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TDWI Upside - Where Data Means Business

Low-Code Versus No-Code: The Differences and Why They Matter

We look at the rising popularity of low-code and no-code tools, including their benefits and limitations.

In a recent TDWI article I wrote about priorities for implementing a successful AI program. One of the top areas I encouraged IT leaders to focus on was no- and low-code offerings. The reason is simple: both enable users to streamline software development tasks. When we streamline processes, our ability to do more work -- and more important, do more meaningful work -- increases. Better results delivered faster propel a business forward. No- and low-code functionality empowers such results.

For Further Reading:

Five Ways No-code Will Make Your Data Engineering Career Skyrocket

How Developers Can Leverage Low-Code/No-Code Tools to Make Themselves Invaluable

Enabling Citizen Data Analysts in the Post-Hadoop World

However, to fully reap the benefits that democratizing software development promises, we also need to understand the limitations of no- and low-code development. As terms very often used interchangeably, it’s also important to understand the differences between the two, including the unique benefits that both no- and low-code tools can offer enterprises.

What Are No-code Tools?

As the name implies, there is no data science expertise necessary to get started. The pool of users includes domain experts -- medical doctors, lawyers, financial analysts, and citizen developers – who have a business understanding of the application’s goals but lack an IT background. Pull-down menus and drag-and-drop features in place of code make it easy for people to get started. Thanks to no-code solutions, projects such as building a website or an app or automating certain business processes have never been easier.

This matters for several reasons. First, and tactically most important, digital transformation projects are growing fast, and the supply of developers to lead them does not meet the demand. In fact, CNBC reports that the rate of unfilled IT jobs increased to nearly a million vacancies at the end of 2021. By 2030, that number is expected to rise by almost 22%. In essence, the problem will get worse before it gets better, and we can’t put progress on hold until then. Additionally, from an ethical standpoint, the more people we have powering our algorithms and applications, the more inclusive our technology will become.

What Are Low-code Tools?

On the other hand, low-code solutions reduce the complexity of software development through visual tools, reusable templates, and automatic workflows. They’re used by professional developers and data scientists to accelerate simpler development projects and automate certain basic aspects of coding. Some expertise is necessary in the use of low-code solutions, and the goal is to free up developers’ time for more important and complex projects. This lends itself to innovation and the ability to make a meaningful impact on the business, not just checking the boxes to get through an endless to-do list.

According to Forrester, about half of enterprises using low-code solutions say they provide a faster delivery than other types of programming. Over 84% cited that a low-code approach helped them improve their time-to-market. In the instance of healthcare, for example, many organizations are working with legacy systems that are not effective for today’s operations or workloads. If a major tech overhaul is not in the cards, low-code solutions can help modernize mission-critical IT functions to support more efficient workflows and get certain digital transformation initiatives off the ground.

Although both no- and low-code development have had a profound impact on business and the democratization of technologies such as AI, neither solution is perfect. One major difference is that they’re designed for very different people. For example, an AI library that provides access to more than 10,000 state-of-the-art models with one line of code still requires a data scientist (albeit a junior one) to run it. On the other hand, a 100% no-code AI tool requires a user interface with a guided workflow, built-in explanations, and hiding of technical details under the hood.

Risks and Limitations

In most cases, the benefits far outweigh the challenges, but let’s explore the pitfalls. First are security concerns. Anytime you integrate data, applications, or tools from a third-party provider, you’re opening the door to risk, so be sure to vet source code. Be wary of shadow IT. Use of no- and low-code tools can be hard to manage because they are often weak on governance protocols and features.

It’s also important to remember that the features that make no- and low-code tech so great are the same ones that restrict them. For patchwork IT projects and process automation, they are a great solution. However, expecting no- and low-code tools alone to achieve all your digital transformation ambitions or fill the void of hard-to-find developer talent will leave you disappointed. Limited functionality also means that coding expertise is often needed for customization. Additionally, kinks and bugs can be harder to mitigate, leaving users frustrated.

Gartner estimates that by 2025, 70% of new applications developed by enterprises will use no-code or low-code technologies -- up from less than 25% in 2020. As the developer shortage persists, it’s likely the proliferation of no- and low-code tools will too. The opportunity for more people to be involved in creating the software and processes that power business is exciting, and it’s something enterprises should encourage. Even with the pitfalls, we can expect big things in the no- and low-code space over the next year and beyond.

About the Author

David Talby, Ph.D., MBA is CTO at John Snow Labs, helping fast-growing companies apply AI, big data, and data science techniques to solve real-world problems in healthcare, life science, and related fields. You can reach him via email, Twitter, or LinkedIn.

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