Don't just learn from the past. Predict the future.
38% of organizations are proactive. Understanding what's happening in your infrastructure today is important — even better is using that to predict what will happen tomorrow.
"Proactive measures are taken based on health checks to reduce the occurrence of known incidents/problems"
Your policies should go beyond the boundaries of your operations to encompass the ecosystems you belong to and the external data you gather (whether that's weather info or threat intelligence).
Policies are no longer static; they're refined and updated dynamically to adapt to context. For example, in high-alert security situation, network polling intervals could be shortened, patch management prioritized, and user authentication policies toughened.
Streaming telemetry should be common at this stage, giving you a real-time view of operations events. Retain that data for historical analysis and as training data for predictive algorithms.
As you gather more data from more infrastructure, storage and aggregation will be pressing issues. Cloud-based data lakes using Hadoop scale and let you aggregate diverse data in a coordinated way.
Effective use of AI and machine learning will be fundamental to get insight from large-scale data sources through predictive models. Real-time analysis can predict outages by spotting their "fingerprints".
Talent becomes a significant focus here: data experts should be paired with domain experts, who have deep experience. When recruiting for data scientists, ensure diversity and inclusion practices are followed, to avoid bias.
At this stage automation focuses on taking the predictive AI model from development to testing and production. Automation is used to normalize data coming in from different parts of the network, validate the model's prediction, and action the response.
Integration between workflow systems — including business process management, IT service management, cloud and network mangement tools — will be key to scaling results.
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