1. Big Data in a Digital City
Key Insights from the Smart City Case Study
Sonja Zillner , Sebnem Rusitschka
Siemens AG

2. @BYTE_EU www.byte-project.eu
Overview
Focus, Approach and Results
• Macro-view of the creation of value from potentially massive amounts of urban data
• that emerges through the digitalized interaction of a city’s users,
• i.e. of citizens and businesses, with the urban infrastructure of resources such as
energy and transportation, social and administrative services, etc.
Focus of the Case Study
1. Desktop Research
2. Focus group consisting of stakeholders from the “city,” “mobility” industry, “energy”
industry, city “technology solution providers”, as well as EU BYTE researchers
3. Interviews complemented the focus group, and enabled us to clear follow-up questions
or collect alternative and/or more detailed perspectives. The interviewees were selected
based on their field of work or study and their seniority.
Our Approach and Methodology
• General overview on the current state of big data in smart cities by examining data
sources, data uses and flow as well as discussing technical challenges
• Identifying positive and negative societal externalities
Results

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Data Sources
1. Mobility Data (violet)
2. Energy Data (green)
3. Environmental / Geo Data (orange)
4. Operational and Process data (blue)
• “Traditionally, like many
other sectors, cities haven
been managing only the
necessary data – not all the
data”
• Digitalisation is seen as
main driver for the creation
of big data sources
• A digitalized city
encompasses a myriad of
existing and potential new
data sources, such as
sensors
Various Origins of Data
More & More Data gets collected

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Data Usage
is facing technical challenges
12= de Montjoye et al. (2013): "Unique in the Crowd: The privacy bounds of human mobility".
• The way how data is being collected and stored massively influences
how easily it can shared
• The majority of data generated remains in silos or old storage
technologies or is not collected at high-resolution
Existing means of data collection and storage hinder the seamless
exchange
1
• Data needs to be anonymized sufficiently
• Data Privacy is seen as challenging task:
“Only four spatio-temporal points, approximate places and times, are
enough to uniquely identify 95% of 1.5M people in a mobility database.
The study further states that these constraints hold even when the
resolution of the dataset is low, mobility datasets and metadata
circumvent anonymity.”12
Privacy and security concerns need to be addressed2

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Understanding the Data Flow
allows to identify critical driver for smart city ecosystem
• to overcome established data silos
• establishing the basis for the intermediation between its users, the citizen and its
partners, the resources
Cities require means for data sharing and exchange (e.g. data platform)
• Investment Dilemma: who pays? who operates?
• Willingness to share data: who participates?
Challenge: How to kick start?
• All stakeholder groups highlighted that they could immediately benefit from integrated
mobility data
• All stakeholder indicated their willingness to share mobility data if this can ensures that
the overall traffic problems can be improved
• Given the situation that mobility becomes increasingly electrified, energy data will follow
to become a data source of high relevance for this ecosystem
• However, for all these data flow scenarios modern platform techniques are required
Critical Driver for smart city ecosystem: Integrating mobility data

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Economic externalities (excerpt)
• Investment dilemma in digital cities
• high ROI is not possible by scaling
• A single city represent s a rather
limited market opportunity
• As basis for data sharing across
stakeholder, common platforms are
needed
• city’s complexity makes the kick-start
of a platform initiative difficult
Key findings
• Open source and open platforms are seen as promising for future data sharing
• Investments by the public sector into the data infrastructure and the subsequent
opening of this infrastructure as a utility / commodity
Recommendation

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Social and ethical externalities (excerpt)
• Immense potential of big data for
social goods
• Privacy and Security concerns need to
be addressed
• A debate on how to assure „enough“
equality as not all citizen will reap
value from data in equal amounts is
required
• The society in general needs to
address the questions: What will men
do when machines learn simple
tasks?
Key findings

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Legal externalities (excerpt)
• New sources of data create new
ways that data can be misused.
• The legal framework needs an
update with the core principal of
putting the individual first
• The penalties of data misuse needs
to be so high that they will prevent
misuse
Key findings

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Political externalities (excerpt)
• Big data business can weaken
European economy, if big data
monopoly of companies like Google,
Amazon, etc. remains.
• Harmonization of legal framework
across the European market is
required for scaling up big data
businesses
Key findings

10. @BYTE_EU www.byte-project.eu
Thank you for
your attention!
Any Questions?
Dr. Sonja Zillner
Email: sonja.zillner@siemens.com

11. @BYTE_EU www.byte-project.eu
Focus Group of the Smart City Case Study
Smart City
Stakeholders
Company Position Relevant Topics
Jaakko Salavuo City of Helsinki Head of Comms & IT Information & Communication Technologies in the City
Bart Rosseau
(repr. Gino
Vertriest)
City of Ghent E-Strategy Open Data
Dave Carter
University of
Manchester
Honorary Research Fellow
Chair, European Connected Smart Cities Network | Centre
for Urban Policy Studies
André Dias CEIIA Portugal Head of Intelligent Systems
Advanced smart mobility concepts: the mobi.me cloud
platform; S3C, an infrastructure for monitoring smart cities.
Lean Doody
(repr. Nicola
Walt)
Arup Associate Director of Consulting Smart City Best Practices, Technology Solutions
Jean Dulac IEA
Energy Analyst from Energy
Demand Technology Unit
Urban planning and development policy, with an emphasis
on transport and building technologies.
Peter Bjørn
Larsen
CLEAN Smart City Manager Smart City Hub
Ajit Joakar Future Text
Innovation - Digital convergence
– Mobile Web 2.0
IoT, Machine Learning, Cities
Francisco
Rincón
Siemens
Corporate Development
Sustainable Cities
Smart City Best Practices, Technology Solutions
Benjami Kott EnergyDeck CEO
Energy platforms for tracking & benchmarking at
community level
Prof. Hans
Uszkoreit
DFKI Chair Artificial Intelligence „A Legal Framework for the Virtual World”
Jamie Cudden City of Dublin Smart City Coordinator Dublink:d Big Data Open Data Platform
Sonja Zillner Siemens Senior Key Expert Semantics Big Data Value Association, cPPP
Mihai Sercaianu Make Better GIS Analyst Data availability, open data and citizen engagement

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Smart City Focus Group Workshop

13. @BYTE_EU www.byte-project.eu
Cartography of Big Data Value in Digital Cities
Through group discussions and break-out sessions with interdisciplinary experts as a focus group

14. @BYTE_EU www.byte-project.eu
Workshop Analysis Preview
End user need of time- & resource-efficient mobility may well be the spark that brings all other
relevant data and stakeholders together

15. @BYTE_EU www.byte-project.eu
Interview Partner
Organization Industry
sector
Technology
adoption stage
Position on data value
chain
Impact of IT in
industry
European City Public Sector Early majority Acquisition
Analysis
Curation
Storage
Usage
Strategic mode
Technology
Provider
Start-up,
Energy
Early adopter Acquisition
Analysis
Storage
Usage
Turnaround mode
Technology
Provider
Non-profit,
Mobility
Early majority Acquisition
Analysis
Storage
Usage
Turnaround mode
Technology
Provider &
Research
Multinational,
Smart City
Early majority Acquisition
Analysis
Storage
Usage
Strategic mode