P for Productivity, P for Process Mining

Why an increasing number of firms worldwide are investing in Process Mining to streamline business models

Regarded informally as the ‘sexiest profession of the coming decade’, data science is all set to be pivotal to the way companies are run and business decisions are made in the coming years. In fact, experts even expect digitally transformed enterprises to lead worldwide GDP growth over the next three years itself. At such a juncture, it is almost an indispensable necessity to ensure one is well versed with the major facets of this transformation – data analytics and process mining – to ensure competitiveness in a rapidly changing world.

Considered direct compliments, process mining and data analytics are two distinct technologies that will be central to future business processes and data-driven decision-making. According to tech conglomerate VentureBeat, “Process mining helps identify inefficiencies or opportunities for improving how companies do things, while analytics help businesses measure performance and identify opportunities. Together, they can deliver the best of both worlds. Better analytics and data prep workflows allow process mining tools to offer a glimpse inside various business processes. And process mining tools help executives understand and improve data science processes used by applications and improve overall reporting.”

Not just a back-burner issue

Historically, some of the most fundamental challenges around business process management has almost always been treated as a ‘back-burner issue’, not being given the primacy it deserves. A major problem, in this regard, is that in most cases, organisations are much more interested in the improved ‘to be’ process, rather than exploring the ‘as is’, or the current process. However, understanding the caveats of the current process is crucial to knowing whether or not the process needs to be reengineered in the first place, where performance problems may exist and the degree of variation in the process across the organisation. This is why some companies often tend to just skip current process analysis altogether, or pay consultants heavily to analyse it. This is where process mining comes in.

Process mining generally refers to a family of techniques in data science and process management to support operational processes based on event logs. The primary objective, is of course, to turn data into insights and actions as quickly and efficiently as possible. Process mining today is currently optimised for processes chiefly within the realm of ERP or CRM applications but requiring hefty manual work to handle other applications. However, owing to the widespread digital transformation and cloud adoption being carried out around the world today, organisations must find a way to automate this rather time-consuming and cumbersome manual process to handle the large swathes of data with utmost efficiency.

According to VentureBeat, “…analytics tools like Alteryx help organize, prepare, and reformat data in a form suited for process analytics. This makes it easier to identify more dependencies or bottlenecks within a process. For example, improved visibility into a driver monitoring app may uncover a manual step that is causing bottlenecks for processing shipping manifest logs. This step can be automated using something like robotic process automation (RPA) technology.”

Bye-bye Bottlenecks

As many organisations have already found, scaling up analytics processes may cause several bottlenecks within enterprises as well. Process mining tools such as ABBYY Timeline, in this regard, makes it much easier to ‘understand, streamline and automate’ analytics as well as facilitate usage in downstream applications.

Consider this example from VentureBeat: “..an Alteryx customer ran a monthly process to calculate its fixed assets that took 40 hours and required a team of 10 contract workers to manage. Modelling this with process mining and creating a repeatable workflow allowed them to reduce that to 2.5 hours. Process mining automatically documented the process, which was useful for compliance and governance.”

Additionally, if one takes into account the challenges involved in coordinating with large dispersed teams across verticals, such as marketing and sales teams interacting with logistics for invoices, shipping or purchase orders, a unified analytics tool can prove to be of major help. By reformatting the data into the format required by the process mining tool, it becomes much easier to generate better data, visualise a larger variety of processes and therefore improve business performance.

The aforementioned, of course, works best when firms have repeatable workflows: such as in analysing volume discounts, running fraud detection or analysing a complex asset mix. Furthermore, process mining comes in especially handy in cases involving a blend of data from multiple databases, applications, spreadsheets or documents maintained by multiple departments, thereby cutting on time and improving efficiency.