20+ years in IT operations and network monitoring. I built and ran a Network Operations Center (NOC — the team that watches infrastructure health around the clock), led Problem Management (the practice of finding root causes, not just closing tickets) at a bank, and audited infrastructure reliability for ITSM clients. I read incident data for a living — OpsLab is the statistical engine I built to do it faster and prove the pattern with numbers.
Cut mean time to repair (MTTR — how long it takes to fix an incident) by 20%. Built a failure-forecast model on historical data, preventing 4+ major network incidents a year.
Ran Problem Management (root-cause practice) plus 12 ITIL practices. Built a monitoring service from zero. Held SLA at 99.9% ± 0.2%.
Cut failure rate by roughly 50% across engagements. Raised availability from 95% to 99.8%.
Radio communications engineer by training. "Master of Communications" award, 2013.
Six core methods out of 20+ in the OpsLab engine — the full catalog also covers correlations, trend diagnostics, anomaly detection, and per-service breakdown. Click a method name for the one-paragraph detail and a real example.
Ranks incidents by how much downtime each one causes, so you fix the few that matter instead of the many that don't. The Gini coefficient (an inequality measure — 0 means all incidents are equally bad, 1 means one incident caused everything) tells you how concentrated the problem is.
Groups incidents by behavior pattern (time of day, duration, frequency) instead of by ticket category. Reveals when a long list of "different" errors is really a small number of root causes.
Finds hidden repeat cycles in uneven time series — a method from astrophysics, applied to incident logs. Reports FAP (False Alarm Probability — the chance the detected cycle is random noise).
Tests whether a change (a deploy, a config update) actually shifted incident frequency, and by how much. Cliff's δ (effect size — how big the shift is, from 0 to 1) turns "it feels worse" into a number.
Estimates the probability of a rare but very long outage — the one that breaks an SLA. Built for planning, not for the average case.
Finds which events tend to appear together and in what order, so an early-warning alert can be set on the leading signal instead of the crash itself. Lift measures how many times more often a pair occurs than by chance.
Send it through OpsLab for the statistical read, or message me directly to talk through what it means for your infrastructure.