The process of selecting KPIs, in general, should be relatively easy, as it is likely obvious what metrics are the best indicators (if online sales are down, then the application is likely not working). But if we want to get a more holistic view of what may be contributing to an operational problem, we must expand our analysis beyond the KPIs to indicators that emanate from the underlying systems and technology that support the application.

Fortunately, there are a plethora of tools that allow for the collection of all kinds of data for centralization in the Elastic Stack. Some examples from within the portfolio of Elastic's free and open source tools are as follows:

• Elastic Metricbeat: An easy-to-use lightweight data shipper that can gather performance metrics from systems and forward them to Elasticsearch
• Elastic Filebeat: Another member of the Beat family that forwards log lines from any application or system log to Elasticsearch
• Elastic application performance monitoring (APM): Allows for the instrumentation and measurement of detailed application performance metrics at the code level

Regardless of what tools you use to gather the underlying application and system data, one thing is likely true: there will be a lot of data when all is said and done.

Remember that our ultimate goal is to proactively and holistically pay attention to a larger percentage of the overall dataset. To do that, we must first organize this data so that we can effectively analyze it with ML.