Harmonising data sources
When integrating sample data form multiple sources, samples may have different subsets of analytes measured for reasons including targeting different target commodities, company priorities, company mergers, and pre-competitive vs company datasets. Imputation can assist in harmonising these data sources.

Change of commodity
Changes in commodity prices can result in a change in company priorities, however the original assay suite may not be useful for a new commodity. Instead of re-analysing the samples at the lab (if the samples still exist), impute the values instead with this tool.

Infilling missing data
Lab-measured analytes may be limited due to cost constraints or legacy analytical methods, and the samples may no longer exist. You can use this method to infill values for the missing desired analytes.

Anomaly detection
Rather than predicting unknown values, compare the predicted values to your lab-measured values. Variations between the predicted distribution and the lab-measured values may indicate geological anomalies worth following up with further analysis or modalities.