Industries

Insurance runs on data that is old, deeply nested and heavily governed. Policy administration, claims and actuarial systems were rarely designed to be read together, and the interesting questions almost always span all three.

Healthcare data is fragmented by design — clinical, administrative and operational systems each answer to different owners. Useful analytics almost always means reconciling records that were never meant to be joined.

Consumer goods businesses are data-rich and signal-poor: shipment, retailer, promotional and consumption data all describe the same demand through different lenses, at different grains, on different calendars.

Electronics generates enormous volumes of process, test and field data. The difficulty is rarely collection — it is joining process history to eventual outcomes across systems built at different times for different purposes.

Mining operations produce continuous sensor, geological and logistics data from sites with intermittent connectivity — and decisions with long lead times and large capital consequences.

Public sector and non-profit organisations often hold data of real social value under tight budget, privacy and transparency constraints — where the method has to be explainable to people who are not analysts.