Emerging low-code development platforms enable Data Science teams to derive analytical insights from Big Data quickly.

As organizations become proficient in capturing, storing, and analyzing data from multiple sources, they are discovering previously untapped business opportunities.

This has been possible with the help of Data Science which has been enabling the companies to make smarter, data-driven decisions, as well as build & deploy Big Data solutions faster. The challenge, however, is that the same services are not yet available at the mid-sized or smaller companies often due to the lack of Data Science professionals. 

Enter Low-code development

With graphical user interfaces and configuration, the Low-Code technology allows non-tech professionals to enter the world of development. They can build applications with no prerequisite knowledge of coding or other database management services. Gartner forecasts the global Low-Code Tech market to burgeon by 23% in the year 2021. 

How does this work for Data Scientists? 

Low-code development platforms enable Data Science teams to derive analytical insights from Big Data quickly. With the co-existence of an array of features like Visual Modelling, Real-time monitoring & reporting, and Cross-platform accessibility among others, the low-code creates templates that replace any repetitive code structure, reducing the load from the algorithms. 

This adds value to the work of developers and data scientists & accelerates the decision-making process. They can then focus on constructing information perceptions, structuring big data projects, or creating new products.

Leveraging Low Code for Big Data Analytics:

The data is still the data, but the ways of getting insights are continuing to improve. The use of Artificial Neural Networks like Machine learning in automating Big Data solutions has augmented exponential growth in the Digital economy. However, with a long & expensive deployment process, organizations are moving towards Low-Code programming for Big Data Analytics. 

With this, they needn’t rely on stressed data scientists for their acumen to extract insights from structured & unstructured data— to enhance business deftness.

This synergistic relationship between the data scientists & those who aren’t programmatically inclined in the organization can foster more data work in a quicker amount of time without any compromise on data analysis. 

Furthermore, as much as Low-Code programming is ideal for non-tech professionals, it can also be used by data scientists to build data prototyping efficiently.

Lastly, while low-code techniques may not replace traditional methods instantaneously, they can help less experienced as well as professional data scientists, in uncovering analytical solutions.