Anomaly Management is the ability to detect, identify, clarify, alert and manage unexpected or unforecasted cloud cost events in a timely manner, in order to minimize detrimental impact to the business, cost or otherwise.
Managing anomalies typically involves the use of tools or reports to identify unexpected spending, the distribution of anomaly alerts, and the investigation and resolution of anomalous usage and cost.
In the context of Cloud FinOps, anomalies are levels of spending that are different from the normal or expected spend.
Anomaly detection identifies data points, events, and/or observations that deviate from a dataset’s normal behavior. Detection tools & procedures allows the FinOps team to react quickly in order to maintain spend levels that an organization expects. To quickly find those needles in your cloud haystack, using automated, machine learning–based anomaly detection is key. These tools are generally offered by cloud providers and third party platforms.
WHERE ARE ORGANIZATIONS IN TERMS OF MATURITY
Organizations operating at a FinOps Run maturity was reflected by practitioner respones that indicated it takes hours for their teams to be aware of unexpected cost increases. Responses indicating that it takes up to a day or multiple days for teams to be aware of cost anomalies were cohorts operating at a FinOps Walk maturity. Responses indicating teams were unaware of unexpected cost increases for a week or more were reflected in organizations operating at a FinOps Crawl maturity
Measures of success are represented in the context of cloud costs and may include one or more key performance indicators ( KPI ), describe objectives with key results ( OKR ), and declare thresholds defining outliers or acceptable variance from forecasted trends.
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How a remote engineering team at a multinational pharmaceutical company used anomaly detection to identify change in their three AWS EC2 x1e.32xlarge instances.
Amy Ashby, FinOps Lead at Under Armor, previews her FinOps X presentation.