
Bain Capital Ventures’ Kevin Zhang: The ‘Unlockable Potential’ in Lending, Investing, and Insurance
Kevin Zhang, partner at Bain Capital, assess the technological tailwinds for emerging players in lending, investing, and insurance....
Casber Wang~quoteblock
According to Bain & Company’s 2021 DevOps Pulse Survey, while 90% of companies consider DevOps a top strategic priority and are actively attempting to become proficient, only 50% of companies have implemented DevOps at scale and even less, 12%, consider their associated capabilities mature.
DevOps has emerged from several paradigm-shifting developments across broader tech and the SDLC. The explosion of software consumption across industries and new technologies like cloud platforms positioned SaaS as the de facto standard for delivering applications.
This expanded market and fast SaaS delivery altered the implicit contract between software developers and consumers. The latter were now demanding feature-rich applications with continuous improvements and dynamic scalability. This fueled an arms race to out-innovate and out-perform competitors. Releasing new features quickly started to make financial sense.
However, this arms race drove dysfunction in engineering organizations. Developers were unable to scale communication and collaboration alongside other aspects of their work. Today, competitive software development requires visibility and communication across historically distinct disciplinary silos. And while Agile promoted integration within teams, practitioners have now turned to DevOps to unify all the relevant parties across the software development process.
To us, DevOps is a cultural shift — a set of practices and an enabling ecosystem of tooling — that connects cross-disciplinary teams and encourages increased collaboration. All of this matters when pursuing the goal of delivering high-quality and sustainable products quickly.
Casber Wang~quoteblock
Q: What can you say more about the frictions being faced in the transition to DevOps?
A: "Legacy environments are the primary hurdle here. IT shops are increasingly standardizing on some combination of cloud, containers, functions, and micro-services. But many are still in a state of transition, supporting a hybrid of both legacy and modern tech stacks. Legacy or so-called brownfield apps can be particularly challenging to ‘DevOps-ify’ because they are often built upon monolithic architectures, leverage static physical infrastructure that can be difficult to automate, and utilize more traditional frameworks and languages."
Q: Can you dive into the main roles Machine Learning will play as DevOps evolves?
A: "There are roles across the cycle of design, testing, maintenance, and more. For example, in testing, it’s standard within the SDLC to write numerous tests that review functionality, stability, and system integration. Test suites are really effort-intensive and tend to balloon as new features are layered into any product or platform. ML-enabled tools are available to automate test generation. There are also ML techniques, including neural nets, which generate synthetic data mimicking production conditions, in order to supply tests with appropriate inputs."
Q: What other important trends are you following closely in this category?
A: "The “shift-left” principle implies that steps should be performed as early as possible in the cycle to avoid costly downstream redesigns. The issue is that staging environments rarely reflect the real world. With DevOps practices, many aspects of the cycle have actually begun to “shift-right,” meaning teams can address issues when they arise, and dispense with onerous early preemption. One example is feature flags, a safer way to ship code by hiding incomplete or untested features. Another is continuous verification, which ingests observability data and uses ML to assess performance and automate the rollback of any incidents, reducing manual checks.
A second trend is a growing focus on measuring and improving engineering productivity. Pockets of scarcity in engineering talent as well as new remote-work cultures have helped push forward new tools focused on code visibility. These aim to more accurately gauge productivity, pinpoint burnout, and onboard new hires faster."
Additional trends, including WebAssembly, meta frameworks, and Web3 dev tools could push the boundary on what is achievable in software delivery. “As application architectures and tech stacks continue to evolve, we have and will continue to see an explosion of new tools and capabilities in this space,” says Wang. These trends, as well as others, may support “the potential for numerous companies of consequence to be produced in the years to come.”
Watch for Machine Learning-driven capabilities that gradually augment the coding and building work. Most DevOps tools share the common goal of automating the tasks that distract engineers from writing code and delivering features. However, ML-driven capabilities will come to augment many coding tasks, as they have begun to do already with features like Github’s Copilot. With time, humans will focus on more high-order reasoning and problem-solving, while computers carry out the actual execution and implementation of code.
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