We’re implementing process mining to analyze our procurement workflows, but integrating with our 15-year-old ERP system has been challenging. The ERP uses proprietary data formats, inconsistent timestamps, and has no native API support.
We’ve built a middleware layer to transform ERP data exports into Appian’s process mining format, but performance is poor with large datasets. Security is another concern - the ERP uses outdated authentication protocols that don’t align with modern API security standards.
Has anyone successfully integrated process mining with legacy systems? What approaches worked for data extraction, transformation, and maintaining acceptable performance while ensuring compliance with security requirements?
Great perspectives. The phased approach focusing on critical processes makes sense given our ERP replacement timeline.
Performance optimization for large datasets requires strategic thinking. We process ERP data in chunks using parallel processing. Instead of transforming everything at once, we partition by date ranges and process in parallel threads. Also, create materialized views in the staging database for frequently accessed transformations. This reduced our processing time from 8 hours to 45 minutes for monthly data loads.
Legacy ERP integration is always painful. For data extraction, we use scheduled batch jobs rather than real-time APIs. The ERP dumps data to CSV files overnight, and our ETL pipeline processes them. Not elegant, but it works. For transformation, we built mapping tables that normalize the proprietary formats into standard process mining event logs. The key is accepting that real-time analytics aren’t possible with legacy systems.