Companies are adopting DevOps at lightning speed, but they are faced with the challenges of adopting it successfully. The difference between successful and unsuccessful adoption of DevOps lifecycle is impact in measurable metrics of faster feedback and recovery times, quality code, less downtime, high availability and better communication between Development and Operations teams.
The changing meme has brought in a radical shift in the responsibilities of development team from just writing code, and doing hand-offs to configure virtual and cloud servers, deploy applications, monitor application health and respond faster in terms of fixing bugs. But is it solving the problem for businesses to release faster, lesser downtime, less production failures? In one word, No, the operations team is still having night mares as software releases have become more frequent when compared to the past releases that occur once in a quarter or half yearly.
Thus, the IT operations is more relevant and need to be better placed in DevOps stack to get the desired business outcomes. As the article is titled, Is your Ops truly integrated into your DevOps lifecycle, we want to emphasize on how adoption of DevOps requires operations to be tied well with automation and real-time monitoring and intelligence initiatives. The role of Operations from classical operations (starting with the servers, keep them running and doing the deployment) to new operations where they are managing infrastructure, configuring and monitoring systems and networking, enforcing policies around security and compliances, and other non-production application related tasks that are crucial for better application quality and less production defects.
Companies believe they are on the right journey when the developers are equipped with continuous integration, build, deploying and managing all environment from development to production. We interact with many companies while helping them with their DevOps initiatives where developers are doing steps mentioned above, probing further they revealed that they still don’t have a measurable impact on speed and quality. This is because since their operations team have moved from managing manual tasks they have not been aligned with the task of continuous and real-time monitoring and feedback required to test effectiveness and defect traceability before it got slipped into production. The reason can be DevOps washing leading to just DevOps tools, processes and technologies and fail to look beyond it to measure that impact of DevOps over desired KPIs.
There’s so much of operations data and how does this impact DevOps Lifecycle
At Qentelli we believe, in DevOps, Ops is not just about the operations team but also about operations data. Companies are holding so much of operations data and development teams are working in separation with these datasets. Companies are collaborating on processes, but data needs special attention to achieve business outcomes with DevOps. The first step of getting Ops in DevOps lifecycle is to decide on the measurable metrics businesses want to achieve. Look for the past data that can be measured to derive insights and actionable items to achieve them.
In the DevOps environment, most of the environment assume transparency and does not realize the lack of intelligent dashboards and have manual dashboards (prone to error and time taking) to know about the real-time intelligence.
The manual dashboards do not suffice the aggressive business outcomes of achieving agility and digital transformation. Operations data must be looped in the DevOps lifecycle to identify patterns and anomalies automatically.
Operations team can use this data to identify inconsistencies early in the testing stage itself and improving them can impact the overall quality of the application or software being released. Automating test runs for speed, quality and accuracy can help in saving time and detecting problems that might take too much of time or in haste, can go unidentified in the end-user environment. With Artificial Intelligence (AI) and Machine Learning (ML), tools available in the market, companies need to put operational data together and, analyse them, define KPIs, measure KPIs from them and create actionable steps for improvement of DevOps lifecyle management. The next action after deriving insights is to keep your operations team in continuous monitoring mode to predict incidents even before they happen and integrate ‘Ops’ truly into your DevOps lifecycle.
The future of Ops is to help developers in just self-serving them with the maximum automation and least intervention and monitoring real-time data to ensure the quality is not compromised with more frequent releases.
Qentelli’s TED as Quality Intelligence Platform:
Qentelli created TED, an Engineering dashboard with capabilities to collect data from various data sources, create metrics and define KPIs and derive insights for improvement. What makes TED a true Quality Intelligence platform and unique, is its ability to derive actionable insights for most key DevOps KPIs out of the box and artificial intelligence (AI) to improve processes, provide predictions for incidents and auto-healing for broken processes. Qentelli helped some of Fortune 1000 companies get better with their Quality and Speed using TED. This can help operations team to have end to end visibility of where companies are leading on their DevOps journey and get it on the right track if it goes here and there.
To learn and explore more in detail about Qentelli’s AI-driven automated testing solutions and DevOps implementations, please write to us at firstname.lastname@example.org. Our experts will be delighted to engage with you.
Headquartered in Dallas, TX with global delivery teams in India, Qentelli is an Industry Thought Leader in Quality Engineering, Automated Testing and Continuous Delivery. With high performing engineering teams working in the dedicated Innovation Group, Qentelli brings design thinking to address complex business problems and enables Continuous Delivery across Enterprise IT through automation for its global customers.