t6 Blog Posts > Five data concepts

Five data concepts

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.

Five data concepts


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.

Data Fusion

Data fusion plays a crucial role in the data preparation process. With t6’s Data-Fusion engine, multiple measurements from various sources are combined to create a unified and enriched dataset with enhanced accuracy. By merging different measurements, t6 provides a comprehensive view of the underlying phenomena, allowing for a more holistic analysis. This fusion process not only improves the quality of the data but also enables the identification of patterns and relationships that might have been overlooked in individual measurements. By leveraging data fusion, t6 minimizes the risk of drawing incomplete or inaccurate conclusions during subsequent analyses.

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.

More recently, Machine Learning and models are extending t6 capabilities.


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.


Formulating a hypothesis is an essential step in the data analysis process. t6 encourages users to develop clear and testable hypotheses based on the data at hand. By establishing a hypothesis, researchers and analysts can define their expectations and predictions regarding the relationships or patterns within the data.


In the context of data analysis, explanation refers to the process of interpreting and communicating the findings derived from the data. After conducting rigorous analysis using t6’s comprehensive toolkit, it is crucial to explain the insights and outcomes in a clear and understandable manner. t6 empowers users to generate meaningful explanations by providing visualization capabilities, statistical summaries, and intuitive storytelling features.