This video shows how big data can drive prioritization of of research topics, leading to more effective research. He shows how a large data set of health data can be classified into 10,000 characteristics of aging, then found current research associated with a given cluster of characteristics. This procedure may sound self-evident, but looking at aging hazards by age/sex/etc. could lead to unexpected insights into what might ameliorate the aging process.
As examples he also analyzes, using big data, the association of senescence with reduced cancers of some types. Finally, he looks at interventions (NR, exericse, time-restricted feeding) in diseased lungs using data sets.
The talk seemed a bit unfocused, but has real insights too, particularly when discussing the rate of aging in various parts of the body.