www.mofa.gov.ae

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

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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

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World Wide interest in Big Data and Predictive analytics

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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

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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

– Prescriptive analytics: how to make specific outcomes happen

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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

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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

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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

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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

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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

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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.

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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

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MoFA interest in business analytics

Building up use cases for analytics

– MoFA mobile app usage and performance

– Staff deployment at UAE missions overseas

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Building PA capability in UAE Government Organizations

Five critical enablers: building maturity in each area

– People

– Processes

– Technology

– Data

– Governance

Raising awareness

Training programs

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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

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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

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Thank you