The COVID-19 crisis has accelerated the digital transformation at most organizations. Americans have become more reliant on online platforms while sheltering in place for everything from groceries to streaming entertainment to banking and other financial transactions.
Much of this shift online is likely to be permanent, with finances providing the most critical case in point. According to Fidelity National Information Services, April saw a 200% jump in new mobile banking registrations, with mobile banking traffic rising 85%—due in part to many bank branches being closed. A survey by fintech company Novantas found only 40% of respondents expect to return to their branches post-COVID-19.
The transaction-processing mainframe is the mission-critical heartbeat powering all these applications. It has played an irreplaceable role in allowing the worldwide economy to keep going in these unprecedented times. Mainframe workloads were escalating even before COVID-19 hit.
A Vanson Bourne survey from late last year showed mainframe-reliant organizations expect deployment frequency for new features to increase at a higher rate (41%) than non-mainframe applications (38%) over the next year. Those numbers will certainly accelerate based on this new normal. Furthermore, according to BMC’s 2019 Mainframe Survey, 93% of respondents believe in the combined long-term and new workload strength of the platform.
These demands will require organizations to ensure development on the mainframe is more agile than ever. In this eWEEK Data Points article, John McKenny, senior vice president and general manager of BMC’s zOS business, offers valuable industry information about best practices he has observed and suggests these approaches.
Best Practice Data Point No. 1: Focus on improving the developer experience.
The slow, inflexible mainframe processes and methods of the past—namely waterfall development approaches—have become intolerable impediments in today’s innovation-centric digital markets. Development teams want to bring the power of DevOps to their mainframe environments, but this transformation can seem overwhelming. The easiest place to start is to make it simple for newer generations of developers to work and drive innovation on the mainframe, as they do on other platforms. This can also help mitigate any mainframe talent drain by increasing all developers’ familiarity and comfort with the platform they eventually will take over.
Best Practice Data Point No. 2: Adopt shift-left, automated testing.
Automated testing is critical for mainframe code, which serves as the backbone for many modern, customer-facing transactional applications. Without proper quality, functionality and integration, these end-to-end applications simply won’t work.
Specifically, unit testing—which involves frequently testing small increments of code, thus allowing developers to proactively make necessary adjustments—has been gaining popularity in DevOps circles. Unfortunately, technical obstacles have historically prevented the kind of automated unit testing that is commonplace in Java from being applied to mainframe development.
Evolving mainframe code to keep up with the pace of modern DevOps requires a similar level of automation for all types of testing—unit, function and performance testing. Furthermore, this testing needs to be “shifted left” and conducted as early as possible in the software development lifecycle to be most effective. Ninety percent of application development managers say that automating more test cases could be the single most important factor in their success as the pressure on IT to accelerate innovation increases, according to the Vanson Bourne survey referenced above. Yet, currently, only 7% of organizations automate the execution of test cases on mainframe code to support their ability to accelerate innovation.
Best Practice Data Point No. 3: Establish and measure against KPIs, including leveraging operational data.
You cannot improve what you don’t measure, and mainframe code must be consistently measured against the same key performance indicators (KPIs) as other code types—namely quality, velocity and efficiency. Given that mainframes support core business processes with little to no margin for error, it is imperative that all three KPIs are consistently maintained and increased, while making sure one KPI does not advance at the expense of another.
The keys to simultaneously increasing performance across these KPIs are the same for mainframe code as they are for all platforms—automation, integration and consistent measurement. Advanced new tools can apply machine learning to developer behavioral data so organizations can quickly identify areas for improvement—allowing them to continuously and proactively enhance development performance and productivity.
Best Practice Data Point No. 4: Embed mainframe code in all aspects of the DevOps pipeline.
To truly enable DevOps on the mainframe, mainframe code must integrate easily and seamlessly within the broader cross-platform DevOps toolchain. Without this integration, essential mainframe code has the potential to become a stumbling block that trips up or slows down the software development lifecycle. Integration into the broader DevOps toolchain (including key processes, such as source code management and test execution) is absolutely essential to the goals of increasing the speed and frequency of new mainframe code drops, optimizing developer productivity and simplifying end-to-end management of the software development lifecycle.
Best Practice Data Point No. 5: Enable coordinated cross-platform continuous delivery.
Mainframe applications and data increasingly serve as a back-end resource for multiplatform customer-and employee-facing applications that include mobile, web and/or cloud components. DevOps teams must be able to fully synchronize the delivery of new, approved code across all platforms, including the mainframe. This is the target state of enterprise DevOps—a natural evolution that extends the principles of software DevOps to surrounding processes across the organization for greater speed and flexibility. This includes a de-siloed environment where the mainframe is “just another platform”—albeit an especially scalable, reliable, high-performing, cost-efficient and secure one that can be quickly modified as needed to meet the needs of the business.
COVID-19 is putting IT teams under even more pressure than before the crisis. With mainframes expected to support what appears to be a permanent spike in workloads, improving mainframe agility becomes imperative. If these core systems of record aren’t agile, the performance of a business will ultimately be hamstrung by the constraints of its mainframe environment. The good news is that best practices and modern tools are available to help organizations embrace intelligent, tech-enabled systems across every facet of the business to thrive during seismic changes and become autonomous digital enterprises.
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