Digital transformation in manufacturing fundamentally involves transforming unprocessed data into valuable insights to guide business decisions through automated systems or human intervention. Consequently, implementing a well-thought-out data modelling strategy is key to successful digital transformation as it helps to express the meaning of the data to digital systems. To learn more about Data modelling for Industrial IoT in general and for Digital Twin use cases in particular, I had a podcast conversation with Erich Barnstedt. Erich is the Chief Architect for Standards, Consortia and Industrial IoT in the Azure Edge and Platform team at Microsoft Here's the outline of our conversation ✅ Importance of data modelling for Industrial IoT.✅ Key Elements of an Effective IIoT Data Model✅ Standardising Configuration Interface for OPC UA Connectivity Mapping✅ Manufacturing Ontologies Reference Solution for Digital Twins✅ UA Cloud Publisher and UA Cloud Twin for Mappping Industrial Assets to Azure Digital Twins using ISA95✅ Significance of UA Cloud Commander at the Industrial Edge✅ OPC UA Information Model Integration using UA Cloud Library✅ Data Modelling Standards✅ Web of Things for Endpoint and Interface Description of Industrial Assets.✅ ChatGPT for fully automating onboarding of non-discoverable industrial assets✅ Converting proprietary interfaces into OPC UA Information Model using UA Edge Translator.✅ The role of the IEC/ISO in standardizing data models for IIoT✅ The Scope OPC UA PubSub Over MQTT in Industrial IoT✅ Metadata and Type Information in OPC UA PubSub✅ Industrial Metaverse Reference Architecture with Open Interoperability Standards