Attending the Data Science Institute seminar at Imperial College
DSI Talk with Dr Gautam Shroff 19/02/2015 – NOTICE: These are only notes and they may not make much sense out of context…
Current business themes – Digital imagination – Simplification – Governance – Suistainability
From = Data “raw material” to = Delivery “means”
- Internet of things
- Big Data
- Mobility, Cloud
- Buzz word of “analytics”
What does big data mean? Not that its a lot of data but its width -> High dimensional spaces.
Things to take into account:
- Data width v Data length
Real-time response v Strategic response
Simulation is very important although it is not traditionally in the arena of predictive analytics.
The typical path:
- analise historical customer transactions
- analyse historical offers
- predict each offer type probability
- for a desired cost-benefit ratio find unique customer-offer strategy
But that is not enough. In practice there are a number of external factors to take into account. So you try or simulate
The typical flow #predictive #analytics is prediction & optimisation. But the real challenge are external factors. So try or #simulate #DSI
Unfortunately trying is not a viable option for a number of businesses out there!
Agent-based modelling and Casual Analysis are not purely statistics, you need assumptions to set them up!
Detecting abnormal behaviour using deep learning. Look at Office power consumption: http://www.cs.ucr.edu/~eamonn/discords
EXAMPLE: Enterprise contextual intelligence: detecting specific event from twitter
- Supplier intelligence
- Detecting events that could adversely impact a supplier (fire, strike, flood, etc) and pushing these based on conversation/transactional context
- Context-aware information advertisements
- detecting nature user-activity; discerning need and pushing top relevant internal/external social network updates
- Correlating unstructured events with structured data
- detecting significant news events using entity-specific indicators and correlating them