This presentation is to create awareness of the use the use of predicative analytics in public sector organizations with emphasis on UAE government organizations.

1. Predictive Analytics in UAE Government Organizations
Dr. Saeed Al Dhaheri
Advisor, Ministry of Foreign Affairs – U.A.E
@DDSaeed
IDC Big Data and Business Analytics Forum, 18 November 2013, Abu Dhabi
www.mofa.gov.ae

2. Predictive Analytics in UAE Government Organizations: Agenda
statistics
Overview and definition
drivers
 challenges
 Potential for predictive analytics in government
examples
Building Predictive analytics capability in UAE government sector
Technology tools used for predictive analytics
conclusion
2

3. World Wide interest in Big Data and Predictive analytics
3

4. Statistics: Analytics big bang
The rate of CIO investing in analytics and big data technologies in the ME is
growing more from 12% in 2012 to more than 40% in 2013 – IDC
Investment is expected to increase at CAGR of more than 20% over the
next five years – IDC
New jobs arising: Data Scientist
4

5. 5

6. Overview and Definitions
Big Data: collection of data sets so large and complex and difficult to
process using traditional data processing techniques
Forces: mobile, social media, information and cloud are driving big data
Predictive analytics (PA): encompasses a variety of techniques from
statistics, modelling, machine learning, and data mining that analyze current
and historical facts to make predictions about future, or otherwise unknown,
events
Based on predictive models and data mining
PA answers what is likely to happen?
Types of analytics:
– Descriptive analytics: insight into what is happening
– Predictive analytics: understand what can potentially happen
6

7. Strategic Drivers
UAE Gov vision to be one of the best government in the world by 2021
Smart city initiatives (e.g, Dubai smart city initiative)
Digital government initiative - increasing volume of data
Internet of things adoption (M2M applications)
– Example: salik by RTA, smart metering by ADWEA and DEWA, security
and surveillance
7

8. Challenges
organizational
– Lack of understanding of the value of PA
– Lack of analytical talent in government
– A general lack of career paths for analysts who do not transition to management
roles is a serious issue for employee retention
Technical
– Perceived complexity of PA
– Building the predictive models is sometimes complex process
Cultural
8

9. Potential for Predictive Analytics in Government
Law enforcement – “why just count crime when you can predict it!”
– Shifting crime fighting work from reactive to predictive and preventative
modes
– Examples:

Police force deployment decisions:

Models to predict area at greater risk for violent crime

Identify suspicious patterns to detect and prevent fraud
Health Care
– To determine which patients are at risk of developing certain conditions,
like diabetes, asthma, heart disease, and other lifetime illnesses
9

10. Best Practice: Singapore example
Singapore has positioned data & Analytics as a key driver for
competitiveness and growth
– Integrated approach
– Developing infocom industry and manpower capabilities
– Establishing data exchange platform
– Formulating the appropriate data policies
– Developing data hubs
Government business analytics program
10

11. Best Practice: Australia Public Service big data strategy
DACoE builds analytics capability across government
- a common capability framework
- sharing technical knowledge, skills and tools
- building collaborative arrangements to develop analytics professionals
11

12. Big data & Analytics example: Etihad Airways
Etihad airways uses big data and analytics in many ways:
 maximizing income opportunities (optimizing pricing strategy)
 forecasting maintenance and spot problems before happening
 Benefits:
 Reduce fuel consumption and shorten turn-around-time at airports
 improve the traveller’s experience while on board.
12

13. Big data and Analytics example: Masdar Institute
Masdar institute is very active in research and development
– Workshop: Data analytics for renewable energy integration
Renewable energy integration
– Multidisciplinary issue
– Example of research topics:
– forecasting of electricity supply
and demand,
– detection of faults
– demand response applications
13

14. MoFA interest in business analytics
Building up use cases for analytics
– MoFA mobile app usage and performance
– Staff deployment at UAE missions overseas
14

15. Building PA capability in UAE Government Organizations
Five critical enablers: building maturity in each area
– People
– Processes
– Technology
– Data
– Governance
Raising awareness
Training programs
15

16. Technology
BI vendors offer advanced analytics tools
New generation BI software offers more user friendly
visual PA for more pervasive adoption
On premise vs. cloud based solutions
Look for free or cheap tools for experimentation
Plan and implement proper Infrastructure for big data
and analytics
16

17. Conclusions
Government needs to move to data-driven decision making culture
More collaboration between the federal and local government is needed in
information sharing
Leadership sponsorship is important
Start small, demonstrate value and grow
Data governance and Data quality is key to successful PA projects
PA needs to be more invisible to users and embedded at points of decision
or action
Don’t rush into PA tools before proper planning
17

18. Thank you
18