The industry is abuzz with a mix of enthusiasm and confusion as advanced analytics are becoming assimilated by IT organizations for a wide variety of use cases, from performance to change and capacity planning, financial optimization, security, and support for cloud and agile. Part of the confusion stems from industry attempts to define advanced analytics for IT -- what EMA calls "advanced IT analytics" (AIA) -- far too narrowly, either purely in terms of discrete technologies or else as simply "big data." Another reason for the confusion is that the more prevalent nomenclature tends to direct advanced analytics for IT purely as an enabler for operations, whereas the data in this report shows just the opposite is true. AIA, as EMA understands it, provides a unifying layer that may support many IT as well as business roles.

Rather than forcing a template of technologies or data sources on our respondents, this exploratory research let the "real world" of active AIA deployments define itself. Our primary requirements included the following:

• AIA should be cross-domain and not restricted to just network, systems, application, database, or even business outcomes.

• AIA should require assimilating many different sources, whether third-party monitoring tools, different data sources, or ideally both. 

• Over and above this data, AIA requires the application of advanced heuristics, such as machine learning, advanced correlation, anomaly detection, or predictive trending.

We also required that all our respondents be actively engaged in their AIA initiative, either as managers or hands-on contributors.