What is process mining?
Process mining is a clear path toward valuable insights by identifying process inefficiencies, bottlenecks and other areas of improvement.
The meaning of process mining
Process mining is a way for organizations to discover, validate and improve upon workflows. By combining data mining and process analytics, process mining software extracts event log data from information systems to analyze how employees complete a particular business process, how well the process works and if there are any deviations.
It gives leaders an inside look at processes to identify any inefficiencies or bottlenecks and, more importantly, how to fix them. Managers don’t have to make assumption-based decisions when it comes to adjusting process models.
Why do organizations need process mining?
In every business process, there is room for deviations. Some are more visible than others, but the result is usually the same: human error, inconsistencies and unhappy customers. Process mining digs deep into the digital footprints left by event log data to understand where exactly these deviations are happening and how they impact the business. Managers get a look into how these data points affect KPIs, including:
- How long it takes to complete this business process
- How much it costs to complete this business process
- If the outcome of this process meets the desired standards
By performing regular checks through large quantities of event data to ensure continuous improvement, organizations streamline and optimize vital, repetitive business.
Process mining vs. data mining
Process mining differs from regular data mining. While they both use large volumes to gather actionable business insights, there are significant differences:
Process mining bridges the gap between data mining and business process management. It takes data from an organization’s information systems and visualizes the steps taken to complete a specific business process. This reveals essential information on deviations, inefficiencies and how to make improvements on an operational level.
Data mining analyzes a variety of data sets to detect patterns within the data, but does not provide an answer as to why those patterns exist. It predicts behaviors by observing major patterns and discarding exceptions to the rule.
Process mining
- Looks at event log data from a particular business process within a specific time frame.
- Highlights any deviations from ideal process models to reveal inefficiencies; exceptions are important in this process.
- Since it focuses on the steps of a business process, it traces how it arrives to a specific result and what can be done to rectify it.
Data mining
- Uses large sets of data available at the time of analysis.
- Focuses on major patterns in data, searching for general rules, leaving out anomalies.
- Limited to identifying patterns and providing predictions with similar instances; there is no answer as to “why” those patterns exist.
Other terms to know
Process discovery
This basic technique extracts an event log and produces an “as-is” model, visualizing how a process functions in reality. It provides details such as reworks, redundant work and where hand-offs happen between employees.
Conformance checking
Conformance checking compares the actual business process to the ideal process model. This identifies any deviations from the intended model.
Model enhancement
This technique takes the knowledge gained from the previous two techniques –– process discovery and conformance checking –– and goes a step further to make the necessary changes to alter costly and time-consuming steps and optimize the process.
What are the steps in process mining?
Step 1: Data extraction
Event log data is extracted and uploaded into a process mining tool. Most event logs have three main attributes needed to process data:
- Case ID: Unique reference to different executions of the same process
- Activity: Which step of the process the case went through
- Timestamp: The time the case ID went through that stage
Tip: The more details provided within the event log, such as vendor, country, facility, or user, the more effective and accurate the results will be.
Step 2: Visualization
All event logs analyzed by the process mining tool are visualized end-to-end into a detailed but digestible workflow. This makes deviations, bottlenecks and rework loops easily identifiable.
Step 3: Process analytics
Next come the results and the model enhancement stage. The visualization and conformance checking steps show how the process flow differs from the ideal model and quantifies its impact on KPIs. These techniques help organizations uncover relationships, hidden patterns and dependencies within their processes. This reveals the causes of discrepancies and highlights the priorities for process improvement. A common solution to these issues is the implementation of automation capabilities.
A practical guide to intelligent automation
How should you choose the right automation tools for your organization’s needs and goals? Start with insights from advisory firm Deep Analysis, including types of automation tools and their differences, seven steps to deciding which automation tools to use and how leading companies approach their automation initiatives.
Automation is everywhere. It’s time to put it to work for you.
What are the benefits?
Gain full transparency over processes across systems and departments. Understand the full context of how certain process flows impact the organization. This can lead to better decisions and improve employee experience and customer satisfaction.
Reduce costs and labor times by identifying process bottlenecks, loops, gaps and more. For example, teams that use Hyland RPA can streamline automation by leveraging bots to minimize repetitive tasks. This results in the reduction of high operational costs due to inefficiencies.
Locate anomalies before they become problems. Don’t wait for visible symptoms of inefficient workflows; discover the issue before it causes problems for users or customers.
Attain stakeholder buy-in and alignment. Process analytics provide quantifiable proof of the impact of process gaps and inefficiencies. Demonstrate the value of data-driven investments to stakeholders even before implementing the fix.
Improve compliance by detecting non-compliant actions and procedures with less time and cost than a traditional audit.
Get to value quickly and easily. Process mining is easy to implement and delivers fast ROI.
What makes Hyland unique in the world of business process solutions?
Business process mining capabilities within the Hyland product suite are purpose-built to identify bottlenecks and fix them fast with advanced pattern recognition and interactive analysis. By creating process models in a fraction of the time and cost of traditional methods, organizations gain full visibility and valuable insights that streamline and optimize workflows, so employees can focus on creating outstanding customer experiences.