So here's the thing, data from industrial sources is inherently messy. For example, a typical PLC system manages thousands of tags from both physical instruments and internal calculations, but this data is often unstructured, not linked to a unifying data model, and uses naming conventions that are vague to the outside world.This makes data from such sources not readily usable in analytics applications and has led to the emergence of a new field of industrial data preprocessing for IIoT, called Industrial DataOps.To discuss more on industrial DataOps, I had a conversation with John Harrington who is the Co-Founder of HighByte, a company pioneering this field with their HighByte Intelligence Hub.Here some of the topics that we discussed.✔️ What is Industrial IoT DataOps✔️ Limitations of the Purdue Model/ISA-95✔️ The importance of Data Quality✔️ Data Standardisation, Normalisation and Contextualisation✔️ Best Practices for integrating industrial data silos✔️ Principles of IIoT Data Modelling✔️ How DataOps enforces privacy and security✔️ HighByte Intelligence HubJohn has previously worked as the VP of Business Strategy at PTC, and he's also served as the VP of Product Management at Kepware Technologies.