Process Mining: A Look at the Actual Process Flow
Process mining reveals the true structures of complex business processes. Instead of merely making assumptions about how processes unfold, real data from various source systems is analyzed. These so-called event logs show which steps were actually executed, when, and in what order.
The result: The actual flow of a process can be digitally recreated and analyzed in detail. In this way, process mining reveals where processes stall, become unnecessarily complex, or deviate from the plan. This is precisely where targeted optimization begins.
Master Data: The Stable Foundation of Operational Processes
Master data is rarely a topic of conversation during coffee breaks, but it is often the reason why processes succeed or fail. It describes key business objects such as customers, materials, or products and is used in nearly every process.
Inadequate master data has an immediate impact: orders get stuck, invoices contain errors, or employees have to intervene manually. Structured master data management ensures that master data is clearly defined, properly maintained, and reliably utilized.
Data Governance: From a Necessity to a Strategic Enabler
If master data is the foundation and process mining is the magnifying glass, then data governance is the structural framework that ensures continuity. Long dismissed as a necessary cost driver, it has long since evolved into a strategic enabler for generating real value from data.
Data governance creates a clear framework for handling data—with understandable rules, clear responsibilities, and shared standards that are actually put into practice every day. It brings people, the organization, and technology together and ensures that data is trustworthy, well-maintained, and can be used effectively. This lays the groundwork for optimizations to take effect and for the full potential of data within the company to be realized.
The Master Data Ping-Pong: Order-to-Cash Under the Microscope
A good example of the interplay between process mining, master data, and data governance is the order-to-cash process. Only when these three drivers work in tandem do processes realize their full potential. The process describes the path of a customer order from the time it is placed until payment is received. The better transparency, data quality, and clear rules work together, the faster performance translates into revenue.
If the turnaround time—that is, the time between order receipt and shipment of the goods—increases, the cause is usually first sought within the process itself: too many approvals, additional checks, or unnecessary loops. However, a closer look through the lens of process mining often reveals that orders repeatedly get stuck in the same checks and approvals.
The reason for this often lies deeper in the foundation of the master data. A classic example is pricing in the customer or product master data: Sales adjusts it to close a deal, Finance later corrects it from a margin perspective, and Customer Service makes additional adjustments in the event of a complaint. Each adjustment makes sense on its own, but in the system, this results in a bit of master data ping-pong. Automated workflows respond promptly with new checks and approvals, and with each additional round, the turnaround time increases a little more.
What initially looks like a process problem thus turns out to be a master data issue. Process mining makes these interdependencies visible and shows that increasing turnaround times sometimes have less to do with the process itself than with what’s happening in the background with master data. Such patterns are not isolated incidents but occur regularly across a wide variety of processes and types of master data throughout the company.
This is where data governance comes into play—the framework of rules that enables well-intentioned adjustments within an orderly structure. Sales, Finance, or Customer Service often modify master data—such as payment terms—each for their own valid reasons. The problem: Without clear rules, conflicting values arise, workflows get stuck in loops, and a reliable, central data source is lacking.
Data governance clearly defines who is authorized to change which data, under what conditions, and how changes must be documented. In this way, it ensures that decisions are based on clear rules. It is supported by Master Data Management (MDM), which implements these rules technically: MDM ensures that master data is maintained centrally, remains consistent, and is available for all processes. Together, data governance and MDM prevent well-intentioned changes from leading to inconsistent data. Every adjustment now supports the process instead of triggering new checks. Orders flow smoothly again from entry to receipt of payment and rely on a reliable central data source—the single source of truth.
Process mining reveals that the measures are working: fewer manual interventions, more stable turnaround times, less master data back-and-forth, and improved employee morale. A one-time error analysis turns into a continuous improvement cycle, and master data, rules, and processes become true value drivers for the company.
Conclusion
Today, process optimization is no longer limited to the analysis of workflows. Sustainable improvements arise when processes, data, and clear responsibilities work together. This is precisely where the strength of the integrated approach described lies. Process mining provides a reality check: It reveals that supposed process problems often stem not from the workflow itself, but from the underlying data. After all, master data forms the foundation of many operational processes. If this foundation is shaky, the processes begin to falter. This is where data governance comes in. It establishes clear rules, responsibilities, and standards for handling data.
The interplay of these three disciplines forms the basis for a continuous improvement cycle. The order-to-cash process demonstrates that companies which consider processes, master data, and governance as an integrated whole lay the groundwork for more efficient workflows and better decisions—and ensure what is perhaps the most welcome side effect of modern data management: less chaos in the system and greater value creation from data.
Especially against the backdrop of the increasing prevalence of artificial intelligence across a wide range of business areas, the value of this foundation becomes clear: Only then can artificial intelligence fully leverage its strengths and transform process optimization from isolated measures into an intelligently controlled, sustained state.
Author Dr. Michael Böttcher
Dr. Michael Böttcher is an expert in data strategy, data governance, and data products, with more than 10 years of practical experience in this field. In his projects, he personally guides clients from a wide variety of industries in increasing their data maturity and leveraging data as a true strategic value driver.
Author Christoph Aleth
Christoph Aleth has been working at adesso since 2023 as a Senior Consultant in the Business Engineering division. There, he brings more than four years of extensive project experience and a deep passion for data-driven process optimization. His professional focus is on process mining, where he has analyzed and successfully implemented a wide variety of end-to-end processes in numerous client projects.
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