A couple of years ago, I came across the echoes of a curious internet drama.
“Learn to code” was the war cry of trolls attacking laid-off journalists from BuzzFeed and Huffington Post. The saga goes back to countless earlier articles about coding as a solution for other people who lost their jobs, and it hasn’t quite ended. But now there’s a plot twist. As one developer disappointed by the rise of codeless software services told me, “Learn to code, they said. It will pay off, they said.” His sentiment raises the question: In our digitized era, should everyone learn to code?
Covid-19 left all of us stranded at home, often with more free time than usual. Understandably, many used this time to learn something new, with the popularity of massive online courses skyrocketing. Whether through platforms like Coursera or bootcamps and other learning programs, coding turned out to be one of the key skills of choice that learners pursued.
It goes without saying that coding is a very useful skill to have, not just for the job market, but sometimes to just be able to make your life a bit easier. With even basic Python, you can write scripts that will save you time on tedious and repetitive tasks, be it generating reports or sorting through your downloads. But even though we often hear how everyone will have to learn to code in the future, the reality may in fact be a little different.
As software-as-a-service (SaaS) platforms place their bets on being “coding-free” as a major selling point, a clear trend shapes up in the high-tech sphere these days. What this often means is that you will not be required to write much code when integrating these solutions with your existing processes and workflows, or even when using them. While coding is usually still an option for more advanced users, such platforms most often grant the user various graphical interfaces and automated tools to work with.
Such solutions are often aimed at fields that traditionally require at least a certain amount of coding skills, even if coding is not necessarily at the core of the job. Solutions like CRFT, for example, allow companies to automate a major share of cybersecurity operations, such as deploying new apps and monitoring their cyber defenses. For cybersecurity people, coding is not always necessary unless they are specifically hunting for zero-day vulnerabilities in the code. Languages like Python, though, are popular in the field precisely for automating routine operations.
In data, too, coding-free is rapidly becoming a prominent selling point. Some companies grant businesses a chance to build up their data flows without having to write custom scripts to collect and process data. The idea is to make tidy data immediately accessible without having to burn through massive amounts of time and money on a data-engineering operation. Data scientists have to spend a lot of time pulling and cleaning the data, which usually involves having to send out several SQL queries before wrangling through the results until you can properly read them into the tool you are using for the analysis.
This does not have to stop with simply pulling the data in, as the most common next step for enterprises — using the data to train AI models — can also be performed without coding these days. Traditionally, this process takes some time and involves modifying the dataset to engineer new features as well as training different models, but no-coding solutions simplify it in many ways. While some companies specialize in niche machine-learning fields such as natural language processing, others offer broader solutions.
The above list only scratches the surface. Companies can do many more things without coding these days, from building websites and web applications to developing chatbots. As the no-code trend picks up the pace, it makes sense to expect some changes in how businesses are hiring and what skills they value.
Both businesses and job seekers must recognize a key takeaway here: Such platforms allow companies to put more value into deep expert knowledge. Companies can focus on retaining people with the strongest skills in statistics, design, marketing or other highly-sought fields without having to worry too much about whether the applicant has the coding skills to back those up. This in itself dents the learn-to-code argument, as failure to do so will remove obstacles for someone with strong expertise in an in-demand field.
At the same time, the value of specialists with an in-depth IT skillset is unlikely to take a significant hit. A custom-built website does not need to limit its design to the list of features determined by vendors. A tailored AI solution, and one built with an in-depth understanding of why this specific machine-learning method is ideal for this specific dataset, will get you further than an off-the-shelf solution.
The key audience for the coding-free solutions are thus not necessarily the tech giants like Google, which are more than capable of handling their own innovation, but smaller businesses and legacy enterprises looking to short-cut their way into the digital era while keeping their business focus in place. And as they do, the rise of the coding-free platforms seems to make things a bit more egalitarian for the workforce: Not everyone will have to learn to code, after all.
This content was originally published here.