Metrics-oriented thinking is key to continuous improvement – and a core tenant of any agile or DevOps philosophy. Metrics are factual and once agreed upon, these facts are used to drive discussions and methods. They also allow for a collaborative effort to execute decisions that contribute towards business outcomes.
DevOps, although becoming a commonly used job title, is not a role or person and there is no playbook or rule set to follow. Instead, DevOps is a philosophy which spans people, process, and technology. The goal is releasing better software more rapidly, and keeping said software up and running by joining development and operational responsibilities together.
Additionally, DevOps aims to improve business outcomes, but there are challenges in selecting the right metrics and collecting the metric data. Continuous improvement requires continuous change, measurement, and iteration. What’s more, the agreed-upon metrics drive this cycle, but also create insights for the broader organization.
Data-Driven DevOps
A successful DevOps transformation focuses on a couple areas. To start, a culture change is needed between development and operations teams. Another core tenant of DevOps is measurement. In order to accomplish a true DevOps transformation, it’s important to measure the current situation and regularly review metrics which indicate improvement or degradation. One of the core tenants of DevOps is measurement, and using said measurements as facts when driving decision making. These metrics should span several areas which may have been considered disjointed in the past.
To help DevOps teams think of possible metrics and how these metrics relate to key initiatives, Gartner recently released this useful metrics pyramid for DevOps:
Many of these metrics span development, operations, and most importantly – the business. They measure efficiency, quality, and velocity. However, Gartner points out that the hardest part is often defining what we can collect, take action upon, audit, and use to drive a lifecycle.
The second challenge (which Gartner does not discuss) is how these metrics should be linked together to offer meaningful insights. If the metrics do not allow linkage between a release and business performance, attribution gaps remain. And unfortunately, many enterprises today analyze metrics that have a lack of linkage or relationship between them.
To help with these relationships, context is critical. Without context, metrics can be open to interpretation, especially as you move up the Gartner pyramid. So it’s crucial to be able to link metrics together and attribute earnings or cash flow with a release or change that represents improvements in the application.
Additionally, metrics should be able to drive visibility inside the application without creating an additional burden for developers. With automated instrumentation, metric data can be produced consistently and comprehensively across all teams. This is extremely beneficial as many teams have different ways of collecting data, which can traditionally lead to inconsistencies. Consistent measurements should always be obtained from the application components and desired business outcomes of the application.
The Latest
Broad proliferation of cloud infrastructure combined with continued support for remote workers is driving increased complexity and visibility challenges for network operations teams, according to new research conducted by Dimensional Research and sponsored by Broadcom ...
New research from ServiceNow and ThoughtLab reveals that less than 30% of banks feel their transformation efforts are meeting evolving customer digital needs. Additionally, 52% say they must revamp their strategy to counter competition from outside the sector. Adapting to these challenges isn't just about staying competitive — it's about staying in business ...
Leaders in the financial services sector are bullish on AI, with 95% of business and IT decision makers saying that AI is a top C-Suite priority, and 96% of respondents believing it provides their business a competitive advantage, according to Riverbed's Global AI and Digital Experience Survey ...
SLOs have long been a staple for DevOps teams to monitor the health of their applications and infrastructure ... Now, as digital trends have shifted, more and more teams are looking to adapt this model for the mobile environment. This, however, is not without its challenges ...
Modernizing IT infrastructure has become essential for organizations striving to remain competitive. This modernization extends beyond merely upgrading hardware or software; it involves strategically leveraging new technologies like AI and cloud computing to enhance operational efficiency, increase data accessibility, and improve the end-user experience ...
AI sure grew fast in popularity, but are AI apps any good? ... If companies are going to keep integrating AI applications into their tech stack at the rate they are, then they need to be aware of AI's limitations. More importantly, they need to evolve their testing regiment ...
If you were lucky, you found out about the massive CrowdStrike/Microsoft outage last July by reading about it over coffee. Those less fortunate were awoken hours earlier by frantic calls from work ... Whether you were directly affected or not, there's an important lesson: all organizations should be conducting in-depth reviews of testing and change management ...
In MEAN TIME TO INSIGHT Episode 11, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses Secure Access Service Edge (SASE) ...
On average, only 48% of digital initiatives enterprise-wide meet or exceed their business outcome targets according to Gartner's annual global survey of CIOs and technology executives ...
Artificial intelligence (AI) is rapidly reshaping industries around the world. From optimizing business processes to unlocking new levels of innovation, AI is a critical driver of success for modern enterprises. As a result, business leaders — from DevOps engineers to CTOs — are under pressure to incorporate AI into their workflows to stay competitive. But the question isn't whether AI should be adopted — it's how ...
