Using affordance analysis to design individual analytics ecosystems
Journal
Laws of large numbers - Use of big data in Asian insurance markets
Series
Swiss Re Risk Dialogue Series
Type
journal issue (edt.)
Date Issued
2016-09-01
Author(s)
Research Team
IWI1
Abstract
Organisations in Asia and worldwide are actively looking for ways to take advantage of big-data analytics. Big-data analytics is, however, mostly applied to well-known use cases in financial analysis and profiling [1]. A high rate of work-related accidents or diseases as well as the rapid ageing of the population around the world not only have an impact on productivity and profitability of enterprises, but also threaten the lives of employees [2]. One promising use case for big-data analytics would therefore be the management and prevention of occupational accidents or work-related diseases. Employee’s work behaviour and health-related data can be integrated to detect correlations and patterns and recognise core drivers of human behaviour at the individual or organisational level [3]. This means that the analytics focus shifts from understanding aggregates (patterns, segments, etc.) to understanding actions and behaviour of individuals. The success of using big data for individual behaviour change and awareness creation is, however, dependent on mutual value creation for both individuals and enterprises – a big difference to traditional use cases of big data. We therefore encourage an alternative approach, one that suggests perceiving and designing such big data infrastructures as an “ecosystem” which can function properly only if the individual and organisational values are aligned and compromised.
Language
English
HSG Classification
contribution to practical use / society
HSG Profile Area
SoM - Business Innovation
Publisher
Swiss Re Centre for Global Dialogue
Publisher place
Rüschlikon
Pages
11
Subject(s)
Division(s)
Additional Information
pp. 55-65
Eprints ID
249131
File(s)![Thumbnail Image]()
Name
RiskDialogueSeries_desease.pdf
Size
436.27 KB
Format
Adobe PDF
Checksum (MD5)
5676287260ce22327211f63f214475e0