Unlike a vintage wine, technology doesn’t get better with age. When considering adopting any emerging technology, industry leaders have to make an objective evaluation of the potential cost vs. benefit. The graph below shows a typical hype cycle, a predictive tool used to assess risk:
The cycle above is instructive for managing expectations and charting the ups and downs of a technology over time. Along the red line toward productivity, however, there are a number of hidden traps. Let’s explore two common scenarios:
- Buoyed by its own hype, a technology reaches maximum visibility at the peak of inflated expectations. But then the technology starts to falter during development or shortly after release. The bad news spreads and adoption levels wane. We’ve now entered the trough of disillusionment. With the product now buckling under the pressure of dwindling returns, confidence evaporates. Finally, investors pull out as the producers of the technology fail to deliver improvements. And because the technology is now holding business back, IT teams are told to replace. The result? An unwanted legacy system.
- A technology achieves mainstream success near the slope of enlightenment and then plateaus. For the time being, the system remains fit-for-purpose and no one complains. But this early satisfaction quickly turns to complacency. Management decides to reduce expenditure, but without budgeting for essential future development. This is a big mistake. What happens next? Cost, complexity and risk all increase the longer things slide. IT maintenance costs alone can trump original budgeting in just 5 years. The upshot is all the effort goes into patching the failing legacy application, meaning there’s no appetite for any upgrades to architecture or technology.
Legacy systems require continuous modernisation. If this key principle is not upheld, costs and security issues all multiply over time. Remember that the advantages of updating ageing technology far outweigh the risks of ignoring the problem. The best way to keep technologies fit and agile is to invest in data innovation over the long term. This development approach requires meticulous upfront planning as well as tailored budgeting. Of course, each strategy will be different depending on the friction points that need to be resolved. You’ll also have to factor in connected issues such as getting buy-in from all personnel (see Skills Shortage). Here are two popular strategies for tackling mounting issues with technical debt, architecture and technology:
- Sprint-release strategy
- This is without doubt the fastest and safest option. Because each legacy issue is handled in isolation, teams don’t get distracted by new features or requirements. And with each release undergoing test automation, the risk of new issues cropping up is mitigated. Also, no functional changes are made, leaving the component to behave reliably in the same way every time. Now one might argue dedicating a whole sprint-release cycle to technology upgrades might delay the delivery of new functionalities. But without these upgrades upfront, implementing new features becomes unsustainable.
- Effort-defined sprint-release strategy
- The benefits of this strategy are contingent on the iteration schedule and total assigned effort. Let’s imagine a release with a projected scope of 100 person-days. If a team operates at 10% capacity, it’s impossible to deliver on something like a Java 1.4 platform upgrade or an old C++ library replacement. The work involved has to reflect resources, but also the level of legacy migration required.
The good news is that the barrier to digital transformation can be overcome. The informed approach to modernisation starts with a full Legacy Systems Assessment.
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