The Intersection Of Process Mining, Automation & Data Integrity

SSON’s Process Mining & BPM Virtual Summit returned for another year and once again delivered great insights to our members. Over the two days, our 2024 thought leaders exhibited the sheer quality of initiatives taking place across the industry. From seasoned veterans within the process mining space to those just starting their journey, the summit exposed all the current process optimization trends and challenges within the shared services space.

The final session of day one was a thought-provoking panel discussion between Andrew Hayden, Senior Product Marketing Manager at Precisely, and Rajesh Rao, the Head of Automation CoE at Nokia. The two discussed the key challenge of maintaining data quality for process mining initiatives.

Common Pitfalls Within Process Mining & Automation

One common mistake is trying to tackle too much at once. Large-scale projects can be overwhelming and often lead to failure as they are difficult to manage. Instead, by identifying and addressing the most significant pain points, organizations can achieve quick wins that build momentum and confidence.

While IT departments play a crucial role, relying solely on IT can be another significant pitfall. Business teams, who are the primary users and owners of the processes and data, should also be deeply involved in these projects. These teams have a better understanding of the day-to-day operations, nuances, and specific requirements of their processes.

Criteria for Process Automation

When deciding which processes to automate, organizations must evaluate both the technical feasibility and the potential business impact.

Organizations should assess whether the current technology and infrastructure can support the automation of a specific process. This includes examining the complexity of the process, the availability and quality of data, and the integration capabilities of existing systems.

The business impact refers to the tangible benefits that automation will bring, such as cost savings, efficiency gains, error reduction, and improved customer satisfaction. A comprehensive feasibility study and impact analysis help in prioritizing processes that not only can be automated but will also deliver significant value.

Maintain Strong Data Quality

One of the most effective ways to maintain strong data quality is to eliminate manual data entry processes. Manual data entry is highly prone to human errors, such as typos, transcription mistakes, and inconsistencies. Automation tools, such as robotic process automation (RPA) and data integration platforms, can capture and input data accurately and consistently, ensuring a higher level of data integrity.

Organizations should also standardize their data entry, handling, and processing methods to ensure that everyone follows the same procedures. This involves creating and enforcing strong data governance, and data standards that dictate how data should be collected, stored, and used across the organization.

These are just three of the key takeaways from Andrew and Rajesh’s collective expertise. To listen to the panel in its entirety, you can view the session on-demand above.