The mainframe market has changed due to recent advances in automated tools for migrating legacy applications to the cloud. Many companies that rely on mainframes are now migrating to modern cloud-based platforms to save relevance and ultimately costs.
Firtech COO Fanie Botha | Image provided
But one of the biggest challenges when migrating from any mainframe is moving transactional and master data. These large-scale migrations typically take more than 18 months, and given the scale and complexity of these projects, businesses have been slow to adopt these automated migration tools.
Data migration always looks easy on paper, but the reality is that 1:1 mappings end up being 1:n:1 mappings, with exceptions to the rules. Reasons behind this include that in today’s enterprise systems, the model information objects are very different from what mainframe systems were designed for.
The reality is that many data migrations marketed as ‘automated’ end up being performed by teams of human data capture and developers. You spend more time programming for differences in the data than for fields that map 1:1.
Imagine if a team of human information seekers could be replaced by human robots. First, it can be trained on the same principles and nuances that require human use in data migration programs.
Digital workers are the solution.
A data migration robot uses front-end GUIs to extract data from the mainframe and automatically migrate and map the data to a new system. This avoids any risks where business rules are written by back-end scripts built into the system’s GUI or terminal.
More importantly, it ensures that exceptions are caught, fixed and re-caught before any data inconsistencies occur in the new system. A single robot works up to 24 times faster than a human and needs no rest or sleep. Effortless, error-free data transfer in hours, not days or months.
Robotics as a Service (RaaS) offers business leaders better access to data captured in these legacy systems. Mainframe data containing years of business transactions can now be used to feed analytics or machine learning that can provide a competitive advantage.
Using the many protocols and interfaces available on cloud services, you can open core workflows and data within the mainframe. Companies can now access mainframe data in real time, allowing RaaS to move away from rigid monoliths and eliminate outdated interfaces and protocols.
Cloud is the future, providing advanced analytics, AI, machine learning and access to data lakes. It also offers horizontal scaling with virtually unlimited potential to increase flexibility and elasticity.
