What is Process Mining (and why is it not enough)?
Businesses live by their processes – the prescribed sets of actions their employees take to get things done. When the processes are working well, the business is doing well. When processes malfunction, the business risks a multitude of dangers, from lost revenue to customer unhappiness to compliance violations. Most companies have a general idea of how their processes should run, but lack an understanding of the day-to-day details of the execution. Without this knowledge, how can they make improvements that work?
Process Mining offers a solution, and for many years it has served businesses well. However, in today’s increasingly complex environment and with increasing pressure to do more, faster and more cheaply, organizations need smarter solutions.
In this article, we’ll explore what Process Mining is, what it can (and can’t) do for companies looking to optimize their processes, and how Process Intelligence offers a more efficient approach.
What is process mining
Process Mining uses actual data from information systems to create a model that accurately reflects how a process is executed.
Applications such as CRM and ERP systems, as well as other recording systems, automatically create event logs that record every action taken. Data from these logs can be collected or “leveraged” to create an audit trail of the processes in which the applications are involved. It works even when multiple apps are used in a single process. Process exploration technology follows these audit trails to create a process model showing the details of the end-to-end process, as well as the variations. Business users can analyze these patterns to determine if processes are performing as they should and, if not, investigate the root causes of deviations from the optimal path.
How process mining works
Before process mining, the only way for companies to analyze the performance of their processes was through interviews with business users and manual reviews of data, a slow and tedious business with a high margin of error. Process exploration enables organizations to leverage automation to paint precise pictures of real-world process performance faster, easier, and more accurately than manual approaches.
Where process mining fails
Process exploration offers huge advantages over manual approaches to process analysis, but it does have its limitations. For example:
- Traditional process exploration identifies process-related issues, but does not provide granular answers to the root causes of those issues.
- Process mining works well in simpler scenarios, but lacks the sophistication for evaluating complex processes with a large number of valid variations.
- Process exploration can only analyze past performance, without the ability to continuously monitor processes and alert users to any deviations.
- Some traditional process exploration tools may be limited in the types of data sources they can connect to, which can limit the value they can provide.
How Process Intelligence bridges the gap
A new generation of process analysis solutions go beyond traditional process exploration. Process Intelligence combines BI-like metrics with a set of process-specific analyzes to provide detailed insight into complex end-to-end processes. Unlike traditional process exploration, Process Intelligence allows businesses to visualize their processes in real time and analyze patterns that cause bottlenecks or disruptions.
Here are the four main advantages that Process Intelligence offers over traditional process mining:
1. Analysis based on timeline
Process exploration uses the process analysis diagram method, which involves converting process data into a flowchart (diagram) and then analyzing the flow of all iterations through that diagram. The downside to this approach is that few business processes fit into a well-organized flowchart. By the time all valid variations of a process are taken into account, the diagram often becomes a tangled mess with limited usefulness.
In contrast, Process Intelligence uses a chronological approach, which creates an unfiltered, unedited history of each process iteration from start to finish. These timelines are then analyzed so that they can be compared, filtered, searched, aggregated, etc., in the same way that a BI application analyzes the records in a table. The chronological approach provides full visibility into the end-to-end process, even when some steps are performed using multiple systems. And the digital analysis approach of Process Intelligence, compared to the diagram-centric approach of process exploration, works equally well on all types of processes, as compared to core process exploration which only works well on processes with little variability in terms of sequence. of steps.
2. Continuous improvement
Traditional process exploration focuses on examining historical data. While this approach can offer valuable insight into what worked well and what didn’t, it does not offer solutions for present and future iterations.
Process Intelligence monitors processes with new data arriving in real and near real time, “monitoring” each iteration and alerting process owners to deviations that could cause delays or other problems. By enabling continuous improvement, Process Intelligence continues to deliver return on investment as businesses capitalize on new opportunities to make processes faster and smarter.
3. Reduces compliance risks
When businesses run traditional process exploration applications, users can examine the output to identify present and past deviations that could lead to compliance issues. This approach relies on the expertise of the users who examine the data.
Process Intelligence enables users to define process rules that align with the organization’s compliance requirements and instruct the system to monitor violations. When one or more of these rules are broken, the system immediately alerts users, allowing them to take immediate action to rectify the deviation and ensure it does not happen again. Process Intelligence alert rules can also be defined to invoke a service when an alert is triggered, in order to automatically deal with the problem. This ability can make the difference between finding a problem just in time, before it affects a company’s compliance state, and finding it when it’s too late to fix and it has already caused. problems elsewhere in the workflow – or worse, become aware of them. after a violation has been reported.
4. RPA improvements
According to Ernst & Young, between 30-50% of initial robotic process automation (RPA) projects fail due to the lack of quantifiable process data. As companies deploy RPA for more complex processes in more complex environments, the pressure to deliver a positive ROI has increased dramatically, and traditional process exploration can only offer limited support to generate the returns. that businesses are looking for.
Fortunately, Process Intelligence can be just as valuable for digital workers (RPA “bots”) as it is for human employees. Today’s Process Intelligence solutions can include process exploration and task exploration. As with process exploration, task exploration searches for significant events in a process. Task exploration adds the ability to record a user’s manual actions on their computer to capture manual process steps to use alongside steps gleaned from the logging system log files. By applying Process Intelligence to manual and automated processes, companies discover new opportunities to improve RPA outcomes:
- Evaluate manual tasks for their suitability for automation
- Capture the steps of a human task and use it as a template to create the required bot
- Identify previously hidden redundancies
- Identify optimizations that can free digital work cycles, improving the productivity of the digital workforce
- Discover and remedy inefficient transfers between digital and human workers
- Provide quantifiable data on the financial impact of digital workers by process
- Compare human and digital work in terms of cost, precision, efficiency and duration
Why Process Intelligence is the future of process improvement
For many years, Process Mining applications have served process owners well, saving countless manual hours and helping businesses discover opportunities for improvement. Process Intelligence offers a new approach to process improvement that improves process mining. Process Intelligence works with all processes, simple and highly variable, manual and automated. Process Intelligence will monitor each process instance at each new stage, alerting or even taking automated action whenever a process behavior of interest is detected.
Process Intelligence supports RPA initiatives by identifying the right candidates for automation, then monitoring and reporting on the process the bots are participating in. Process improvement can now be taken to a new level by delivering on its promises of increased productivity, reduced risk of costly compliance violations, and efficiency that can create happier customers, happier employees and greater competitive advantage.
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