approach No. 1: Establish a Relationship of Topologically Grounded Causality Among the Variables Describing the System
- Approach No. 2: Situate the Variables Describing the System in a Bayesian Causal Network
- Approach No. 3: Establish the Relationship of Granger Causality Among Variables Describing the System Arranged in a Time Series
- Approach No. 4: Use Pre-Existing, Distributed and Mostly Partial Knowledge of System Causality to Prune the Correlational Variable Networks
- Approach No. 5: Establish What Changes Have Recently Been Introduced to the Environment and Try to Correlate Variable Value Fluctuations to Those Changes
Combine Bayesian Causal Network Analysis With Change-Focused Analysis for the Most Effective Combination of Causal Analysis Methods
- Add Causal Analysis to Pattern Discovery and Anomaly Detection to Make the Data Stored in ITOA Platforms Truly Actionable
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