How Process Mining is Supporting the Push for Digital Government Services
Gaining a comprehensive understanding of agency business processes is essential as governments deploy more digital services to meet the growing needs of the public. Agencies need to have insight into existing processes before they can undertake digital transformation initiatives.
But discovering and evaluating existing digital business processes in order to improve and evolve them is difficult for several reasons. First, existing processes tend to be complex and poorly documented. Second, most agencies lack automated tools to find and analyze these processes. A study found that 72% of IT organizations use manual methods to uncover the details of digital business processes.1
Process mining technology enables agencies to automatically discover and improve digital business processes in their IT environment, where processes can be spread across hundreds of systems that don’t necessarily work well together.
WHAT IS PROCESS MINING?
The term “process mining” describes an analytical discipline for discovering, monitoring and improving digital processes on an ongoing basis. Process mining technology scours event logs generated by agency business systems to automatically analyze how digital processes are working.
Process mining uses artificial intelligence (AI) and machine learning (ML) technology to create a deeper understanding of digital business processes and identify inefficiencies and opportunities for improvement. The analysis also helps IT determine the best plan to eliminate bottlenecks, automate certain steps, or redesign the entire process.
Process mining capabilities can be integrated into runtime management platforms that automate process mining tasks such as data integration, process analysis and modeling, compliance checking , benchmarking, simulation, prescriptive automation, and integrations with existing automation technologies.
Execution management platforms provide a real-time view of process performance by collecting and analyzing data across multiple systems and event logs. These platforms also offer tools for planning and simulating potential business process changes, which can help IT understand the impact of process changes before implementing them in business systems.
MORE INSIGHTS FOR BETTER PROCESSES
The combination of automated process mining analysis and execution management gives IT two key advantages for process improvement.
First, complete visibility into business processes. Process mining provides a complete and up-to-date view of how business processes work, including details about data, systems, and connections at every step. In addition, the analysis shows where processes need to change. With this information, IT teams can focus on implementing improvements, not finding process gaps.
Second, detailed process insights help IT move from a system-centric organization to a business-centric organization. An up-to-date, detailed view of business processes helps IT and process stakeholders make more informed decisions about future automation efforts.
Automated process mining and execution management offer great advantages over manual methods. Manual reviews provide a single snapshot view, making it difficult to fully understand bottlenecks, failures, and needed process improvements. Most manual reviews are subjective and limited in scope, so they may miss important factors in process design. And even limited manual reviews can be time-consuming and expensive because they typically involve multiple teams. By the time a manual process analysis is performed, it may already be out of date due to changes in the IT environment or agency business.
The practice and technology of process mining addresses these challenges in a way that overcomes the limitations of manual review and supports continuous process improvement. Government processes that can benefit from process mining include shared services such as finance and procurement, systems migration, and constituent services such as health and social services.
DEVELOP A PROCESS MODERNIZATION PROGRAM
Establishing a formal process modernization program will help support and accelerate a government’s digital transformation efforts. A comprehensive understanding of business processes is critical as agencies adopt more digital services and adapt them to meet growing audience needs.
Process exploration is a central part of a formal maturity-driven program for process modernization.
Most government IT organizations are in the early stages of process maturity, meaning they are beginning to develop their understanding of processes or have begun to standardize certain processes. Later stages involve optimization and innovation in the execution of business processes.
A process mining program can be adopted gradually, starting with a test project that analyzes the modernization of a system. Analyzing a system with high-volume, mission-driven processes will help quickly identify the value of a process mining program.
An important strategy for this initial project is to appoint a Process Mining Champion. This person can educate agency stakeholders on the value of process mining and how it can help the agency accelerate digital transformation.
Finally, set clear criteria to define the success of the trial project, such as key performance indicator (KPI) goals and deadlines. Include process stakeholders in the discussion to define these criteria.
CLARITY TO IMPROVE DIGITAL PROCESSES
Business processes that work well for users and streamline internal operations are key goals of digital transformation. Given the complexity of government processes, achieving these goals requires an in-depth and ongoing analysis of how well the processes are currently working and how they can be improved to work better. By embracing technology and a process mining agenda, the agency will be better able to focus on improving operations, adapting to change, and meeting new citizen needs and expectations.
This article was written and produced by the Center for Digital Government Content Studio, with information and contributions from Celonis.