Gold Coast IT Solutions: "An Ironman" Approach toward Big Data

The evolution of the Ironman suit from the rugged MARK I forged out of missile parts to the state-of-the-art J.A.R.V.I.S-enabled Mark XLIII wasn’t an overnight transition. It was a calculated process, streamlined primarily by necessities and scenarios rather than an outcome of quickly assembled in-vogue technologies. This approach helped Ironman build tools, that evolved with the features required to overpower the enemies he was facing and eventually defeat them. Such is the approach adopted by Gold Coast IT (GCIT) Solutions, an applications and services company that delivers enterprise solutions by understanding their client’s environment, deploying a Minimum Viable Product (MVP), and letting the client observe the value it brings before scaling it up. This gives the clients an opportunity to scale out slowly and efficiently rather than taking an all-in approach which could be disastrous as requirements and environments change over time. “We take a very small piece of our customer’s ecosystem, build a suitable application and let it perform, thereby ensuring our clients that we are in the right direction,” says Prashanth M. Ramakrishnan, the CTO and co-founder of GCIT Solutions.

Over the past decade, global enterprises have been used to the practice of upgrading and modifying their systems to refashion their mode of operations and keep up with the ever-changing business environment. As a result of frequent updates and patches, companies are struggling to derive data from legacy systems. Disparate components are now failing to communicate with other systems in terms of translating data of different formats and transferring it in real time due to the layers and layers of customization, making the process of deriving and analyzing data cumbersome. GCIT Solutions was born out of this need for solving collocation interoperability issues that were present in enterprise applications across all industries. “We bring a unique selling proposition to the table wherein, we don’t just look at big data from a volume perspective, but we take into consideration, the number of systems it affects,” says Ramakrishnan.