As organizations strive to implement a modern architecture, moving off legacy mainframes is often a primary objective. Mainframes present a particularly difficult modernization problem. Indeed, they are often a “black box” which data enters, is acted upon, and exits--critical to business operation, yet completely mysterious to most of today’s IT staff. For this reason, they can pose serious risks to business operation. Upkeep can also be an immense drain on resources.
When legacy infrastructure has critical data and business logic locked inside of it, organizational leadership must address the problem. Most organizations do this by moving the data--often archiving it in a data lake or other repository for use when needed--and sacrificing the complex logic inside the mainframe which acts upon the data. Yet that logic is essential to business processes, so organizations are obligated to reengineer it in costly and time-consuming ways. Sadly, these ways rarely produce a completely satisfactory result, leaving organizations with an incomplete, hypothetical picture of what that logic was. This incomplete picture creates gaps in their business, and a formidable obstacle to ever achieving true system modernization.
Dataacs has developed an approach that can extract both the data and the logic from legacy mainframes. Their approach is faster, more efficient, and less costly than traditional approaches. More importantly, it results in accessible data as well as an extremely accurate understanding of the mainframe logic, accomplished with minimal disruption to daily business. Once armed with this knowledge, organizations are free to achieve a decoupled architecture, fully integrated with all their data sources, without any loss of integrity or compromise in business processes.