Why Low-Code Could Help Drive the Next Wave in Analytics and Automation?

Why Low-Code Could Help Drive the Next Wave in Analytics and Automation?

Mohammed Jarood Mustafa
February 5, 2024
5 min read

According to Gartner he global market for low-code technologies is set to grow to $26.9 billion in 2023. Gartner's Senior Market Research Specialist, Varsha Mehta, outlines that businesses are "increasingly turning to low-code development technologies to fulfill growing demands for [rapid] application delivery and highly customized automation workflows."

The growing labor shortage and operational demands are also prompting enterprises to adopt hyper-automation tools and technologies. This includes businesses spending on low-code development platforms (LCDPs) to support process automation, decision analytics, and business intelligence.

Traditional analytics typically requires a considerable time investment from data analytics experts. On the other hand, low-code platforms can leverage artificial intelligence (AI) and machine learning (ML) to simplify data analytics. In fact, Gartner reports that by 2025,  50% of data analytics  solutions will be developed by busineAss users using low-code and no-code tools.

So, can low-code platforms drive the next wave in data analytics and automation? Let's discuss this in the following sections.

Applications of Low-Code Development Platforms in Analytics and Automation

Among the major challenges, AI and machine learning applications consume a lot of development time and require constant maintenance from skilled resources. This is the reason why  70% of businesses  report zero (or minimal) business impact from implementing AI.

Besides AI-based applications, a traditional approach to data analytics is also time-consuming and inefficient. For any data-intensive application, developers spend a considerable amount of time in application programming and less time working with the data.

To that end, it's noteworthy that Gartner projects a 30.2% growth forecast in 2023 for citizen automation development platforms or CADP. Some of the typical use cases of CADP include:

  • Workflow automation
  • SaaS applications
  • Web-based form automation
  • Data visualization

In essence, low-code development tools can overcome the shortage of skilled resources to create high-quality AI and ML-driven applications. 

One of the major applications of low-code development is data analytics in Industrial IoT (or IIoT). Using functional block programming, low-code analytics tools provide customizable block libraries for applying advanced analytics. LCDPs enable users to work on data by:

  • Organizing data processing in a visual and hierarchical flow
  • Visualizing the entire flow of data processing in real-time
  • Adjusting the data algorithms at any given time

Citizen developers can also adopt the  low-code approach  to develop intelligent ML solutions. For instance, a low-code process monitoring application can monitor any synthetic, organic business process. It can also leverage its ML capabilities to notify operators whenever there is a significant drop in quality.

Here are some other use cases of low-code data analytics:

  • Banks can leverage low-code analytics to map their customer journey and improve customer experience
  • Low-code analytics tools in fleet management help in mining data from various sources with minimum programming

Next, let's discuss the benefits or advantages of using LCDPs in data analytics.

Benefits of Low-Code Development Platforms in Data Analytics

Why do most organizations fail to maximize the value of their ML applications and data science initiatives? Well, they face several challenges. Some of them are:

  • Building an efficient ML data model or application consumes a lot of time and effort.
  • Data models lose their efficiency due to constant market changes, thus leading to poor decision-making.
  • Lack of skilled talent slows down the implementation of accurate data analytics.

A low-code development platform can overcome these analytics-related challenges with the following advantages:

1. Faster Time-to-Market

Low-code platforms accelerate application development using reusable components. This includes data connectors, ML algorithms, data handlers, and front-end modules. This enables data science teams to iterate and optimize the data model until it addresses the business problem.

2. Easier Maintenance of Data Models

Organizations need to constantly refresh data science models and ML algorithms to keep them relevant for specific business needs. Low-code tools help efficiently monitor and maintain data models, as they can easily detect any degradation and help take appropriate action.

3. Improved Flexibility

Low-code development tools provide data-dependent organizations the flexibility to leverage their business data. They provide a flexible approach that allows  business users  to collect data from various sources. Further, they enable the creation of flexible BI dashboards relevant to the business use case.

4. Addressing the Skill Gap

Due to the prevailing shortage of skills, organizations find it challenging to hire and retain competent data science professionals. Low-code development platforms overcome this problem with their intuitive interface and drag-and-drop functionality. On low-code platforms, organizations incur lower costs and efforts for retraining their software development team for data analytics. With reusable components and repeatable workflows, new hires don't need to retain knowledge about AI data models and applications.

The Bottom Line

It's exciting to see the synergy between low-code platforms and data analytics. LCDPs enable organizations to create more agile and resilient solutions pertaining to data science and analytics. The low-code technology is highly adaptable for custom applications built for changing business needs.

As an experienced IT consultant, Novigo Solutions has long been providing tailored  data science and analytics  solutions. Our services enable you to decipher data to extract valuable insights for improving business decision-making.

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