SPOTFIRE
This helps users find valuable starting points for their analyses.
“ That‘ interestingness’ is part of how we use machine learning to surface anomalies in data,” Brad goes on.“ As an engineer or as a scientist, you might say,‘ that looks interesting, I want to drill deeper into that’.
“ You might mark a section of that distribution that looks unusual, and Spotfire will automatically identify correlations to that difference in behaviour.”
More recently, Spotfire has integrated generative AI capabilities, allowing users to interact with the software through natural language. Users can ask questions about how to use the software, request specific visualisations or seek explanations of existing analyses.
The platform also supports integration with programming languages like Python and R, enabling users to create custom analytical functions – with or without coding expertise.
In the same way that upstream oil and gas engineers rely on subsurface models to make sense of complex geological data, manufacturing engineers often face similar challenges in visualising vast volumes of operational data across equipment.
Analogous to building a 3D geological model, Brad shares a recent moment that highlights the value of Gen AI in data-heavy environments:“ Just the other day, I wanted to create a series of 3D surfaces from 16,000 pieces of operating equipment. I asked the copilot,‘ write a Python function for me that can grid and interpolate this information’.
“ It wrote the program, injected it into Spotfire and all I had to do was point and click to say,‘ this is the data table I want to use; these are the values I want to display; go ahead and do your work’. Engineers and data scientists can work together to directly implement new functionality in the platform on their own or with AI assistance.”
Engineer personas and use cases Spotfire serves several key engineering roles in semiconductor manufacturing, each with distinct needs and responsibilities.
60 May 2025