Demystifying the term ''AIOps''

We learned in Chapter 1, Machine Learning for IT, that many companies are drowning in an ever-increasing cascade of IT data while simultaneously being asked to ''do more with less'' (fewer people, fewer costs, and so on). Some of that data is collected and/or stored in specialized tools, but some may be collected in general-purpose data platforms such as the Elastic Stack. But the question still remains: what percentage of that data is being paid attention to? By this, we mean the percentage of collected data that is actively inspected by humans or being watched by some type of automated means (defined alarms based on rules, thresholds, and so on). Even generous estimates might put the percentage in the range of single digits. So, with 90% or more data being collected going unwatched, what's being missed? The proper answer might be that we don't actually know.

Before we admonish IT organizations for...