Legacy business process management is obsolete.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.
- Ingestion and Orchestration. This is where the 'mining' in process mining takes place. Specialized algorithms are used to extract data from business information systems. This data may take many forms, including event logs, audit reports, transaction records, and customer support tickets.
- Discovery. The process mining platform leverages the consolidated data from the first step to create models of each process being evaluated. Referred to as process graphs or process maps, these are frequently interactive and explorable, and in some cases may include multiple process paths.
- Conformance. As processes are charted out, the algorithm references an intended process model, flagging any deviations so that they might be addressed. Insights from conformance may also be used to improve the existing model, a process typically referred to as performance mining.
- Analytics. The platform applies a range of different metrics to its process models, which may further assist in identifying root causes for the application of targeted fixes. This also allows a business to determine the impact each inefficiency has on business outcomes, and by association the return on investment that may be gained by optimizing that process.
- Benchmarking. By creating a digital twin of the process, the business can explore multiple scenarios to determine the most effective model. They can also benchmark this twin against their existing process, or leverage an external process model for the same purpose.
- Application. The final step is for the business to apply the insights generated in the previous five steps. This is typically best achieved as part of a greater business optimization initiative.
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:
- Human Resources. Improving recruitment, hiring, and onboarding practices. Optimizing employee management, including training, compliance, and performance tracking.
- General Business Operations. Optimization of common business processes such as report generation, account creation, and approvals.
- Finance. Enhancing procurement, improving invoice and payment processing, and identifying opportunities to reduce overhead through automation.
- Software Delivery. More efficient lifecycle management, migration, and deployment. Greater visibility into testing and development.
- Information Technology. Optimizing network operations, server management, and security controls. Running simulations to ensure more efficient deployments, particularly in hybrid environments.
- Support. Identifying bottlenecks in ticket routing, and ensuring more effective, efficient resolution for both internal and external support requests.
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:
- Standardization of business processes
- Improved accuracy, revenue, and outcomes through business process automation.
- Reduced lead times and reaction times when dealing with customers.
- Address noncompliance in real-time and analyze audit data more efficiently.
- Identify bottlenecks and address process conflicts.
- Eliminate redundant workflows.
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.