Process Mining VS. RPA
The markets for Process Mining and RPA tools are booming. Here’s a look at how these two “buzzwords” compliment each other
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Robotic process engineering (RPA) is the use of computer software 'robots' to handle repetitive, rule-based digital tasks. RPA bots work directly from an application’s user interface, mimicking human actions, including logging in and out, copying and pasting data, opening emails and attachments and filling out forms.
As companies look to dramatically increase efficiency in the wake of the COVID-19 pandemic, the market for RPA solutions is experiencing explosive growth. In fact, the global RPA software revenue is projected to reach $1.89 billion in 2021, an increase of 19.5% from 2020, according to the latest forecast from Gartner, Inc.
On the other hand, while RPA is considered a tool or a solution, process mining is more of a technique the goal of which is to turn event data into insights and actions.
By applying specialized algorithms to event log data to identify trends, patterns and details of how a process unfolds, process mining enables companies to automate and streamline operations. The insights garnered from mining processes can then be used to reduce waste, allocate physical and human resources more efficiently, and enable faster responses to internal and environmental changes.
In other words, process mining solutions read event logs in IT systems (i.e. ERPs and CRMs) to learn about business processes, while RPA automates those processes.
Similar to RPA, companies are adopting process mining solutions at a remarkable rate. From 2018 to 2019 alone, the market for process mining solutions grew by 140-160% from US$230 to US$250 million, according to Everest Group. By 2026, it’s expected to reach $3.5 billion.
Though these RPA and process mining are entirely different technologies, they complement each other incredibly well. Here’s a look at how.
Process Mapping & Simulation
Process mining tools paint a picture of what a organization’s current business process look like. Not only do the insights gleaned from these systems shed light on potential inefficiencies or performance gaps, they also allow the organization to simulate and validate re-engineered processes.
By starting with such analysis, you can bypass one of the most frequent failure points of RPA: automating faulty processes. Since RPA needs standardized, data-centered, and easily repeated tasks in order to be most effective, process mining is an essential component of successful deployments.
In addition, process mining serves as a sort of GPS for RPA deployments. By outlining the steps conducted by human employees, process mining data can provide a template or roadmap for RPA bots to work off of.
Last but not least, process mining can also be used to monitor RPA performance.
The Road Towards Hyperautomation
Hyperautomation refers to the extension of legacy business process automation beyond the confines of individual processes. By pairing RPA with complementary solutions like process mining, AI, analytics, and other advanced tools, one can automate complex business processes that rely on unstructured data.
For those looking to embark on their hyperautomation journey, process mining does more than just pinpoint potential candidates for automation, it also connects the dots between various IT systems and illuminates invisible workloads. Furthermore, process mining tools also generate data that is primed for machine consumption and enables, essentially, the automation of automation.
