Data validation

SigTech has a curated verification system that allows our engineers to ensure data quality.

Our data cleansing process is broken down into two steps:

1. Validation: an iterative process where we select checks to apply to datasets and resolve any errors that arise.

2. Cleaning: corrections and deletions are loaded in. The data is then stored to a separate storage facility.

Errors that surface following validation:

Icon	Status	Description
	Accepted	Accepted with individual errors.
	Amended / corrected	On the basis of an error, the data has been corrected. Example: an anomaly.
	Deleted	Data that shouldn’t be included. Example: an accidental entry.

Tip: a fully validated timeseries or instrument is marked with a .

Note: default is clean data, but you have the option to select raw data.