With governments across the globe wrestling to keep inflation under control and grumbles of recession growing louder, many businesses are set to face a period of considerable economic pressure.
To help weather the turbulence, businesses are scrambling to find ways to cut back on costs. And, naturally, as a large contributor to expenditure, technology budget is among the items under review.
With this in mind, TechRadar Pro spoke to CTOs from a broad range of industries, who highlighted the best areas of the technology stack to target for savings. In this edition, we focus on reining in the cost of development and data infrastructure.
Embrace low code
High-quality developers come at a premium these days, with businesses in their thousands rushing to digitize their operations and a widespread skills shortage driving up rates.
According to Richard Farrell, CTO at customer experience company Netcall, savvy companies can therefore realize significant savings on the cost of development by leaning into no-code and low-code.
“To overcome the developer shortage, businesses need to think outside traditional developer roles and recruitment. Development with low-code application platforms requires lower skill levels than traditional programming and is suitable for a wider range of lower-cost workers,” he explained.
“Organizations should consider opportunities to utilize the people they already have more effectively, by equipping talent with new digital skills. They should also augment the IT workforce by empowering business technologists with low-code, automation, analytics, data science and machine learning platforms and tools. This fosters collaboration across teams, drives impact and reduces time to value.”
“Those that begin expanding such capabilities now, through low-code adoption, are going to be given a head start – positioning themselves for long-term success in spite of the ongoing dearth of developers.”
Look to platforms with wide support
Another way businesses can strike a healthy balance between cost and performance is by making sensible decisions about the new technologies they adopt, building into the calculation any indirect expenses they might incur.
Andrey Korchak, CTO and co-founder of payments firm Monite, points to the need to ensure deployments are built atop widely used languages and to exercise caution when embracing emerging technologies.
“For all СTOs, especially when you are working in a startup, there is always the dilemma of how to provide the best tools for the IT infrastructure to work, but at the same time justify these costs,” he told TechRadar Pro, before offering up a series of tips:
- Use popular technology: Elixir may be a good programming language, but not many engineers use it - you'll spend a fortune to hire them
- Be conservative with new features: EdgeDB might be a great database, but it's new, and it might have unknown problems that can destroy your data
- Automate everything: Instead of hiring release engineers, it's worth creating deployment automation pipelines. In this case, rather than hiring dozens of manual QA engineers, you can bring in two QA automation engineers and have them cover your code with reproducible automated tests.
Think long-term
Although some transformations may entail a high upfront cost, the long-term cost saving potential of these projects should not be overlooked.
Dael Williamson, EMEA CTO (Field Advisory & Engineering) at data lakehouse company Databricks, says this is a common mistake when selecting data architectures. Although cheaper in the short term, less advanced technologies can bleed cost over time.
“For CIOs under pressure to reduce spending, data architectures are one key area they should be looking at. Legacy architectures – like data lakes and data warehouses – are cumbersome to operate, leading to information silos, and inaccurate, duplicated datasets. Ultimately, this can impact businesses’ bottom lines,” he noted.
“Yes, migrating to a modern data architecture, such as a data lakehouse, comes with an obvious initial cost. But it’s an investment in the future. Lakehouses, for instance, offer tailwinds from increased spending pressure - they’re easier to operate, saving crucial time, and are also open platforms, freeing organizations up from vendor lock-in. Lakehouses also greatly simplify the skills needed by data teams, as they rationalise their data architecture. The costs of migrating will quickly outweigh the cost of working with inaccurate data, or the time spent navigating a clunky and outdated system.”
“For some, making this jump might feel like a leap amid the recession. They may feel inclined to build on the architectures they already have. But that would be like building a skyscraper on top of a shack. You might get a few storeys in, but the higher it gets, the more problems emerge, and with mounting costs to boot.”
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