Predictive analytics in uae government organizations


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

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Predictive analytics in uae government organizations

  1. 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
  2. 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. 3. World Wide interest in Big Data and Predictive analytics 3
  4. 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
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  6. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 18. Thank you 18