Why Organizations fail with their DevOps?
Quality and Speed are the two most important outcomes of a successful DevOps journey. Be it an organization just taking baby steps towards the journey or an enterprise with most advanced CI-CD implementation, improving their product quality and delivery speed is a constant endeavor. Your organization has a failed DevOps implementation if you were not able to achieve any significant improvement in Quality and Speed! DevOps failure is not uncommon and even most mature organizations fail at it in the first go. There’s no secret recipe or ingredient to get DevOps right but, if you want to succeed, read on, on how you can put your journey back on track.
Getting to Quality and Speed
Quality and Speed are the result of continuous improvement and learning. For continuous improvement, organizations need to derive insights and learn from their data.
Data can come from various data sources, both structured and unstructured, real-time and historical. The key to deriving insights is collecting the data from the various processes, tools, servers, resources in your journey. Right from your collaboration and planning tools to your test logs, build logs, application monitoring logs and support tickets, everything needs to be collected. The data thus collected, can then be leveraged and insights can be built to help improve processes.
Insights and Intelligence
So how do you create Insights and intelligence from data? Metrics and KPIs are a good start, but are limited in providing enough insights to take corrective actions. Being able to combine various key KPIs is important to derive insights. For example, one of the most important metric in DevOps, MTTR (Mean Time To Recovery), can give you understanding of where you stand currently, but unless you have the ability to gain insights into which processes would help you reduce the MTTR value, it will be difficult to continuously improve. A Quality Intelligence platform helps to connect various data points and KPIs to derive the insights. As in the example of MTTR, the most important processor data point to improve would be incident detection. If you take more time to detect, the time to recovery will increase. Connecting these two key KPIs helps provide actionable insights for improvement.
As you start deriving insights from your data and metrics, the next step of improvement is to derive intelligence, which can then help predict incidents before they occur and in few cases use the intelligence for auto-healing.
Easier said than done. We, at, Qentelli understand the importance of deriving intelligence from business data and metrics and tying them to KPIs to give businesses a complete picture of where they stand in their DevOps journey. Qentelli has developed its own intelligence platform to help businesses adopt continuous delivery and DevOps without failed attempts.
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.
To learn and explore more in detail, please write to us at email@example.com. Our experts will be delighted to engage with you. Also, you can visit Qentelli’s social links for more details-
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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.