Why mainframe migration

Re-factor from legacy code to a modern programming language, using semi automated tooling to mitigate risk relating to legacy skills, increase agility and reduce costs.

Re-imagine the business: Rewrite the application based on newly developed requirements. The resulting application goes beyond current capabilities, allowing for technology modernization as well as the modernization of what were likely outdated business processes. Related posts.

A Cloud Transformation at Guardian Life November 8, Accenture explains how a life insurer improved its capabilities in areas most critical to business growth using cloud transformation. Subscription Center. Stay in the know with our newsletter Stay in the know with our newsletter. View Transcript. This value is not valid This value is not valid This email address is already in use. Field Empty Input text here. Send E-mail. There is already a separate, active Accenture Careers account with the same email address as your LinkedIn account email address.

Please try logging in with your registered email address and password. You might wonder what mainframes have to do with CO2 emissions standards. Well, European regulation requires car manufacturers to calculate the CO2 emission of each car they make, and companies might be subject to huge fines when they go over their assigned quota. European car manufacturers found that the expected number of such calculations per second from their various brands, dealerships and partners will be over tenfold the maximum capacity of their production mainframe that runs these calculations.

They found that if they could reduce mainframe queries, they would not need to grow their mainframe footprint, and in parallel be able to deploy modern services for their customers. They save millions of dollars, provide millisecond response time and have a modern data platform that can migrate them to the cloud.

And the higher the transaction volume, the more expensive it gets. One example is a top-3 US and multinational bank running up to 1 billion transactions a day supporting over 35 million daily customers found out that if they reduced Mainframe queries, they could reduce MIPS related license fees while seamlessly scaling their deployment to service their rapidly growing digital service loads. Along with that, Financial Services organizations are struggling to monetize the Open Banking API for new applications and better customer experience.

States such as Connecticut and New-Jersey learned this the hard way when they faced challenges processing hundreds of thousands of unexpected unemployment claims from people who lost their jobs due to the pandemic.

If you are a mainframe user, there would be major nervousness at undertaking a comprehensive lift and shift. A CIO using mainframe would argue certain workloads are better suited to each system. Perhaps Y2K? Mainframe migration should start with looking at what you currently have, understanding the relationships and inter-dependencies. The identification of the right migration strategy is crucial for the success of modernization.

A strategy begins with discovery. What do you have? What is your outlook? Do you have a target destination? After these assessments have been made, start developing the stepping stones to get you to the place you want to go — while mapping it, implementing, and deploying it.

A method to think about this is to break it down:. Influences discovery: detailed information on all elements that provide each component and function. Bear in mind that everything on a mainframe has an equivalency elsewhere. If your migration strategy is correctly planned, anything can be migrated. Moving off mainframe requires deep and accurate understanding. This article began with the concerted attack on mainframe environments from cloud service providers. Core Modernization.

Corporate Banking. Customer Experience. Customer Experience Journey. Customer Experience Strategy. Customer Intelligence. Cyber Security. Cloud Computing. Debt collection process. Digital Identities. Dark Data.

Data Aggregation. Data Analytics. Data Ecosystem. Data Efficient Learning. Data Ethics. Data Governance. Data Hygiene. Data Ingestion. Data Lake. Data Migration. Data Mining. Data Platform. Data Privacy. Data Science. Data Transformation. Deep Learning. Deep Neural Networks. Design Thinking. Digital Banking. Digital Contact Center. Digital Engineering. Digital Experience Platform. Digital Platform. Digital Product Development. Digital Transformation.

Digital Wealth Management. Edge Computing. Embedded Engineering. Energy Data Analytics. Energy Management. Enterprise Application Services. Enterprise Information Management.

Experience Design. Experience Transformation. Facial Recognition. Field Force Management. Fraud Detection. Game Theory. Genetic Programming. Gesture Recognition.