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As progressively more organizations and enterprises, regardless of size, adopt DevOps principles, the modern workplace is evolving. Legacy practices and systems are slowly fading away, giving way to newer, more streamlined and Agile approaches. According to recent global surveys, around 50 percent of enterprises have already adopted DevOps principles. This, along with advancements in technology that lead to more efficient R&D, is a rallying cry for progressively more companies to adopt the best in agility and organizational positivity.
Despite this, the rapid adoption, among other factors, has brought up a number of challenges for those who are currently either looking to DevOps, or are in the process if implementation and have already encountered issues. For enterprises who have functioned in traditional fashion till now will undoubtedly find it difficult to move out of their comfort zone and adhere to a starkly different culture that DevOps brings.
However, just like any other form of organizational change, DevOps can be implemented with the right approach. More importantly, technical training consisting of DevOps process structures, and formulated according to the principles can drastically improve adoption and implementation, while imbuing teams and managers with skills and functional changes that will benefit both them and the enterprise as a whole.
To that end, let’s discuss some of the commonly faced challenges by DevOps managers and their teams, as well as how technical training can help mitigate them.
At the core of DevOps is a culture built by and based on, collaboration. The term itself indicates collaboration between two uber-important aspects of development, namely Development and Operations. This is not as simple; however, especially with enterprises who have experienced a culture very different from what DevOps brings about, which then leads to lack of actual communication and collaboration between the devs and operations teams.
Additionally, an integral element of the new methodology is the breaking and restructuring of traditional departments into more collaborative single units which focus on a common goal. An example of this would be bringing business reps and testers into the development team and make it more cross-functional and product-oriented instead of project management-centric group. This requires an extensive amount of adjustment, both on the managers’ and team members’ end; a shift that is not always optimized and seamless.
These facts make the enterprise guilty of them appear as if it’s going against the collaborative culture trends forecast for 2018.
On the technology side of things, the very first problem that occurs during DevOps implementation is transitioning from legacy systems towards hybrid or cloud-based infrastructures. Also, since DevOps is all about collaboration, IaaS-based tools such as Azure, AWS and Google Cloud, coupled with collaboration tools such as SharePoint is standard practice, which tends to throw off legacy users.
Knowing which tools perform what function on which platform is vital if the technology is to be integrated into operations quickly. Easier suggested than done since it is quite difficult to create a hybrid environment and implement new SaaS tools into a traditional setup while also teaching teams how to use the new tools.
Another hurdle regarding tech adoption is getting the completely different toolkits of the operations and development teams to somehow integrate and allow users to collaborate. This problem connects to the earlier one of human collaboration, seeing as modern software tools are indeed capable of collaboration, if only the users are trained how to function with optimal synergy.
The CFO and operations teams can sometimes be at odds, especially with technologies that blur the line between fixed assets and continuous utilities. This often leads to budgeting constraints and disputes, since the operations budget gets impacted by the capital expenses during new process adoption and introduction of new tools.
Consistent changes to the budget are also an important issue, especially with operational spending becoming more diverse and distributed. This can create odds between the budgeting officials and development teams, which slows production.
Since we’re already discussing tools; another major issue that arises with new technology is an excessive focus on newer, more powerful software and systems. These systems need to be properly introduced to the staff, vetted for any security issues and optimized to work in perfect harmony within existing infrastructures, or even new ones.
All of the above takes away from the human aspect of DevOps implementation, which means that the teams are not integrated as solidly as the technology.
Very rarely do we see teams incorporate analytics into their decision-making process. This is true for even currently DevOps-oriented teams, that trust their collaborative skills, automated systems and algorithm-based deployment to deliver results. A major driving behind organizational success, regardless of DevOps implementation, is recognizing analytics data and leveraging it into efficient development, ergo a better product.
The lack of a solid, dependable analytics platform will have enterprises losing their edge as well as their hold over the market, which is counterintuitive to DevOps adoption because after all is said and done, the bottom line does matter.
The single most measure before the adoption of any new methodology is training the teams to handle and operate according to said methodology. DevOps managers need to place DevOps training at the top of their list of priorities when transitioning to the more Agile ideology.
Instilling functional understanding of collaborative tools, as well as continuous process integration will help teams collaborate more frequently and effectively. Delivering technical training prior to systems transitions will allow developers to acclimatize quicker to the newer setup. Training the staff to leverage automation and algorithmic process management will cut costs and help with staying within budget. And finally, gathering data and using it to determine the intricacies of consumer demand and technological shifts will help teams improve on all of the above.
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