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22% of organizations are predictive today. To take that last step to preemptive operations requires more data, more automation, and more intelligence in the infrastructure.
Both known and potential incidents/problems can be predicted before they occur along with providing recommended actions to avoid them
Policy frameworks at this level are likely to move well beyond the boundaries of standards like ITIL and NIST. The most advanced organizations in our research developed policies internally to govern end-to-end automation.
Policies will have evolved beyond static business rules and instead define which processes can be automated completely without human intervention, using historic behavior trends to validate autonomous actions.
Data collection at this stage should focus on validation and impact assessments. Before and after snapshots of infrastructure state can help validate the impact of preemptive automated systems on overall IT health, as well as explain root cause. The goal is to verify that the automated systems are performing in line with business intent.
Use reinforcement learning systems to train the preemptive systems. E.g., Once the operator approves a change made by the predictive model, it shouldn’t look for approval during similar subsequent occurrences.
Monitor customer actions to incorporate the recommendations and measures taken for different scenarios in the algorithm. KPI metrics should expand to focus on total business impact.
Automate high frequency and low risk tasks, like changing local authentication passwords, and high risk tasks that require operator validation and reinforcement learning. The goal should be to implement infrastructure automation to automate the entire compute, storage and network process flow.
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