introduction on the cultural issues in the big data era, with focus on innovative initiatives to utilize big data for cultural affairs

1. BIG DATA …
AND CULTURAL LANDSCAPE
UAE CONTEXT
PRESENTED IN THE MCYCD WORKSHOP – UAE INNOVATION WEEK
22 – 28 NOVEMBER 2015 – # UAE INNOVATES
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Sufyan Al Barghouthi
Director of Org. Development – FCSA - UAE
Member of Global Working Group on BIG DATA for Official Statistics – UN
26 NOVEMBER 2015

2. My Route Map
• What's BIG DATA
• BIG DATA and Culture sector
• BIG DATA and Cultural Landscape
• Some Cases .. Food 4 Thought ..!
• Some Main Conclusions
• Open Discussions

3. Information every where…

4. We are living in an ‘online information society’
Internet of Things
“Remember … MORE
numbers doesn’t mean
more INORMATION!!!!.”

5. Growth of Global Internet usage by 2015

6. What does that mean?
Big Change … Big Shift ..!!
• Big data
New
Production
Factor
• Claude Computing
New Public
Services
• Internet
New
Infrastructure
network

7. Around
6 Millions Tweets
per day in the
Arab countries
11% fro UAE
90000
2011
410000
2014
???
2021

8. What's Big data definition..?
Many definitions, but most of them are
summarized as follow:
Big Data is a term applied to data sets whose size
is beyond the ability of commonly used software
tools to capture, manage, and process the data
within a tolerable elapsed time. Big Data sizes are
a constantly moving target currently ranging from a
few dozen terabytes to many petabytes of data in a
single data set.

9. What they say about Big data ..?
New Oil / wealth which increase by use!!

10. Main Sources for Big data
• Smart meters and sensors:
Traffic cameras, GPS devices, price scanners, power
monitors, smart watches, smart phones, etc.
• Social Interactions:
Talks and publications on social networks like Twitter,
Facebook, FourSquare, etc.
• Business Transactions:
Movements of credit cards, electronic cash registers, cell
phone records, etc.
• Electronic files:
Documents which are available in electronic formats such as
PDF files, websites, videos, audio, digital media broadcasting
• Broadcast media
Digital video and audio produced on real time
It comes as Structured and Unstructured forms

11. BD characterized by Vs term:
we can add Value added

12. Data Confidentiality / Security in Cultural
Landscape
• Personal information is individual & precious to
each one of us – it’s vital that we treat it
properly.
• There is a long-standing and healthy debate
about the balance between the right to privacy
and the necessity to hold and share data.

13. Challenges of B D in Culture Sector
(International Exp.)
• There is concerns about the accessibility, quality, and
comparability of cultural data, different infrastructure;
• Pre judgments and inherited norms: many cultural
practitioners assume that data—and especially
quantitative data—are of limited value when it comes
to making programmatic and artistic decisions;
• Different culture sector stories: lack of coordination
and standardization in existing cultural data collection
efforts.
• In addition to organization level challenges .. Learning
institutions are most likely better in using BD.

14. B D Driving Future Cultural
Landscape
 Data-driven organizations
perform better on measures
of financial and operational
results than those who do
not
 Data facilitate efficient
processes, saving time and
money
 Data lead to innovation
 Data will ultimately lead to
funding.
 Data-driven decision making
require organizational cultural
change
 Strong Leadership is necessary
to set clear goals and to ask the
right questions
 Skillful and talented Data/IT
Specialists must be on staff.
 Lack of statistical and technical
skills in the labor force

15. BD and Culture sector in the UAE Context

17. Interactive Cycle of Engagement and Empowerment
Public
Policies;
YES
Gover
nment
Society
Environ
ment
Individ
uals
Sustainability
Engage
ment
Empow
erment
Accountability

18. Macro View: BD Impacts Domain
Utilizing
BD
opportunit
ies
Economic
growth of
information
industry
Social and
economic
benefits to
people
Improving
public
service
delivery
Citizen
awareness
and
developme
nts

19. BD for Development in Culture Sector
Big Data for Development
is about turning imperfect, complex, often
unstructured data
into actionable information
By the time hard evidence finds its way to the
front pages of newspapers and the desks of
decision makers, it is often too late or
extremely expensive to respond…!!!

20. Can we link B D to the Development Agenda Post 2015?
Lets have a look on the SDGs
Remember
Monitoring means:
Measuring and tracking

21. B D & Sustainable Development Agenda Post 2015

22. B D & Sustainable Development Agenda Post 2015

23. B D & Sustainable Development Agenda Post 2015

24. B D & Sustainable Development Agenda Post 2015

25. Practical view: Where we are standing?
Wisdom
intelligence
Understanding
Knowledge
Information
Data / statistics
Noise / Numbers

26. Where we are standing? Information Journey …
Seeing what you asked for
Versus
asking yourself what you see;
1st defines the info you want; the 2nd the info defines the model you need.

27. Org. Dev.!?
Development
Modernization
Standard-
izations
Strategy
Dev.
Operation /
KPIs
Corporate
performance
&
Excellence
Innovative Solutions
Foresights / Shaping the Future

28. So …
Who is influential ?
People influence the culture or culture influence the people?
How Big Data can help?

29. What sort of project we can think about with BD?
Role of
Culture..
• What is the
role of ‘the
arts’ in
people's lives
relative to
their other
activities and
interests
Audience
Habits
• How do UAE
nationals /
Non-nationals
satisfy their
needs for both
creative
expression and
artistic
enjoyment
Culture and
UAE Mix
• What is the
culture mix fits
demographic,
social, cultural,
technological
shifts and the
UAE mix?
Cultural Shift
Drivers
• What macro
trends in
consumer
behaviors and
public tastes
are driving
demand for
new arts and
future culture

30. What sort of project we can think about with BD?
Cultural
turning Points
• What line of
change on
culture
audience
behavior and
consumption
is taking
place? Is
there turning
points
Culture values
• What links
between arts
on the ground
and people
values? Who
drive? Where
we are going
from now?
Demand for
Culture
• Why people join
activities they
may previously
disliked or wasn’t
aware
Designing
Demand
• What role can
our institution
play in building
demand for
the arts and
culture?

31. What sort of project we can think about with BD?
Time Use and Youth needs
• What motivates youth in our
society to invest their time and
their physical presence in the
arts as participants rather than
merely audiences , how they
make their choices?
Impacts
• What is the rewards /
impacts of culture
season?

32. How can we work with Big Data?

33. How we can work with Big Data?
Step 1. Source Data: Speed, Type and Amount.
What kind and how much data are we working with?
• Assessing how hard it is to access
• Determining how it needs to be transformed
• Identifying the technologies to facilitate the process
Step 2. Data Preparation: Cleansing and Verification.
What do the data need for operational requirements?
• Define methods required for data prep such as:
• Standardization, verification, filtering, etc…
Step 3. Data Transformation.
What is required to leverage the data?
• Unstructured data may be broken down and presented in a structured
format
• Data sources can be aggregated to determine not-so-obvious
relationships between data types

34. How we work with Big Data?
Step 4. Business Intelligence/Decision Support.
Tools, methods, techniques to leverage data
• Data Mining
• Visualization/Simulations
• Keyword Searches & Syntax Analysis
Step 5. Analysts/Visualization.
How should the data be used?
• Present data visually so it can be explored
• Use data as is to support/enhance/improve existing
organizational processes / policies
• Monitoring performance on strategy outcomes
• Informed decision making (improved inputs).

35. Moving ahead …Organized Big Data Project …
Assess StrategyDefine
BIG
DATA
Project
Inputs Plan
Execute OutcomesReview

36. What we need to Run Big Data Project in
Culture Sector?
Sufficient and necessary conditions
 Adequate Staffing
 IT and supporting Infrastructure
 Adequate budgets
 Road map
 A Data-Driven organizational
culture
 Openness to organizational
change
 Leadership support
 Cooperation with partners
 Project Planning
 Data Analysts/IT Specialists,
etc…
 Data storage, Software,
Hardware, Connectivity, etc…
 Technological investment
 Data Prioritization
 Evaluation process

37. Food 4 Thought
Some Cases from others experiences

38. Quick wins .. Start simple ..
Social media as example ..!!

39. What Social media tell us?

40. Multidimensional Topic

41. Culture Sector Domains
AFTER SCHOOL GAB .. Food 4 THOUGHT
Young people face a number of dangers during the hours
after school. There are approximately 20 to 25 hours per
week that children are out of school while most parents are
at work, creating an “after-school gap.”
Self-care and boredom can increase the likelihood that a
young person will experiment with drugs and alcohol by as
much as 50 percent.
Youth tend to develop patterns of alcohol, tobacco, and
other drug use - or nonuse – from ages 12 to 15.
On school days, 3-6 PM are the peak hours for teens to
commit crimes, be in or cause car crashes, be victims of
crime, and smoke, and other social diseases.
Teens who do not participate in afterschool programs are
nearly three times more likely to skip classes at school than
teens who do participate.

42. Country Practices: Success stories
The case of Museums - UK:
• Big Data can be a smart project to encourage discovery
and learning,
• Online tourism topics can generate from multiple sources
new stories about culture;
• it's also an opportunity for both virtual and physical
audiences to tell cultural stories on their own terms
• Creating evidence-based cultural stories
• This Interactive process will create new ways of working,
as well as new forms of storytelling that allow cultural
institutions to develop new models of participation with
audiences.
Example: sharing some collections on line and collect and
analyses responses!

43. Case Example: Consumer confidence (Netherland)

44. Consumer confidence: Twitter Data

45. Youth at Risk Policy has been insufficient in fighting serious/life-threatening youth problems
Analyzing social data – buzz patterns related to suicide among youth – allows formulation of
policies to effectively prevent suicide by better understanding the circumstances and
psychological states of the youth
(Source) National Information Society Agency Pilot Project (Jul-Sept, 2012)
3 / 15
e.g.) Suicide Context Pool e.g) Online spread of harmful content e.g.) Influence by Major Players Online
Case Example: Supporting Youth At Risk Policy (S K)
Suicide
risk
factor
Harmful Contents

46. Wikipedia as a big data source (E C case)
Insights on world heritage from analysis of Wikipedia use
Chart shows people's Popularity of WHS over time
(What happened in March 2013?!)

47. How the Society feels ? (N L case)
4
7
A number of basic emotions
Happy
Sad
Angry
Scared
Tender
Excited

48. How the Society feels ? (N L case)

49. Youth Empowerment Strategy (YES) in UAE
and Innovation Enablers
YES
Entrepreneu
rships
starters
Patent
Rights
Auth.
Investment
Capital
Innovation
empowerment

50. Making it Works ,,,
• Leader
• Training
• Policy notes
S.C Index
• analytics
• Private
Sector
• Academia
• Social
media
• Tell. Com.
Data
Inputs
BI Tool
TeamResults

51. Developing Social Composite Index…

52. This project can help the Ministry to Track YES
in Different Fields …
MCYCD
B.D Project
Ministry
policies 4
development
Impact
Tracking
Intervention
measures
Promoting
Innovation
Partnerships
Society
engagement

53. To conclude …
Something we can start on the Fast Track …

54. PARTNERSHIPS … Core stone
• Expand and invest in the talent pool by creating a formal track for IT/Data
managers with training and certification in BIG DATA Analytics and data
scientist technologies.
• Establish and broaden coalitions between culture academic and
associations to develop professional standards and shared best practices
for the field.
• Expand “college-to-government service” internship programs focused on
technical aspects of BIG DATA for Cultural agenda.
• Align incentives to promote data sharing for projects in culture activities.
• Provide further guidance with data sources and stakeholders on privacy
and data protection practices.
• Develop intellectual property policies to promote innovation.

55. Things 2 do on the Fast Track
• BD Vision supported by leadership: leadership that
can authentically build bridges across the different
spheres and interests that make up the cultural
sector in UAE.
• Orchestrated and engagement programs with all
partners in the culture sector, more ideas more
success, (Partnerships).
• Shift the interest of BD from data’s value as an
accountability tool to data’s value as a decision-
making tool, and until we are using data to inform
decision-making about programming, we can’t truly
be said to be engaging in data-informed decision-
making.

56. Things 2 do on the Fast Track
• To develop an agenda for future research and data
collection, with clear objectives and a plan of
action to utilize BD for culture sector.
• Develop and encourage training and professional
development in data-related skills for staff
members; open eye on BD analytics.
• Open the gate for innovation through improving the
cultural data infrastructure and partnerships.

57. • Leading the shift: Managing the change; transform
personal and organisational ways of dealing with
culture issues, improve training, and consider
credentials.
• Clarify and streamline legal framework for BD
usage: develop policies for using BD for culture
needs, develop a fast-track procedure where there
is a strong case for quick wins.
• Short time to market: Ensure effective enforcement;
implement fine provisions quickly; provide new
powers; ensure adequate resources; and revise
structure where needed.
Generic and policy Recommendations

58. Generic and policy Recommendations
• Develop mechanisms for safe and secure research and
statistical analysis: develop ‘safe havens’ as
environment for Culture-Based Research for the
Ministry to serve also national needs in this sector.
• Safeguard and protect publicly available (online)
information: coordination with relevant partners in the
government.
• Develop a project: BID Data for Culture Sector, with
complete strategy and action plan and well defined
deliverables according to time schedule.

61. Some use full references?
• New Data Directions for the Cultural Landscape: Toward a Better-
Informed, Stronger Sector
• Alan Brown, Principal, WolfBrown
• John W. Jacobsen, CEO, White Oak Institute and President, White Oak
Associates, Inc.
• Roland J. Kushner, Associate Professor of Business, Muhlenberg
College
• Lawrence T. McGill, Vice President for Research, The Foundation
Center
• UNSD big data for official statistics, UAE 2015.