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In this session we discuss the industrial digital transformation, and what are the key capabilities necessary to provide a step-change in delivering, scaling and maintaining data products (ML, analytics, applications), – a prerequisite for succeeding in becoming data-driven at scale. In particular, we deep-dive into industrial data problems and how industrial companies can go from data silos to contextualized data, and how that drives competitiveness in the years to come.
– The successful digital transformation is the aggregate of many small operational improvements fueled by data: analytics, applications, ML models, visualizations, in short, data products.
– The ability to deliver, scale and maintain data products efficiently is a result of having the right data architecture.
– The right data architecture must 1) provide a data model that can represent your operations digitally, 2) Means to populate the data model with data from previously siloed systems and applications, and 3) Tools to efficiently use the data model in the creation of data products.
– Industry has some particularities in data types and data relationships that are absolute imperative for industrial companies to solve, to get to a step-change in the marginal cost of delivering data products.