In this episode, we'll dive deeper into the changes and challenges brought about by AI, particularly since the November 22 moment with ChatGPT. We'll explore how AI has impacted industrial systems engineering, data quality, and the utilization of massive amounts of data in factories and supply chains.
Introduction Let's start with the big picture. AI has been around for a while, but the renewed focus on data quality and the challenges of handling vast amounts of data have become more prominent. The November 22 moment with ChatGPT triggered a significant shift in how we approach AI and data.
Importance of Discovery One of the key aspects of an industrial-grade data fabric is the importance of discovery. Access to data, metadata, and data models is crucial for understanding and comprehending the information. This enables tooling, whether AI-assisted or analytics environments, to work effectively.
Normalization is another critical factor. Ensuring common data formats at the consumption layer, such as everything in degrees Fahrenheit, is essential for consuming, discovering, and transforming data. Multistream contextualization also plays a significant role in this process.
Data Storage and Manipulation Where does the data live? This is a separate issue that needs to be addressed. Whether the data is dumped into a data lake or handled ad hoc, the ability to manipulate data at cloud scale has seen significant changes. The technical obstacles are dropping, making it easier to bridge into source systems using well-described interfaces.
Overall, the changes brought about by AI have had a tremendous impact on industrial systems engineering. The focus on interoperability, open systems, and solving large-scale problems has become more prominent. The technical obstacles are getting easier to overcome, and the opportunities for using AI and data fabrics are exciting.
Let's continue the conversation and explore more about industrial-grade data fabrics and their role in the era of AI.
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