The Power of Open-Source RPA and Parallel Processing

Over the past few decades, technology has made incredible leaps and bounds — from the creation of the internet to the development of AI. Waves of innovation have boosted enterprises’ productivity and reach, but with every new iteration, it becomes more complex to manage multiple technologies in your stack.

A Brief History of Enterprise Technology

Mainframes and customized homegrown platforms were the norm through the 1970s and 80s. ERP systems became the rage in the 90s and early 2000s. Most companies adopted these monolithic solutions to manage enterprise support functions and service delivery — and many still rely on them as core systems of record. 

Integration consulting and data center outsourcing became popular later in the 2000s. Then, around 2015, cloud computing and SaaS offerings gave enterprises new and exciting options that kicked off digital transformation initiatives aiming to capture greater value and agility.

As companies assessed their technologies, they encountered several challenges — many legacy systems were still viable and needed to work seamlessly with newer cloud and SaaS-based applications. To make all this work, enterprises began to look to robotic process automation (RPA).

The Limitations of Proprietary RPA 

Think of RPA as a digital workforce that automates repetitive and rule-based tasks without altering existing systems. 

Over the past few years, proprietary RPA start-ups recognized an opportunity to automate tasks across new and old technologies using screen scraping and workflow rules to ingest and process data. This helped many enterprises with specific use cases but also exposed two limitations — the inability to directly interface with legacy systems and the lack of processing scalability. These limitations create “broken bots” enterprises want to avoid.

Since screen scraping is the primary way for proprietary RPA solutions to interact with business applications, when user interface elements like text, colors, and fields change, bots tend to get confused and “break.” Unfortunately, it's a frequent occurrence and takes time and effort to fix. Recent research shows that nearly 70% of companies experience broken bots at least once a week — and 41% said it takes them over 5 hours to fix a broken bot, with some taking more than 24 hours.

The other cause of broken bots is a lack of computing power needed to process large workloads. Proprietary RPA providers license their technology by the bot and set pre-agreed-upon infrastructure limits. As a result, customers must strike a balance between cost and resources. 

When processing demand surges, you hit the ceiling and tasks can break down. The other side of the coin isn’t very attractive, either. If you overbuy licenses and computing resources for occasional surges in demand, then you’re paying for overhead you seldom use. 

The Flexibility of Open-Source RPA 

Open-source software is used within mission-critical IT workloads by more than 95% of IT organizations worldwide, whether they are aware of it or not. This is because software engineering leaders integrating diverse technology ecosystems often explore open-source technology for cost savings, autonomy, and architecture flexibility. 

Since enterprises maintain a mixture of new and old technology that doesn’t always work well together, many are adding open-source RPA to their toolbox. By its nature, open-source RPA  (including the Python programming language) offers a powerful, flexible, and agile way to glue together disparate enterprise applications and transact business without disrupting core legacy systems or existing RPA workflows.

Beyond its openness and adaptability, open-source RPA enables a programmatic approach to automation. It works deeper within application layers — beyond the UI — using locators, X-Path, and other means to ensure that data flows accurately and doesn’t cause broken bots. Open-source RPA also leverages the power of the crowd for best-in-class automation libraries and prevents vendor lock-in, meaning you can use your bots for life.

Open-source automation teams can manage their entire infrastructure and application development lifecycle using GitOps/DevOps best practices — which is uncommon with proprietary RPA solutions. This allows for greater collaboration and coordination between teams and results in fewer errors and faster fixes.

The Power of Parallel Processing 

Vendors in the open-source RPA space offer orchestration solutions that provide exceptional speeds and elasticity through parallel processing. This is the process of dividing tasks into subtasks or threads and executing them simultaneously across multiple computing resources.

You may think parallel processing has existed for a long time — and you are right. However, proprietary RPA providers earn money through inflexible licensing and infrastructure schemes. Offering on-demand scale and accelerated processing speeds cannibalize their revenue, which isn’t their business model. Open-source RPA isn’t constrained by these limitations. 

Parallel Processing in the Real World

Emerson is a Fortune 500 industrial solutions company and an early adopter of RPA. They chose a proprietary RPA provider years ago to automate many core processes, including finance operations, complex order management, and supply chain processes with tight SLAs. Unfortunately, they struggled with inflexible architectures and infrastructure constraints that compromised their ability to meet mission-critical business processing SLAs.

Emerson surveyed the RPA landscape for traditional and open-source options. With a robust Python-based framework for building bots and the ability to scale automation up and down and accelerate speeds with parallel processing, the company chose open-source RPA.

As a result, Emerson is 100% SLA compliant. They increased speeds by 72% and reduced infrastructure by 75%. In addition, their stakeholders are happier and they have virtually eliminated rework.

Parallel Processing and Open-Source Technology: A Dynamic Duo

Open-source RPA technology paired with parallel processing drives improved enterprise performance and financial outcomes. In addition, it offers control and stability so robot creators can reduce broken bots, gain new automation possibilities, and build portable bots without vendor lock-in. 

It can be integrated with virtually any technology, including other RPA tools, to create powerful and flexible automation solutions — and is free to use. Deploying open-source bots with GitOps offers better traceability, compliance, and governance than most proprietary RPA platforms.

Open-source enables developers to securely orchestrate bots for virtually any process — at scale — with no licensing fees and a consumption-based pricing model, depending on the vendor. In addition, developers can choose cloud, on-premise, or hybrid orchestration options to distribute, monitor, and manage robot workforces in real time.