Every company is made up of thousands or even millions of individual processes or workflows waiting to be discovered.
Today, all the different types of tasks, processes and workflows can be measured accurately with process discovery software. In this guide we go through what enterprise leaders need to know.
What is process discovery?
Process discovery is a series of techniques and tools used to define, outline, and analyze business processes. It provides a measurable understanding of how people carry out daily operations and processes in the workplace.
You can think of process discovery as the exploration of the "as is" of an organization's existing processes while process redesign is the exploration of the "to be" goal of improved 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 or business process transformation.
Five key benefits of process discovery
- Process visibility
Process discovery observes the task-level data and performance, thus capturing business processes on a larger scale. It gives better visibility of user interactions with different information systems.
- Improved processes
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.
- Reduced process risk
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 isn't a match. Process discovery cuts that time by mapping the processes and recognizing the new initiative's value.
- Maximized ROI on tech investments
Large-scale systems and software rollout requires 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.
- Increased cost-efficiency (and shorter time to value)
Process discovery can 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.
How did process discovery emerge?
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. Interviews by business process analysts (BPA) were time-taking but effective ways to get an understanding from subject matter experts (SMEs).
Examples of questions asked during a business process discovery interview:
- What usually triggers this process or workflow? Are there other triggers that can start this process?
- What is the first process step to start the process? What output is created from starting this step?
- Who performs this step? What are the applications / IT systems used to perform this step?
- How often is this step performed every day/week/month?
- Can you identify business challenges to this step specific in the process? What is done to resolve these challenges?
- How is this process step measured? Are there key performance indicators (KPIs) for performance?
- How much time does it take to perform this step of the business process?
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, automated process discovery has clear advantages on several fronts.
Who is involved in a process discovery?
Process discovery can be used by any business unit, function or industry that has key processes that can be measured and improvement.
Process discovery is typically conducted in enterprise businesses by a team of experts or external consultants. The people involved in a process discovery typically include:
- process engineers - responsible for understanding and optimizing the efficiency and quality of business processes.
- subject matter experts - the individuals or experts with specialized knowledge in a field or process.
- data analysts - the data scientists who analyze data and creates business intelligence solutions and reports.
- business analysts - an expert in the use of data and analytics to meet business goals and objectives.
Depending on the nature of the process, additional stakeholders can include product or project managers, IT personnel, and executives. All of these stakeholders will work together to understand the current state of the process, identify areas of improvement, and develop recommendations for optimizing it.
Key steps to process discovery
One way to view process discovery is from the perspective of continuous improvement. In other words, the goal of process discovery is to methodologically explore the "as is" current state of processes and aim to implement an improved "to-be" model. This method can be broken down to eight concrete steps.
Step 1. Discover the as-is processes
Step 2. Document the as-is processes
Step 3. Analyze the as-is processes
Step 4. Define to-be processes
Step 5. Develop to-be processes
Step 6. Introduce to-be processes
Step 7. Implement to-be processes
Step 8. Maintain to-be processes
In this method, the aim is to discover and document clearly the way current processes are and how to introduce and implement improvements.
Automated process discovery
Process discovery software 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.
Process discovery software uses highly sophisticated data science and analysis methods. Examples of process discovery algorithms in process mining include:
- Alpha Miner
- Heuristic Miner
- Fuzzy Miner
- Inductive Miner
- Genetic Miner
Different process discovery tools in use
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.
Use cases for process discovery tools
With the emergence of automated process discovery tools, enterprise businesses have found a number of use cases for conducting process discovery with dedicated software.
1. Improve business processes
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.
2. automate business processes
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.
3. improve employee satisfaction and efficiency
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.
4. Facilitate RPA automation
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.
Want to find more use cases for Process Discovery?💡 Read our blog on 8 examples of time waste in enterprises.
Process Mining vs Process Discovery?
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.
Where does the data come from?
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, can come from a number of sources, including interviews, surveys or recording of user interactions. In a manual process discovery 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.
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 vs Task Mining
In the technology world, everything changes very rapidly, and thus, the terminology gets altered, confused, and replaced very often. Several vendors might use the same term for quite different technologies and tools, making it hard for consumers to identify what exactly they need.
While we have discussed the differences between process mining and process discovery, task mining is another very similar term that gets confused a lot. Although underlying technology-wise task mining and process discovery are very close relatives, the final goals and how they’re used by the end users are what sets them apart. To understand that, one could have a look at the most prominent vendors in the industry. The common pattern is that process mining companies have task mining as their complimentary service, while process discovery is used by RPA vendors.
Both technologies trace users’ digital footprints beyond event logs across IT systems and apps. While task mining does it to identify process inefficiencies, process discovery needs the data to find automation opportunities more effectively. As a result, although they're very close to each other, they might slightly differ in terms of the outcome they bring.