Effectively adding analytics to your monitoring

Effective monitoring for todays agile environments is both science and art. (Analytics can provide the science while experts and business context can provide the art.) There is no perfect solution, but a framework for integrating these varied information sources as collaborators can drive continuous improvement. Elizabeth Nichols highlights (anonymized) examples from real environments.

Effective monitoring for today’s agile environments is both science and art. (Analytics can provide the “science” while experts and business context can provide the “art.”) There is no perfect solution, but a framework for integrating these varied information sources as collaborators can drive continuous improvement. Elizabeth (Betsy) Nichols highlights examples from real environments and demonstrates how to leverage analytics as part of a larger framework of collaborators to continuously improve health and performance monitoring. Betsy begins with a survey of analytics techniques that have applicability in environments that range from very small to huge, including deterministic and statistical analytics, machine learning, univariate models, and multivariate models—for each, providing examples from (anonymized) cases that illustrate where a given technique can succeed (and where it can fail) and why. Betsy then describes a framework—which provides key services such as integration of collaborators, orchestration of tasks, feedback/control loops, scenario replay, and sensitivity analysis, packaging, and incremental improvement—in which analytics plays a role as one of many collaborators, as well as a use case that shows how a framework can drive continuing improvement as business conditions evolve and the collaborators mature.