Having worked on industrial first of its kind pilots like CarPass project for an Automotive major in Germany, FAVES as an autonomous machine project for a major in France, etc. we have figured out one basic principle in terms of the industrial adoption for Data Optimization, and that happens to be having a Convergence ideology.

To address a specific business problem, a siloed/disparate approach to stacks will not be enough to string a holistic solution. We need to understand the Data prudence completely to ideate a solution. As we try break down Convergence into smaller chunks, we need to envision IoT as a Data Sourcer, Machine Learning as a Data Analyzer, AI as a Data Automator and Blockchain as a Data Provenance medium.

As a part of our workshop we will demonstrate a couple of specific usecases correlating these stacks together to narrate Convergence ideation.

Swapnendu Mukherjee

Data Convergence Expert

Digiotai