In our previous post, we introduced the idea that data analytics can significantly improve IT outcomes and facilitate the transition to DevOps practices. We described the three data types available to IT departments (Operations Data, Monitoring Data, and Event Data), and we delved into more detail about how to use Operations Data. This post focuses on the value of Monitoring Data.
Diving Deeper: Monitoring Data
Infrastructure monitoring data can provide IT departments with insight into the virtual or physical, systems they manage. Analyzing these data can provide value across multiple axes, including outside the IT organization itself:
- Cost Optimization – Earlier this year, Datapipe’s Data & Analytics Professional Services team discussed how analyzing patterns in metrics such as CPU usage could allow for public cloud capacity cost reductions. These types of analyses provide an empirical understanding of patterns in an application’s resource needs, allowing IT departments to better tailor resource allocations and only pay for what they use.
- Microservices Performance Optimization – Public cloud container services such as AWS’s Docker or GCP’s Kubernetes facilitate the use of microservices software architectures. These services provide metrics that allow DevOps teams to monitor the performance and interaction of various microservices. This data can be used to improve software architecture by identifying inefficiencies in the microservices’ design.
- Customer Insight – Outside the IT organization, infrastructure-monitoring data offer the potential for unique insights into customer behavior. In the CPU usage example above, the regular daily and weekly usage patterns indicated underlying patterns in customer behavior. These insights can help businesses better understand their existing user base and design products accordingly.
These examples speak only to the value of the continuous data streams provided by monitoring systems. As we discussed in our previous post, monitoring systems also provide event logs detailing system changes. We will explore this further in our next, and final, blog in this introductory series.