By completing this tutorial, you’ve learned how you can detect anomalous behavior in a simple set of sample data. You created anomaly detection jobs in Kibana, which opens jobs and creates and starts datafeeds for you under the covers. You examined the results of the machine learning analysis in the Single Metric Viewer and Anomaly Explorer in Kibana. You also extrapolated the future behavior of a job by creating a forecast.
If you’re now thinking about where anomaly detection can be most impactful for your own data, there are three things to consider:
- It must be time series data.
- It should be information that contains key performance indicators for the health, security, or success of your business or system. The better you know the data, the quicker you will be able to create jobs that generate useful insights.
- Ideally, the data is located in Elasticsearch and you can therefore create a datafeed that retrieves data in real time. If your data is outside of Elasticsearch, you cannot use Kibana to create your jobs and you cannot use datafeeds. Machine learning analysis is still possible, however, by using APIs to create and manage jobs and to post data to them.
In general, it is a good idea to start with single metric anomaly detection jobs for your key performance indicators. After you examine these simple analysis results, you will have a better idea of what the influencers might be. You can create multi-metric jobs and split the data or create more complex analysis functions as necessary. For examples of more complicated configuration options, see Configure anomaly detection.