Misunderstood, overlooked and poorly applied: Agile and rapid approaches to software, long sought after, may not be quite ready for businesses.
20 years after the formulation of Agile manifesto, and 12 years after the onset of DevOps, these two interdependent philosophies are still projects in progress – and for some companies, very slow projects. These methodologies have the potential to change the way software is designed and delivered, but it takes a lot of organizational work to get them adopted. What is the problem ? The long-sought agile and fast approach to software may not be quite business-ready, and that’s the problem. Especially when game-changing initiatives are underway.
Consider the push towards advanced analytics and artificial intelligence. In this area, “Agile and DevOps are still largely misunderstood, neglected and poorly applied,” says Chris Bergh, CEO of DataKitchen. For example, DevOps is widely accepted in the software engineering community, “but sporadic in the data analysis space, often made up of data scientists who use code and configure tools to create analytical information”, he explains.
Trying to apply agile and DevOps methods across the enterprise “is quite difficult to adopt consistently,” admits Manoj Karanth, associate vice president and global head of data science and engineering at Mindtree. « This must be supported by a sustainable development policy. It must be supported by a change management function given the enormity of the effort. Sometimes the underlying technology does not allow the level of automation required by DevOps teams, which leads to a lot of spaghetti code that adds to maintenance issues. »
What advice from the experts ?
Industry Experts Provide The Following Insights To Improve The Chances Of Success For DevOps And Agile Methodologies :
- Broaden the definition of the term “product”. A common theme that Manoj Karanth observes in successful efforts is “forming global product teams as opposed to siled product teams,” he explains. « These product teams think through the entire product lifecycle, from design to production, taking into account the maturity of the product and the tools. This forms a good ecosystem to apply and refine the agile principles that the maturity of technology and the team allows when embarking on the road to automation. » What is needed, he adds, is the ability to break down organizational silos, as well as an openness to the adoption of new technologies.
- Switching to low code and no code not only opens possibilities for end users, but also paves the way for more resilient Agile and DevOps approaches. The adoption has been cohesive because “many solutions are either low code or no code, which allows organizations to implement technology quickly and easily maintain it,” says Borya Shakhnovich, CEO and co-founder of airSlate.
- Leave the tedious work to APIs. The key ingredients of Agile and DevOps businesses “are easy to automate technology components that are enabled by software APIs – hence why cloud native technologies and low code platforms are essential for this activation,” says Manoj Karanth. « Taken together, this helps bring greater efficiency through hyper-automated operations with lifecycle automation, smart monitors, and bots across the business process, cognitive, and conversations. »
- Encourage adoption of common tool sets. Teams tasked with delivering a solution across different parts of the business (IT, data engineering, data science, visualization and governance) “may be in different management chains, competing for limited resources, or reside in different locations. », Explains Chris Bergh. « Sometimes they behave more like warring tribes than members of the same team. » As a result, he continues, “the day-to-day existence of a data engineer working on a master data management platform is very different from that of a data analyst working on Tableau. Tools influence the optimal length of their iteration cycle – months, weeks, days. Tools determine their approach to problem solving. Tools affect their risk tolerance. In short, they see the world through the lens of the tools they use. Dividing each function into a silo of tools creates a sense of isolation, which prevents groups from considering their role in the end-to-end data pipeline ».
Source : ZDNet.com
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