Tech leaders in non-tech companies face the challenge of combining fuzzy human IP with modern data tools. They must balance traditional processes with innovative algorithms. Their companies aspire to become data driven, which require solid data practices. This talk exhibits how the collective knowledge of an organization can be amplified through inclusive data products.
Commodity Trading is digitizing rapidly, and incumbents rely increasingly on data-driven innovation to get an edge on markets. One of the key topics involves price forecasting and predicting trends. We provide a compelling example of how organizations in traditional industries can build successful use cases by:
model.fit().predict().The talk is intended for those who want to apply AI techniques in traditional industries. We propose a technology stack that allows traditional industries to leverage modern data tools to rapidly develop transparent data products.