Often confused with Process Mining, Process Discovery is a powerful mapping technique that helps organizations put their actual work in perspective. In this article we explain how it works and touch on its benefits and limitations.
Companies used to engage in digital transformation initiatives to gain a competitive advantage and be at the forefront of the industry. In the 2020s, the landscape is slightly different - you need to automate to survive the competition at the least.
To replace legacy systems and have streamlined processes, organizations adopt automation tools such as RPA, low-code/no-code applications, etc. However, it doesn't make sense to merely automate for the sake of "automating" - decisions on which processes and tasks are subject to change must be based on facts. Here's when process discovery comes into play.
Process discovery is a series of techniques and tools used to define, outline, and analyze existing business processes. It provides a profound understanding of how people carry out daily operations processes.
Discovering processes is one of the most critical steps in understanding how the organization works and, frankly speaking, is a prerequisite for any successful automation project.
Process discovery observes all of the actions performed by humans in their daily work within different systems. It records everything users do and then analyzes it to find suitable processes for either automation, process improvement, or even process replacement.
Unlike other process mapping tools, process discovery covers a whole spectrum of information systems that it can work with - from CRM/ERP tools to productivity apps such as Calendar and Slack. Collecting the data from users' digital traces, process discovery analyses, and structures it using advanced computer vision and machine learning algorithms. These analyses help find process variations or patterns that could be a basis for finding automation potential.
Which processes qualify as automatable based on discovery? The processes need to be: repetitive or cyclical, highly manual, rule-based with little to no exceptions, and preferably have structured data.
Process discovery doesn't need any prior development before it can start working. It only requires an installation of software plug-ins that run on people's computers in the background without interrupting them or affecting their work in any way.
In the past, companies would need to conduct multiple workshops and interviews with different teams to map out current business processes and manually find any inconsistencies or bottlenecks. It would take a considerable amount of time and resources, yet most decisions would still be based on assumptions and intuition.
Companies needed a better way to visualize the processes within different systems and applications to understand which activities need to be automated and which ones need to be prioritized. Compared to other more traditional mapping methods, process discovery has clear advantages on several fronts.
If the company reaches a point where it knows that something should be improved in its business-related activities, this is where process discovery can come in handy. It's a very effective first step to see the reality of work in the company and to better understand current processes before committing to multi-million automation projects.
Process discovery helps to find automation potential in a non-biased, data-driven way. Data is the new oil; valuable, high-quality data that tells a lot is a pure treasure. Process discovery's robust data collection techniques help to find better automation cases.
Manual and repetitive tasks can be really tedious and cause decreasing job satisfaction and productivity loss. However, it's hard to identify them when integrated into a routine and interrelated between various processes. Process discovery can be your company's eyes and really map the processes making them more transparent.
Companies have used process discovery as an "x-ray" in their RPA initiatives. RPA is a software tool that automates repetitive, rule-based tasks by creating "bots" that mimic people's actions and execute them within a particular task. Process discovery supports in identifying which processes are ideal candidates for RPA in terms of automatability and ROI. It also creates automated workflows, significantly reducing the time in RPA implementation.
Process mining and process discovery are the two terms that get confused a lot; they are even used interchangeably in some instances. This is reasonable if we look at the ultimate goal they aim to achieve - providing companies with insights into making work more smooth.
Demand for these two approaches has skyrocketed over the last years and is expected to grow more with the growth of RPA. Despite such a similar position in tools facilitating digital transformation, process mining and process discovery have a few significant differences that shouldn't be overlooked.
Process mining analyzes the data from event logs, while process discovery observes people's digital footprints.
Process mining gets the data from event logs and uses different data points to re-engineer the process and compare it to the ideal target process through conformance checking. If event logs fail to capture all the critical points or have many inaccurate records, then the created process model might not be as reliable, deviating from the real picture. In other words, process mining relies on distinct steps within the process. Some of the systems, such as Slack or Teams, simply do not produce logs, making the use of process mining limited, and as a result, overlooking people's interactions with systems.
Process discovery, in contrast, records ad-hoc human work throughout the whole process and then decomposes it. Through this inverse approach, process discovery can document even the "shadow work" and "white space" that usually gets unnoticed through the event log approach. Because process discovery provides a more continuous approach to observing the work, even seemingly unimportant parts of the process get recognition and add value to the analysis.
Process mining needs development efforts before use.
Another significant difference is the need to make integrations with information systems to get started with process mining. It requires upfront back-end integrations with different software and applications that will be monitored in the mining process. Process discovery doesn't need any integrations, so users can quickly install the software agents on users' computers uninterruptedly.
That being said, process discovery and process mining can and should be used together to achieve the best results in capturing all the necessary insights.
Process discovery observes the task-level data, thus capturing business processes on a larger scale. It gives better visibility of user interactions with different information systems.
Having smooth and streamlined processes is one of the essential criteria for improving business performance overall. Optimizing processes is crucial in increasing employee performance and customer satisfaction.
In most cases, it's hard to see a real impact of a new system/application on the company's performance quickly. It takes months, if not longer, to identify that the new application wasn't a match. Process discovery cuts that time by mapping the processes and recognizing the new initiative's value.
Large-scale systems and software rollout require large-scale investments too. By finding the process inefficiencies and automation cases based on them, companies can better use the acquired tech and improve ROI on those investments.
Process discovery tools help eliminate manual processes, thus freeing the time for people to focus on the more important stuff. It also helps cut costs on unnecessary tech investments that don't bring much value to the company.
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