BIG DATA IN ACTION WORKSHOP: 2017 WORLD GOVERNMENT SUMMIT

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EXECUTIVE SUMMARY

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“�Technology�and�globalization�is�a�source�of�disruption�and�creation...�But�the�transition�from�technological�promise�to�the�solution�of�public�sector�problems�is�not�going�to�happen�automatically.�It�will�require�effort�on�the�part�of�governments�to�better�articulate�what�problems�technology�could�help�them�solve,�an�enabling�policy�environment�for�technological�solutions,�better�public-private�partnerships,�and�lastly,�mindset�and�cultural�shifts�to�form�the�right�attitudes�for�change.”� Renaud Seligmann, Practice Manager, World Bank Group

�e Governance Global Practice (GGP) supports client countries to help them build capable, e�cient, open, inclusive, and accountable institutions. �is is critical for countries to underpin sustainable growth and is at the heart of the World Bank’s twin goals of ending extreme poverty and boosting shared prosperity.

For the 2017 World Government Summit, �e World Bank’s Governance Practice partnered with the Big Data Program in Global Operations Knowledge Management (GOKMU) and SecondMuse to deliver a “Big Data in Action” workshop and high-level panel. Both activities explored the opportunity to use big data applications in government service delivery, policy-making, and citizen engagement.

�e workshop gathered 25 big data experts in public and private sector to advance thinking on these areas. �e workshop included:

• Seven lighting talks featuring case stories of big data in government

• Exercise to identify high priority areas for applications of big data within service delivery, policy-making, and citizen engagement

• Team based exercises to design practical big data prototypes in areas like public health, transport, government integrity/accountability and Jobs

EXECUTIVE SUMMARY

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M A I N I N S I G H T S • Big data is not just about the size or quantity of the data now available. It is also about how governments use

data driven capabilities to gather citizen feedback on services and put it into action toward more responsive and proactive service delivery, enabling governments to continuously improve public service delivery, and build trust.

• As governments look into adopting the use of big data, we need clear regulations and guidelines developed for data use, data ownership, privacy, civil liberties and equality.

• �e private sector is generally the owner of the most valuable datasets. Public-private data collaboratives are emerging that provide access to private sector data for public and social good. �ese arrangements will be critical moving forward to ensure that big data can be accessed and responsibly used.

PART I: BIG DATA IMMERSION

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�e �rst half of the workshop provided participants with a grounding in big data in Governance. Seven lightning talks were delivered.

Stefaan Verhulst, Co-founder of GovLab, spoke about the role of data collaboratives in making data more liquid. Verhulst highlighted the impact of data collaboratives in the following �ve areas: situational awareness and response, public service design and delivery, knowledge creation and transfer, prediction and forecasting, and impact assessment and evaluation. He shared examples in which big data was used for emergency response in Nepal, improving access to electricity in India, and preventing new outbreaks of Ebola in West Africa. Verhulst also noted six key incentives for the private sector to share data: reciprocity, research and insights, reputation, revenue, regulatory compliance, and responsibility.

Rhiahanan Price, Director at Digital Globe spoke about the value of high resolution imagery and geospatial analytics for governments. Price shared Digital Globe’s work in household enumeration to help governments prioritize deployment of resources to curb malaria. In Zambia, this work involved crowdsourcing to narrow down the dataset and cover over 500,000 sq. km in four months. Price also shared her team’s work in helping the Indonesian government prevent forest �res from spreading-- by leveraging crowdsourcing, high-resolution short-wave infrared imagery, and real-time analytics. She spoke about the opportunities that lie ahead in terms of machine learning and convergence of data, and emphasized the need for new business models and data licensing agreements.

Stefaan�Verhulst Rhiahanan�Pr ice

PART I: BIG DATA IMMERSION

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Steve Adler- Chief Data Strategist at IBM spoke about the importance of paying attention to culture, emphasizing that when we try to get culture to change with technology, our results are not as good as they would be if we adjusted technology to �t the culture. Adler shared lessons from his time serving as Chief Data O�cer for the city of Medellin, Colombia. His experience planning a data show for the city’s residents illustrated the importance of understanding and addressing the habits and characteristics of di�erent segments of society. Among other things, this included understanding the local population’s schedules, their perception of the value of data in their lives, reasons for the limited engagement and understanding data analysts had with regards to the general population.

Brooke Patridge, CEO of Vital Wave, spoke about some of the challenges that are evident in the area of public private partnerships in big data. �ey include lack of evidence base, lack of regulatory and policy frameworks, lack of sustainable, scalable business models for the private sector to see opportunities, and lack of local and global capacity. Patridge spoke of the role governments can play in improving such partnerships—particularly by contributing to and requiring relevant use cases and case studies; articulating the value of data use to partners, civil servants, and citizens; embracing risk-mitigation strategies relating to privacy—rather than keeping a distance; and cultivating data science as a skill set.

Steve�Adler Brooke�Patr idge

PART I: BIG DATA IMMERSION

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Chan Cheow Hoe, Chief Information O�cer, Government of Singapore spoke of how the Government of Singapore uses big data for data-driven decision making. In one case, the government estimated the level of economic activity in an industry by analyzing patterns in water usage, power usage, and transportation patterns to and from the industrial zone. In another case, the government used data aggregated from commuters travel cards to understand commuter travel patterns and design express bus lines—thereby reducing citizen travel times and tra�c congestion. He spoke of the government’s challenges around data sharing, creating a data mindset, and recruiting and retaining talent. He also highlighted opportunities for governments and the private sector to turn data into a service and to use data for dynamic resource allocation.

Daniel Szomoru, Data Scientist, Uber spoke about Uber Movement, a website that uses the constant stream of data generated by Uber users to help urban planners make informed decisions about their cities. Szomoru shared current and future capabilities and insights that such data can provide-- including zone-to-zone travel times, tra�c patterns at di�erent times of the day, street segment speeds, and data on dangerous intersections and road quality.

Linus Bengtsson, Director at Flowminder spoke about how the organization uses big data to support vulnerable populations around the globe by providing data on the number of people, their characteristics, and their mobility through high resolution maps. Bengtsson shared how the organization combined traditional household surveys with micro-censuses and extrapolation to help the government of Afghanistan build population models in the absence of a census. He also shared the organization’s capabilities to complement traditional sources of data with estimates of land cover, temperatures, night lights, tweets, mobile movements etc.

Chan�Cheow�Hoe Daniel �Szomor Linus�Bengtsson

PART 2: SOLUTION PROTOTYPING

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�e second half of the workshop involved participants working through hands-on group exercises. Participants �rst worked through a series of questions to identify priority areas for using big data in government. �is was followed by a hands on exercise where the group assembled into four teams to develop prototype solutions within high-priority areas.

PART 2: SOLUTION PROTOTYPING

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G R O U P E X C E R S I Z E : M A P P I N G O P P O R T U N I T I E S A N D P R I O R I T Y A R E A SQUESTION 1: What�v is ion�does�your �organizat ion�have�with�regards �to�big �data� for � the�next �3 �years?

PART 2: SOLUTION PROTOTYPING

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G R O U P E X C E R S I Z E : M A P P I N G O P P O R T U N I T I E S A N D P R I O R I T Y A R E A SQUESTION 1: What�v is ion�does�your �organizat ion�have�with�regards �to�big �data� for � the�next �3 �years?

PART 2: SOLUTION PROTOTYPING

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QUESTION 2: What�government�services �have�the�potentia l � to �be�changed�most�profoundly �through�big �data� in �the�next �3 �years?

PART 2: SOLUTION PROTOTYPING

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QUESTION 3: How�wi l l � these�services �change� in �the� four �selected�areas?�

FOCUS AREA: JOBS AND SKILLSFOCUS�AREA:�ACCOUNTABILITY,�

TAXATION,�AND�FRAUD:

Changes in focus of education and skills training Shifting if means if production to user with smartphones

Education standards changing rapidly when a new competence is needed

New Skills needed for new jobs Careers giving way to robust short-term engagement marketplace

Mapping of jobs and skills Dynamic capability to match industry needs with skills

More fluidity introduced to the labor market Social Media and online jobs website as new source of labor data

Digital transformations Clear understanding of job marker

Better policy planning and decisions.

Change in burden Increased citizen engagement and feedback loops to encourage

transparency Radical transparency increasing the cost of engaging in corruption

Accountability and greater transparency Automatic reports on transactions

Decisions based more on facts and less on politicsRise of E – services in government

Budget Transparency Move from statistics to precise measurement and reporting More effective delivery of services to vulnerable populations

FOCUS AREA: HEALTH FOCUS AREA: TRANSPORTATION

Better understanding of the cause of pathology Central aggregation of records

More personalized Diagnosis and Medication Improved causal indicators or predictors of health outcomes

Precision health Evidence-based performance managementMove towards using generic information

Improved interoperability around healthcare systemsMove to preventive medicine

Improved matching of demand and supply of product and servicesHealth monitoring

Reduced pressure on curative treatmentsElectronic patient records

Private sector involvement to improve public transportation Better insight on mobility patterns

Dynamic Planning Rise of innovative products and services

More transparencyFast feedback

Intelligent traffic managementBetter public transportation

Optimization of private transportation services Minimized congestion and optimized routes

Data-driven optimization of public transit

TEAM DESIGN SESSIONS

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T E A M 1 : S O L U T I O N A R E A : J O B S A N D S K I L L S�e big data for jobs and skills group considered a few areas for exploration at the outset—from how big data can help mitigate automation’s impact on jobs to understanding why some jobseekers may be more adaptable than others. �e group eventually gravitated towards an approach that was broad enough to encompass speci�c areas individual group members wanted to explore. �e question the group chose was: “How can we use Big Data to better understand labor market dynamics?”

D A T A G E N E R A T I O N�e group explored how granular, real-time data from big data sources could complement existing macro-level data from the government. In this case, such data would include information that can be obtained from job boards, employer websites, professional networking sites, other social media, call detail records (CDR), etc. Data on vacancies, industry, skills needed, and salary would be critical on the demand side, while data on available skills, educational backgrounds, location, current salaries, activity on professional networking sites, network size, and member’s chances of �nding opportunities would be critical on the supply side.

A C T I O N SA key action item in this area would be standardization of data—particularly on skills. �is would be followed by gathering and aggregating data on demand- and supply-side factors from the various sources.

�is would allow for the creation of a database/data-sharing platform that can be used by governments, businesses, skills providers (universities and vocational training institutes), and individual job-seekers. �e group also discussed how actions to address data privacy need to complement each of these steps.

I N S I G H T SFirst, the data-sharing platform would provide governments, businesses, and individuals with information on the number, type, and location of jobs; salaries; and the �ow of jobs within and between regions and countries. �is would also provide key information on the supply side—on what skills are available and where, and allow for the matching of jobs and skills. In addition, this would allow governments and businesses the opportunity to answer more complex questions such as: How can the labor market be made more resilient to change (globalization, automation of jobs)? How can the country/city best prepare for changing demand for skills? Which markets/skills are most likely to be viable/transferable? Which ones are most at risk? What characterizes individuals that are easily able to adapt to new labor market conditions? Why do workers with certain skills respond better to change than others?

R E S U L T S�e solution prototype would allow for better matching of jobs and skills. Governments could use these insights to anticipate demand for skills and allocate resources to skills-training and education accordingly, resulting in a dynamic, proactive, highly competitive, and resilient labor force.

TEAM DESIGN SESSIONS

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T E A M � 2 : � S O L U T I O N � A R E A : � A C C O U N TA B I L I T Y,�T A X A T I O N , � A N D � F R A U D�e big data for accountability group chose the following question for exploration: “How can we improve the integrity of Government Civil Servants” Other notable questions that were not selected included: How can big data be used to improve civic trust? How can big data be used to detect illicit �nancial �ows? How can big data be used to detect bene�ts fraud?

�e group’s solution focused on issues of reporting, under-reporting, �nancial disclosure, and integrity of civil servants.

D A T A G E N E R A T I O NCadaster data, Financial Transaction Data, Travel Data, Satellite Data, Social Media, Video

I N S I G H T S�e solution would use machine learning algorithms to detect patterns and instances of collusion, non-compliance and corruption in civil servants. For instance, we would be looking for property that was undeclared, luxury gi�s or purchases, patterns in networks between individuals and entities that suggest collusion or clientelism. We discussed importance of putting checks in the data and algorithms to protect individuals through anonymization, or to report not on individuals, but on properties of the network or system (not at the individual level) that characterize illegal, non-compliant behavior, and to have processes with checks and balances to follow up.

A C T I O N SOne action item is to create indicators for monitoring and to �ag instances for follow up. Another action item is to address the challenge of making the disincentive strong enough while keeping the data anonymized.

R E S U L T SMaking this data accessible to the public would serve as a deterrent for civil servants to engage in non-compliance.

TEAM DESIGN SESSIONS

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T E A M 3 : S O L U T I O N A R E A : H E A L T H�e big data for health group began with a scoping discussion, which focused on whether the end state should be an incremental improvement on existing approaches (e.g. make the existing model quicker, more e�cient, and less costly) or a transformational approach, which would seek to move emphasis and incentives from a focus on interventions (treatments) to a focus on preventative health maintenance. �e group decided to focus on the more aspirational e�ort to refocus on preventative measures. �e group noted that for the �rst time in history, food-related health problems were more about poor quality than insu�cient quantity. Simply put, junk calories were a bigger health risk than malnutrition.

D A T A G E N E R A T I O N In exploring data sources, the group focused on proxies for poor preventative health measures and highlighted Google Online Marketing Challenge. Some of the challenge winners could provide useful location proxies for where to target new approaches. For example, ResQ , which helps consumers “rescue” le�overs from restaurants and acquire quality meals at a cheap price, could provide heat maps of areas where preventative measures could be better received.

By combining more traditional data sources (e.g. social media, mobile data, senor data, city, county, national government data, etc.) with proxies from partners such as ResQ, the group sought to create a “health data ecology.”

I N S I G H T S�is could inform targeted e�orts to improve health maintenance in demographic and geographic outliers (e.g. urban food deserts and rural smoking and obesity clusters in the U.S.). �e health data ecology could be mapped to the disease/health burden (where and why life expectancies are below national averages), both to inform programmatic activities as well as to �nd where new data sources/proxies are needed.

Other insights can be found by exploring intersections between health outcomes and exogenous factors such as con�ict, migration, natural disasters, climate change, etc. �is analysis is important to help identify factors that could swamp otherwise successful preventative health initiatives.

A C T I O N SA key actions item identi�ed by the group included data driven advocacy materials focused on in�uencing country-level health care policy change from interventions (treatments) to preventative health maintenance. �e group acknowledged that incumbents in the intervention �eld (pharma, insurance, “medical industrial complex”) have deep pockets and a strong vested interest in the status quo. �e case study on how European activists out maneuvered the ag biotech industry (e.g. capturing the public’s mindshare with the image of “frankenfood”) could help inform a successful campaign. Data without “sticky” and relatable stories will not be enough to achieve change.

�e group also highlighted the need for successful behavior change programs, citing the success in smoking reduction, which was as much a factor of socially ostracizing smokers as convincing people of the health hazards of smoking.

Another important data driven strategy for policy makers would be return on investment modeling as well as performance management and tracking to demonstrate the possible impact achievable by scaling successful pilots.

TEAM DESIGN SESSIONS

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R E S U L T S�e group identi�ed key results, such as improved health outcomes, reduced healthcare costs and wider health insurance coverage. �ey also �agged an important bene�t of changing an asymmetric and sometimes adversarial relationship between heath care providers and patients into a peer-partnership approach that created more trust.

TEAM DESIGN SESSIONS

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T E A M 4 : S O L U T I O N A R E A : T R A N S P O R T A T I O N�e big data for transportation group selected the following question for exploration: “How do we optimize the use of transportation in dense cities?” Other notable questions that were not selected were: How does big data inform public transportation options in a city? How can big data support regional transportation infrastructure and services? How can big data be utilized to forecast transportation services?

Big Data in Action Solution prototype: �e solution focused on adapting pricing and transportation choices using big data derived insights to deliver more responsive transportation services to end-users going from A to B locations.

D A T A G E N E R A T I O N Key data sources considered were bus data (when, where), private sector data (needs to be shared with governments), surcharge data from car sharing, beacon data, private car data (less available), GPS data. �e case of Uber Movement was presented as potentially valuable as an example from the private sector. Uber Movement is a platform that shows recent data regarding travel times and time ranges from Uber rides. Sharing of data needs to be done increasingly through APIs to encourage usage.

A C T I O N S • Optimize transportation usage by spreading the tra�c load through adjusted incentives. �ese incentives will be

informed by insights derived from transportation and related data.

• Pricing adjustments (a city delivering public transportation services cannot price accurately without the appropriate data).

• Arrangements with private sector organizations to open critical datasets towards pricing adjustments.

• Mobility as a service, all public modes of transport need to be increasingly connected.

�e conversation led to a focus on dense cities given that populations are increasingly moving towards cities, and it is in dense cities speci�cally where big data can have the most impact regarding transportation. Locations such as Singapore are already utilizing big data to optimize the usage of transportation services and movement of end-users. However, there is much yet to achieve, as a large amount of critical data is still in hands of the private sector. Cities can work towards integrated, multimodal transportation service solutions by utilizing data insights from across di�erent transportation services, which can lead to an optimization of transportation usage across a city. Incentives such as transportation pricing adjustments are a key lever to allow for optimization to happen. Additionally, improving the enabling environment towards public-private arrangements can lead to critical datasets being utilized for transportation improvement. Optimizing the usage of transportation services will lead to more sustainable and less congested cities.

TEAM DESIGN SESSIONS

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I N S I G H T S�e solution would be looking to increase the e�ciency of public transportation systems in dense cities by delivering a platform that allows end-users to plan and optimize their trips from A to B. �e solution would need to map intermodal tra�c �ow to optimize the choices of users by combining the best options across transportation modes. Additionally, through big data insights, public transportation such as buses would be able to deliver express routes where most convenient. Data insights can also provide critical information about pain points such as intersections with the most accidents or bus stations that have peak �ow at speci�c times. City level interventions were also considered such adjusting pricing at speci�c peak hours to alleviate the usage of speci�c modes of transport and compensate by delivering others at the same time.

R E S U L T STwo key outcomes that the prototype seeks to in�uence: 1) Using big data and pricing adjustments to help deliver better transportation choices to end users, 2) Making the transportation system in dense cities more responsive through the use of adequate data.

APPENDIX

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1 . � P A R T I C I P A N T � R O S T E R

Rhiannan Price Director, Digital Globe

Steven Adler Chief Data Strategist, IBM

Brooke Partridge Chief Executive O�cer, Vital Wave

Syed Raza Senior Director, Mobile Data Digital Impact Alliance

Bruno Lepri Data Scientist, DataPop Alliance

Daniel Szomoru Data Scientist, Policy Research and Economics, Uber

Enda Ginting Assistant Deputy, O�ce of the President of the Republic of Indonesia

Marian Panganiban Regional Policy and Research Manager, Grab

Bruno Sanchez Data Scientist, Global Leader and WBG Data Scientist

Vasiliy Pushkin Deputy Head of Analytical Center, Government of the Russian Federation

Chan Cheow Hoe Government Chief Information O�cer, Government of Singapore

Karen Kee Advisor to Government Chief Information O�cer, Government of Singapore

Stefaan Verlhust Co-founder, Govlab

Linus Bengtsson Executive Director, Flowminder

Richard Wood Researcher, Flowminder

Brandon Barnett Director Corporate Strategy, Intel Corporation

Hatem El Sherif Data Analytics Expert, Federal Competitiveness and Statistics Authority UAE

Todd Khozein Partner, SecondMuse

Neisan Massarrat Director - Strategic Programs, SecondMuse

Richard BolyConsultant, World Bank Group

Dharana Rijal Consultant, World Bank Group

Trevor Monroe Senior Operations O�cer, World Bank Group

Rachel Winter-Jones Communications Lead, Governance Global Practice World Bank Group

Edouard Al-Dahdah Senior Public Sector Specialist, Governance Global Practice, World Bank Group

Renaud Seligmann Practice Manager, Governance Global Practice World Bank Group

APPENDIX

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2 . � B I G � D A TA � I N � A C T I O N � F R A M E W O R K�is framework helps participants delve more deeply into how big data can be used to advance a solution. Starting with a question(s), each team moves toward a desired future state. �is involves thinking through a number of questions at each stepL

Question: What is the right question

Data Generation: What data can be applied to answer this question?

Data Insights: What insights to we expect to �nd from this data?

Action Implementation: What actions can be taken based on the insights?

Results: What outcomes or behaviors do we want to in�uence?

3 . � L I N K S � T O � W O R K S H O P � R E S O U R C E S• 2017 World Government Summit: https://worldgovernmentsummit.org/home

• Workshop Collateral: Issues Paper, Presentations, Pictures, Posters - https://wbg.box.com/s/ozq261thgmhh3hs7wc7whsjujr86n9bg