Talk of low-code, no-code platforms have been making the rounds of late, with Goldman Sachs injecting USD90 million into low-code software maker WSO2, while data automation platform Cascade Labs recently raised USD5.3 million.
And as observed in a Washington Post report this month, the rapid rise of low-code has allowed non-computer scientists to create digital applications that were previously the domain of computer science graduates, while simultaneously opening the door to deliver fast and meaningful impact to organizations.
But what exactly is a low-code, and what implications does it have on building a vibrant data culture or developing data-centric and AI applications?
At its heart, low-code is essentially a development environment for creating application software by leveraging scripting and a graphical user interface (GUI). The ability to visually configure applications significantly speeds development over traditional programming languages such as C or Python.
Low-code platforms are not new. Some trace their origins to 2011, and it is arguable that even traditional programming platforms have long sought to incorporate GUI elements to facilitate coding – Visual Basic comes to mind.
Fueled by increased digitalization, interest in low-code platforms has surged, along with a new generation of low-code platforms that are even easier to use. Businesses are attracted by how low-code platforms lower the barriers to producing the apps needed to accelerate their digital transformation journey.
Low-code platforms are relevant to data scientists or employees versed in programming, too. For instance, Streamlit, an open-source app framework for Machine Learning and data science teams, offers a ready platform to quickly build and share data apps.
While data scientists might have some amount of programming skills and the ability to access open-source projects and build machine learning apps, Streamlit argues that they often lack the technical aptitude to make all the parts work together.
Enter Streamlit, which allows data scientists to focus on their algorithms and ML training to put together complete apps such as the Face Mask Detection System we wrote about recently. Investors are convinced, and Streamlit clinched USD35 million in Series B funding earlier this year.
Selecting the right no-code platform
In a nutshell, low-code platforms help businesses grapple with a shortage of tech talent, build and roll out apps much quicker than before, and allow “citizen” developers with a web browser and an idea to get started. This is a game-changer that puts the power of data within reach of even small organizations.
A report in the Harvard Business Review identified some crucial features for a competent no-code platform for data and AI. The first is a simple interface that makes it easy to integrate data from multiple places within the organization, whether a spreadsheet software, CMS and ERP system, or a software-as-a-service platform.
Uploaded data should also be automatically classified and encoded, such as dates, categories, or numbers. Moreover, the platform should also automate model selection and training, as well as the ability for non-data scientists to navigate through the optimal model without the need to know the intricacies of each model.
Finally, the low-code platform should be easy to deploy and work alongside existing processes and systems. Models should be monitored, and as new data becomes available, models are retrained to keep them up-to-date.
So how can businesses push their data agenda using low-code platforms? The advice for getting started with a low-code platform to build data apps is similar to that for standard IT projects.
Don’t boil the ocean. Enable quick wins by focusing on low-hanging fruit with high impact to pilot data projects. Fail quickly if the projected return on investment (ROI) doesn’t appear feasible after the start of a project – switch to other high-return applications instead.
A low-code platform empowers employees to think of creative ways to leverage data to drive and optimize their work, effectively democratizing data across the organization. Still, a certain level of digital competence will likely be needed for success. This means identifying employees with the enthusiasm and digital aptitude to champion or spearhead initial projects.
Low-code platforms are set to increase. Some will be extremely easy to navigate, others might be much more powerful but have a steeper learning curve. Be open to exploring new platforms, and always keep your citizen developers at the center of your efforts.
And yes, even low-code platforms will benefit from the know-how and expertise of your coders. So, remember to keep your in-house experts in the loop.
Paul Mah is the editor of DSAITrends. A former system administrator, programmer, and IT lecturer, he enjoys writing both code and prose. You can reach him at [email protected].
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