Vinoth Kannan

Daimler Truck journey towards Data Products

For the vast majority of organizations, investments made on AI have not led to meaningful performance improvements. Companies still struggle to scale promising Data science proof-of-concepts. One of the main reasons is due to the cultural aspect of how the company is organized around data. By focusing on technical capability of data management which are infrastructure driven - acquiring, storing and consuming data, has diminished the focus from the higher objective – treating data as a product. At Daimler Truck, a data product is treated as a data asset, which is optimized for consumption. To handle such data products we had to reinvent our data platform, data management capability, policies and our overall data strategy- that includes embracing new architectural paradigms such as data mesh and most of all empowering the organization to be data product driven. This talk walks the audience through this journey and provides some key insights on the dimensions of People, Process and Product For the vast majority of organizations, investments made on AI have not led to meaningful performance improvements. Companies still struggle to scale promising Data science proof-of-concepts. One of the main reasons is due to the cultural aspect of how the company is organized around data. By focusing on technical capability of data management which are infrastructure driven - acquiring, storing and consuming data, has diminished the focus from the higher objective – treating data as a product. At Daimler Truck, a data product is treated as a data asset, which is optimized for consumption. To handle such data products we had to reinvent our data platform, data management capability, policies and our overall data strategy- that includes embracing new architectural paradigms such as data mesh and most of all empowering the organization to be data product driven. This talk walks the audience through this journey and provides some key insights on the dimensions of People, Process and Product For the vast majority of organizations, investments made on AI have not led to meaningful performance improvements. Companies still struggle to scale promising Data science proof-of-concepts. One of the main reasons is due to the cultural aspect of how the company is organized around data. By focusing on technical capability of data management which are infrastructure driven - acquiring, storing and consuming data, has diminished the focus from the higher objective – treating data as a product. At Daimler Truck, a data product is treated as a data asset, which is optimized for consumption. To handle such data products we had to reinvent our data platform, data management capability, policies and our overall data strategy- that includes embracing new architectural paradigms such as data mesh and most of all empowering the organization to be data product driven. This talk walks the audience through this journey and provides some key insights on the dimensions of People, Process and Product. For the vast majority of organizations, investments made on AI have not led to meaningful performance improvements. Companies still struggle to scale promising Data science proof-of-concepts. One of the main reasons is due to the cultural aspect of how the company is organized around data. By focusing on technical capability of data management which are infrastructure driven - acquiring, storing and consuming data, has diminished the focus from the higher objective – treating data as a product. At Daimler Truck, a data product is treated as a data asset, which is optimized for consumption. To handle such data products we had to reinvent our data platform, data management capability, policies and our overall data strategy- that includes embracing new architectural paradigms such as data mesh and most of all empowering the organization to be data product driven. This talk walks the audience through this journey and provides some key insights on the dimensions of People, Process and Product.

An accomplished Data & Cloud Architect and Product Leader with over a decade of hands-on experience with large-scale distributed systems, Big Data, and Cloud technologies. As a result-oriented thought leader, I have a track record of strategizing, designing multiple Advanced Analytics platforms, and building teams that drive value from data investment that matters.

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