What Is 'Process Mining' and Should You Be Doing It? Pinpointing and addressing process inefficiencies is a top priority for most businesses. Is process mining the way to go for your company?
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Everyone knows what data mining is, but the term "process mining" may be new to some. According to Gartner, process mining is a methodology used to discover, monitor and improve processes that already exist within a business by relying on data. The goal of using process mining is to explore where existing business processes are inefficient and address those critical areas. Unfortunately for many businesses, this consideration is usually given a low priority. Here we look at what process mining offers to a company and why it should be considered a pressing issue.
Much of the trepidation that modern business has with process improvement comes from how they approach process improvement. Far too many companies focus on the "to be" of processes rather than the "as is" of how methods perform currently. Naturally, unless a business knows how a process operates now, it can't possibly figure out how to improve it and make it more efficient later on. This lack of interest in the current process leads businesses to take shortcuts to define the current situation or spend exorbitant sums on consultants to discover their own operations.
The other side of the coin is just as bad. Some businesses view process improvement through a slow, iterative methodology, where small fixes are applied throughout the improvement process. Focusing on the "as is" of a process carries with it the risk of losing sight of the forest for the trees. Much of the information used to discover the process's faults are based on personal interviews, which are subjective. Many teams conduct these interviews as a matter of course but then disregard the information since the data collected is subjective. Professionals view these results with well-grounded skepticism.
In addition to these issues is the enduring disconnect between a business's information systems and its processes. Many companies use different enterprise systems, some of which are data-oriented while others are process-oriented. These suites provide limited support to process improvement because they aren't designed for this use. The data they produce or collect remains on the systems, but it requires a lot of work to extract that data into a usable form for process improvement. Process mining becomes ever more complicated the less connected and integrated the business's information systems become.
Finding and addressing issues
Process mining could be considered a business analog to big data analytics. The difference is that instead of focusing on the consumer and improving marketing techniques, it focuses on the business and improving processes. Process mining gained in popularity over the last decade. Even so, many companies aren't using the process to their advantage. Process mining software can integrate alongside existing data suites and collect process mining data while the system creates it. A good example of this is Celonis, a software that works in tandem with SAP and other transactional systems. It accesses and aggregates data not just from SAP but from other client software such as SalesForce and Oracle through APIs.
Businesses face a lack of connectivity between their information systems and the processes that use them. They stand to benefit the most from process mining in methods that are only partially digitized. A registered agent, for example, may benefit the most in processes that happen outside the IT system and require feedback from clients. It's important to remember that process improvement requires more than just raw data.
Process mining provides a solution that's elegant in its implementation. Anyone who has to delve into state process flows or oversee complex business processes would appreciate the improvements that process mining provides. The techniques are improved by aggregating data and taking feedback into account. The result is a more efficient process that's building on its past successes. It may not seem such a massive impact for those closer to the ground in terms of operations. But even at this level, process mining offers solutions that may otherwise have been missed. Process mining can highlight issues within each segment, allowing workers on the floor to fix problems and perform preventative actions before something happens. As long as a company uses enterprise systems to support business processes, process mining should be high on its list of priorities.
Three process mining techniques
Process mining falls into three broad categories, depending on what it's going to be used for.
Discovery: This methodology is used when there aren't any past records to base the process's improvement on. The core element of this type of process mining is data gathering and aggregation. A model can then be produced from the data collected so that later iterative approaches can use it. An algorithm is then developed to analyze the process and highlight where it can be improved based on the collected data.
Conformance checking: When a process model is already established and performing, conformance checking ensures that the data from the process logs match the process model. Any discrepancies or deviations are analyzed within variances to see what could be causing these problems. This methodology either highlights that the process needs to change or that it's performing as expected.
Performance mining: Businesses can consider performance mining as a tune-up of their processes. It's used when a process model has already been established but the business believes it can be significantly improved from its current level of operation and efficiency. Performance mining allows a company to investigate how it can enhance a process based on changing budgets and technology improvements.
Tools of the trade
Several companies put out tools that businesses can use in their process mining. These tools make it much easier to spot issues within the business's processes through visualizations and mapping. These advancements help break down the functions into their component parts and help those at the top of the hierarchy to more comprehensively analyze the situation given the existing data. It's far better than trying to explain reports cobbled together from collected data systems. The outputs from these tools can guide management into making the right decisions to improve their processes.
Weighing the cost
If a business wants to take advantage of innovations in its industry, there's no better way to incorporate them than through process mining. From here, a company can see exactly what's wrong with its processes and what is required to make those processes work more efficiently. Data guides conclusions so that the businesses can benefit from a quantitative approach to improving their processes. A company that wants to stay relevant should consider investing in process mining, especially if its competitors are already doing so.