An exploration of process mining, how it works, the value it provides and some organizational use cases.
Organizations — from lean startups to Big Tech — can only approach maximum efficiency and make key business process improvements when they understand how their workflows truly work. Humans alone can’t perform this data extraction and analysis fast enough to make their organizations competitive.
With process mining, organizations can expose what’s really happening in their processes instead of operating under assumptions. They can then identify the root causes of bottlenecks in real-time, optimize their resources and scale with full productivity and confidence.
What is process mining?
Process mining is a tool for modern business process management (BPM). It lives at the intersection of data science and management science. Process mining is comprised of a group of strategies with a common goal — to glean process insights within information systems via units called event logs.
The technique an organization uses for process mining depends on the stage where its process models reside. Here are the three main process mining techniques:
Process discovery: This is the most common technique and doubles as a building block. Discovery yields a process model that can be used to check for future conformance and enhancements. You can think of process discovery as “finding out what’s happening.”
Conformance checking: With conformance checking, algorithms hone in on any deviations to the expected process model outcomes. You can think of this technique as a “finding out where we’re going off the rails.”
Model enhancement: With discovery and conformance checking in place, you may want to go one level deeper with process enhancement, which begins with model enhancement. You can think of this technique as a way to “find opportunities to improve.”
Together, these data mining techniques help organizations maintain a high level of standardization, predictability and continual improvement. Companies can use process mining for the optimization of a single process, individual department or entire organization.
The value of process mining
Companies that have identified and can track their process KPIs have the benefit of deeper process analysis. They can track weaknesses in their business processes and find low-risk paths toenhancement, making them more agile and competitive in the marketplace.
Here’s how process mining helps them achieve and maintain this agility and competitive edge:
Boosting business intelligence: Data-backed insights and suggested enhancements help stakeholders reach common ground and make business operations decisions faster.
Identifying process bottlenecks: Process mining helps organizations gain a sense of where previously undetected bottlenecks exist so they can address root causes and regain efficiencies in common processes.
Surfacing process deviations: Organizations know how a business process should play out, but with any process comes the potential for deviations that cause workflows to get off-track. Process mining surfaces real processes and actual process steps — including any deviations. These represent opportunities to improve.
How process mining works
Process mining applies algorithms to proprietary log data sets from IT systems. The granularity of the log data (fine to coarse) determines how easily it can be analyzed and leveraged (with coarse data being easier to analyze and leverage than fine data).
A multisystem process mining approach might include event data from across an organization. Therefore, it’s possible that the sources of data are both systems (e.g., enterprise risk planning (ERP) and customer relationship management (CRM)) and departments (e.g., human resources).
What is an event log?
Let’s step back and look at our source: the event log. An event log is a digital record that helps organizations understand what is happening within a network. In many respects, it’s like a snapshot that captures a moment or specific action.
Another simple way to look at it — something happened (an event) at a certain time (timestamp). Attached to that date and time might be additional useful information. For example, someone tries and fails to log on to a SaaS application. In addition to the event and timestamp, there would be an incident associated with the log. Coupled with the analysis and visualizations that process mining technology can provide, the event log can help paint a more complete picture of what took place in a complex transaction.
Process mining use cases
Process mining can be applied to any organization (in any industry) that employs and logs business processes. Below are some common process mining use cases:
Manufacturing and distribution: The process of getting products to market has never been more closely scrutinized than it is today. The health and productivity of people across the globe has hinged on the speed of manufacturing and distributing vaccines and related health equipment. Process mining can remove bottlenecks between the many distributed players, with critical outcomes for all.
Sales: Process mining can examine sales cycles for deviations and other inefficiencies. This can help companies better allocate resources and make process enhancements that shore up metrics such as days to close — a boon for B2B companies whose cycles can range from three to nine months, introducing opportunities for competitors to increase market share.
Finance: Process mining can help finance departments make business processes like auditing more efficient by identifying deviations, such as maverick buying, which is the practice of purchasing goods and services outside an established procurement process.
E-commerce: Process mining can help organizations better understand how a given customer process works in order to increase sales and improve the customer experience. For example, an e-commerce company can produce models that quickly surface key metrics, such as relationship between web pages, critical paths to purchase, order-to-cash and time to issue resolution (on the support side).
Healthcare: Process mining can improve the efficiency of patient encounters and the efficiency and cost associated with interdepartmental workflows within a healthcare setting. The impact extends to the patient/user — shorter encounters and wait times are typically beneficial for both patients and healthcare providers. It also helps the organization with things like high-cost imaging workflows, for example.
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