In the ever-changing business environment, staying competitive requires efficiency. At QPR, we continually adapt to our customers’ evolving needs. In this blog, we unveil the newest advancement: Data Augmented Process Modeling. This innovation, integrated into the Digital Twin of an Organisation suite, aims to revolutionise the Process Modeling landscape by seamlessly combining the best practices of Process Modeling and Process Mining methods.
Process Modeling Evolution
Reflecting on the first encounter with professional process modeling software, the user recalls a journey spanning over two decades. While the tools have evolved, becoming sleeker with enhanced features, the fundamental concept remains unchanged—a canvas for creation and a palette of symbols to convey ideas.
Around 2010, a shift towards automating process modeling through transactional data from IT systems emerged. This ambitious vision gave rise to process mining, a valuable tool providing profound insights into processes by analysing operational data. Despite the advent of process mining, traditional process modeling software remains a fundamental cornerstone among the essential tools for optimising business processes.
With a front-row view of the evolution of both process modeling and process mining landscapes, QPR has closely followed market changes, customer needs, and the challenges and shortcomings of different technologies. Armed with this comprehensive understanding, QPR is driven to go beyond the status quo and address the evolving landscape of process improvement tools.
Process Modeling and Process Mining users are often living in isolation
Until recently, Process Modeling and Process Mining methods were often siloed, even within the same organisation, resulting in missed opportunities for collaboration. This separation hindered efforts to enhance efficiency and reduce costs, as process modelers lacked real-time performance data and struggled with adapting to changes in specific processes. On the other hand, process mining users faced challenges with dynamic processes that didn’t always leave clear digital footprints, limiting their analysis.
Recognising the need to bridge this gap for accurately depicting current workflows and designing efficient operating models, an innovative solution emerged last year. The solution, known as Data Augmented Process Modeling, combines the strengths of both worlds, offering a promising approach. This innovation is on track to revolutionise the process modeling landscape.
Process Modeling Evolution
Reflecting on the first encounter with professional process modeling software, the user recalls a journey spanning over two decades. While the tools have evolved, becoming sleeker with enhanced features, the fundamental concept remains unchanged—a canvas for creation and a palette of symbols to convey ideas.
Around 2010, a shift towards automating process modeling through transactional data from IT systems emerged. This ambitious vision gave rise to process mining, a valuable tool providing profound insights into processes by analysing operational data. Despite the advent of process mining, traditional process modeling software remains a fundamental cornerstone among the essential tools for optimising business processes.
With a front-row view of the evolution of both process modeling and process mining landscapes, QPR has closely followed market changes, customer needs, and the challenges and shortcomings of different technologies. Armed with this comprehensive understanding, QPR is driven to go beyond the status quo and address the evolving landscape of process improvement tools.
Data Augmented Process Modeling combines the best of both worlds
Data augmented process modeling represents a powerful integration of two crucial methodologies: process modeling and process mining. Its benefits extend beyond addressing challenges inherent in existing methods and tools, offering a wealth of advantages.
Traditional process modeling software serves as a comprehensive tool for comprehending, managing, and enhancing various organisational aspects. However, the manual nature of traditional process modeling demands significant effort and incurs high maintenance costs. Moreover, manually crafted models of the current organisational state may not accurately reflect reality, leading to a potential loss of trust in process modeling descriptions.
Utilising the Data Augmented Process Modeling solution facilitates modeling work by leveraging transaction-level information from the organisation’s IT systems. This approach yields various benefits.
Modeling is accomplished more efficiently and at a reduced cost.
Leveraging actual data ensures that the models capture real-world variations and nuances not always accounted for in theoretical process models, resulting in a more precise representation of the process.
The transparency provided by accessing underlying data enhances the credibility and trustworthiness of the modeling descriptions, a critical aspect for making reliable business decisions based on these models.
Unlike traditional process modeling, which quickly becomes outdated due to the dynamic nature of business operations, data augmentation ensures that models evolve in real-time, ensuring an accurate representation of the current situation.