Legacy technology is a compatibility issue. The solution, continuous modernization, keeps aging tech stacks integrated with more innovative options.
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The definition of legacy is changing.
Today, legacy technology is less about the infrastructure in place and, instead, a philosophy on how technology can impede the business. It’s not just mainframes and databases, but anything that shuts down business agility, efficiency and progress.
“Legacy technology is any technology that makes it difficult for organizations to change their application systems to support changing business requirements,” Anne Thomas, distinguished research VP at Gartner, said in an email to CIO Dive. “And, therefore, it impedes business agility.”
After divorcing the definition of legacy from just the age of the technology, companies can then rethink long-term strategies and create a plan for applications as they sunset. Modernization becomes a continuous process, constantly evaluating the out-of-date tech that needs to go.
Companies deal with legacy debt that talent can no longer maintain and customized applications that are difficult to update as they reach end-of-life, according to Thomas. “These situations are the traditional heavy-duty modernization challenges.”
Six in ten organizations dealing with legacy technology are pushed to modernize to improve efficiency and security, according to a Morning Consult survey of more than 500 government IT decision-makers on behalf of IBM. Three-quarters (78%) found migrating away from legacy systems somewhat or very challenging. 
Legacy technology, in the broadest sense, is a compatibility issue. If an old tech stack can’t integrate with more innovative options, it’s considered outdated.
“What makes something legacy is typically when a new technology is introduced and becomes the industry standard,” making other technology incompatible, said Cyndi Tackett, VP of product marketing at Flexera.
A vendor can sunset a version or technology when it decides to no longer offer support, Tackett said. Without support it automatically becomes legacy, which is what happened when Microsoft discontinued Skype for Business in favor of focusing on Microsoft Teams.
“They’re making something legacy, so that they can invest in something that’s more strategic for them,” Tackett said.
The last 18 months of the pandemic and the shift to remote work accelerated modernization for many companies, leaving legacy debt to pile up. Nearly one-third (31%) of organizations plan to increase transformation spending due to COVID-19 through 2021, according to a Capgemini survey.
Technology which does not support new ways of working, such as tower computers with their implicit lack of portability, causes efficiency issues which can be addressed,” Alastair Pooley, CIO at Snow Software, said in an email.
While the need to transform quickly may have solved some of the legacy conundrums, “businesses are now facing a new set of challenges,” Pooley said. “As ever in tech, progress brings ever more challenges.”
While it’s hard to maintain the discipline to regularly revisit working systems, the benefit of ever marginal gains is often a hallmark of successful organizations,” Pooley said.
Legacy technology will never go away, it will only change shape, according to Tackett. Companies will have to “ruthlessly prioritize” how modernization takes place.
“Prioritization should primarily be based on making sure that your customer-facing or revenue-generating services are modern to provide the best customer experience,” Tackett said.
For many, improving customer experience is the main motivator behind modernizing legacy systems. More than half (58%) of 1,420 IT decision-makers surveyed said improved CX drove legacy application modernization, according to a Rackspace survey.
But modernization is more complex than just adopting newer technologies.
While the obvious solution to some of the database legacy problems may seem to be the cloud, it’s not always the right option, Emma McGrattan, SVP of engineering at Actian, told CIO Dive in an email. The cost for the cloud infrastructure to support the workload and transaction volume could be too costly.
“If we think about the systems that power many of the world’s biggest businesses, such as banks, airlines, retail and government, the underlying technology may date back 30 years or more,” McGrattan said. The systems are usually built on database technologies supporting several layers of infrastructure around them.
Business can dissect the data and application data and application infrastructure and move the pieces and parts that are portable, but this introduces latency and data governance concerns that might negate any benefits gained from that migration, McGrattan said.
Instead, organizations are better off approaching legacy technology with the right strategy and leadership to continuously tackle it before debt becomes unmanageable.
To overcome legacy technology’s impediments to agility, Thomas recommends companies: 
Track the debt and execute continuous and iterative modernization.
Avoid creating unnecessary debt.
Track and address modernization in regular application maintenance. 
Plan regular upgrades to avoid end-of-life. 
Develop an assessment and decision model to proactively schedule modernization.
“Organizations that are doing agile, DevOps, and continuous delivery generally avoid creating new legacy software because they are always refactoring and maintaining the code base and always delivering new features in support of changing business requirements,” Thomas said. 
Even with continuous delivery in motion, companies can still wind up with legacy systems caused by technical debt accrued based on more recent tech infrastructure going out of style, according to Thomas.
“For example, a once-popular open-source framework can be replaced by another and quickly reach end-of-life, or a formerly-popular application server technology isn’t compatible with modern application architectures or platforms,” Thomas said.
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With $11 million on average to spend on data-driven initiatives, companies want to rely on AI and ML to enhance internal and external processes.
To compete in their industries, businesses rely on a growing set of digital tools that bring workers together, making the whole greater than the sum of its parts. But the balance is delicate. 
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With $11 million on average to spend on data-driven initiatives, companies want to rely on AI and ML to enhance internal and external processes.
To compete in their industries, businesses rely on a growing set of digital tools that bring workers together, making the whole greater than the sum of its parts. But the balance is delicate. 
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