Critics have predicted the death of the mainframe for more than 30 years. But, in spite of these dismal diagnoses, mainframe technology has proven that it has staying power, and isn’t going anywhere fast.

June 30, 2017

3 Min Read

By Pino Vallejo

Critics have predicted the death of the mainframe for more than 30 years. You can’t teach an old dog new tricks, as they say. But, in spite of these dismal diagnoses, mainframe technology has proven that it has staying power, and isn’t going anywhere fast. 

Even in a world dominated by software as a service (SaaS) apps and the dizzying amount of internet-connected devices out there, mainframes hold their own. Today, the majority of global Fortune 500 companies still rely on the technology to power everything from ATM transactions to airline reservations. IBM’s recent decision to equip its z Systems mainframe with new tricks – specifically, machine learning capabilities – is the latest move to cement the mainframe’s reputation as anything but an old dog. 

Doubling down on mainframe dependence

IBM’s objective is simple: to give mainframe users access to the same benefits machine learning provides organizations in the cloud. Using elements from the IBM Watson platform, z System users will be able to automate the creation, testing and implementation of analytical models regardless of programming language or data type – without the cost of migrating data off-premise. 

The implications of this announcement will reverberate beyond IBM’s product ecosystem, as it will affect business users, IT hiring strategies and channel firms alike. Here are three specific impacts to watch out for:

The mainframe becomes “stickier”. The more organizations can tap into advanced computing techniques on the mainframe, the less likely they are to migrate away from these trusted systems. IBM’s announcement aside, some of the world’s top banks, insurance companies, retailers and healthcare organizations still run mission critical workloads on the mainframe. 

These companies also have troves of data that can be transformed into predictive analytics and other business intelligence. As a result, bringing machine learning to the mainframe helps build an even stronger business case for why IT departments should stick with their legacy systems.

The time is now to rethink mainframe talent strategies. With Baby Boomers exiting the workforce at an increasing rate, many organizations are losing their top mainframe experts – not only in terms of technical skills, but also tribal knowledge and best practices. As the mainframe perseveres, employers face a growing challenge: recruiting and holding onto new mainframe talent. 

The U.S. is projected to create upwards of 30,000 new mainframe jobs by 2020, underscoring the need for organizations to shore up their succession strategies. From internal training initiatives to mentorship programs, there is an array of options for employers hoping to attract and develop the next generation of mainframe professionals. Ultimately, vendor plans to modernize the mainframe (e.g., swapping COBOL for Python or Scala) may help entice Millennial and Gen Z candidates who want to work with cutting-edge concepts like machine learning and artificial intelligence.

Channel firms need to keep up. The mainframe’s longevity will affect managed service providers’ and value-added resellers’ businesses for years to come. Channel firms with clients or prospects in mainframe-dependent industries must ensure infrastructure, service offerings, a sustainable “Next Gen” succession plan and support solutions to adapt as well. 

With IBM expected to introduce its next generation of the z System sooner rather than later, the channel can’t afford not to stay on top of this hardware evolution, and invest accordingly. Now is also an apt time for firms to reassess their approach to client engagement. IBM’s machine learning announcement was borne out of customers’ business (not simply technical) needs, a perspective the channel should embrace when positioning their own services. 

It would be inaccurate to frame IBM’s machine learning move as part of the mainframe’s “rebirth” – it never really “died.” Rather, the mainframe is adapting and evolving to provide technical solutions for today’s business issues, finding its footing in a world where business and IT leaders demand faster, better and cheaper from all of the technologies they deploy. 

Survival in the IT industry – for employees, products and organizations – hinges on an ability to change. The mainframe’s evolution is a blueprint for getting it right.

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