Overcoming data connectivity challenges with process mining

Process mining can help organizations overcome data connectivity challenges in business processes, according to industry experts from Sanofi, Watson Buys, process.science and Software AG.

Speaking to PEX Network for our Leveraging process mining to overcome data connectivity challenges report, these experts explained why data connectivity can cause business process challenges, how process mining can offer solutions to such challenges and how technologies such as artificial intelligence (AI) and machine learning (ML) further enhance process mining capabilities.

In this article we summerise key lessons learned in this recently launched industry report.

Accessibility challenges

For many businesses, assessing how processes are being performed in practice can be particularly challenging when they are not connected to enterprise information systems. They need to be supported by metrics and statistics hosted in business information systems for businesses to have visibility over how they are performed.

According to Christian Müller, head of process intelligence at Sanofi, this lack of connectivity offers a significant challenge for businesses looking to measure process performance.

“When it comes to process improvement, the goal is to improve the performance of something which can be measured,” he says. “Without connectivity, you do not have the necessary data, you are limited in analyzing and you cannot measure the process performance and improvements.

“If there is no awareness of the problem [you are seeking to solve] or the opportunity [you want to capitalize on], then you cannot determine the root causes or the solution,” he adds. 

This lack of visibility over what is really happening in business processes limits organizations’ ability to implement process improvements as there is no accurate way to determine where they are necessary.

According to Müller, the problem of limited data connectivity can be a difficult one to solve, as it can seem like businesses require data connectivity, in order to generate the level of visibility needed to eliminate connectivity issues.

“It is a little like the chicken and egg problem we always face when we look into a process and the data is not there,” Müller notes. “There is no system connectivity, many processes still involve copying and pasting in Excel, and they are managed in team chats between staff.” 

Related content: Why process mining is the starting point for transformation

Process mining as the solution

Process mining does offer a solution to the problems Müller exposes, as it is primarily used to collect, store, analyze and link process data. As such, process mining can be used as a tool to build out data connectivity gaps by identifying and eliminating the visibility issues that characterize them.

Müller explains how process mining does this. “Process mining can show a process from the system’s perspective [based on the end results of the process] and compare it with the process as it is [performed in reality based on performance metrics],” he says. “From here we can identify the blind spots and point out the parts of the processes where we are lacking data or connectivity [with the information systems] and then we can assess how to catch up, generate data and eliminate those blind spots.”

Once businesses begin eliminating blind spots, the corresponding improvements in process visibility make it easier to continue identifying further blind spots. In this manner the process of building out data connectivity becomes somewhat exponential and allows businesses to push on further.

To find out what our expert contributors have to add, check out Leveraging process mining to overcome data connectivity challenges, and do let us know in the comments below if your business has enjoyed any of the benefits of process mining.