Legacy business process management is obsolete.

Not that it worked particularly well to begin with. Per The Harvard Business Review, too often when reengineering a process, businesses fail to focus on how the process currently performs. While charting out the way they want things to work, they lose sight of the inefficiencies, bottlenecks, and performance problems within their existing ecosystem.

On the opposite end of the spectrum, there are the businesses that embroil themselves too deeply in analyzing their existing processes. In the absence of any effective process management tools, they spend inordinate time and resources on interviews and qualitative observation. The end result is the same.

Nothing gets done.

Even in organizations where roadmapping and analysis are well-balanced, there is a lack of visibility. A lack of connectivity between processes and the business's enterprise information system. Process mining represents the solution to that problem.

What Is Process Mining?

Process mining is essentially the intersection of data science and business process management. The basic idea is that in a modern context, every business process leaves behind a series of digital footprints. Although these are not always visible to data scientists, these footprints can functionally serve as 'breadcrumbs' through which an organization can gain a more complete picture of how its processes and systems intersect.

Process mining isn't actually a new idea. Although the technology to enable it only recently became available, it has existed as a theoretical field of research for over twenty years. The concept was first introduced in 1999 by Will van der Aalst, a Dutch computer scientist who is widely regarded as “the godfather of process mining.”

How Does Process Mining Work?

Process mining can typically be broken into six stages. Note that not every process mining platform applies all of these techniques.

It's important to understand the process mining is ongoing. It is not a project that one can simply mark as finished and forget. There will always be new efficiencies to discover, new bottlenecks to address, and new opportunities to leverage.

What Are The Most Common Use Cases for Process Mining?

Typically, process mining is most commonly applied for one of the following purposes:

What Are The Benefits of Process Mining?

The first, most obvious benefit of process mining is that it provides direct, objective insights. It looks 'below the hood' of a business, automatically mapping each process from tangible data. In addition to being less biased, process mining is considerably more accurate than legacy techniques such as surveys or workshops.

The second major benefit is tied to automation. Compared to legacy business process optimization, process mining is significantly more efficient. Because it doesn't require any manual work, this also means it's more cost-effective, as well.

Most process mining platforms also run without interrupting existing systems and processes, meaning you needn't worry about interrupting workflows or having to implement any complicated workarounds.

The application of process mining, as one might expect, can also improve a business in a multitude of ways:

Data-Driven Decision Making

Business process optimization has long felt like an uphill battle, but it no longer needs to be. With the advent of process mining, businesses can leverage data science and analytics to gain a level of visibility into business operations which would have previously been impossible. And through that visibility, they can make better, more informed decisions.

And more importantly, they can do so in a way that allows them to clearly demonstrate their efforts to senior leadership, both justifying their budget and providing executives with peace of mind.