Speaker
Description
With all the data being collected today, there is a strong focus on how to generate value from that data for various stakeholders and society as a whole. While many analytics tasks can be solved efficiently using local data only, typically, their solution can be substantially improved by using data of others. Obvious examples would include (i) supply chains where stakeholders can highly benefit from data upstream (production side) and downstream (consumption side), as well as (ii) tourism, where for instance the hospitality industry may find value in data coming from transportation. Another important application area is that of energy systems, where many stakeholders collect and own data, would benefit from each other’s data, but are reluctant to share. Sharing limitations are often motivated by privacy concerns (individuals), or by the potential loss of a competitive advantage (firms).
We explore various approaches to support collaborative analytics to incentivise data sharing. Eventually, this leads to discussing monetisation of data and of the contribution of features and data streams to the solving of common analytics tasks. We will zoom into the specific examples of regression and prediction markets, with application to energy system operation problems.
| Classification | Both methodology and application |
|---|---|
| Keywords | Analytics, Forecasting, Markets |