Data Intelligence
The study of how raw observations become structured information, evidence and ultimately understanding.
Data is not understanding.
Data is a representation of observations, not knowledge by itself. Before data can support a conclusion, it must be collected, structured, measured and interpreted in context.
The Quant Society studies structured and unstructured data, relevance, signal and noise, measurement quality, missing information, correlation, dataset bias and the methods used to organize evidence.
More data does not automatically create more understanding. Poorly selected, biased or irrelevant data can increase confidence while reducing the quality of a conclusion.
Data Intelligence is the evidence layer of TQS: the discipline concerned with the path from raw data to information, evidence and defensible understanding.
What are we trying to understand?
What is data, and when does it become information?
How should structured and unstructured data be interpreted?
What separates relevant evidence from noise?
When can more data make a conclusion worse rather than better?