Big Data and Smart Government

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1 Big Data and Smart Government Institute for Public Administration Australia Nov 20, 2014 Ramayya Krishnan W.W. Cooper and Ruth F. Cooper Professor of Information Systems H. John Heinz III College, Carnegie Mellon University

2 Seizing the Data Revolution Data Tsunami: Explosive Growth in Size, Complexity, and Data Rates Enabled by mobile phones, social media, , videos, images, click streams, Internet transactions and sensors everywhere! Opportunity to integrate and leverage with existing legacy data sources HOWEVER, what is important is not whether data is big or small but that it has context and relevance to the task or decision at hand. The Age of Data: From Data to Knowledge to Action Widespread use of data to create actionable information leads to timely and more informed decisions and actions. Fundamentally, this is about evidence-based management and policy making Image Credit: Chi Birmingham 2

3 Imagine By coupling roadway sensors, traffic cameras, and individuals GPS devices, we can reduce traffic congestion and generate significant savings in time and fuel costs. By accurately predicting natural disasters such as hurricanes and tornadoes, we can employ life-saving and preventative measures that mitigate their potential impact. By integrating emerging technologies, such as MOOCS and inverted classrooms, with knowledge from research about how people learn, we can transform formal and informal education. By mining data from electronic health records and through experiments, develop a causal understanding of cost-efficient and personalized practice guidelines associated with the best health outcomes 3

4 Smart Sensing, Reasoning and Decision Environment Sensing Emergency Response Situation Awareness: Humans as sensors feed multi-modal data streams Percepts (sensors) Agent (Reasoning) Credit: Photo by US Geological Survey Actions (controllers) Pervasive Computing People-Centric Sensing Social Informatics Smart Health Care Evaluate Sense Intervene Identify Personal Sensing Public Sensing Social Sensing Assess 4 Source: Sajal Das, Keith Marzullo

5 Jack and Suri, 2013

6 Mobile Devices & Cellular Networks are Pervasive The number of mobile-connected devices will soon exceed the number of people on earth. vs Mobile data traffic will grow at a compound annual growth rate (CAGR) of 66 percent from 2012 to 2017, reaching 11.2 exabytes per month by

7 ACMA, 2013

8 8 Slide Credit: Intel Corporation

9 Statistical Agencies Surveys vs. social media? Legacy Data Surveys remain very relevant and social media is a complement not a substitute! Public Health Education Business data Adoption of Evidence-based Management and Policy Making is key to improve quality and value of government data

10 Characterizing Data What matters is data that is actionable not big data or small data!

11

12 The Data and Analytics Value Chain Network and access Infrastructure Devices and data rates Mobile phones are the dominant device but we are on the cusp of embedded sensors for a variety of applications Secure Data storage and compute infrastructure Role of cloud Services Regional vs. National Strategies Data governance and sharing infrastructure Role of Open Data initiatives from the Public Sector Role of Exchanges (e.g., health information exchanges with private and public data in the US) Data sharing standards Data for the public Good: (see Orange s Data for development) Data Privacy Policy and its interaction with business models

13 Exemplars: Analytics in Action

14 Ushahidi: Collective Intelligence using social and mobile

15 Source: IBM Research

16 SURTRAC: Scalable, Real-Time Adaptive Signal Control for Urban Road Networks Traffic 21 and The Robotics Institute, Carnegie Mellon University Controller Video Cameras CMU SURTRAC System 1. Video cameras measure traffic conditions. 2. System optimizes phase schedule at intersection and sends commands to the control box. CMU SURTRAC System Controller 3. Schedule is communicated to downstream intersections to indicate what is coming. 4. Scheduling cycle is repeated every few seconds.

17 Surtrac Pilot Results and Status Penn Circle Field Test (Jun 2012): % Improv. AM rush Travel Time % Mid Day % PM rush % Evening % Overall % # of Stops % % Wait Time % % 8.89% % % % % % Emission s 23.83% 29.00% 18.41% 14.01% Bakery Square Expansion (Nov 2013): 21.48% % Improv. AM rush Mid Day PM rush Travel Time # of Stops Wait Time Emissions 17.02% 33.81% 32.76% 16.21% 21.35% 37.23% 38.09% 17.62% 28.61% 44.87% 46.40% 24.77% Overall 24.07% 40.39% 41.54% 20.67%

18 Crime prediction in Chicago Since 2009, we have been working with the Chicago Police Department (CPD) to predict and prevent emerging clusters of violent crime. Our new crime prediction methods have been incorporated into our CrimeScan software, which has been used operationally by CPD for deployment of patrols. From the Chicago Sun-Times, February 22, 2011: It was a bit like Minority Report, the 2002 movie that featured genetically altered humans with special powers to predict crime. The CPD s new crimeforecasting unit was analyzing 911 calls and produced an intelligence report predicting a shooting would happen soon on a particular block on the South Side. Three minutes later, it did

19 Enablers of Analytics

20 Data Management Technology Stephen O Grady, Redmonk

21 Statistics New and Existing methods Machine Learning Optimization Social Science Methods

22 Cloud-based Services

23 Scalable Computation Many important applications must process large streams of live data and provide results in near-real-time - Social network trends - Website statistics - Intrusion detection systems - etc. Require large clusters to handle workloads Require latencies of few seconds Exploit data parallelism or graph parallelism based on task Adapted from source: AMPLab, UCB

24 Frameworks and Services Graphlab (CMU), AMPlab (Berkeley) Computational frameworks Amazon (EC), EMC, Oracle, Storage and computation via the private market Bundled and specialized offerings Salesforce

25 The Data and Analytics Value Chain Network and access Infrastructure Devices and data rates Mobile phones are the dominant device but we are on the cusp of embedded sensors for a variety of applications Secure Data storage and compute infrastructure Role of cloud Services Regional vs. National Strategies Data governance and sharing infrastructure Role of Open Data initiatives from the Public Sector Role of Exchanges (e.g., health information exchanges with private and public data in the US) Data sharing standards Data for the public Good: (see Orange s Data for development) Data Privacy Policy and its interaction with business models

26 Business and Policy Enablers

27 Technology alone will not solve all of society s challenges. Must consider economic, social and cultural barriers to adoption and use of solutions. 27

28 Policy Innovation Lean Innovation Key enabler of a number of ICT applications Example: Mobile money; Balancing KYC requirements with enabling airtime agents to do both cash in and cash out Business model innovation incenting individual engagement with compensation models See airtime compensation in Jana.com

29 Policy Considerations Be Problem Driven: Support both bottom up and strategic planning on problems and initiatives that are likely to benefit from data analytics and enable their use of these technologies Disaster preparedness, Intelligent Water Management, Smart Retail, Education Leverage Existing Investments and Nurture New Data Sources: Statistical agencies, public health, CDR s from telco s, social media data Public-private partnership Provide incentives for good data governance and stewarding:. User protection via privacy and security technologies. Need to create policies that will enable data flow! Education and Workforce Development: Develop, recruit and grow the skills needed to fuel and support the data-driven economy. Partnership with universities and private sector to train human capital Make enabling infrastructure accessible and affordable: Mobile broadband reach and pricing Cloud infrastructure and services

30

31 Education and Workforce Development Data Science: The Sexiest Job of the 21 st Century 1

32 Credits Farnam Jahanian, VP-Research, CMU

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