Four reasons to use process mining

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While digital transformation has become something of a business holy grail, often with automation at its heart, many businesses have no idea where to start. Forrester believes 'process mining' offers a solution.

Nearly 40% of businesses and technology decision makers who embark on a digital transformation journey find their road is potholed with misunderstood and often undocumented business processes.

That’s according to a recent Forrester Research report, “Process Mining: Your Compass for Digital Transformation”, which notes that while process automation is essential for digital transformation, many organisations have no idea which processes to automate – or even how those processes work.

A trap many businesses fall into is to try and automate as many processes as possible. However, as lead author Rob Koplowitz points out, automation for the sake of automation is a real problem, because “digitising everything is not a realistic goal”. Businesses need to prioritise in order to establish a road map for transformation.

The question is – how to identify which processes to automate in order to achieve the greatest value?

That’s where process mining comes to the fore. Process mining applies the concepts of data mining to business process. The technology is largely used for process discovery (understanding the business processes in order to document them); compliance auditing (in cases where the understanding of a process is essential to ensure compliance with, for example, financial process regulations); and process enhancement (to enable the cataloguing and prioritising of manual processes – and possibly even inefficient automated processes – as a starting point for end-to-end automation).

Knowing which processes to automate is only part of the challenge. Understanding exactly what is going on within that process is another issue entirely. “To optimise efficiently, you need to cultivate an accurate depiction of what’s going on beneath the surface of your processes,” Koplowitz says.

Using traditional manual discovery techniques such as interviewing participants in the process, or observing them at work, is not only time consuming, it is also subject to human error – and human subjectivity.

“Manual discovery is a surface-level, bias-heavy approach to understanding how things operate. Without tapping into data, your transformation journey is subject to personal perception and lacks a grounding in observable, quantifiable face,” he says.

Process mining, a technology that has been around for many years and can be regarded as 'mature', makes it possible to understand end-to-end processes objectively and dispassionately through a fact-based lens.  

How to leverage process mininig

In the report, Koplowitz outlines four suggestions for the effective leveraging of process mining in order to drive digital transformation initiatives.

1. Learn what process mining is and how it can help. This is essential if a business is to obtain a baseline understanding of process mining how it can contribute towards an understanding of the intricacies of the business’ processes

2. Use process mining to prioritise automation efforts and to “pick your automation battles” so as to reduce handing errors, boost compliance efforts and/or reduce costs.

3. Include customer experience professionals in process mining efforts. Because many digital transformation initiatives are directed at improving customer experience, process automation efforts should not be run in parallel with customer experience efforts. Unless the two are integrated, the chances are that the entire digital transformation exercise will be less than successful.  integrated process.

4. Let process mining tools be your objective third party. When people are brought together to try and unravel organisational processes, chances are there will be little consensus about how the processes actually work, let alone which are the most important. Process mining technology effectively eliminates tedious, error-prone, potentially contentious manual discovery.

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