Data Historian: put the process trace next to the experiment
A batch comes back off spec, or a scale-up run behaves differently from the lab run. The temperature trend sits in the plant historian and the batch result sits in the experiment record, so someone exports both and lines up the timestamps by hand, and every new question about the run repeats that work.
The Uncountable Data Historian brings process signals from your plant historian, control system or data logger into the Manufacturing Suite and binds them to the equipment, experiments and lab requests they explain. Run rules flag the excursion, Points shows several signals on one time axis with each experiment banded over them, and Experiment Trace shades each workflow step over the trace. Your plant historian and control system keep running the plant.
This solution brief is for R&D, process and quality leaders who want investigations and scale-up reviews to start from the run, not from an export.
Inside this solution brief:
- Integration jobs built with the Uncountable Python SDK, and backfills without duplicates
- Tags with units, reference names and run rules
- Bindings to equipment, experiments, lab requests and monitoring locations
- Points and Experiment Trace for reading a run step by step
- Access by tag, and Bodie AI Agent for questions about a run
- A worked example: reactor R-101 runs hot during a batch
See what the process did, next to the result. For more depth, read the guide See what the process did next to the result.
FAQs
How the Uncountable Data Historian brings process signals into the Manufacturing Suite and ties them to equipment, experiments and lab requests, from integration jobs and tags to run rules, bindings, Points, Experiment Trace and Bodie.
No. Your plant historian and control system keep running the plant, and alarms and interlocks stay in your control system. The Data Historian keeps the signals R&D, process and quality teams need next to the work they explain.
A job built with the Uncountable Python SDK runs on a schedule or a webhook and sends up to 1,000 values for one tag in each request through the external API. Each value carries its observation time and time zone, and sending a window again replaces stored values rather than duplicating them.
Bind the tag to the experiment for its run window. Experiment Trace shows the bound tags over the run with each workflow step as a shaded band, and Points shows several tags on a shared time axis with Experiment Bands.
Each tag carries its run rules. A range rule can flag 2 of 3 points beyond 2 standard deviations, and trend rules flag runs of rising or falling points. Flagged points show in red on the chart, next to the bands of the experiments that ran.
.png)
