Data is invaluable to an organization, but it can also represent a major stumbling block if an enterprise hasn’t optimized how data is used to support processes that run operations. Process mining can play a role in helping organizations get an objective and detailed view of process flows – including fixing bottlenecks and delays that cost time and money. Let’s examine the importance of process mining, and how this activity can be aided by the right underlying data layer to support the level of granular and iterative analysis that process mining requires.  

Why Process Mining Is Needed

Every business process consists of a series of steps and tasks that need to be carried out – either by humans or technology – in order to deliver a desired outcome. While simple processes like receiving

and reconciling payments for goods sold are fairly straightforward, organizations can struggle with the more complex processes that define most enterprise operations today. This might include conducting complex portfolio analysis, transaction monitoring at a global bank, or managing a multi-tier supply chain to support a manufacturing operation. 

Gaining insight can be difficult under these conditions, requiring a patchwork of perspectives from multiple individuals and piecing together data insights across multiple IT systems. Unfortunately, this process discovery is a process in and of itself that has traditionally broken down amid the size and complexity of most enterprise operations. Many companies continue to struggle with an overreliance on manual analysis that can be time-consuming and error-prone – especially when it’s happening at scale in the production environment.  

Process mining was established as an objective way to discover, monitor, and contextualize how data is used to drive a particular business activity or function. By combining data from sources across an organization, process mining generates insights that provide a more complete and accurate picture of how processes are actually being executed. These insights can help teams perform end-to-end process adjustments and uncover automation potential to improve process design and execution.

Building the Right Data Foundation for Process Mining

Process mining has the potential to enhance clarity, control, and efficiency in leveraging data across various enterprise functions. The caveat is that results will be severely limited if the process mining methodology itself is not operating atop the right underlying data architecture. The reason for this is that process mining is a simple term for an exceedingly complex set of activities that play out at the most granular level of an organization’s data architecture. If data silos and misaligned standards are present at this level, time and money can be wasted on data ingestion from multiple sources, with excessive staging, cleansing, batch processing, and other curation tasks.

These issues are magnified by the fact that process mining requires repeated adjustments and performance analysis – continuous improvement loops that typically overwhelm personnel and systems that aren’t optimized for the detailed and iterative nature of process mining. Fortunately, agile data management architectures that can support process mining exist in the form of several options that now include data lakes, data mesh, and data fabric architectures.  

All these approaches are designed to break down silos and better connect data, but of the three, data fabric architectures provide the most visibility and control. This is due to an abstraction layer that creates a unified view of all data – regardless of where the data is physically stored – so there’s no longer the need to physically migrate, reformat, and configure data across multiple systems. By eliminating ETL and connecting all data where it resides, data fabric creates the ideal staging ground for process mining.  

Reaping the Benefits of Process Mining

When implemented correctly, process mining capabilities grounded in an agile foundation like data fabric will accelerate insights into true operational performance, give a complete view of customer and employee journeys, drive enterprise modernization through better use of automation, and bolster compliance and fraud detection through continuous process monitoring. Compliance is also enhanced by flagging any areas of non-conformance in a process by comparing the actual process behavior with a target model. 

The best process mining approaches will include machine learning algorithms, feature KPIs and dashboards to conduct root cause analysis, monitor ongoing performance, and ultimately enhance the process model. Throughout, process mining accelerates the speed of business by quickening cycle times, including the whole lifecycle of processing time, idle time, transport time, and wait time required to complete a process, process variant, business case, or activity. 

These benefits can drive powerful use cases. As just one example, a large retail grocery network can use process mining on a data fabric foundation to optimize supply chain across hundreds of stores – identifying variances at certain locations and pinpointing their causes. As this example illustrates, effective process mining solutions are technology agnostic – designed to operate across multi-vendor environments of third-party suppliers and transportation services, as well as delve into the inventory management and staffing processes within a particular store. 

Conclusion

Processes and data are at the core of every organization. Process mining allows IT teams to put data to work strategically to achieve a high level of transparency in these processes and move quickly from insight to action. Data fabric is the ideal foundation for this, allowing a level of broad access and granular control of all data to create business processes that are more transparent and efficient – ultimately enabling enhanced decision-making and improved operational performance.