Digital, low code, and elephants

Ritesh Varma, Global Head - Business Solutions, Newgen Software

With Delta still prevailing, and Omicron in the midst, we welcomed the year 2022 with a new Covid variant, Florona. They say these variants are less fatal and on the verge of making Covid a common virus. Or is it so?

While many countries are opening up and many others plan to remove restrictions, economies are still hampered with the slightest hint of an increase in new cases with the new variant. Businesses have quickly realised that they have to live with it, and the only way to survive the unexpected is to become increasingly digital.

Achieving Digital, The Low Code Way

Digital, in today’s terms, has become superfluous; it changes by the day, and so do business realities. The only way to keep pace with the rampant change is by approaching digital the low code way. Today, it seems that every software company calls itself a low code provider. But the difference is in the way the whole solution is architected.

“The only way to eat an elephant is to cut it into bite-size pieces,” were the words once shared by a wise guru, an industry stalwart, an individual who digitally transformed a bank using the power of low code.

It takes both technology and business experts to lay out an organisation’s artificial intelligence and machine learning roadmap.

Allow me to clarify; it is not that one is interested in eating an elephant or cutting it into bite-size pieces. Here, the elephant represents the business problem at hand, and the bite-size pieces represent how it needs to be broken down into manageable pieces. Low code is the ideology, and just like the business problem, low code itself has to be flipped on its head.

Let me try and explain this a bit more.

Time For The Perfect Symphony 

We all view low code as a tool or an all-encompassing application that would have answers to all our questions. It should, however, be looked at as an orchestrator of components. The components here represent services that have their APIs and data sets. There can be various types of services – internal or external. Internal would mean something that the tool presents, and external would mean something that the environment and ecosystem provide.

Rolling out artificial intelligence involves procuring and setting up the various hardware, software, and human resources required for the artificial intelligence and machine learning projects.

Internal services could be interfaces, rulesets, masters, etc. Each of these services has its API and data sets and is independent of each other with minimum dependencies. On the other hand, external services would be like the core banking, ERP, core insurance, loan management system, or even simpler systems such as MS Word. Again, these would have their APIs and data sets. These services would do their part at the relevant stages of the application and processes as demanded.

The process or application has to be looked at as an amalgamation of these services into relevant stages by a concept referred to as an ‘orchestrator.’ Depending on the organisation and its current state, the internal services may be heavy, or the external services may be heavy. However, these are just orchestrated to provide the end state. For instance, if we take the example of banking and lending, the lending application stages remain the same.

Building artificial intelligence and machine learning take time. The data science team needs to understand the requirements clearly, collaborate with the business experts.

Depending on a bank or a financial institution, how and what components are called for can vary, making each application different. An organisation with a portal may not require investing in a portal service, but another may need to subscribe to an internal portal service. Similarly, a financial institution that invested in credit scoring and underwriting may plug in its external credit service compared to another that subscribes to an internal credit service.

A real-time data modelling capability is crucial to any artificial intelligence and machine learning system. Users should quickly adapt to the changing data definition and volumes.

The end objective is to understand how an organisation componentalises or servicifies the entire application and orchestrates them together to deliver the perfect made-to-order, custom-fit app. That, in turn, helps the organisation transform and get that leverage to address the ever-changing business environment.

Ritesh Varma, Global Head - Business Solutions, Newgen Software
Ritesh Varma, Global Head – Business Solutions, Newgen Software.