“When we want to use the data to draw broader conclusions about what is going on around us, then the quality of the data becomes paramount, and we need to be alert to the kind of systematic biases that can jeopardize the reliability of any claims.”
[The Art of Statistics, David Spiegelhalter]
In the age of Big Data, we can collect tremendous data from many sources that have different qualities (e.g. accuracy, resolution, or fidelity). Using all the data we can easily draw statistics about what we measured. These statistical results help us to understand what is going on by comparing the previous statistical results. However, if we want a deep understanding of hidden patterns for accurate future prediction (statistical inference), the quality of data becomes the main factor for accurate prediction; higher quality, higher accuracy. Collecting data, however, has a general trade-off between the quality and the quantity. High accurate data require expensive data acquisition costs (e.g. expansive measurements, fine-scale simulation using more computer resources) while less accurate data are relatively cheap to obtain.
As the book mentioned, the data-driven predictive model totally depends on the quality (and the quantity) of data. First, we check the accuracy (or fidelity) of data and use only the high-fidelity data to make a data-driven model for decision or prediction. Due to the aforementioned trade-off, however, we generally have a few high-fidelity data and/or many low-fidelity data. Then how to make a data-driven model? since a few high-fidelity data provide only partial information, it is hard to make an accurate model globally. the use of many low-fidelity data enables us to make a global model but it has a systematic inherent bias, leading to a wrong prediction. Hence, in data science, many researchers have focused on multi-fidelity data fusion, which enables us to make an accurate global model using both high and low fidelity data; chasing both the quality and the quantity.