Data may well be the lifeblood of our digital economy, yet it will only deliver strategic business value if it can course freely through the veins and arteries of your organisation. In other words: if your critical business processes are not complicated or hampered by inaccuracies, inconsistencies and inefficiencies. By allowing you to visualise, analyse and optimise your business processes, Process Mining helps you to get rid of those bottlenecks.
Order-to-Cash, Procure-to-Pay, Plan-to-Produce, Request-to-Service: business processes are flat-out essential for any organisation to function properly and to achieve its growth objectives. Essential though they may be, however, today’s business processes are still far from perfect. According to market research firm IDC, every year companies lose no less than 20 to 30% in revenue due to process inefficiencies.
Data Mining: similar but not the same
Process Mining not only gives you a better understanding of those inefficiencies, it also allows you to remedy them and improve your business processes. Indeed, with the real-time actionable insights that Process Mining provides, you can take immediate action when notified of any issue or discrepancy in your processes. In addition, the constant monitoring of processes that goes with Process Mining, can lead to process conformance and compliance.
Sounds great, right? But how does it work exactly? Process Mining, as the term itself suggests, actually shares some clear and obvious similarities with Data Mining. To begin with, it also analyses Big Data to provide important insights into business processes and support business decisions. Not surprisingly, therefore, in both these fields of business intelligence (BI) the role of artificial intelligence (AI) and algorithms is becoming increasingly prominent. Discovering and visualising causal relationships and hidden patterns, which would otherwise not be visible to the human brain, is absolutely central to both.
But whereas the input for Data Mining consists of tables with data, Process Mining instead uses so-called event logs, audit trails, data and events from operational or transactional IT systems, such as your ERP system, which are moreover provided with a timestamp. This allows you to avoid a checklist-based process discovery which is time-consuming, incomplete, and subjective. Process Mining applies specialised algorithms to these event log data in order to identify trends, patterns and details of how an entire process runs rather than a singular incident.
More than just a fast, efficient, and affordable way to get a comprehensive as well as objective view of your business processes, Process Mining also helps you to (better) automate and optimise those processes. More particularly, it helps you find out which step(s) in the execution of a process you need to prioritise for automation. No wonder then that Gartner categorises Process Mining as a subset of Hyperautomation, along with AI (artificial intelligence), ML (machine learning) and RPA (robotic process automation). Combining Process Mining with those other key technologies that drive Hyperautomation, RPA in particular, can further improve your business process automation. As does combining Process Mining with a quality-control methodology like (Lean) Six Sigma.
If implemented correctly, Process Mining leads to continuous process improvement. Ultimately evolving into a real-time tracking solution for your process health, it effectively becomes your ‘process pulse’, one might even say.