Intelligent process mining: The next stage in digital transformation

The ongoing adoption of digital transformation by almost all industries has brought the importance of data and processes into focus.

Silos of data and standalone business applications, usually built upon legacy IT architectures, which have carried businesses forward over decades, are no longer suitable in modern business environments.

Businesses today need to be much more agile to changes in their target markets whether they are focussed on consumers in a B2C model or businesses in a B2B model.

To drive operational and financial efficiencies, they need to have their data repositories integrated and accessible across their end-to-end enterprises.

They need their operational and system processes to be transparent and optimised so that they can move towards increasing automation.

Repositories of data whether static or dynamic yield information about the volatility of business and help to integrate the front, middle and back offices of the enterprise. But it is processes that are increasingly becoming the foundations of digital enterprises.

For large global enterprises with hundreds of processes, which decide the fabric and culture of their business, having a well-documented and transparent library of processes is a must, and multiple tools are available for building such libraries.

However, custodians of data digital transformation, in their quest for business excellence continue to worry about deviations in their fabric of processes and how to document such deviations and correct the processes.

Whether an organisation is a startup tech organisation or a diversified conglomerate, improvements are possible when they fully understand their processes and workflows. Increasingly the challenge is that humans cannot understand, manage, and monitor these processes well enough and fast enough, to keep pace with digital business today.

With the volatile nature of business and with workers working on multiple platforms, with data being saved in multiple frameworks, conformance and deviations in business processes are now being assessed and managed by artificial intelligence and algorithms.

Digital process trail

Mission-critical business applications like ERP, core banking and payment gateways, for example, create a data repository of event logs with date and time stamps.

An event log is a snapshot of a process that captures a defined set of data parameters, the workers involved, date and time, system configurations, and other predefined characteristics. Event logs capture the state of processes and workflows as they happen in software applications, system and network infrastructures, among others.

As processes and workflows progress every hour and every day, these event logs are generated, captured and saved to be accessed later.

It is virtually impossible for any human to manually interpret the vast repositories of event logs and generalise the efficiency of the workflow and business process that have generated those logs.

It is even more improbable that they could meaningfully suggest changes and improvements.

Data management: Automated process mining

Process mining is a modern-day tool that sits at the intersection of data management and business process management with a layer of analytics for visual representation.

It embeds organisational strategy by being laser-focused on a singular goal to reveal insights into processes and workflows captured by event logs inside information systems.

Process mining is applying data science to discover and validate workflows and processes. Process mining combines data mining and process analytics to recreate the state of an enterprise’s workflow and processes and by doing so, expose bottlenecks, congestion, logjams and deviations.

Process mining utilises event logs from business applications and information systems to build and represent the underlying workflows.

This helps to identify the root cause of deviations and whether the solution rests in process changes or resource allocations. Process mining is also the ideal approach to build the use cases for introducing robotic process automation.

Intelligent process mining applies predefined algorithms to repositories of event logs to objectively rebuild the process and workflow as it is happening in the enterprise. This representation is objective and does not rely on team managers and experts to rebuild the existing state of the process.

Types of process mining

In general, intelligent process mining can take various forms. In the discover mode, algorithms search event logs and recreate and rebuild the process that has generated the event logs. The process and workflow model that is generated is without any influence or bias towards what the process is expected to look like or should look like.

In the conformance approach, intelligent process mining uses event logs to generate a deviation pattern between an expected process and how the process behaves in real life. By demonstrating the deviations, process mining also indicates the root cause of the deviations.

Using the root cause of deviations, intelligent process mining in the enhancement approach monitors the efficacy of the steps taken to improve the process. In other words, after detecting deviations and the root cause of the deviations, process mining is used to track the success of the remediation in the process.

To summarise, intelligent process mining can vastly help build an objective approach towards process improvement, paving the way for the adoption of additional digital technologies, like blockchain and robotic process automation.

However, process mining also has challenges, such as data quality, data consolidation and enterprise complexity.

Partnering with trusted and specialised partners can help enterprises in their intelligent technology journeys and is a proactive way of moving forward.

The writer is the CEO of Omnix International.