The DevOps approach to software engineering has revolutionized release speed by unifying development and operations. It’s quite likely that, today, DevOps principles guide your development team’s processes by providing a framework that enforces continuous integration and deployment. When you develop software with the DevOps approach, you follow a lifecycle of development, testing, acceptance, and production (DTAP).
Organizations with large, and especially distributed, development teams have realized significant improvements in their software development lifecycle (SDLC) processes by employing a DevOps approach. That’s why employing the same DevOps approach to development of Robotic Process Automation (RPA) bots at enterprise scale just makes sense.
Today, most Robotic Process Automation (RPA) solutions offer capabilities that let you move bots from one stage in the development lifecycle to the next. Does that mean they support a DevOps approach to bot lifecycle management (BLM)? No. The idea that simply moving bots along the DevOps lifecycle constitutes BLM is a myth. But like many myths, it sounds plausible if you don’t examine it too closely.
Each stage in the DevOps lifecycle takes place in a separate environment. You develop in one environment and test in another. Production is separate too. So, to manage the lifecycle of a bot, you need to be able to maintain separate environments for bots based on their stage in the lifecycle. And you need to be able to move entire bot packages between the stages.
Let’s say you create bot A and, in order for it to run effectively, bot A depends on individual processes A.1, A.2, and A.3. The bot and its dependencies need to be managed and moved along the bot lifecycle as a package. “Obviously,” you say. But most RPA solutions don’t readily do that — they just import and export the bot between environments that you provide. You manage bot dependencies separately. “Really? That sounds tedious,” you might say. Really. And, yes, as any RPA program manager will tell you, it is quite tedious.
Without a true BLM framework in place, you have to establish and manage your development and test environments. You also have to manage and advance dependencies individually. That might work well when you’re doing a proof of concept for RPA, but it doesn’t scale. The more bots you have to manage using disconnected processes, the longer it takes to get bots into production. Plus, if dependencies get missed, you may find more errors that delay continuous integration and deployment.
Compliance can be an issue, too. For bots touching processes with compliance requirements, such as financial processes covered by the Sarbanes-Oxley Act (SOX), the lack of a true BLM framework forces you to develop and manage your own controls over bots.
The BLM framework that is included in Automation Anywhere Enterprise does much more than simply import and export bots — it integrates readily with the DevOps workflow. Support for separate development, testing, acceptance, and production environments — including complete version control and roll-back features — comes built in.
A highly granular role-based access control (RBAC) is another enterprise must-have that underpins security of the Enterprise RPA platform. With granular RBAC, the bots seamlessly transition between stages in the DevOps lifecycle.
But what about dependencies? Dependencies transition along with bots. That’s because Automation Anywhere Enterprise manages the entire bot package as part of the bot lifecycle. This control over versions, roles, and bot packages lets you develop more bots faster even when you have stringent compliance requirements. This enables you to scale RPA rapidly across your enterprise and experience higher return on investment even faster — a hallmark of a well-crafted enterprise-class RPA strategy.
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As VP of product marketing, Kashif Mahbub culls his more than 15 years of experience in product marketing, product management, and sales enablement strategy and execution to lead product marketing efforts, channel and partner development, and demand generation.