BDOD 2013 WORLD SUMMIT ON BIG DATA AND ORGANIZATION DESIGN Paris, May 2013

1. UNIVERSITY/PUBLIC
DRIVEN APPLICATIONS
How to access, organize, and develop values
from Big Data to meet societal challenges, to
optimize public utility usages and to reduce
waste?

2. Group Participants
• A. Letizia Allegra Mascaro
• Andrew Rindos
• Albert Lejeune
• Alessandro Bria
• Andrea Lodi
• Benoît Otjacques
• Carlo Cavazzoni
• David Nguyen
• Eiman Kanjo
• Elias Carayannis
• Fabien Mieyeville
• Fabrizio Piccolo
• Florent Pratlong
• Francesco Saverio Pavone
• Gabriel Juhás
• Georgios Theodoropoulos
• Giorgio Pedrazzi
• Giovanni Erbacci
• Giovanni Righini
• Giulia Adembri
• Giulio Iannello
• Giuseppe Fiameni
• Jean-Patrick Péché
• Samuel Javelle
• Juan M. Vara
• Kathleen M. Carley
• Leonardo Sacconi
• Ludovico Silvestri
• Maria Chiara Pettenati
• Maria Teresa Pazienza
• Mladen Vouk
• Nesrine Zemirli
• Paolo Frasconi
• Paolo Nesi
• Paolo Tubertini
• Patrik Hitzelberger
• Renaud Cornu-Emieux
• Renaud Gaultier
• Rick Edgeman
• Roberta Turra
• Sanzio Bassini
• Senese Francesca
• Sergey Belov
• Simone Tani
• Stavros Sindakis
• Svetlana Maltseva
• Yves Denneulin
Group chair: Maria Chiara Pettenati
20 papers, 47 authors

3. Group 2 geography
FR, IT, USA (Washington DC, PA, MD, NC), Saudi Arabia, Russia, Spain,
UK, Luxembourg, Denmark, Thailand

4. Table of Content
A.
B.
C.
D.
E.
State of the art
Current Work
Near future Work
Challenges and issues
Recommendations

5. STATE OF THE ARTA.

6. How organizations view Big Data
Schroeck, M., Shockley et al (2012) Analytics: The real-world use of big data How innovative
enterprises extract value from uncertain data
18%
16%
15%
13%
13%
10%
8%
5%
Greater scope of information
Non traditional forms of media
New kind of data and analysis
Real time information
Large volumes of data
The latest buzzword
Data influx from new technologies
Social Media Data 1144 respondents

7. Three elements
• Big data is seen as data
(different kinds of), storage of
data and analysis of the data
1
• Social aspects of data are
minor2
• Big data is a hyped term
3

11. Schroeck, M., Shockley et al (2012) Analytics: The real-world use of big data How innovative
enterprises extract value from uncertain data

12. Data is there and we need to make the best out of it

13. We produce and consume Data for a specific purpose

14. “Big Data is the set of
technical
capabilities, proc
esses, strategies
and skills for
continously converting
vast, fast, varied data
into Right Data to
obtain actionable
insights and
foresights”
Adapted from: Beacon Report – Big
Data Big Brains – 2013

15. CURRENT WORKB.

16. Marco Pospiech and Carsten Felden ( July 29, 2012). AMCIS 2012 Proceedings.
Big Data
A State
of the
Art
Group 2:
improving final
users
experience in
Big Data – new
methodologies,
theories, techn
ologies, new
educational
curricula

17. Big Data – Group 2 Survey
1
•Application
Domains
2 •Use / consumption
3 •Functional Areas

18. SURVEY RESULTS
(17/20 RESPONDENTS)

19. Group 2 – Big Data production
& utilization
32%
68%
Data Production &
Utilization
Data Utilization

20. Group 2 – Application domains
7
2
7
8
5
2 2
6
1 1
2
5
0
1
2
3
4
5
6
7
8
9
Application domain

21. 12 12
6
2 2
8
10
2
8
0
1
2
3
4
5
6
7
8
9
10
11
12
13
Group 2 - Functional Areas
covered

22. Group 2 - Type of AnalyticsGroup 2 – Type of Analytics
35%
30%
13%
22%
Type of analytics
Descriptive
Shallow Predictive
Deep Predictive
Prescriptive

23. CHALLENGES & ISSUES
B.
C.
D.

24. Government /
Healthcare /
Soc. Sciences
Information Sciences
Data-
intensive
Sciences
Application Domains
State of
the Art
Near
Future
Work
Challenge
s and
issues
EnterprisesEducation

25. Government /
Healthcare / Soc.
Sciences
Information Sciences
Data-intensive
Sciences
Application Domains (1/2)
State of the
Art
Near Future
Work
Crisis
management, Pollution
monitoring, Sentiment
Analyisis, Service
Personalization, Traceabilit
y management,
Organizational learning and
enterprise intelligence
Big Data Production
Laboratory-restrained
processing functions
Scalable temporal and
network approximation
Algorithms for information
extraction and text mining
Data modelling
Data sharing
Remote processing
Open Data
(beginning), Decision
Support
Systems, Labour
Planning Healthcare,
Social network
analytics, Mashups
Web of Data
Unstructured content
analysis
Innovation dynamics
and organizational
ambidexterity
Social media analytics
Geo-network
analytics

26. Government /
Healthcare / Soc.
Sciences
Challenges
and issues
Application Domains (1/2)
Knowledge discovery and sharing, data storage, format conversion, data
fusion, multi-media content metadata analysis
Metadata std., Data
gathering, Analytics
evolution, Exascale
computing, Advance
d Open and Linked
Data, Policy changes
to support use of big
data,
cross-border data
transfer, data supply
chain
Interdisciplinarity (skilled professionals), large-scale Information visualisation, easily
accessible frameworks for developing intelligent applications, address a common/
shared semantics, system of systems optimization
User-friendly data
browsing and information
retrieval
High performance
computing requirements
Users (scientists) training
Information retrieval
models, scaling
algorithms, Information
relevance /utility, limited
access to social media data
, multi-lingual data, user
participation, law, security, p
rivacy, ethics, smart social
user profiles/semantic
modeling, inference
algorithm, pervasive
technology
Information Sciences
Data-intensive
Sciences

27. EnterprisesEducation
Application Domains (2/2)
State of the
Art
Lot of professionals dealing with data
management, a relatively small number
of professionals building models, and a
large number of users who are
downstream from those models who
have to make decisions
University of Milano
Ecole Central Lyon
The North Carolina Virtual Computing
Lab (VCL)
Grenoble Ecole de Management (GEM)
and Grenoble INP – Ensimag
Universty of Roma Tor Vergata
Near Future
Work Living Lab
Design Thinking
Organizational design to support
efficacy, robustness and resilience
Data-driven decision making
Business model sustainability
Business Process Management
Systems
Knowledge, information & data
analytics – more computationally
intensive
Process Mining, Discovery

28. Challenges
and issues
Application Domains (2/2)
Educating to Big Data
Big Data to support Education
Mining techniques, Analytical tools
Reference models
Exascale computing challenges
Memory and Storage
Reliability and resiliency
Concurrency and Programming
models
System Software
Co-design of hardware and
applications
Sustainable Enterprise Excellence
(SEE)
Data Governance
large-scale Information visualisation, easily accessible frameworks for developing
intelligent applications, address a common/ shared semantics, system of systems
optimization
EnterprisesEducation

29. RECOMMENDATIONSE.

30. Organizational Educational
Strategy &
Policy
Recommendations
Expected
benefits
Functions &
Technology
Expected
benefits

31. Organizational Educational
Strategy &
Policy
Recommendations (1/2)
1) Support curricula design in Mngt
Science, Analytics, Data Sc., Eng. and
Operations Res. with interdisciplinary
competencies (Math, Computer
Science, Economics and
Engineering, Social and Human
Sciences)
2) Integrate mathematics didactics with
topics addressing value extraction
from data
3) Complement traditional math
branches (Calculus, Geometry and
Algebra) with Operations
Research, Data Mining and Machine
Learning
4) Address educating to Big Data as
well as Big data in education
1) The message must come from outside
(companies, public institutions, funding
agencies)
2) Make the case for the value of Big Data
and put in place the appropriate business
process
3) Open real valuable (big) data and align
processes and policy
4) Sustaining cultural shift: Right Data is
more valuable than Big Data
5) Direct research funds to interdisciplinary
research in Analytics and Data Science and
Engineering
6) Provide open governance on decision
making tools and means
Expected
benefits
Collapse efforts, realize economic, ecologic
and societal potential of
information, Promote
sustainability, efficiency, innovation, open
democracy through transparency. Address
societal challenges with scientific
techniques instead of using a techno-centric
New generations of experts trained to
address big societal challenges with
appropriate tools, skillset and mindset
More efficient educational system

32. Organizational Educational
Functions &
Technology
Recommendations (2/2)
1) Translate Big Data, Smarter
Planet, Analytics and Optimization into
better recognizable keywords like
Operations Research/Management
Science, Computational
Mathematics, Statistics
2) Start «analytics» programs (learning as
well as academic analytics)
3) Develop new metrics of quality of the
educational process and the
educational institutions
4) Мodeling tools for
education, Integrated with Big Data
analytics
5) Provide access to large datasets for
educational purposes
1) Invest in (technological) capacity
building
2) Implement good information
processing practices
3) Define new frameworks and
architectures for big data
analysis
4) Address languages diversity
5) Support careful analysis and
massive data warehousing of
heterogeneous and distributed
pieces of data.
6) Contribute to standardardization
Expected
benefits
Improve Big Data accessibility for
several applications, support better
deployment, easier access to big and
open data, easier identification of
processes that need optimisation
Smarter education
Smarted administration
Innovation in Education & Research

33. Improving final users
experience in Big Data
with new
methodologies, theories,
technologies, educationa

35. UNIVERSITY/PUBLIC
DRIVEN APPLICATIONS
How to access, organize, and develop values
from Big Data to meet societal challenges, to
optimize public utility usages and to reduce
waste?