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JPS Public Policy Consulting 1
Big Data for the Analysis
of the Digital Economy and Society Seville, 22 September 2015, JRC-IPTS
Wrapping up ?
Jean Paul Simon (JPS Public Policy)
Summary:
Some key words
Statistics and economic development
Some issues
Challenges
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“Don’t ask for meaning; ask for the use”
Usually attributed to Wittgenstein (Philosophical
Investigations)
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Big data: a quick overview with pictures
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Some key words
Granularity
Flexibility
Making visible:
strongly connected regional networks become visible through the
analysis of call volumes e.g. Sri Lanka
Vizualisation techniques: interactive visualization tool, EIPE 1&2
Mobile Networks Data:
MNBD (Samarajiva): to improve and extend measurement
Impressive identification of communities based on call analysis (Sri Lanka)
Rising new policy questions.
De Cordes: reuse technical network management data.
ISTAT (Barcaroli): call data records.
Eurostat (Wirthmann): Mobile phone data for tourism and
population statistics.
Internet sources: web surveys, web scraping (generic/
specific) and text mining, Wikipedia, Google searches.
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Statistics and economic development
“statistics 'everywhere‘”
Combining big data with traditional data sources
Blending of sources and multipurpose statistics
Multiple sources/ single view
D4D.
Acknowldged transformative impact (UNO, OECD)
Some funds available
Heatlh applications: Ivory Coast, Senegal, Sri Lanka
Urban planning: Sri Lanka
Applied economics: Webdatanet
Tapping into current opportunities
Web surveys: wageindicator a useful source of information
Wide international scope (including emerging countries)
High industry and occupation details
High flexibility (provides specific information)
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Some issues
Quality:
Applied economics: Webdatanet
Data not generated by any of the existing scientific methods
Uncertainty about generation process & sources of error
Problems depend on Research goals
Representativeness
Of web surveys (Wageindicator)
But can be corrected
Of MNBD: e.g. coverage
Validation
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Challenges (1)
Data analytics is a frontier field with tools and techniques under
development
Big data source scouting.
Continuous update of methodological research agenda
Expertise in short supply/ capacity building
Quality and transparency
Privacy and confidentiality: preserving privacy and openness
Access to third party data sources / data sharing
Scientific and technical standards and methodology
Professional ethics
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Challenges (2)
How to create a proper market and find the appropriate
business models
Structural issues of asymmetry: between data owners/suppliers
(billion) v. a handful of data collectors
Symmetry may be needed to foster digital innovation
Hence a need for adequate business models i.e. for access to data: rights
management, contracts and pricing
Need for the right ecosystem: governance/ regulation,
private public partnerships, data sharing, industries and
universities
BD does not answer the why question (Samarajiva)
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Conclusions
“Big data is a big pain in the arse. There’s a lot of it, and it’s
all over the place.” (Robert Strickland, ex T-Mobile USA)
"The irony of Big Data is that the places where success is
quite clear are often the places where the term simply isn’t
used. In those places, the term may actually be disliked".
C.Taylor (Wired, 2015)
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Thanks With illustrations from Dear Data a year-long, analog data drawing
project by Italian artist Giorgia Lupi and US artist Stefanie Posavec.
Available at: http://www.dear-
data.com/?et_mid=736984&rid=240765462
g, analog data drawing project by Italian artist Giorgia Lupi and US artist Stefanie Posavec.
Available at: http://www.dear-data.com/?et_mid=736984&rid=240765462
[email protected] [email protected]
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