Data are a centric point in t6. As a consequence, a lot of tools are in place to provide you with the full control over your data within the platform.
Data Preparation aims to clean and transform raw data prior to process and analyse. t6 is embedding multiple preprocessors to validate (or reject), format, transform and correct data ; as well as a Data-Fusion engine to combine multiple measurements together and enrich them with a better accuracy in a result. Major goal of this data-preparation is to have best in class quality on the measures + eliminate bias during analysis phase.
Additionally, t6 is able to drop irrelevant measurements or fix incorrect values before storing it, avoiding misleading analysis.
Annotation & Classification
Data-annotation or Data-labelling expect to classify every single measure on categories. This classification aims to identify an input pattern. Binary, Multi-Class, or Multi-Annotation (or Multi-Label) Classification are customizable on t6.
Exploratory Data Analysis on t6 brings graphical and non graphical information about your data measured in a certain Flow. The Exploration process will help understand how does your data looks like and is a prerequisite for any analysis.