What Is Process Discovery?
Process discovery is defined as a set of tools, technologies, and techniques used to visually represent and analyze the business processes currently ongoing in an organization and how they are related. Process discovery is a key pillar of strategic automation since it allows you to map the (formal) business processes before deciding what to automate.
In 2022, Deloitte found that 63% of organizations can utilize process intelligence to “accelerate discovery” and quickly identify the best automation use cases.
Learn More: What is Process Mining
How Does Process Discovery Work?
Process discovery is one of the key use cases of process mining, finds Gartner. Essentially, you analyze event logs housed in information systems — such as customer relationship management (CRM), enterprise resource planning (ERP), etc. — to reveal workflows and data exchanges in the organization. Process discovery helps build a model showing how the business process works.
While, on paper, you can do all of this manually, process discovery can be a complex and time-consuming activity. Human analysts are limited by their personal knowledge, and hidden dependencies and correlations are easy to miss. That is why most companies rely on process discovery software. Today’s cutting-edge tools use artificial intelligence (AI) and machine learning (ML) to identify processes in an organization’s information systems and model possible process variations using ML algorithms.
However, it should be noted that process discovery only works when there are structured datasets housed in formal business systems. Process discovery cannot map or analyze unstructured data emanating from interactions across emails, chats, and documents.
Here is a step-by-step breakdown of how process discovery works:
The process discovery tool connects with existing enterprise systems.
- It extracts data from event logs, performance records, and other machine-readable data available in your systems.
- The tool analyzes the data and uses AI and ML algorithms to find dependencies, relationships, and variations.
- A visual representation of the findings is made available through process discovery dashboards
- Some tools can also detect process deviations that are causing inefficiencies.
- A complete map of as-is business processes is created, giving you the “who, what, when, and where” of each process.
Learn More: What is Process Intelligence
Why Process Discovery is Necessary
Process discovery is necessary for the following reasons:
- Cost optimization
This is one of the primary reasons why companies undertake process discovery. As the saying goes, you cannot improve what you cannot measure — possess discovery turns all business activity into a visually represented, quantifiable entity that can be analyzed, measured, and optimized. With this data, business leaders can make decisions that save costs, increase revenues, and positively impact the bottom line.
- RPA effectiveness
“Wasting effort on overly complicated processes” is among the top 10 reasons why RPA fails, according to Gartner. Without a detailed process map, companies risk investing in the wrong processes or overlooking the ones that matter. Before embarking on an RPA project, it is advisable to conduct process discovery to understand the “as-is” state of the organization. It also helps set automation priorities so that you know exactly how to scale RPA and reap the expected benefits.
- Security, compliance, and audits
Unmapped processes pose a security risk, as the data involved may be exposed without your knowledge. Process discovery also reveals important security vulnerabilities, such as loose access privileges. Further, it is helpful for compliance as it generates ready-to-use data logs and reports for audits. If you need to comply with external industry regulations, the results of a process discovery initiative can make it easier to create and submit the requisite reports.
Learn More: What is Task Mining?
Process Discovery vs. Task Mining: 2 Key DifferencesDespite the benefits of process discovery, it cannot give you a complete picture of what’s happening in your organization. That is because it looks at only structured information from transactional systems and not user activity data (e.g., keystrokes), desktop-level data (e.g., shadow IT apps), and unstructured data (e.g., text messages). As a result, task mining tools become important — they differ from process discovery in the following ways:
- Task mining focuses on the smaller components of work: Task mining tools capture data between processes and subprocesses; this is often where inefficiencies creep in. For example, copy-pasting data is a task that takes place between receiving and fulfilling an order request.
- Task mining relies on different data sources than process discovery:Process discovery uses log data from information systems like Salesforce, SAP SuccessFactors, etc. in contrast, task mining monitors and records how employees use apps at the desktop level.
Process discovery is often just the first step in a larger transformation project. Companies may use this information to make strategic decisions like increasing revenue, reducing costs, and managing risk and compliance.
A major challenge with process discovery is that it reveals the as-is process but can’t provide granular insights into applications. While process discovery helps catalog the business process, it can’t provide visibility into the many tasks that add to its success. For example, you might be able to spot a common inefficiency that frequently occurs in your organization but not understand which employee actions led up to it.
Today, over 60% of the teams’ workday is spent on unstructured interactions in documents, emails, communications, custom applications, and websites – outside of ERP, CRM, and other systems of record. This massive unstructured and undocumented interaction dataset between people and software is untapped and contains a goldmine of insights that could give a significant competitive edge to enterprises. Traditional task mining solutions have significant privacy, accuracy, and scalability issues and fail to capture work done outside the systems of record, such as EPRs and CRMs.
Through Soroco ScoutTM, powered by the world’s first work graph platform, organizations can unlock this data source to discover areas where inefficiencies occur and are prime candidates for RPA-centric automation.
Click here to kickstart the process discovery journey with Soroco ScoutTM today!