The exact distinction between process mining and Business Intelligence (BI) can be confusing because the two terms are closely related. Both have a common goal, which is to contribute to the quality of different decisions at different levels of an organization. But significant differences should be considered when deciding whether to use process mining or Business Intelligence for your organization.
Business Intelligence and process mining often analyze the same processes to gain new insight, but the level of analysis of the two technologies is different. Business Intelligence analysis is at a higher level than process mining and is more concerned with meeting productivity and efficiency standards of processes or customer needs and satisfaction.
Relevant data is essential for Business Intelligence that looks for business trends and KPI alignment issues in sales, marketing, and other operations. This technology is interested in analyzing your critical business processes to find out if any problems in the work process affect the business activities.
Process mining focuses entirely on the discovery and analysis of end-to-end business processes. Therefore, it is only interested in event log data. Process mining uses this data to automatically capture the end-to-end process and define known or unknown activities to create process workflows. Process mining then performs a detailed analysis of each exercise to look for bottlenecks, rework, deviations, and other inefficiencies that increase process time and costs.
What is process mining?
Process mining is a technique used to analyze, monitor, and optimize business processes based on existing data used in the organization. Process mining, like data mining, uses algorithms to discover knowledge from data sets. Organizations can find and analyze their processes using process mining, create new functions, correct inefficiencies in their existing processes, and make more informed decisions.
As Gartner stated in its 2018 report on process mining, there are many potential uses for process mining. Some common examples are:
- Business Process Management (BPM)
- Business process improvement
- Business process mining
- Supporting the implementation of digital process automation (DPA) technologies
What is Business Intelligence (BI)?
Business Intelligence (BI) is a set of strategies and techniques that an organization uses to transform business data into formats that stakeholders can use to identify, develop, and create new strategic business opportunities. The term “Business Intelligence” was coined in 1958 by IBM researcher, Hans Peter Luhn. Luhn defines Business Intelligence: as “The ability to understand the interrelationships of presented facts in such a way as to direct actions towards the desired goal.”
BI has many potential uses, including:
- Analyze customer behavior and identify trends
- Budgeting and financial planning
- Risk analysis
- customer relation management
- Improve logistics and operational performance
The similarities between process mining and BI
Business Intelligence and process mining help stakeholders make more informed decisions by analyzing organizational data. These two technologies often use the same analytical processes to perform analyses in different scopes. Business Intelligence tools show patterns and correlations between data points. Analysts look at this information to form their perspectives on various organizational issues. However, process mining provides actionable insight and leaves less information to blame.
Process mining and Business Intelligence have mutual understanding in several cases that both deal with managing and analyzing business data to provide actionable insights that support business decision-making. Process mining and Business Intelligence typically use the same data stored in datasets. The critical business processes that process mining typically uses for analysis are often also analyzed with Business Intelligence.
Data is the starting point of process mining and Business Intelligence. Both drive organizational improvement by breaking down data and analyzing what has or hasn’t happened in your business operations. The datasets used, the depth of analysis, and the results generated different insights.
But despite having similar goals and dealing with the same processes, their performance and scope are different.
The differences between process mining and BI
There are significant differences between process mining and Business Intelligence. The most crucial difference is in the depth and scope of the analysis. Business Intelligence involves a higher level of analysis to help stakeholders understand how process performance issues affect the organization’s overall business performance.
However, process mining closely deals with a specific business process. Performing root-cause analysis determines where the process performance problems originate and, more importantly, what is the cause of their occurrence. In addition, with process mining technology, organizations can identify previously unknown instances of process performance problems. This is a fundamental difference between process mining and traditional Business Intelligence.
Business Intelligence requires previous information from the organization’s processes and continuous interpretation of data by analysts to obtain new perspectives. For example, Key Performance Indicators (KPI) may indicate that a process is not performing well, but it is up to the analyst to decide on corrective action. Business Intelligence is an excellent tool for monitoring KPIs, but it is flawed because it assumes the business process is fine.
The process mining goes deeper and discovers the bottlenecks that cause poor performance. The result is that the perspective of process mining has little interpretation and is generally applicable. For example, you may find that approval requests are delayed because employees are slow to submit manual requests. Process mining tells you that manual verification processes are prone to deletion.
Another critical difference is automation opportunities. Business Intelligence does not tell stakeholders what tasks can benefit from automation. Process mining identifies automation opportunities and helps organizations implement them faster. You can also use Business Process Management (BPM) software with a process validation engine to test automated “To-Be” processes before launch in real time.
Business Intelligence (BI) or process mining?
Organizations should benefit from both Business Intelligence technology and process mining software. BI plays an essential role in managing business processes. For example, stakeholders can view and monitor Key Performance Indicators (KPI) using business process management systems to advance business initiatives and strategies. Organizations can control and analyze their business processes using process mining software to increase efficiency. Additionally, stakeholders can identify automation opportunities that organizations can implement by using an industry-leading low-code BPM platform.
