Process Mining, an emerging player in the market, is poised to bring radical change and become a trendsetter to how we approach business process management. The technique refers to extracting and analysing relevant data stored by information systems, leading to improved and automated process discovery.
Process mining brings efficiency to existing techniques utilized by organizations to analyse, optimize and automate processes. It stands out as a differentiator in the market, bringing a more holistic approach to process analysis, enabling automation with end-to-end transparency and integral data insights.
This technique reduces the overall cycle time for process analysis and removes human biases and reliance on fragmented teams which often have limited visibility to the entire process. It also allows for advanced simulation of potential business improvement solutions and enables the user to create and test different scenarios to measure the impact.
Symbiotic Relationship with Technology Landscape
Organizations are increasingly playing catch-up with their technology to sustain and grow in today’s environment and bridge the gap with rapid process modernization. Today, with more and more processes getting digitized, the use case for process mining becomes even greater. It is this digitization of processes that has been the greatest driver behind the evolution of process mining from a theoretical concept to a practical application, enabling the move toward digital transformation of processes across organizations.
Process innovation is a dominant force driving transformation of financial services organizations, helping them adapt to an evolving and growing business landscape. However, with complex business functions spilling across a maze of geographies and systems, critical assessment of current state processes becomes challenging and potentially slows down how organizations adapt to an increasingly disruptive environment. It often results in temporary solutions without addressing the challenges rooted at fundamental levels, resulting in an ineffective business optimization strategy.
This is where process mining can bring a data-backed spotlight to otherwise hidden issues and dependencies by providing critical insights and increased transparency. The engine traverses through relevant data to generate a robust end-to-end process map, outlining the activities and interactions taking place between actors and enterprise technology, and other supporting information, such as business rules, exception flows, and organizational models. It encourages a problem-based approach where the identified drawbacks and bottlenecks form the base of the optimization strategy and help maximize process efficiencies and cross-functional collaboration.
The efficacy of process mining relies heavily on the quality of data being fed into it. Comprehensive but focused data will lead to superior output with a sharper view into the inner workings while sub-prime data marred with too much noise, wasteful material, and missing essential information will result in a poor process mining exercise. Preparation of data requires considerable time and effort for the organizations that are looking for quick turnarounds. Another challenge is the continued reliance on unstructured manual activities, which slip through the cracks and remain unaccounted for through process mining. Hence, it is critical to identify suitable processes before applying process mining methodology to ensure efficient utilization and correctly prioritize improvement initiatives.
The Pareto principal dictates that 80% of the cost and efforts arising due to process deviations are caused by 20% of exceptions. It is hard to optimize a process and remove inefficiencies unless these undocumented process exceptions are identified and fixed. Process mining allows organizations to detect these unintentional exception paths in the defined process flow, determine their root causes, and work on removing process exceptions.
Although process mining is an independent concept, it has natural synergies with other emerging technologies, such as Robotic Process Automation (RPA). RPA strives to automate manual front-end tasks by installing “software bots” to get high-volume tasks performed fast and without errors. Process mining lends to a more effective RPA implementation by providing an accurate understanding of the organization’s processes and uncovering hidden details that could potentially be detrimental to the overall success of the RPA implementation and substantially reduce the value of IT investments.
Another advantage of process mining is the ability to run simulations and measure the effect of process optimization and automation strategies. For example, process mining can provide required metrics to assess the effectiveness of an RPA implementation vis-à-vis the desired outcomes and determine if processes are executed as designed. Such process benchmarking and compliance measurement capabilities provide KPIs for more focused and accurate investments, ensuring a high rate of return for modern businesses.
Process mining brings vital insights into the current and desired process state through a data-driven approach, helping organizations in laying the essential groundwork for process optimization and automation. With its growing popularity and continuous evolution, process mining is primed to expand as an essential building block in the field of business process management.
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