Data is certainly the fuel for AI. Yet there is a source of valuable data that usually does not garner much attention. It is from mainframe systems. They hold enormous amounts of data—which go back decades—for mission critical operations.
But then again, there are difficulties working with mainframes and AI. “The biggest challenge is the lack of compatibility of emerging technologies,” said Chida Sadayappan, who is the Cloud AI/ML Offering Leader at Deloitte Consulting LLP.
But the benefits of AI are too important to ignore. So what can be done? Well, one strategy is for leveraging cloud platforms outside of the mainframe environment.
“New approaches to cloud migration replace the traditional ETL (extract, transform, load) approach with a more modern ELT (extract, load, transform) approach that moves mainframe formatted data directly to any object storage target before using the target platform to transform it for use in AI applications,” said Gil Peleg, who is the CEO of Model9. “This pioneering method adds mainframe data to data lakes quickly, easily, and securely so leaders can maximize the ROI of their cloud BI and analytics applications.”
But like any IT effort, there needs to be a clear-cut plan and the goals must be achievable. The reality is that AI efforts can take considerable time to generate ROI.
“Companies should be aware that these types of initiatives don’t always cut costs,” said Sudhir Kesavan, who is the Global Head of Cloud Transformation at Wipro FullStride Cloud Services. “They may have the opposite effect, so having them be business-led can help overcome the challenge of business benefits seeming less tangible to start.”
IBM Mainframes and AI
The capabilities of mainframes have been evolving quickly. For example, IBM has been retooling its Z system for AI and this has involved the integration with many common open source platforms like Spark, PyTorch, Keras, and TensorFlow.
“We are enabling our clients to embed AI into their mission critical enterprise workloads and core business processes with minimal application changes and giving them the ability to score every transaction while meeting even the most stringent SLAs (Service Level Agreements),” said Elpida Tzortzatos, who is an IBM Fellow and the Chief Technology Officer of z/OS.
By generating the AI insights on Z, this allows for real-time responses at the point of interaction, which can be critical for applications like fraud detection. There is also a major security benefit because sensitive data is not moved.
Leveraging AI For Mainframe Environments
The power of AI for mainframes does not have to be about creating projects. For example, there are emerging AIOps tools that help automate the systems. Some of the benefits include improved performance and availability, increased support speed for application releases and the DevOps process, and the proactive identification of issues. Such benefits can be essential since it is increasingly more difficult to attract qualified IT professionals.
According to a recent survey from Forrester and BMC, about 81% of the respondents indicated that they rely partially on manual processes when dealing with slowdowns and 75% said they use manual labor for diagnosing multisystem incidents. In other words, there is much room for improvement—and AI can be a major driver for this.
“Mainframe decision makers are becoming more aware than ever that the traditional way of handling mainframe operations will soon fall by the wayside,” said John McKenny, who is the Senior Vice President and General Manager of Intelligent Z Optimization and Transformation at BMC. “The demand for newer, faster digital services has placed increased pressure on data centers to keep up as new applications come online, the volume of data handled continually increases, and workloads become increasingly unpredictable. In today’s fast-paced digital economy, this creates a perfect storm of higher customer expectations, faster implementation of an increasing number of digital services, and a more tightly connected mainframe supported by a less-experienced workforce.”
Tom (@ttaulli) is an advisor/board member to startups and the author of Artificial Intelligence Basics: A Non-Technical Introduction, The Robotic Process Automation Handbook: A Guide to Implementing RPA Systems and Implementing AI Systems: Transform Your Business in 6 Steps. He also has developed various online courses, such as for the COBOL and Python programming languages.