According to a 2021 Gartner survey, only 16% of CFOs see the real potential of data to guide decision-making. The good news is that process mining can help reverse this scenario.
In this article, understand in more detail the steps of process mining, the benefits, and see how to apply it to your IT!
What is Process Mining?
Process mining is a data analysis technique that enables the discovery, modeling, and optimization of business processes through historical data.
This means that, through process mining, it is possible to understand how processes are executed in practice, identifying flaws and improvement opportunities. It is a technique that uses machine learning approaches to extract information from a company’s databases.
This data can come from business process management (BPM) systems, customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, and other information systems that record process events.
Types of Process Mining
Process mining is generally performed in three ways: discovery, compliance, and improvement. Below, learn more about each of them:
Discovery Process Mining
This is the most basic type of process mining. It is used to identify the actual process, meaning how the process is actually executed. This is because the actual process may differ from the prescribed process, i.e., how the process should be executed.
Discovery process mining can be used for various purposes, including:
- Validating the prescribed process: verifying if the prescribed process aligns with reality;
- Identifying deviations from the prescribed process: in this case, it can point out compliance issues or inefficiencies;
- Updating the prescribed process: the goal is to make the prescribed process reflect the actual process.
Compliance Process Mining
Compliance process mining is used to verify if processes are being executed according to the policies and procedures established by the company. It is very useful for:
- Checking compliance with regulations: the technique can verify if processes comply with government or industry regulations;
- Checking compliance with internal policies: it can also check if processes are following internally established company policies;
- Identifying opportunities to improve compliance: this aspect leads us to the next topic.
Improvement Process Mining
Improvement process mining helps identify inefficiencies as well as opportunities for automation and optimizing process performance. Explained in more detail:
- Identifying inefficiencies: it can uncover activities that are unnecessary, redundant, or time-consuming;
- Identifying opportunities for automation: it helps managers understand which tasks do not require human intervention to be executed effectively and, therefore, can be automated;
- Improving customer experience: improvement process mining can also help optimize the customer journey throughout the process.
Benefits of Process Mining
Process mining makes process understanding factual, transparent, and dynamic, as it is purely based on data without interference from biases caused by human intuition. In this way, it enables more precise and well-founded analyses based on facts to guide a continuous business improvement strategy.
In other words, your organization has the ability to understand the complexity of processes with a comprehensive view of operational capacity, bottlenecks, lead times, triggers, among many other aspects related to the company’s business.
It is worth noting that, regardless of the size and maturity of the company, it certainly has processes that determine the entire flow of activities and resource involvement (human or material) to some degree. Therefore, it has data suitable for analysis, with the possibility of course correction and improvements.
In summary, process mining offers several benefits:
- Time savings from manual analyses, presenting diverse data on a single screen;
- Much more accurate decision-making based on facts rather than human intuition or instinct;
- Access for all teams to the same set of factual information;
- Reduction of operational costs through the combination of process mining and intelligent automation;
- Reduction of the risk of human error;
- Agile process cycles compared to manual execution;
- Quick analysis of process compliance and fraud detection.
Differences between Process Mining, Data Mining, RPA, and BPM
Process mining, data mining, RPA, and BPM are techniques that can be used to improve business processes. However, there are some important differences between them.
Process mining, as explained so far, is a data analysis technique that allows the discovery, modeling, and optimization of business processes from historical data. On the other hand, data mining is a broader and more generic term used for the practice of analyzing large volumes of data, aiming to identify patterns and gain insights or discover trends. In this sense, process mining is a form of data mining, employed to find patterns within a company’s processes.
RPA in IT, on the other hand, is a technology that automates repetitive and manual tasks. It can be used to automate business processes, reducing the need for human intervention.
Finally, BPM is a discipline that aims to improve business processes. It involves the identification, mapping, analysis, redesign, and optimization of these processes.
How to Use Process Mining and RPA?
Process mining and RPA are two complementary technologies that can be employed together to improve the efficiency and effectiveness of business processes. While process mining provides an accurate and up-to-date view of the actual flow of processes, RPA automates repetitive and error-prone tasks.
According to the Process Mining Sector Scan study published in January 2020, 78% of process mining customers say that this technology is key to enabling their RPA efforts. It’s also worth noting that, according to Grand View Research published in April 2021, the global robotic process automation market size is expected to reach 11 billion dollars by 2027.
To use process mining alongside RPA, it’s important to have an understanding of both technologies. Process mining tools can be used to collect data from event systems, analyze this data, and generate visualizations of the actual flow of processes. On the other hand, RPA robots can be used to automate tasks according to a set of rules or business logic.
For example, if process mining shows that a task is performed manually in multiple steps, it can be automated with an RPA robot, executing all the steps in a single operation.
Practical Example
An insurance company can use process mining to identify that its claims processing is slow and inefficient. In this sense, process mining can reveal that there are many delays between process steps, and some tasks are performed manually. The company can then use RPA to automate manual tasks and restructure the process to make it more efficient. Do you understand how these two technologies are great allies?
Benefits of Combining Process Mining and RPA
The successful implementation of process mining and RPA requires careful planning and the involvement of all stakeholders. It’s important to define clear objectives for the project, identify suitable processes for automation, and develop an implementation strategy.
Once these prerequisites are met, the organization can identify benefits such as:
- Improved productivity: Employees can focus on more strategic and high-value-added activities by being freed from automatable tasks.
- Cost reduction: Automation of repetitive and error-prone tasks can lead to cost reduction.
- Improved compliance: Monitoring processes with process mining can help ensure compliance with regulations and internal company policies.
Optimize Your Company’s Processes with Open Source RPA
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