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The business environment pressures organisations to turn to technology to reach and maintain leading market positions. Process mining uses the data from users existing systems, for example, ERP and CRM, and provides comprehensive, real-time visibility into how current business processes run. This collated information allows users to identify inefficiencies within their existing business processes.

Process Mining is a Technique to Analyse and Track Processes

Process mining can be understood as an overlapping of business process management (BPM) and data mining. Process mining utilises event log data to generate process models which can be used to discover, compare, or enhance a given process.

Data mining itself is broader, less specific, and extends to a variety of data sets. Data mining is often used to observe and predict behaviours.

Process mining offers a data-driven approach to BPM, which generally collates data informally through workshops and interviews and uses software to document that workflow as a process map. Since the data that informs these process maps tends to be qualitative, process mining offers a quantitative approach to view event data.

The starting point for process mining is an event log. All techniques assume that it is possible to sequentially record events. An event refers to an activity (i.e., a well-defined step in some process), an event is related to a particular case (i.e., a process instance). Event logs may store additional information about events.

Whenever possible, process mining techniques use extra information such as the resource (i.e., person or device) executing or initiating the activity, the timestamp of the event, or data elements recorded with the event (e.g., the size of an order).

The Benefits of Process Mining

Before process mining, the primary way to analyse business process performance was through interviews with business users and manual data reviews—a slow, tedious undertaking with a high margin of error. Process mining allows organisations to leverage automation to paint accurate pictures of real-world process performance—faster, easier, and more accurately than manual approaches.

Other benefits include:

  • Reduced costs: Process mining reveals inefficiencies, bottlenecks, and tasks that can benefit from automation. This means that organisations can significantly reduce their operating costs.
  • Increased transparency: With process mining, stakeholders are able to locate relevant data and create actionable insights. Leading to a higher level of transparency across specific processes as well as at the organizational level.
  • Improved performance management: By automating the collection of key performance indicators, stakeholders can continually monitor processes in real-time and ensure operational excellence.
  • Improved customer experiences: Process mining enables organisations to get to the root causes of issues quicker, this allows them to react fast and provide enhanced customer experience.
  • Improved compliance: Auditing is costly and often time-consuming. Process mining can analyse data quickly and stakeholders can identify compliance issues in real-time.

How Does Process Mining Work?

Step 1: Data

Business processes are often complex, they can operate across a range of systems that are likely to use various types of data, have several users, and may belong to different departments.

Process mining tools offer several approaches for extracting data from the underlying systems. An example of a simple data extraction method could be a user exporting an event log from a system as a .csv and uploading it to the process mining tool. Real benefits could be seen from real-time data ingestion to continuously sync process data.

When cases (business objects) are in operation they leave behind data. For example, the data could be in the form of a service ticket status moving from ‘waiting for response’ to ‘response received’. This could be thought of as an ‘event’.

Process mining collects the data from events by consuming system logs, event logs, databases etc. The data is then visually reconstructed to display what is occurring within the process.

Event logs include three key pieces of data for individual events:

  • Case ID
  • Activity
  • Timestamp

Additional information is often present, which could be used to obtain further details and more in-depth for the process analysis.

Step 2: Discover Processes

Process discovery uses event log data from the first step to produce an end-to-end visualisation of the process that superimposes every phase that every case (i.e., a process instance) took as it moved through the cycle into one visualisation. This is sometimes called a digital twin of the organisation.

The visualisation is a chronological sequence of events that displays all of the different paths that cases took to get from the beginning of the process to the end. Each unique path can be referred to as a variant, with variants that don’t follow a standard or accepted path being called deviations.

Process discovery allows you to explore this data often displayed in a process map and see all of the different variants.

Under this classification, no previous process models would exist to inform the development of a new process model. This type of process mining is the most widely adopted.

Step 3: Analysis

Process performance analysis is where organisations can begin to understand the causes of inefficiencies and problems within their operational processes.

Process mining tools can offer information about which cases are; running late, overloaded, deviating, skipped, or bottlenecking.

With a second-generation process mining tool, the user can also conduct a Root Cause Analysis to find out what is causing those problems.

The user is able to evaluate different days, facilities, vendors, etc., show relationships and dependencies in the process, or look at distributions to find majorities, outliers and problem areas.

Step 4: Monitor Process

Monitoring the process gives the user the ability to keep track of KPI values. Most process mining tools provide the ability to send notifications to the user if the KPIs cross a specified threshold.

The user can monitor the percentage of cases that match their desired process, and the percentage that doesn’t. In addition, the user can see if steps are being skipped, executed in the wrong order, or taking longer than expected at a certain stage of the process.

Users could use the insights from the process mining tool to determine if they need to alter business process strategy or determine if there are new areas for improvement.

Process Mining: The Key to Successful Robotic Process Automation (RPA) Efforts

Robotic Process Automation (RPA) is a type of business process automation technology, which uses software robots/artificial intelligence (AI) to perform automated functions. RPA software offers solutions to administrative and repetitive tasks that generally require manual human completion.

RPA technology provides a platform that enables the user to build, deploy and manage software robots that imitate human actions by interacting with digital systems. The robots can navigate systems, understand what’s on the screen, complete keystrokes, extract data and perform defined actions. Additionally, RPA leverages aspects of AI for refined decision-making and has the ability to adapt, assess and execute cognitive tasks from detecting, predicting and inferring.

Accessing the true value of RPA in a sustainable and transformational manner requires a comprehensive approach considering technology and infrastructure, governance and risk management, process flow and human capital impact.

Part of the shortfall of RPA lies in recognizing the ideal candidates for automation. Not all high-volume, lower-value tasks require automation, and often these may have an unforeseen impact in other areas of the business.  Automating the incorrect business process may create more work in the long run, whereas perhaps removing a step in the process, or adjusting rules regulating could enhance the efficiency.

Enter process mining, 78% of people who automate say process mining project is key to enabling their RPA efforts (Process Mining Sector Scan, January 2020).

How does process mining benefit RPA processes:

  • Highlights the most impactful and valuable areas in the business for automation.
  • Process mining continuously monitors ROI and KPIs.
  • The automation program can be assisted by data and ongoing measurement.
  • Provides an end-to-end perspective that is required to improve process performance and ensures delivery of results.
  • Indicates where processes require improvement before automation.

Process mining facilitates the deployment of RPA in a sustainable fashion and uses the resulting productivity gains to redeploy employees to tasks with direct benefits.

Next steps

To sum up, process discovery could revitalise operational processes that were put aside years ago. Problems include creating process descriptions that tell us about the way the business operation takes place. Another big problem in software applications is poor connectivity from a business process to a business enterprise information system.

Process mining will address this challenge but also revitalize the field of process management with innovative technologies. In business process improvement, organizations generally want to restructure and improve processes, which often are not interested in examining “as is” processes.

If everything is prepared and thought through, then implementing automation should go smoothly.

Talk to an expert and see how you can get started.

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