Messy Data Hurts Business AI — New Study Reveals Fixes

Think of process mining as an X-ray for your company. Every time an order ships, a ticket closes, or a customer calls, software leaves behind a digital breadcrumb. Event logs are the collection of those breadcrumbs. Process mining studies them to reveal where work really happens — and where it gets stuck. But there's a catch: if the breadcrumbs are scattered or misleading, the X-ray gets blurry.

This new study, a systematic review by researchers Ghita El Alaoui Talibi, Oleksandr Kosenkov, and Anastasija Nikiforova, looked at that exact problem. The researchers combed through existing academic literature and found 29 distinct challenges that arise when companies try to build event logs. They then organized those challenges into five broad categories of requirements — think of them as 'checklists' for doing it right. Some solutions already exist, and they come from two fields: data engineering (cleaning and organizing data) and domain knowledge engineering (bringing in experts who actually understand the business process).

Why should you care? Because process mining is becoming a core tool for the digital economy. Banks use it to speed up loan approvals. Hospitals use it to shorten patient wait times. E-commerce companies use it to find why deliveries are late. If the data feeding these systems is poor, you get confident-sounding but wrong conclusions — leading to wasted money and frustrated customers. The study's insight is simple but powerful: get the logs right from the start, and everything downstream improves.

In short, this isn't just an academic exercise. It's a practical guide for anyone working with business data: prepare carefully, involve the people who know the process, and you'll get results you can actually trust.