Data Modernization is the Foundation of your AI Success Story | Cognizant
[Music] Data modernization is an essential foundation for AI to scale. If you don’t have data stored in an accessible way, historical data captured in an accessible way, you’re not gonna be able to achieve any success with AI machine learning. So, the first factor that I would put in that is essentially, how accessible and easy is it for folks, your data scientist, your machine learning experts, to access all kinds of data that exists in your organization. You know there’s always controls that apply, but within those controls and those boundaries, how easy is it for them to really fail fast. Because you want these experiments and ideations to fail fast as quickly as possible, and then you know for the experiments that are successful for them to scale.That’s a big component of how we’re implementing AI. The second part, I mean in terms of architecture – API driven architecture, micro-services driven architecture, that allows people to easily access data and be able to test those hypotheses is going to be key. From a model perspective, we talk about AI models machine learning models, they require neural networks require very significant infrastructure – GPU, CPUs, that can scale and compute these models in a timely manner. To do that, really setting up on-prem infrastructure is not a choice, you have to have modern data architectures on the cloud to be able to achieve success. If you’re looking at a digital platform, if you’re looking at delivering insight you know on a real-time basis to your clients, it has to be on the cloud. It has to enable this sort of real-time integration of data and output of models, in that timely manner that allows you to deliver recommendations and deliver optimizations to your clients.