Better Process Analysis Using Analytical Techniques

This will be a radical shift for many business (process) analysts. Currently, some business analysts do little analysis at all, but provide more of a business documentation function. This may be due to lack of training to provide them with the required analytical skill set, or could be the organization’s view of the business analyst role as a documenter rather than an innovator. Documenting current processes and procedures is valuable, but it’s not really analysis, and it is unlikely to discover opportunities for radical improvement. Because of this view of business analysts as documenters, many are provided only with documentation tools: modeling tools for building BPMN, CMMN and/or DMN models of their processes and decisions; wireframe or other diagraming tools for sketching user interfaces; and the usual suite of office productivity tools for writing text-based descriptions of their findings.

Gathering information as input to this documentation is typically done through observation: job-shadowing someone while they perform a task (which is useful for task procedures but not overall process flow), interviewing teams about their operational processes (which is often of very little use because of the subjectivity and interpretation of the interviewees), or viewing operational metrics (which show what is measured but not necessarily aligned with corporate goals). At the end of this information gathering, the business analyst is left with an incomplete and/or incorrect view of what actually happens, and may have little understanding of how the processes link to goals.

I don’t want to downplay the role of in-person observations – I’ve made a lot of use of these techniques over the years, and they can provide insights – but to identify that they don’t do a very complete job of analysis, and they should only be one part of the data gathered. With the current remote work and social distancing, most observations need to be done remotely, which adds an extra filter between the observer and the task performer.

As a starting point for a more complete, data-driven analysis, it’s important to gain understanding of the actual end-to-end business process, both the human tasks and the activities in the systems with which they interact. This can be done using analytical process discovery techniques, namely process mining and task mining, which measure what really happens rather than someone’s interpretation of what happened (or was supposed to happen).

Process mining tools take history logs from systems such as ERP and CRM systems, then automatically derive the end-to-end process models. But rather than just providing a single process model, process mining shows all the different ways that the process is actually executing, and helps identify the reasons for the variance. This is a great way to find the broken processes, where someone looks at the discovered process and says “I didn’t know that we did that”, or to show situations where people have been able to bypass certain controls, such as issuing a PO without the right level of approval.