smart city as a service - granicus

Smart City as a Service: Using Analytics to Equip Communities for Data-Driven Decisions

A Frost & Sullivan White Paper

Brian Cotton

frost.com

contents

Challenges Municipal Officials Face Delivering Services .......................................................... 3

Analytics Applied to City Problems ........................................................................................... 4

Unlock the Value of Data to Deliver Better Services, Faster .................................................. 6

Cloud Computing Enables Smart City as a Service .................................................................. 8

Equipped to Make Data-Driven Decisions: Smart City as a Service ........................................ 9

3


All rights reserved © 2015 Frost & Sullivan

Regional and municipal governments and large public organizations are striving to deliver higher levels of service, but are challenged with flat or decreased funding to support their efforts. One solution is to use the vast stores of data available to gain a better perspective to identify opportunities to deliver services more efficiently, and engage stakeholders with open data initiatives to create innovative programs – in effect becoming a smart city. A framework approach to apply advanced analytics to these floods of data provides a roadmap to achieve these aims, but city IT organizations are often limited by staff size, skill and budget, and are unable to implement the framework on their own. Cloud computing and software as a service can provide the means to quickly acquire data-driven decision-making capabilities that support smart city initiatives, putting advanced services within reach of virtually any size city.

CHALLENGES MUNICIPAL OFFICIALS FACE DELIVERING SERVICES

From public safety, transportation, to water and energy utilities, to city planning, effective stewardship of natural and financial resources is critical to providing public services. Reducing waste and increasing efficiency in government operations to sustainably deliver improved citizen and business services is the heart of many local government agendas.

What to do with a Flood of Data

Public safety organizations, municipal administrations and public service departments, including transportation and municipally owned utilities, generate and consume a lot of data.1 Local government amasses volumes of data on everything from public works, public safety and public facilities to elections, permitting and taxes. Every day, new data is created from across a city’s infrastructure through sensors and video cameras, as well as from people interacting with the government and from mobile apps. Legislation mandating retention periods2 guarantees that legacy data accumulates, and the mass is perpetually growing from new data that is continually arriving in structured and unstructured formats. Although this flood of data is challenging, there is ample opportunity to fundamentally change the way services are delivered.

Meeting the High Demand for Services and Providing a Better Experience on a Limited Budget

Public organizations and utilities are facing demands from citizens for more convenient access to services and expectations for a seamless, easy-to-use experience.3 This means less waiting in lines, problems fixed the right way the first time and less disruption from incessant construction on the streets. At the same time, government expenses – particularly at local levels – are increasing due to rising urbanization and aging infrastructure, as well as new program requirements passed down from national and regional governing bodies without sufficient funding to support them.4 This conundrum puts a premium on finding innovative ways to increase the value of government on a limited budget.

Government can solve this conundrum by digging deep into the data that it already has and surfacing connections and insights into how city goals and work projects are interrelated. Improved analytics can help city leaders and public officials better predict what can happen in the future, as well as guide their activities to achieve optimal outcomes for citizens. When analytic tools are shared, department managers can work across administrative boundaries and organizational silos, and collaborate around integrated service delivery models.5 Thus, distinct departments and agencies can see where activities can be mutually supportive (e.g., excavating the streets once to repair water mains, identifying areas where crime is a problem, resurfacing streets and replacing outmoded street lights) and operate from a common, single view of the city so that work is done more efficiently and more

4

frost.com


effectively. At the municipal level, this kind of collaborative approach can direct budget savings to new service initiatives and enable the city to be managed as a coordinated, holistic system of systems. This is the foundation for a city to become a smart city.

Many forward-looking organizations seeking to implement their smart city vision are now considering cloud computing as a way to quickly obtain powerful insight

capabilities without committing to large capital expenditures.

Making it Happen

Gaining deeper insight involves gathering all available data, organizing it, and then performing several types of analysis on it. However, most municipal governments have insufficient staff or skill to devote to this effort. Given constant demands placed on already limited budgets, city leaders need to acquire these capabilities quickly, with low upfront investment. This isn’t restricted to the local level; individual departments, such as water or transportation, or state and provincial levels facing floods of data will also seek to acquire capabilities quickly and cost-effectively.

Many forward-looking organizations seeking to implement their smart city vision are now considering cloud computing as a way to quickly obtain powerful analytic capabilities without committing to large capital expenditures. Rather than investing in city-owned and operated analytic tools, another option is to consider analytics offered as a service. City IT managers can determine if implementing smart city as a service is the right approach for their city by understanding the main types of analytics a smart city uses and the value they deliver.

ANALYTICS APPLIED TO CITY PROBLEMS

Business intelligence and analytics have a strong foothold in modern government.6 In public safety, for instance, data mining and analytics are helping to shift crime fighting work from reactive to predictive and preventative modes in cities such as Vancouver, Memphis and London. The use of analytics in other domains, such as education, water and public transportation, illustrate how cities are integrating analytic capabilities into many domains to solve problems confronting them.7 The resulting benefits include improved program success, cost savings and higher levels of services for citizens. Municipal governments seeking to generate value from their data will use analytics to learn from the past, look into the future, guide policy and activity to obtain optimal outcomes, and understand relationships between events and locations.

Geospatial Analytics: Understanding Where Things Happen

Geospatial analytics allow city managers, agency operations staff and other stakeholders to visualize events and conditions as they happen in specific places and how they may change over time. Geospatial techniques are often used with other types of analytics to enable users to associate cause and effect relationships with specific places under their jurisdiction. Geospatial analytics can produce density and heat maps, time-distance relationships and group optimization that can assist city and departmental officials allocate resources, plan for zoning or

5



temporary events, or understand population movement. Moreover, combining geospatial analytics with other types of analytics can enable cities to choose optimal routing for new roads or pipelines, siting fleet maintenance facilities or selecting community healthcare locations.

Descriptive Analytics: Learning from the Past

Descriptive analytics provide city leaders and departmental managers situational awareness and an understanding of historic trends. This could cover, for instance, establishing baseline departmental budgetary performance and the achievement indicator of each department, the frequency and nature of inquiries or complaints registered and response time to address them, and the current operational readiness of a city’s fleet of road maintenance vehicles. Descriptive analytics can also enable program administrators to quickly determine eligibility for benefit programs and match the right services to citizen needs, prevent fraud and waste of funds, as well as be used for planning purposes.

Philadelphia, for instance, uses descriptive analytics to identify tax delinquent properties that are available for auction.8 A property in Philadelphia is considered delinquent if taxes owed are not paid within nine months of the city’s deadline. These properties are subject to interest and other fees, which can accumulate and eventually overwhelm the value of the original tax. Often these properties are abandoned, creating a growing backlog of delinquent properties that can linger for decades. The city publishes an interactive map showing these properties, with which prospective buyers can easily identify opportunities. Interested buyers use the results of descriptive analytics to find investment opportunities, which can become revenue opportunities for the city.

Predictive Analytics: Transitioning from Reactive to Proactive

Predictive analytics develop insight into possible future conditions or events within a city or region, probable impacts of programs and policies, and potential citizen or business reaction to new initiatives. Predictive analytics measure the efficacy of services delivered, which can help fine-tune programs. Predictive analytics can also help city managers and officials test scenarios, for instance during emergency conditions or extreme weather events, and develop contingency plans to enact should any of these scenarios become realized. Predictive analytics can also help agencies identify risks associated with new initiatives, streamline the delivery of services and prevent duplication or fraud in the system for current initiatives.

Chicago is one example of a city using descriptive analytics to solve problems. Chicago, like other cities, constantly battles rat infestations. Using citizen complaints to its 311 non-emergency services line, Chicago’s Department of Streets and Sanitation developed models that can predict where rat populations are high, such as where garbage accumulates, and when rat populations are likely to increase quickly.9 By combining the predictive models with geospatial analysis, pest control officers can have a visual representation to indicate the times and places where their efforts can be most effective.

Prescriptive Analytics: Make Things Happen

Based on an understanding of how systems have behaved in the past, and how they are likely to behave in the future in a variety of circumstances, prescriptive analytics not only enable insight into the likely results of decisions, but also suggest the optimal path to achieve desired outcomes. They can be applied to determine the best way for a municipal program to achieve its policy goals, for instance. Prescriptive analytics can also support informed decision-making around developing new service delivery programs, providing personalized services to specific segments of a city’s population, or to optimize the use of limited city resources and assets.

6

frost.com


Prescriptive analytics are relative newcomers to municipal government implementations, but one example is found in the City of Des Moines, Iowa’s Wastewater Reclamation Authority (WRA). The WRA uses predictive analytics to optimize the operation of the motors in the city’s treatment plants.10 By understanding the performance cycles on the motors, the WRA was able to design operational and maintenance schedules that lower energy usage by 20%, helping make better use of the WRA’s budget.

UNLOCK THE VALUE OF DATA TO DELIVER BETTER SERVICES, FASTER

The data a municipal government has from which to generate analytics can come from legacy systems, devices and from people. Once city leaders understand how analytics can unlock the value in this data and envision how analytics can help deliver higher levels of service and optimize lean budgets, the next step is to map out implementation goals and plans that will drive their smart city transformation. Municipal planners and CIOs can use a framework approach, depicted in Figure 1, as a guide.

The framework approach in Figure 1 recommends three methods that create increasingly more value from the application of data analytics. Starting from a single domain or department, the approach can be extended to include other stakeholders, including citizens and businesses, and then to other domains across the municipal government. A municipal CIO or IT manager can also implement all three methods concurrently. As the relative scope of change associated with each method increases, the amount of value realized in making a city smarter increases.

Figure 1: Three Methods to Unlock the Value of Municipal Data

Integrate New Datawith Legacy DataV

alue

Rea

lized

Aggregate, organizeand manage data,

apply analytics

Leverage analytics andinsight across government

domains, optimizeprogram management

Empower citizens bydistributing dataand encouraging

citizen participation

Combine Insight AcrossMultiple Domains

Add Open Data

Improve service levelsIncrease decision accuracyRaise situational awarenessReduce redundancy

Increased accountabilityIncreased transparencyWider range of apps developedDeeper citizen involvement

Integrated program managementAutomation of citizen supportInterdepartmental responseinteroperabilityCollaboration around shared insightReduce operating costsRelative Scope of Change

Source: Frost & Sullivan

7



Integrate Existing Data with New Sources of Data and Apply Analytics

A city’s CIO or departmental IT manager will prepare for data-driven decision-making by making all municipal available data consumable by analytic tools, and then integrating it. This involves aggregating and organizing the data, and understanding the relationship between different types of data. Legacy system data, for instance, is static and structured, often housed in different storage locations. Other data is from a multitude of sensors placed around the municipal infrastructure, as well as from surveillance cameras located around the city. Increasingly, newer forms of data are generated by people – from interactions with 311 services, smartphones or monitoring social media – and these also need to be integrated. Once the data is integrated, different types of analytics can be applied.

Immediate value can be realized through reducing or eliminating redundant data in the system. At the same time, the results of analysis can help municipal officials and department heads improve service levels, increase the accuracy of their decisions and raise their overall situational awareness across the city. The framework approach recommends starting small with a single department or domain to gain experience with implementing analytics, and achieve some successes that can be used to promote the approach to other departments.

Deliver Transformative Insights: Combine Insight Across Domains

The hallmark of collaborative, integrated service delivery models is breaking down traditional silos and integrating key functions across domains to encourage collaboration between them. Once a municipal CIO or IT manager prepares the data and applies analytics in one department, the approach can be extended to other departments. The goal is to create shared insights across domains, thereby achieving higher value for municipal and department decision-makers that transcend individual domains.

Combining insight across domains gives departmental users the capacity to make associations between formerly siloed facts and events, people and trends. By using this approach, a department can identify new or previously unknown issues, root causes of problems, or affect a new policy. Government itself will realize value from a broad application of the approach when departments collaborate around shared data, and interoperate more easily through a common understanding of a situation and follow a collective response plan. Moreover, government can integrate and optimize program management, reducing operating costs and making the most of available resources.

Increasing Value with Open Data

The value of shared insight can be increased when municipal data is published in open formats, within accepted security limits, to the public. This is Open Data, and granting citizens, businesses and institutions access to municipal data amortizes the value of the data management and analytic tool investments. New value is created when third parties develop new applications and services to address public and private needs, which extend beyond governments’ capacity. Open data empowers citizens and encourages deeper participation, and it encourages large and small businesses to innovate through new applications and services. Many governments, such as Canada11, recognize increased citizen engagement under these programs and are actively expanding them to encourage participation in policymaking and regulatory processes.

Open Data increases government accountability and transparency. Mitigating concerns that staff in government offices may have over potentially increased workloads caused by citizen complaints requires leadership and vision to demonstrate that increased transparency will, in the long run, improve citizen satisfaction and lower complaints. One department’s move to open data can serve as a role model of positive change, guiding other departments’ transition to Open Data.

8

frost.com


CLOUD COMPUTING ENABLES SMART CITY AS A SERVICE

All the tools and enablers that support data-based decision-making in a smart city magnify the flood of data challenges confronting municipal IT departments. Analytics, collaboration and Open Data can place substantial storage burdens on municipal IT systems. This is loud and clear in a recent survey we conducted in which 33% of government IT managers in the US cite “data storage growth” as their top IT challenge.12 Compounding the problem, the always-on, 24x7 expectations of government service availability means that IT managers need a sophisticated backup strategy, which may also need to comply with strict measures on the duration and means of storage.

With the exponential growth in the amount and type of data, as well as the additional computing resources required by analytic-based decision tools, municipal IT departments will struggle to continually invest to maintain a traditional, largely inflexible, on-prem infrastructure to support these workloads. What municipal IT departments need is a new way to procure IT resources that can supplement current systems; one that enables the mayor, city council and departments to engage in data-driven decision-making and transition their city into a Smart City. Cloud computing is that new way.

Government has adopted cloud less quickly than other industries. Security and compliance concerns have historically been the main reasons keeping governments from being early adopters. However, this is changing as cloud service providers focus more on security and compliance issues, including in some cases offering government-certified versions of their services. As a result, government IT departments are increasing their use of cloud services. In fact, a recent Frost & Sullivan survey found that 37% of regional and local government IT managers in the US and Europe are currently using cloud computing, including software as a service (SaaS), while 57% are expected to be using it by 2016.13

The Tactical Benefits of Using Cloud to Enable Data-Driven Decision-Making

The key drivers for increased government adoption of cloud are the tactical benefits of shortening the time to value through rapid deployment, and making IT costs more “budget-friendly.” This combination of benefits enables departments to quickly prove the value of cloud-based analytics, which can drive support for expanding the deployment of cloud-based analytics to other departments. Rather than trying to obtain a rigid capital budget to implement storage, collaboration and analytic tools on a traditional, on-prem infrastructure, IT managers can procure these tools through the cloud using a more flexible operating budget. This is supported by our survey data that shows fully 50% of government IT managers cite “shifting costs from capital to operating budget” as a “very important” reason to procure IT services through the cloud.14

In addition, IT managers can match planned growth of smart city services (e.g., water, public safety, traffic) or additional users to operational budgets so the cost keeps pace with actual needs and available resources. A further tactical benefit of cloud is that there is a much lower management burden on a municipal IT staff to support the advanced analytics capabilities that underlie many smart city services.

The Strategic Benefits of Using Cloud to Enable Data-Driven Decision-Making

Most organizations, including government, start with the tactical benefits of cloud but soon shift to more strategic considerations that underpin a deeper transformation of overall IT operations to align with changing business goals. A powerful and open cloud platform enables municipal CIOs and IT managers to build a flexible foundation to support a comprehensive smart city service strategy that can grow to support future needs. Another benefit of a cloud strategy is that it gives IT managers an option to deploy on a hybrid cloud model, so workloads can be deployed on traditional platforms, cloud, infrastructure or software as a service, depending on the cost, performance and security needs for each workload.

9



Mayors and department heads will also find strategic benefits from a cloud-based implementation of smart city services. Analytic and collaboration capabilities, and the smart city services that rely on them, can be rolled out quickly and scaled up as citizens increase their usage of the services. Moreover, cloud supports high levels of service resilience, so critical smart city services are always available, no matter the time or location in which they are needed. Finally, municipal CIOs can optimize costs because the entire environment can be managed from a single console, simplifying their administrative workload and maximizing infrastructure flexibility.

EQUIPPED TO MAKE DATA-DRIVEN DECISIONS: SMART CITY AS A SERVICE

Citizens and stakeholders are demanding higher levels of service from government and municipally owned organizations. City leaders and department heads are applying descriptive, predictive, prescriptive and geospatial analytics to municipal data to make better informed decisions. By integrating all available data and applying analytics to it, municipal CIOs and IT managers are equipping their cities to make data-driven decisions – and this value increases when multiple departments and citizens themselves are able to collaborate around these capabilities. Philadelphia, Chicago and Des Moines are demonstrating that data-driven decision-making is a wise path to become a Smart City and meet demands for higher levels of service.

Although the analytic and collaboration capabilities that support data-driven decision-making can be implemented on a municipal or department computing platform, many cities do not have the personnel or skill to do this. Instead, CIOs and IT managers can obtain these capabilities as a service through a cloud platform and maintain the flexibility to grow data-driven decision-making capabilities as needs and resources allow. Moreover, by implementing a smart city strategy on cloud, municipal IT managers gain the freedom to support the mayor’s policy goals and departmental needs in a variety of ways. With these capabilities available as a service, virtually any city can easily, cost-effectively and quickly evolve into a Smart City.

The author acknowledges and thanks Lynda Stadtmueller, vice president of Frost & Sullivan’s Cloud Computing Services program, for her guidance and support on this paper.

This paper was developed by Frost & Sullivan with IBM assistance and funding. This report may utilize information, including publicly available data, provided by various companies and sources, including IBM. The opinions are those of the report’s author, and do not necessarily represent IBM’s position.

frost.com

ENDNOTES

1. “The Open Society.” The Economist, 25 February 2010 edition. http://www.economist.com/node/15557477. Retrieved 13 November 2014.

2. State of California, Archives Division. “Local Government Records Management Guidelines.” February 2006. http://www.sos.ca.gov/archives/local-gov-program/pdf/records-management-8.pdf. Retrieved 12 November 2014.

3. Fruechting, Ashley. “The Digital Town Hall: Meeting Citizen Expectations through Government Websites.” Canadian Government Executive, Volume 20, Issue 8. 03 November 2014. http://www.canadiangovernmentexecutive.ca/category/item/1675-the-digital-town-hall-meeting-citizen-expectations-through-government-websites.html. Retrieved 05 November 2014.

4. Ibid.

5. Janowski, Tomasz and Ojo, Adegboyega. “Transforming Government through Knowledge Management and Communities of Practice.” United Nations University, Center for Electronic Governance. Presentation proceedings from the China E-Government Forum 2009, 16-17 April 2009. www.egov.iist.unu.edu. Retrieved 11 May 2012.

6. Levine, E.S. “Challenges for Public Sector Analytics.” Analytics Magazine, March/April 2012. www.analytics-magazine.org/march-april-2012/537-challenges-for-public-sector-analytics. Retrieved 20 November 2014.

7. Cotton, Brian. “Smarter Analytics for Government: The Importance of Infrastructure for Insight.” A Frost & Sullivan White Paper. January 2013. http://public.dhe.ibm.com/common/ssi/ecm/en/xbl03024usen/XBL03024USEN.PDF. Retrieved 18 November 2014.

8. Tax Delinquent Properties in Philadelphia. http://www.philly.com/philly/news/special_packages/inquirer/in_HT_tax_delinquency.html. Retrieved 17 December 2014.

9. Thornton, Sean. “Using Predictive Analytics to Combat Rodents in Chicago.” Data Smart City Solutions, July 12, 2013. http://datasmart.ash.harvard.edu/news/article/using-predictive-analytics-to-combat-rodents-in-chicago-271. Retrieved 17 December 2014.

10. Goldsmith, Stephen. “3 Ways to Optimize Urban Infrastructure.” Government Technology, February 24, 2014. http://www.govtech.com/data/3-Ways-to-Optimize-Urban-Infrastructure.html. Retrieved 17 December 2014.

11. Boyle, Jim. “Stop, Collaborate and Listen: An Assessment of Canada’s Open Dialogue Implementation Strategy.” Dalhousie Journal of Interdisciplinary Management, Volume .10, Number 1 – Spring 2013.

12. Frost & Sullivan. “2014 Cloud User Survey.” Unpublished survey data collected in 2Q2014.

13. Frost & Sullivan. “The Impact of IT in Government: An End-User Perspective.” Report ND1E-72. July 2014.

14. Op cit. Frost & Sullivan 2014 Cloud User Survey.

10

frost.com


http://www.economist.com/node/15557477

http://www.sos.ca.gov/archives/local-gov-program/pdf/records-management-8.pdf

http://www.canadiangovernmentexecutive.ca/category/item/1675-the-digital-town-hall-meeting-citizen-expectations-through-government-websites.html

http://www.canadiangovernmentexecutive.ca/category/item/1675-the-digital-town-hall-meeting-citizen-expectations-through-government-websites.html

www.egov.iist.unu.edu

www.analytics-magazine.org/march-april-2012/537-challenges-for-public-sector-analytics

www.analytics-magazine.org/march-april-2012/537-challenges-for-public-sector-analytics

http://public.dhe.ibm.com/common/ssi/ecm/en/xbl03024usen/XBL03024USEN.PDF

http://datasmart.ash.harvard.edu/news/article/using-predictive-analytics-to-combat-rodents-in-chicago-271

http://www.govtech.com/data/3-Ways-to-Optimize-Urban-Infrastructure.html

For information regarding permission, write:Frost & Sullivan331 E. Evelyn Ave., Suite 100Mountain View, CA 94041

Silicon Valley331 E. Evelyn Ave., Suite 100Mountain View, CA 94041Tel 650.475.4500Fax 650.475.1570

San Antonio7550 West Interstate 10, Suite 400San Antonio, TX 78229Tel 210.348.1000 Fax 210.348.1003

London4 Grosvenor GardensLondon SW1W 0DHTel +44 (0)20 7343 8383Fax +44 (0)20 7730 3343

[email protected]

AucklandBahrainBangkokBeijingBengaluru Buenos AiresCape Town Chennai ColomboDelhi/NCR Detroit

DubaiFrankfurtHouston Iskander Malaysia/Johor BahruIstanbul JakartaKolkata Kuala LumpurLondonManhattanMiami

MilanMumbaiMoscowOxfordParisPuneRockville CentreSan AntonioSão PauloSeoulShanghai

Shenzhen Silicon ValleySingaporeSophia Antipolis Sydney TaipeiTel Aviv TokyoToronto Warsaw

Frost & Sullivan, the Growth Partnership Company, works in collaboration with clients to leverage visionary innovation that

addresses the global challenges and related growth opportunities that will make or break today’s market participants. For more than

50 years, we have been developing growth strategies for the Global 1000, emerging businesses, the public sector and the investment

community. Is your organization prepared for the next profound wave of industry convergence, disruptive technologies, increasing

competitive intensity, Mega Trends, breakthrough best practices, changing customer dynamics and emerging economies?

2Big Data and Analyticsfor Government Innovation

Abstract

This chapter discusses the transformation of the public service provision modeldue to big data, and in particular due to public engagement in the context of opengovernment initiatives. We outline the changing role of governments insocieties, and the technological enablement towards direct online democracy andactive citizen engagement, as well as the utilization of big data enabledgovernance as a competitive advantage for attracting resources and talent tomaintain a global smart megacity status. To this end, this chapter discusses theutilization of (a) new sources of data, such as Crowdsourcing, Internet of Things,(b) engage public talent, (c) institutionalize private–public partnerships and(d) seeks for new models of value-for-money public provision, but also thechallenges that big data present us with respect to data ownership, data quality,privacy, civil liberties, and equality, as well as public sector’s ability to attractbig data analyst talent. We demonstrate different aspects of this discussionthrough two case studies: Barcelona Smart City and Haiti’s emergency supportduring the 2010 earthquake disaster.

2.1 Introduction

This chapter discusses the impact of big data in the public sphere on public serviceprovision and new opportunities for public service organization and structure thatmay transform the role of governments in societies.

The utilization of ICT to improve public sector services has started with thewhole e-government discussion. Transforming government services using ICTs hasbeen a complex and costly task, often associated with the automation of publicservices and business systems integration. While e-government projects focused onoperational efficiency, initiatives such as Open Government efforts sought to fosterpublic service transparency, civic participation, and inter-departmental collabora-tion. This could be achieved by sharing public sector infrastructure, seamlessinformation sharing with other agencies, bundling core competencies to improve

© Springer International Publishing Switzerland 2015V. Morabito, Big Data and Analytics,DOI 10.1007/978-3-319-10665-6_2

23

service delivery and engaging external entities, such as universities and businesses(Executive Office of the President of USA 2014).

While these changes definitely seek to effect efficiencies, they are also qualitativein nature, changing fundamentally the nature of the relationship between govern-ments and citizens. Big data initiatives come to underpin their progress. Since 2012,both EU and the US are seeking ways, though legislative and policy changes, toremove obstacles in the use of big data which promise greater effectiveness withlower costs in the public sector (Nagy-Rothengass 2013). Civic participation viasocial media, for example, can also reduce the cost of public service delivery.Crowdsourcing information on potholes, for example, can cut down on inspectioncosts. Big data also promise clockwork provision of public service. Intelligent assets,such as intelligent traffic lights can notify a central asset management system abouttheir state of maintenance ahead of time and larking issues in their working condi-tion, so repair work can be streamlined without disruption of service (Thomas 2013).

Before we go on to elaborate on the role of big data in civil life, it is important tounderstand some underlying shifts in the role of Governments and its relationshipcitizens.

2.1.1 New Notions of Public Service: Towardsa Prosumer Era?

Everything about civil service, even its very naming “service”, has emphasized thetransactional relationship between citizens and government. The relationship issimple. Civilians pay taxes and in exchange they are served in various fields, health,education, road maintenance and the like.

Recently, however, a new understanding of public provision put citizens in therole of partners. The key idea is that the pursuit of public ends is the responsibilityof everybody—private and nonprofit entities, the public, and government. Part-nership with citizens and community involvement was a big part of the 2010Manifesto of the Conservative party in UK under the auspices of the “Big Society”program (Conservative Party 2010). The US Open Government initiative,announced only a year before in 2009 also seeks foster civic participation(McDermott 2010). Both are premised on the idea of enabling people to take care ofthemselves and of each other. Social media and smartphones can facilitate theinteraction between citizens and governments on the go. They can also amplify thecommunication and engagement of public though communities of interest. Tocombat crime, for example, citizens need to coalesce with police in monitoring andreporting suspicious activities. Recently, this is also happening in other areas too ofcivic responsibility.

Application developers, such as Citysourced.com have developed applicationsenabling citizens and residents to report and provide information to local govern-ment about all sorts of civic issues, from potholes to graffiti, fly tipping, brokenpavements or street lights. People can do so anonymously or not, they can upload

24 2 Big Data and Analytics for Government Innovation

http://Citysourced.com

photos, and pin them on a street map. The report is sent to councils and there is atracking of progress on the issue online (CitySourced Inc. 2014). This is a typicalexample of how technology has facilitated citizens to play the role of councilinspectors and this is a free service to the community and to the government too, asit minimizes inspection costs. Charities and interest groups work together toamplify the message. Cyclist communities, for example, have a big interest inpotholes as it is a big nuisance for them, so pothole reporting is promoted bycycling charities and associations (The National Cycling Charity 2014).

2.1.2 Online Direct Democracy

And even more fundamental change might take place due to social changes andtechnology advancements; one that aspires to give citizens decision making poweron social issues, much like the type of direct democracy of ancient Greece. PartyX(Nelson et al. 2015), is such an initiative. They seek to take advantage of develop-ments in online collective decision making, to involve every stakeholder inpolitical decision making. As this is currently on beta version and used only at locallevel for local issues, it does not fall under the big data agenda as yet. Should thiskind of technology however go into adoption phase and used to debate globalissues, then we can start seeing big data making inroads into the political andlegislative sphere. Real-time, big volume, unstructured information aside, globalpolitical debating will add another dimension of interest in our discussion of big data;that of ‘multilingualism’. Dealing with multilingualism is a dimension alreadyhigh in EU agenda (Nagy-Rothengass 2013).

2.1.3 Megacities’ Global Competition

Since 2011, more people live in cities than in rural areas for the first time in humanhistory. Megacities, i.e. cities larger than 10 million people, are an emergingphenomenon. According to the UN, the number of megacities will have grown fromfive in 1975 to 26, with 24 of them located in the developing world (United NationsDepartment of Economic and Social Affairs 2006). Megacities are not a local ornational issue. They will affect the future prosperity and stability of the entire worldas they will shape the balance of power of national economies in a global world,affect population mobility and configuration of talent, and will influence the socialand political dynamics of the world (United Nations Department of Economic andSocial Affairs 2006). Megacities have a functional and a symbolic role. What wouldUK be without London? And what would The Emirates be without Dubai?

Megacities are not just key instruments of social and economic development atall fronts but also harbors of social innovation for the private and public sector dueto their unique dynamics. Megacities are an attractive proposition for those seekinga better quality of life in terms of a higher standard of living, better jobs, fewer

2.1 Introduction 25

hardships, and better education. In a globally competitive environment Megacitiescompete for capital resources including global talent. They typically face a 5 %population growth rate, which challenges the quality of living indices (such assecurity, cost of living, mobility, employment, environmental) that put pressure onurban infrastructure and public policy. To raise their attractiveness, megacities needto improve on those indices (United Nations Department of Economic and SocialAffairs 2006; Mostashari et al. 2011). Hence, Megacity Mayors face uniquedilemmas, primarily on how to raise standards of living across a number of well-being indices in the face of high population growth rates, while compete in theglobal environment.

Smart city infrastructure, the bundle of Internet of Things solutions for cityinfrastructure management and intelligent infrastructure-citizen interfaces are con-sidered to provide a way forward. The quantity of data produced and the criticality ofinfrastructure management will raise the bar for big data analytics and management.We will discuss this opportunity as part of the content in the next section.

2.2 Public Service Advantages and Opportunities

2.2.1 New Sources of Information: Crowdsourcing

Crowdsourcing is becoming an increasingly common term and opens new avenuesfor creating free public value, civic engagement, and transparency. It can take manyforms. ‘Crowdreporting’, for example, is a common form of crowdsourcing in thepublic sphere, at the moment, and in line with the new conception of citizen as apartner. The “SeeClickFix.com” is a typical example (SeeClickFix 2014). It is anonline service designed to help citizens report non-emergency issues in theirneighborhood, via a web interface, Facebook or smartphone apps. The issue han-dling process is tracked online. After the issue is reported, it is tracked online thesame way logistics companies track the delivery of packages to their destination,only that information is published via Twitter and Facebook to inform the public(SeeClickFix 2014). “Nothing new” one might say, reporting issues like this couldbe done in the past using other means, like calling the council or writing a letter.What is so different after all? I guess the answer should be immediacy and trans-parency, and perhaps non-evasiveness.

The public does not need to go out of their way to report such issues anymore.There is an app for that or they can just log into Facebook. There is no wait on thetelephone to reach an operator, there is no time consuming writing of memo. Theprocess is blended into our everyday social life. Now everybody with a smartphonecan go around and report civic issues. In addition, direct feedback and traceabilitycan give people satisfaction and a sense of achievement that they have contributedto common good. In the past, reported information was not acknowledged andfollow-up information was non-existent. So, direct interaction between public andgovernment agencies in this civic reporting encapsulated three governmental goals:


http://SeeClickFix.com

(i) to engage the public into civic life, as citizens actively engage in the process; (ii)to decrease the cost of civil service, as citizen engagement is voluntary and free ofcharge; and (iii) to improve transparency of public service processes, as the issuehandling process is now traceable online at par with private organizations (Viciniand Sanna 2012).

The creation of public goods via crowd reporting is not, however, the soleprivilege of government agencies. Weather underground, for example, combinescrowd-sourced human observation with weather station data to establish a new levelof accuracy within weather reporting. Weather data is assimilated from 2,000weather stations maintained by the Federal Aviation administration, 26,000 stationspart of the Meteorological Assimilation Data Ingest System (MADIS) and a 16,000of personal weather stations adhering to quality controls and standards. Coupledwith crowd observations and meaning scientific analysis from meteorologistsprovide valuable insights for the co into the science behind the data and the rela-tionship between weather and climate change (The Weather Channel Inc. 2014).This blend of human insight with private and public sensor systems, gives rise tothe idea of the Internet of Everything (Danova 2014), the merge of structured andunstructured data in a variety of forms, from textual to pictures, videos and audiomaterial.

Crowdreporting can also take the form of feedback. For example, the “Did YouFeel it” service in the US surveys people on a number of earthquake parametersregarding their experience of particular earthquake incidents. Enriching numericaldescriptors with empirical data can enrich knowledge of qualitative descriptors suchas intensity (USGS 2014). Using crowd feedback to train intelligent Internet ofThings (IoTs) technology utilizes the wisdom of the crowds in artificially intelligentpublic systems. We discuss this idea of public organization in the subsequentsection, when we introduce the concept of Cognitive city as a potential domain forbig data analytics application.

2.2.2 New Sources of Information: Internet of Things (IoTs)

While there is not a commonly agreed definition, the Internet of Things (IoTs)refers to the network of intelligent devices which include sensors to measure theenvironment around them, actuators which physically act back into their environ-ment such as opening a door, processors to handle and store the vast data generated,nodes to relay the information and coordinators to help manage sets of thesecomponents (Zhang and Mitton 2011).

IoTs have made way into utilities, smart homes, healthcare and wellbeingapplications, and they are expected to proliferate into other areas, such ascommuting and transport (as shown in Fig. 2.1).

IoTs will push our data storage, connectivity and architecture limits to a newhigh. The socio-economic implications for how will live our lives might be huge.For example, McKinsey Global Institute (Manyika et al. 2011) reports a 300 %

2.2 Public Service Advantages and Opportunities 27

increase in connected machine-to-machine devices over past 5 years and a sheer80–90 % decline in microelectronics pricing which is anticipated to lead in 1 trillionmore ‘things’ connecting to the Internet across industries such as manufacturing,health care, and mining with a potential $36 trillion cost saving in operating costs(Manyika et al. 2011).

While big emphasis is given currently into the development of intelligent assetsby equipping them with sensors that beam information about their properties andconditions, what really makes up the internet of things is their distributed, pur-poseful collaboration, and this requires architecture, i.e. organization and structure,in line with the ethics of transparency (Bentley et al. 2014). Hence most of suchtechnologies are clustered around common purpose for example smart city tech-nologies, or smart car technologies to denote what drives the logic of their archi-tecture. One of the trendiest such clusters is, of course, Smart City, which suggestsan advanced connectivity through a highly networked city infrastructure viaintelligent assets. The promise of course is a fertile foundation and over timecapable to attract new businesses and investments and further urban growth andsocio-economic development (URBACT 2012).

While the Internet of things can provide efficient use of resources, it neverthelesslacks in responsiveness and agility. To instill responsiveness into city management,we may be required to take a step further into developing cognitive city systems.Decisions about cities need to be based on principles, values, and thereby quali-tative metrics as to how people want to live their lives, and what well-being means

0

20,00,000

40,00,000

60,00,000

80,00,000

1,00,00,000

1,20,00,000

1,40,00,000

1,60,00,000

1,80,00,000

2,00,00,000

Connected Cars Wearables Connected Tvs Internet of ThingsTablets Smartphones PCs

Fig. 2.1 The internet of everything adapted from Danova (2014). The number of devices in useglobally are shown on the y-axis


to them. Hence, a smart city is as different to a cognitive city, as a robotic arm is tosocial anthropoid. A platform that can combine citizens’ active engagement intodecision making about local government’s decisions and community well-being.

Given that architecture is central to our notion of the Internet of things, we canview cognitive cities as providing the principles for humanizing their design. Notonly sense and perceive but also learn, memorize, recall relevant experiences inorder to adapt their responses accordingly. Almost like biofeedback, people canprovide real-time feedback teaching the system how to behave. After all, cities seekto improve their quality of life of their citizens; and quality is in the eye of thebeholder. People can train machines to include feature extraction, classification andclustering, all of which they can then be perfected through successive optimizationof their algorithm (Warner 2011).

Smart city sensors can therefore be trained by people to learn, memorize, recallrelevant experiences, who teaches them and who makes the final value judgment onthings? This is an area of debate still in its infancy, but one that breeds big legal,political, and ethical questions about the future of social life. EU for example,supports initiatives that foster social innovation and inclusiveness together witheconomic innovation and environmental sustainability to engage citizens in publicservice co-creation and local authority support. Most critical to crowdsourcingsuccess is the feeling by participants that their efforts were considered and thatresults came from the initiative (Evans-Cowley 2011). This requires moderatorswho are competent social networkers, able to used crowdsourcing tools that linkand provide feedback easily, as well as a change in processes used to developgovernmental services (Brabham 2009).

2.2.3 Public Talent in Use

Consultation is nothing new in public life. In developed nations it is actuallyinstitutionalized. City and regional planners are always looking for ways to engagethe public in the process of planning. As a consequence Crowdsourcing of ideasand solutions has emerged as a web-based model to help in solving difficultchallenges. This Section explores the use of crowdsourcing to support problemsolving in planning. A case study of designing a planning curriculum usingcrowdsourcing is highlighted. In this case, crowdsourcing was a successful modelfor generating creative ideas to support curriculum revision (Evans-Cowley 2011).

The study discussed in Evans-Cowley (2011) that people chose to participate foraltruistic reasons, such as an opportunity to contribute to the community, to con-tribute their knowledge, and that they wanted to be part of a conversation on thetopic. Thus, public involvement is a central concern for urban planners. Consid-ering the difficulties inherent in the typical public involvement process, thechallenge for planners is how best to implement such programs. The Web can beused to exploit collective intellect among a population in ways one-to-one meetings(Brabham 2009).


Consequently, at the state of the art, for example, (Brabham 2009) argues thatthe crowdsourcing model is appropriate for enabling citizen participation in publicplanning projects. Starting with an exploration of the challenges faced by publicparticipation in urban planning projects, (Brabham 2009) supports the argument ofthe Web as an appropriate technology for harnessing far-flung genius. Then, itconcludes with an exploration of crowdsourcing in a hypothetical neighborhoodplanning example, together with a description of the challenges of implementingcrowdsourcing.

Also, governments gradually use the Internet to aid in transparency, account-ability, and public participation activities, and there is growing interest in innova-tive online problem-solving to serve the public good. The crowdsourcing modelinfluences the collective intelligence of online communities for particular purposes.To develop better tools that engage the public, it is important to understand howand why people participate in these kinds of activities. In 2009, for example, theFederal Transit Administration in US used crowdsourcing for public participation intransit planning (The Next Stop Design project). Based on interviews with 23participants, they analyzed the motivations of those participants to engage theproject (OGP 2014).

Taking the above issues into account, it is worth noting that while data is the rawmaterial of knowledge, it is really the interpretation of such data that can be actedupon by providing insights, foresight, knowledge or skill, etc. Interpretation ofinformation requires a schema, a way of looking at data and drawing conclusions.In modern societies this was traditionally the job of experts. In the digital era, this ischanging! Printing empowers individual ownership of ideas, the digital eraempowers co-ownership. Wikipedia is a prime example of how data and infor-mation is organized and interpreted in a collaborative way online to provide anunderstanding on each subject matter through a constant ever evolving debate. Theidea is premised on Marshall McLuhan motto that “the medium is the message”, inthat a certain medium facilitates certain interpretations that other media do not(Bentley et al. 2014).

To this end, online communities have become an important source for knowledgeand new ideas. Making “big data” available to a large number of analysts means thatmore ideas can converge on how to mine such data. A platform for doing so isKaggle (2014), a world’s leading an online platform, which operates as a knowledgebroker between companies aiming to outsource predictive modeling competitionsand a network of over 100,000 data scientists (Kaggle Inc. 2014). Currently, clientsinclude companies such as General Electric (GE), Ford, Facebook and Microsoft,and health service organizations, such as Heritage Provider Network, in Californiawho seeks to develop a predictive algorithm that can identify patients who will beadmitted to a hospital within the next year. Such predictive analytics in the service ofhealth care provision can inform the development of more accurate Public healthbudgets and cost cutting plans (Pinsent Masons LLP 2013).


2.2.4 Private–Public Partnerships

Private–Public partnerships can be seen to emerge in almost every aspect of publicservice. With the privatization of most utilities and the trend towards outsourcing,much of the public sector in advanced societies is run by the private organizations.Countries in the periphery of Europe, such as Greece are going through the teethingpains of making this transition now. With global competition on public servicemanagement, we are entering of course a new phase of relations between public andprivate organizations. That of partnership; the pursuit of a long standing mutuallybeneficial and loyal relation that will ensure long-term planning and prosperity(Miller 2013).

Big Data management has a core role to play in supporting decisions on all ofthese partnerships, which is why progressive governments are aiming to mobilizeand support in the field. The event organized by the World Resource Institute on“Public–Private Partnerships for Open Government” in London 2013 is charac-teristic. It sought to share the task of mobilizing citizen engagement, developingmore transparent and accountable governments (Stefan and Kisker 2011). This hasimplications for data ownership and data management at all public service frontsfrom healthcare and natural resources to publicizing data on contracting, govern-ment expenditure infrastructure, and government aid to third parties. One aspectthat deems these partnerships fundamental is the lack of expertise within the publicsector and the ability to vet and support them. Another, of course, is the upfrontinvestment in infrastructure, big data clouds for example, that large organizationscan afford with the view to recoup them though long-term government contracts.Yet another reason is the ability and urge of private companies to experiment withnew technologies in less sensitive contexts and provide governments with ‘safe’technology options that are not like to raise public criticism.

Due to the power to profile people and triangulate information about individuals,big data analytics offers deals with some fundamental concerns of public services.Identity management, for example, is a big issue for most public services, from taxcollection to health provision to parking ticket collection. Big data analytic tech-nologies may make it possible for public service organizations to combat fraud,improve data breach monitoring and authentication (Zhang and Chen 2010). On theother hand, getting it wrong may have some serious repercussions. We discuss thisin more details in Sect. 2.3.

2.2.5 Government Cloud Data

Governments have slowly embarked on Cloud computing to tackle the much to bedesired transparency, participation, and collaboration amongst its agencies but alsowith the public. The idea, the concept, and the term, that is cloud computing, havepassed into common currency in an ambiguous manner. The very conceptcharacterized by three main entities—Software, Hardware and Network to describe


the fusion of Virtualization, Grid computing, Utility computing and Webtechnologies that result in new models of IT service delivery (Clark et al. 2013).

Cloud computing permits central governments to uniformly cover the wholecountry with e-government solutions, independently of divergence of localadministrative units that may be better or worse prepared to provide e-services.Service-oriented architecture facilitates provision of compound services coveringwhole customer processes, where a customer may be a citizen or an enterprise. Theroll out of such systems can happen simultaneously and cost efficiently, as licensingand support can be negotiated on the whole.

By now consumers, corporations, and governments are used to store their data to“the cloud” so they can be accessed from any device, anywhere anytime. Accordingto Lerman (2013) over 69 % of Americans now use webmail services, store dataonline, and utilize online applications. This trend is only going to continue, withindustry analysts predicting clouds to be $40 and $160 billion over the next fewyears, not accounting for the internet of things applications (Lerman 2013). Withthe wider adoption of cloud computing, the term C-Government was coined toconnote agility due to its virtualization, scalability due to grid computing and thesimplicity of Web 2.0. Clouds need to be interconnected to make it easier for usersto switch between cloud service providers as well as the providers to supply infiniteresources (Yiu 2012).

While the relevance of clouds for governments and their potential, e.g., forvoting information systems has been greatly welcomed, enthusiasm has beencurbed by identify vulnerabilities involved in the digitalization of governmenttransactions and the electoral process, thus surfacing issues with trust andtransparency (Manyika et al. 2011; Al-khouri 2012). The issues will be dealt with inlater sections.

2.2.6 Value for Money in Public Service Delivery

The whole move from paper-filling to e-government services was to facilitate costcutting partially though integration across public services. Big Data offers Gov-ernments the possibility to do so without going through the pains of Businesssystems integration.

The UK alone, according to the Head of Digital Government Unit, Chris Yu,estimates that some 16–33 billion per year can be saved by taking advantage of bigdata in the public sector, which can lead to £250–£500 per capita gains. This iscalculated based on a number of initiatives having to do with performancemanagement to improve the overall efficiency of government operations, reducefraud and error, and tax collection alone (Laney 2014). McKinsey Global Instituteestimates the potential savings at a European level to mount up to €150–€300billion a year (Yiu 2012).

A number of analytical tools can also mine data regarding citizen sentimenttowards public services to provide feedback and highlight opportunities to


customize service delivery by helping employees better understand the needs ofeach citizen. This is no different to how commercial organizations use it tocustomize their service to paying customers. Providing quality service to citizenshas been government agenda for most developed nations and in particular thosewho want to attract international talent. For example, predictive analytics in the areaof health informatics, particularly epidemiology, can also be a huge help for gov-ernments who need to gear up for crisis management. The more accurately peoplecan predict the spread of disease the more cost effective prevention and treatment isexpected to be, the less the disruption to public life.

Such benefits accrue without further changes in the current public servicestructures, yet even more benefits may accrue if we consider the potential ofadopting predictive analytics to advance crime prevention and reduce policingresources or introducing smart grid technology to improve the efficient use of utilityresources, (gas, electricity and water).

2.3 Governmental Challenges

2.3.1 Data Ownership

With data ownership come great responsibility for its management, storage, use andmisuse. But in the age of open (public) data who really bears such responsibility?Who is the legal guardian of it and what is written in the implicit contract betweencivilians and government regarding its use and protection? In one sense all publicdata is private data in that it is either civilians personal or lifestyle information orrelates to the functioning of the public service which is nevertheless accountable tothe public, at least in democratic settings. In that sense, governments and publicorganizations are custodians of our data, granted permission to use it in exchange ofproviding us with public services and promote public good (Gudipati et al. 2013).Take the United States Patent and Trademark Office online database, for example,which contains over 8 million patents and 16 million filings dating back to SamuelHopkins’s 1790 and receives 200,000 patent applications and 100,000 trademarkapplications each year (Hoffman and Podgurski 2013). Who does this informationbelong too? Obviously, the patent holders and submitters. Yet, such informationwould be useless for both the person and the country unless the US governmentsafeguards its existence and integrity. Indeed, personal data can be created by avariety of sources from people, machines, devices, and the rightful owner of suchdata is the authority which can verify its veracity as being the ‘True Owner’ of thedata (Al-khouri 2012).

This will bear interesting questions when devices will monitor our behavior andcondition beaming information about our whereabouts and state. While devices aresusceptible to error, people are susceptible to deception and self-deception.Governments and public agencies will have to make rules to decide how to handlethe inconsistencies.


2.3.2 Data Quality

Big Data can amplify the repercussions and implications of poor data quality, and isa particularly important issue for governments and civilians alike. Recorded datacan be flawed (erroneous, miscoded, fragmented, or incomplete) due to workloadpressures and user interface workarounds (Junqué de Fortuny et al. 2013). Datashould be checked for completeness, conformity, consistency, accuracy, duplica-tion, and integrity, and good practices around data quality do exist. In privateorganizations, non-quality data can be ignored from consideration, without com-promising the integrity of analysis or affecting the user. For example, if a retailerdoes their analysis only on the ‘clean’ data of their customers to profile and predictfuture sales, the customer is not greatly affected (Crawford 2013). The same cannotbe true for the health service, for example, particularly if ‘unclean’ data arecharacteristic of a locality (e.g. a local hospital). Poor data quality can result fromintegrating data sources. Data issues can also emerge from the integration, feder-ation or conglomeration of data, and given the variety and volume of big data,testing this data can be a big task. Various big data testing procedures have startedto emerge, using grid processing technologies, such as Hadoop, to support a timelyprocessing (Crawford 2013).

While, data stewardship and data testing procedures can deal with the “Garbagein” problem, another issue is lurking in the seams of public sector decision-making;and this is all forms bias [selection bias, confounding bias, and measurement bias(Kerr and Earle 2013)]. Confounding occurs when we correlate two phenomena,which has been studied independently (Howarth 2014; IQ Analytics 2014). Forexample, pulling together smoking habits from Facebook profiles and associatingthis with youth diabetes statistics, might erroneous speak of causal relationship thatcould be mediated by another factor, for example unemployment or heredity or thefamily’s economic status. Selection bias can come from all possible sources andmost importantly due to differential devise use amongst different countries, agesand socioeconomic groups. Kate Crawford principle research in Microsoft andVisiting professor of MIT, gives brilliant accounts on the hidden biases in big datain Harvard Business Review blog (Crawford 2013). Given the sensitivity of publicservices that can bear life threatening and legal implications, treating informationwith utmost rigor is fundamental. Thus, we need to outline and progress the con-versations on regulatory and other interventions to address data analysis difficultiesthat could result in invalid conclusions and unsound public health policies(Microsoft 2014).

2.3.3 Privacy, Civil Liberties and Equality

Privacy and civil liberties are the two sides of the same coin. User profiling is theprocess of collecting information about a user in order to construct their profile. Theinformation in a user profile may include various attributes of a user such as


geographical location, academic and professional background, membership ingroups, interests, preferences, opinions, etc. More, ominous applications includecell-phone tracking and the proposed creation of a national biometric database.

As Kerr and Earle (2013) argue profiling individuals on the basis of their health,location, electricity use, and online activity raise risks of discrimination, exclusionand loss of control. When these involve access to public services, repercussions areexacerbated. The promise of big data is based on prediction with the view topreempt possible threats. If, for example, one can predict increased burglaries in anarea, local government can increase policing of this area to preempt such incidents(Barcelona City Council 2014).

Preemptive action is based on prediction and prediction on predictive algorithmbased on social information and this curtails civil liberties replacing proof with riskestimates. With increased predictive capability comes increased responsibility toavoid such threats that can make governments more conservative in how theyapproach social risks. To what extent can governments afford to let unemployedyouth roam the streets freely, once we have established that it is highly probable tocommit petty theft or drug dealing? On the other hand, shall we detain or curtail thefreedom of youth to meet and socialize and lead them to isolation and depression?To what extent policemen should be informed about such correlations and wouldthat lead to discrimination of every unemployed youth, who treated with suspicionmay even turn to crime as self-fulfilling prophesy theory would predict?

Also, Professor Lerman (2013), for example, raises another issue; the issue ofequality regarding public treatment of people and groups who do not fully partic-ipate in the information society, because they don’t have the means, time or appetitefor. Statistics about the digital divide show great variation in digital engagementfrom country to country, age group participation, socio-economic class, urban orrural living and, of course, between countries (Lerman 2013). Some interesting2014 statistics about the global internet and social media use can be found at thesocial media community blog hub (Kemp 2014). The risk here is that governmentsmay come to rely so much on big data that they forget to ensure that they engagepeople to understand their needs and considered these during decision-making.

2.3.4 Talent Recruitment Issues

Given the scarcity of data analysts talent in the market, the public sector will have ahard time attracting such talent as permanent stuff. In the UK, for example, publicsector organizations are obliged to recruit below market going rates to justifyHuman Resource (HR) expenses (PageGroup 2014). In addition, the once upon thetime job security and fringe benefits of working for the public sector increasinglydisappear, making public sector organizations even less attractive. With largeorganizations such as banks, insurances, large online retailers and consultanciescompeting for such resources, governments will have a hard time attracting andkeeping such talent. On the other hand, governments could use University talent.

2.3 Governmental Challenges 35

A resource much underutilized particularly in Europe, despite the depth and breadthof skills relevant to the public sector (Campos 2008).

On the other hand, there is always training as an option to up skilling publicsector staff. In February 2014, for example, the UK government announcement that£150,000 of government funding would be dedicated to Open data training of morethan 150 Public Sector employees (Gangadharan 2013).

2.4 Case Studies

Barcelona embarked on the smart city journey 10 years ago in an informal fashionresulting in many smart city projects now dispersed in various departments acrossthe city, currently being collated under a single program. The 22@ Barcelona region,once in need of redevelopment, has been transformed into a living test site forpiloting new technologies (Department for Business Innovation and Skills 2013).

Xavies Trias, mayor of Barcelona since 2011, has recognized the importance ofdigital technologies for the future prosperity of the city. In his words in outlining hiscommitment states that Barcelona “…should not waste the opportunity we have toapply these new technologies to improving people’s quality of life, by generating anew “economy of urban innovation” based around smart cities. This is another ofour future commitments” (Department for Business Innovation and Skills 2013).

To progress this agenda he formed Urban Habitat, a government wide man-agement structure to promote collaboration across water, energy, human servicesand environment agencies. Whereas, housing and urban planning were alsogrouped together. To further cement cross agency collaboration, the Smart CityPersonal Management Office, oversaw all projects with a smart city aspect. Whilethere are over 100 projects with a smart city angle, 13 are highlighted as strategicfor the smart future of Barcelona, tackling the necessary infrastructure to supportsmart city applications, kit out city assets with intelligent sensors, and define smartcity public services. To this end, the telecommunications network is revamped tointegrate fiber optic networks, and Wi-Fi networking, public and a centralizedmanagement system enabling the interoperability and prioritization of mobility,public transport and urban infrastructure, applying concepts such as priority andintermodality to make more efficient and sustainable mobility in cities. This isunderpinned with intelligent data project collating information from smart assetsand public service organizations with the view to opening these up to the public.New public services are progressed such as energy projects relating to the urbanlighting of Barcelona, creating microgrids to create local generation and con-sumption of green energy, telemanagement of irrigation urban green spaces andelectric car mobility options, as well as, smart parking options to enable speedyparking avoiding unnecessary city traffic. Citizens will have contactless and mobileapps to use city services. Some projects focus more generally on a mentality changearound smart city agendas. The O-Government project, for example, seeks to gainsupport for Open Government, strategy and roadmaps and improve transparency,


open data and civic participation. The “Citizen compromise to sustainability2012–2022” seeks to gain definition and traction for a city roadmap that can pro-vide a more equitable, prosperous and self-sufficient environment to its people(Department for Business Innovation and Skills 2013).

The regeneration of the 22@ Barcelona region was a public-private partnershipwere companies, universities, research, and communities work in close proximitywith municipal leaders to exchange knowledge and streamline innovation, but alsoensure inviting and engaging urban planning by subsidizing housing and developinggreen spaces. Local and international, private and public funding was used forinfrastructure development and the testing of new public services. The governmentfacilitated access to public funds by institutionalizing InnoActiva, a consultancyagency which to supports private companies to make their case to public authoritiesand institutions. Following the Silicon Valley cluster model, it is setting up clustersin areas that they can develop a competitive advantage. Hence, 22@ is orientedto attracting talent and expertise in Media, Information and CommunicationTechnologies, medical technologies, energy and design.

As to big data and analytics issues, open data is actually a core part of Barcelona’ssmart city event. Public and business access to information such as election results,population, public facilities, or economy sits in a public repository called Open DataBCN. Microsoft for example utilized data relating to a town festival called“La Merce” as a pilot to providing improved crowd management solutions. To thisend, data feeds from social media, credit card transactions, web site visits, customerservice inquiries, GPS data, traffic status, weather data, and parking was collectedand analyzed. These data sought to gain insights about people’s perceptions of thefestival’s entertainment and food venues, citizen interests, people mobility patterns,and medical and crime incidents that can help the planning and management of thenext event (Vienna University of Technology 2014).

The city also pilots the provision of services based on mobile identificationtechnologies. Through a smartphone app, citizens can access information aboutparking tickets and car towing destinations, request public subsidies for nonprofitactivities, and the like, providing a proof of concept and of technology and gettingthe necessary public engagement to move to the next level (Davenport and Prusak1997). Consequently, public transport smart apps are ahead of their generation dueto popular demand.

In Barcelona transport information is based on a hyper-reality app. Anyone canobtain information on bus stop locations, location, lines and even be directed to itby simply pointing a smartphone camera in any direction, working wonders forcitizens new to an area, tourists and even blind people who can be oriented towardstheir target destination using voice directions or Microsoft (2014).

Public engagement also manifest in developmental work for the smart city.Sentilo, for example, is an open source sensor and actuator platform sponsored bythe Barcelona City Council, and designed by an open community.

2.4 Case Studies 37

Point of Attention: Barcelona’s open-source, smart city platform, engageslocal talent in smart city development, ensures technology and providerindependence and data stewardship and remains with the public, under itsstewardship and safeguards civil liberties.

With the view to establish the city’s reputation as a smart city, Barcelona alsodrives the Smart City Protocol initiative which seeks to connect global cities in pilotprojects to address common challenges (Bain and Sentilo 2014). Barcelona is asmall cosmopolitan city with the vision to grow and an exemplar for smart citydevelopment that remains open, transparent, and democratic through an exchangeof all capital resources from capital and infrastructure to knowledge and talent.

While for most, smart city applications are still considered a nice-to-have featurein our city life, crisis management is the acid test for any smart application. Allemergency services share a common requirement, when it comes to informationmanagement. They need to accurately analyze life critical, real-time informationfrom diverse sources, in order to deploy and manage emergency service workflows(Gangadharan 2013).

During the Haiti earthquake in 2010, emergency services needed to be dispatchedto the area to support the government cope with the circumstances. InStedd, a com-pany specializing in technology design for emergency services such as naturaldisasters and diseases, offered support to the emergency services and people. Within48 h the company has set up the telecoms infrastructure and gain buy in for setting upan emergency response number. The company offered a message-integrated com-munications from two mobile network companies; incoming aid requests werereceived inHaitianCreole. Thesewere routed toRiff/EIS for analysis (Alehegn 2010).

Riff has the capability to automatically extract features, classify data and tag dataand their metadata (e.g. source and target geo-location, time, route of transmission)and before it can process it via algorithms. The analytics module can detect rela-tionships between these extracted features within a collaborative space or acrossdifferent collaborative spaces. Riff can also combine information from GeoChat, acollaboration tool geolocating human comments, observations and reports to makeinformation richer and relevant. Riff then shared information with Crowdfloweranother workflow provider, handling the distribution of tasks to a bilingual volunteerworkforce for translation, tagging, and geocoding. Information was then forwardedto Ushahidi, a website initially developed to map reports of violence in Kenya nowturned a global crowdsourcing platform with humanitarian goals. ‘Ushahidians’, as acommunity of interest helped to map, accurately geotag information to provideaccurate coordinates to the search and rescue team on the ground (Meier 2012).

Point of Attention: Smart apps and open collaboration platform canbecome the critical infrastructure platform for the application of big data andanalytics to disaster recovery.


The value of swarm intelligence-based approaches for workflow-based emer-gency management systems has been outlined as far back as 2007 (Bentley et al.2014). This was an example par excellence for a bundle of crowdsourcing servicescombined to provide an emergency response information architecture working withthe added complication of bilingualism. Data quality is of paramount importance toprioritize calls and minimize erroneous dispatching of scarce rescue resources.Timely and accurate information processing was life critical. International crowdsof volunteers were utilized and important safety critical decisions had to be taken onthe fly by the government and participating companies alike. The venture’s successwas based on companies’ technical capability and social responsibility, andopenness to collaboration with other companies and volunteers for the same cause.

2.5 Recommendations for Organizations

Governments will have to find their feet and strike the right balance betweenprogress and the challenges of the big data era and redefine its relationship to thepublic and to private capital in a world of global competition. Smart city hasbecome perhaps a pillar of competitive advantage for those who can grasp theopportunity, while others will lag behind. As to these issues, in what follows wepoint out some key factors for an effective application and exploitation of big dataand analytics in public sector digitalization, particularly, for smart cities and serviceoriented initiatives.

2.5.1 Smart City Readiness

Each country will have to access the readiness of its cities to become and itspositioning in a smart cities global landscape. The European Smart Cities initia-tives, audits cities on the basis of the six factors shown in Table 2.1 (ViennaUniversity of Technology 2014).

Smart city strategy at a national level is likely to be faced with budgetarytensions between rural and urban development and a nation should have a visionand a view of how to engage its people and private investors in the conversation.Moreover, both local and central governments will have a ‘good cop, bad cop’ roleto play between role modeling the opening up information and effecting transpar-ency and ensuring that information is safeguarded from abuse by involved parties.

In addition, smart cities will divulge responsibility for city services to machines,partnerships with private companies and the public and this requires not onlyeducated citizens but also a change in mentality about civic responsibility from allthese parties.

2.4 Case Studies 39

2.5.2 Learn to Collaborate

Like most sociotechnical changes, to realize the benefits of big data and smart cityinitiatives we need to change the way we do things. In particular, these technologiesrequire the diverse stakeholders collaborate. Government agencies and departmentshave been traditionally separated by internal rivalries and financial competitiondeveloping into an embedded silo mentality and culture. Information has been seenas power and it has been hoarded to make people indispensable in the face ofdownsizing, cost cutting, and other modernization attempts. Davenport and Prusakhighlighted such issue as far back as 1997 (Davenport and Prusak 1997).

Focus on efficiency and years of recruiting people and managers focusing oncost cutting exercises have stripped the public sector from innovative humanresources and know-how and practices (Parker 2014). Public sector practices interms of renewing their staff and policies about paying their staff at the low end ofmarket prices makes it difficult for them to attract human talent, or indeed tomanage external associates who recruit such talent.

The public sector will need to rethink its internal recruitment processes toemploy smart people who will focus on creating successful partnerships with theprivate sector and the public. If governments are to show the way of developingsmarter cities, they should orient themselves to attracting people with high quali-fications, affinity to life-long learning, social and ethnic plurality, creativity, flexi-bility, cosmopolitanism and open-mindedness. In addition, managing successfulcollaborations will require new managerial skills. High partnering skills involve:

1. creating rapport via openness and self‐disclosure and feedback,2. trust building through actions and words,3. creative conflict resolution and problem solving,4. appetite for change, and5. welcoming interdependence (Dent 2006).

Table 2.1 Factors for smart cities initiatives audit

Factors Description

Smarteconomy

Innovative spirit and entrepreneurship, productivity, workforce flexibility,ability to transform and international embeddedness

Smart mobility Local and international accessibility, availability of ICT infrastructure,innovative transport systems

Smartenvironment

Attractive natural conditions, pollution, environmental protection andsustainable resource management

Smart people Level of qualification, affinity to life-long learning, social and ethnicplurality, creativity, flexibility, cosmopolitanism and open-mindedness

Smart living Cultural facilities, health conditions, individual safety, housing quality,education facilities, touristic attractively and social cohesion

SmartGovernance

Participation in decision making, public and social services, transparentgovernance, political strategies and perspectives


Furthermore, partnering in the sphere of emerging technologies will also requirereviewing public sector procurement policies to allow wider participation in thesupplier pool and perhaps even participation of newly established technologyventures that might be considered risky (Uyarra et al. 2014).

Finally, for Governments to continue to be relevant in a big data world, withlimited resources, they need to become smarter and this means fostering publicparticipation in decision making, public and social services, and making gover-nance, political strategies and perspectives transparent and lean.

2.5.3 Civic Education and Online Democracy

The key aspirations of open government are the engagement of the public in thepolitical processes and their involvement in self-service public services. This willrequire heightened levels of interest, knowledge, and maturity from the public, aswell as new modes of participation by governments. One means to achieve theformer is education. A United Nations review of such program in the US, showedthat such program had changed both people’s engagement levels and feeling ofadequacy to engage in the political process, but not people’s respect for differentpolitical viewpoints, social cohesion and trust (United Nations Publication 2010).

Online participation in democratic processes can provide an affordable ways toconsult governments and take part in decision making in ways that it was notpossible before. Relevant initiatives spring up slowly in different countries. InJanuary 2014, California, for example, institutionalized the California Report Card,mobile-friendly web-based platform that encourages citizens to engage in thedeliberative process via chat rooms where they would enter their own suggestionsbut also rate others’ suggestions (Newsom and Goldberg 2014).

2.5.4 Legal Framework Development

Legal frameworks lag behind technological developments at all fronts of virtuallyenabled living. The persistence and rise of Cyberbulling is a testament to that. Bigdata profiling raises many issues regarding privacy, civil liberties, and equality, asthey were described above. With on demand public services via smart applicationsentering the mix, such issues, particularly those of inclusion and exclusion from thisvirtual world, can achieve another level of inequality. Thus, Governments need todefine new legal frameworks to regulate life and perhaps they even need to do so at aglobal level, as internet engagement is a global phenomenon. Big data analytics canbe used as the tool to help international government bodies to analyze people’ssentiments but also integrate best practices on such matters, but also to make lawmore understandable by its law enforcement groups and the public (Morabito 2014).

2.5 Recommendations for Organizations 41

2.6 Summary

This chapter discussed the impact of big data in the context of public serviceprovision and new opportunities for public service organization and structure thatmay transform the role of governments in societies. We started our analysis bydiscussing developments in public service provision, which treats citizens asprosumers (proactive consumers) of public service delivery, moves towards directonline democracy, and finally, to active engagement and a global smart megacitiescompetition for resources and talent.

In this context, governments seek to gain an advantage by utilizing a) newsources of data, such as Crowdsourcing, Internet of Things, b) engage public talent,c) institutionalize private–public partnerships and d) seeks for new models of value-for-money public provision. Despite its potential, the adoption of big data andanalytics are not without challenges, particularly for central governments. Of par-ticular interest are the challenges regarding data ownership, data quality, privacy,civil liberties, and equality, as well as public sector’s ability to attract big dataanalyst talent.

We showcased two case studies demonstrating how new forms of public serviceprovision. Barcelona Smart City provides an example par excellence of collabora-tion between the private and public sector for regional redevelopment. Haiti’semergency support during the 2010 earthquake disaster demonstrates how big datain the hands of passionate volunteers can organize and support with life-criticalemergency services, providing a life example as to what can be achieved throughthe blend of human intuition and available big data integration and advancedanalytics. Like most sociotechnical changes, challenges reside in the social sphere oftechnology acceptance and use, as well as with the regulation of such technology,hence our recommendations are directed towards auditing readiness for Smart Citydevelopment, reskilling public servants with partnership management skills,developing public’s mentality of civic participation and updating legal frameworksto cope with developments in the big data area.

References

Alehegn, T.: Crisis mapping and collaboration between Western and African ICT developers forHaiti quake. http://www.tadias.com/01/19/2010/crisis-mapping-and-collaboration-between-western-and-african-ict-developers-for-haiti-quake-response/ (2010). Accessed 15 Nov 2014

Al-khouri, A.M.: Data ownership: who owns “my data”? Int. J. Manag. Inf. Technol. 2, 1–8 (2012)Bain, M.: Sentilo—sensor and actuator platform for smart cities. https://joinup.ec.europa.eu/

community/eupl/document/sentilo-sensor-and-actuator-platform-smart-cities (2014). Accessed15 Nov 2014

Bentley, R.A., O’Brien, M.J., Brock, W.: A: mapping collective behavior in the big-data era.Behav. Brain Sci. 37, 63–76 (2014)

Brabham, D.C.: The public participation process for planning projects. Plan. Theor. 8, 242–262(2009)


http://www.tadias.com/01/19/2010/crisis-mapping-and-collaboration-between-western-and-african-ict-developers-for-haiti-quake-response/

http://www.tadias.com/01/19/2010/crisis-mapping-and-collaboration-between-western-and-african-ict-developers-for-haiti-quake-response/

https://joinup.ec.europa.eu/community/eupl/document/sentilo-sensor-and-actuator-platform-smart-cities

https://joinup.ec.europa.eu/community/eupl/document/sentilo-sensor-and-actuator-platform-smart-cities

City Council: Mobile digital ID for Barcelona citizens. http://www.mobileid.cat/en (2014).Accessed 15 Nov 2014

Clark, B.Y., Brudney, J.L., Jang, S.-G.: Coproduction of government services and the newinformation technology: investigating the distributional biases. Public Adm. Rev. 73, 687–701(2013)

CitySourced Inc.: Citysourced. http://www.citysourced.com (2014). Accessed 15 Nov 2014Conservative Party: Building a big society. London, UK (2010)Crawford, K.: The hidden biases in big data. http://blogs.hbr.org/2013/04/the-hidden-biases-in-

big-data/ (2013). Accessed 15 Nov 2014Danova, T.: The internet of everything. http://www.businessinsider.com/the-internet-of-

everything-2014-slide-deck-sai-2014-2?op=1 (2014). Accessed 15 Nov 2014Davenport, T.H., Prusak, L.: Information Ecology: Mastering the Information and Knowledge

Environment. Oxford University Press, New York (1997)de Campos, A.: Literature on university-industry links: towards an integrated approach in the study

of influencing factors. Merit. Unu. Edu. 44, 0–32 (2008)Dent, S.M.: Partnership Relationship Management (2006)Department for business innovation and skills: global innovators: international case studies on

smart cities smart cities study (2013)Evans-Cowley, J.: Crowdsourcing the curriculum: public participation in redesigning a planning

program. Soc. Sci. Res. Netw. Work. Pap. Ser. (2011)Executive Office of the President of USA: Big data. Seizing Opportunities. Washington, D.C.

(2014)Gangadharan, S.P.: How can big data be used for social good? http://www.theguardian.com/

sustainable-business/how-can-big-data-social-good (2013)Gudipati, B.M., Rao, S., Mohan, N.D., Gajja, N.K.: Big data: testing approach to overcome quality

challenges structured testing technique. Infosys Labs Briefings 11, 65–73 (2013)Hoffman, S., Podgurski, A.: The use and misuse of biomedical data : is bigger really better? Am.

J. Law Med. 39, 497–538 (2013)Howarth, B.: Big data: how predictive analytics is taking over the public sector. http://www.

theguardian.com/technology/2014/jun/13/big-data-how-predictive-analytics-is-taking-over-the-public-sector (2014)

IQAnalytics: big data training a priority for the public sector. http://www.itqanalytics.com/community/blog/big-data-training-a-priority-for-the-public-sector#.VDJhABbILcs (2014). Accessed 15 Nov2014

Junqué de Fortuny, E., Martens, D., Provost, F.: Predictive modeling with big data: is bigger reallybetter? Big Data 1, 215–226 (2013)

Kaggle Inc.: The home of data science. http://www.kaggle.com (2014). Accessed 15 Nov 2014Kemp, S.: Social, Digital and mobile around the world. http://wearesocial.sg/blog/2014/01/social-

digital-mobile-2014/ (2014). Accessed 15 Nov 2014Kerr, I., Earle, J.: Prediction, preemption, presumption how big data threatens big picture privacy.

Stanf. Law Rev. 66, 65 (2013)Laney, D.: Who owns (really owns) “Big Data”. http://blogs.gartner.com/doug-laney/who-owns-

really-owns-big-data/ (2014). Accessed 15 Nov 2014Lerman, J.: Big data and its exclusions. Stanford Law Rev. Online. 66, 55–63 (2013)Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., Byers, A.H.: Big Data:

The Next Frontier for Innovation, Competition, and Productivity. McKinsey Global Institute,Washington, DC (2011)

McDermott, P.: Building open government. Gov. Inf. Q. 27, 401–413 (2010)Meier, P.: How crisis mapping saved lives in Haiti. http://newswatch.nationalgeographic.com/

2012/07/02/crisis-mapping-haiti (2012). Accessed 15 Nov 2014

References 43

http://www.mobileid.cat/en

http://www.citysourced.com

http://blogs.hbr.org/2013/04/the-hidden-biases-in-big-data/

http://blogs.hbr.org/2013/04/the-hidden-biases-in-big-data/

http://www.businessinsider.com/the-internet-of-everything-2014-slide-deck-sai-2014-2?op=1

http://www.businessinsider.com/the-internet-of-everything-2014-slide-deck-sai-2014-2?op=1

http://www.theguardian.com/sustainable-business/how-can-big-data-social-good

http://www.theguardian.com/sustainable-business/how-can-big-data-social-good

http://www.theguardian.com/technology/2014/jun/13/big-data-how-predictive-analytics-is-taking-over-the-public-sector



http://www.itqanalytics.com/community/blog/big-data-training-a-priority-for-the-public-sector#.VDJhABbILcs

http://www.itqanalytics.com/community/blog/big-data-training-a-priority-for-the-public-sector#.VDJhABbILcs

http://www.kaggle.com

http://wearesocial.sg/blog/2014/01/social-digital-mobile-2014/

http://wearesocial.sg/blog/2014/01/social-digital-mobile-2014/

http://blogs.gartner.com/doug-laney/who-owns-really-owns-big-data/

http://blogs.gartner.com/doug-laney/who-owns-really-owns-big-data/

http://newswatch.nationalgeographic.com/2012/07/02/crisis-mapping-haiti

http://newswatch.nationalgeographic.com/2012/07/02/crisis-mapping-haiti

Microsoft: City deploys big data bi solution to improve lives and create a smart-city template.http://www.microsoft.com/casestudies/Microsoft-Excel-2010/City-of-Barcelona/City-Deploys-Big-Data-BI-Solution-to-Improve-Lives-and-Create-a-Smart-City-Template/710000003415(2014). Accessed 15 Nov 2014

Miller, H.E.: Big-data in cloud computing: a taxonomy of risks. Inf. Res. 18, 571 (2013)Morabito, V.: Trends and Challenges in Digital Business Innovation. Springer International

Publishing, New York (2014)Mostashari, A., Arnold, F., Maurer, M., Wade, J.: Citizens as sensors: the cognitive city paradigm.

International Conference & Expo on Emerging Technologies for a Smarter World, pp. 1–5.IEEE (2011)

Nagy-Rothengass, M.: European Activities in the Area of Big Data. META-Forum, p. 27. Berlin (2013)Nelson, S., Milberry, K., Richardson, J., Martin, M., Thomson, K.: PartyX. http://partyx.ca/

(2015). Accessed 8 Jan 2015Newsom, G., Goldberg, K.: Let’s amplify California’s collective intelligence. http://citris-uc.org/

lets-amplify-californias-collective-intelligence-op-ed-ken-goldberg-gavin-newsom-california-report-card/ (2014). Accessed 15 Nov 2014

OGP: Open Government Partnership. http://www.opengovpartnership.org (2014). Accessed 15Nov 2014

PageGroup: Public sector salary survey: insight and trends. http://www.michaelpage.co.uk/salary-survey/public-sector.htm#salary-results (2014). Accessed 15 Nov 2014

Parker, L.: Cuts mean a smaller public service must be smarter. http://www.governmentnews.com.au/2014/06/cuts-mean-smaller-public-service-must-smarter (2014). Accessed 15 Nov 2014

Pinsent Masons LLP: Calls for EU public private “Big Data” partnership. http://www.out-law.com/articles/2013/november/calls-for-eu-public-private-big-data-partnership/ (2013). Accessed15 Nov 2014

SeeClickFix: Report neighborhood issues and see them get fixed. www.SeeClickFix.com (2014)Accessed 15 Nov 2014

Stefan, R., Kisker, H.: Sizing the cloud. http://www.forrester.com/Sizing+The+Cloud/fulltext/-/E-RES58161 (2011). Accessed 15 Nov 2014

The National Cycling Charity: Fill that hole. http://www.fillthathole.org.uk (2014). Accessed 15Nov 2014

TheWeather Channel Inc:Weather underground. http://www.wunderground.com (2014). Accessed 15Nov 2014

Thomas, J.C.: Citizen, customer, partner: what should be the role of the public in publicmanagement. Public Adm. Rev. 73, 786–796 (2013)

United Nations Department of Economic and Social Affairs: World Urbanization Prospects: The2005 Revision (2006)

United Nations Publication: e-Government and New Technologies: Towards better citizenengagement for development. Geneva, Switzerland (2010)

URBACT: Smart cities: Citizen innovation in smart cities. http://urbact.eu/en/projects/innovation-creativity/smart-cities/homepage/ (2012). Accessed 15 Nov 2014

USGS: DYFI background—the science behind the maps. http://earthquake.usgs.gov/research/dyfi/(2014). Accessed 15 Nov 2014

Uyarra, E., Edler, J.,Garcia-Estevez, J.: Barriers to innovation through public procurement: a supplierperspective. Technovation 34(10) 631–645 (2014). doi: 10.1016/j.technovation.2014.04.003

Vicini, S., Sanna, A.: How to co-create Internet of things-enabled services for smarter cities. TheFirst International Conference on Smart Systems, Devices and Technologies, pp. 55–61.Stuttgart, Germany (2012)

Vienna University of Technology: European smart cities 3.0. http://www.smart-cities.eu/ (2014).Accessed 15 Nov 2014

Warner, J.: Next steps in e-government. In: Proceedings of the 12th Annual International DigitalGovernment Research Conference on Digital Government Innovation in Challenging Times—dg.o ’11, p. 177. ACM Press, New York, USA (2011)


http://www.microsoft.com/casestudies/Microsoft-Excel-2010/City-of-Barcelona/City-Deploys-Big-Data-BI-Solution-to-Improve-Lives-and-Create-a-Smart-City-Template/710000003415

http://www.microsoft.com/casestudies/Microsoft-Excel-2010/City-of-Barcelona/City-Deploys-Big-Data-BI-Solution-to-Improve-Lives-and-Create-a-Smart-City-Template/710000003415

http://partyx.ca/

http://citris-uc.org/lets-amplify-californias-collective-intelligence-op-ed-ken-goldberg-gavin-newsom-california-report-card/



http://www.opengovpartnership.org

http://www.michaelpage.co.uk/salary-survey/public-sector.htm#salary-results

http://www.michaelpage.co.uk/salary-survey/public-sector.htm#salary-results

http://www.governmentnews.com.au/2014/06/cuts-mean-smaller-public-service-must-smarter

http://www.governmentnews.com.au/2014/06/cuts-mean-smaller-public-service-must-smarter

http://www.out-law.com/articles/2013/november/calls-for-eu-public-private-big-data-partnership/

http://www.out-law.com/articles/2013/november/calls-for-eu-public-private-big-data-partnership/

http://www.SeeClickFix.com

http://www.forrester.com/Sizing+The+Cloud/fulltext/-/E-RES58161

http://www.forrester.com/Sizing+The+Cloud/fulltext/-/E-RES58161

http://www.fillthathole.org.uk

http://www.wunderground.com

http://urbact.eu/en/projects/innovation-creativity/smart-cities/homepage/

http://urbact.eu/en/projects/innovation-creativity/smart-cities/homepage/

http://earthquake.usgs.gov/research/dyfi/

http://dx.doi.org/10.1016/j.technovation.2014.04.003

http://www.smart-cities.eu/

Yiu, C.: The big data opportunity-making government faster, smarter and more personal, Policyexchange. London, UK (2012)

Zhang, L., Mitton, N.: Advanced Internet of things. 2011 International Conference on Internet ofThings and 4th International Conference on Cyber, Physical and Social Computing, pp. 1–8(2011)

Zhang, W., Chen, Q.: From E-government to C-government via cloud computing. 2010International Conference on E-Business and E-Government, pp. 679–682. IEEE (2010)

References 45

http://www.springer.com/978-3-319-10664-9

2/11/2016 www.governing.com/templates/gov_print_article?id=327218711

http://www.governing.com/templates/gov_print_article?id=327218711 1/2

How New York City Is Mainstreaming DataDrivenGovernanceWhat began as a niche innovation is creating a wider transformation of government culture.

BY: Stephen Goldsmith | September 16, 2015

In the early days of online government, cities at the vanguard rightly bragged about taking on "egov" as aproject. Within just a few years, though, egov had moved from a set of individual projects to an ingrained partof innovation and customer service. Now, cities around the country are working to make their immensequantities of data more public and actionable. As these initiatives take hold, the success stories of dataanalytics will, like egov, move from the anecdotal to the mainstream.

That process is well on its way in New York City, an early leader along with Chicago in using big (and little)data to drive innovation. New York is looking to leverage data science more broadly and deeply into existinginstitutions in an ambitious bid to remake governance.

In 2010, thenmayor Michael Bloomberg appointed me as the city's deputy mayor of operations. Bloomberg,a leader in technological innovation in both business and government, knew the importance of automatingprocedures and harnessing data analytics. He gave me the task of advancing the city's innovation agenda inthe Office of Operations, which coordinates the agencies that provide the services most visible to NewYorkers. Data was a crucial part of our strategy.

At the same time, other officials in the city's government were brewing their own datapowered solutions.Mike Flowers, New York's first chief analytics officer, gathered a team of young data scientists to produceinsights from the city's largely untapped troves of data. His work ventured where there was little precedent,and so he worked quietly and fiercely on a few specific initiatives that he thought could benefit the most fromdata analytics. That project was a success, and in 2013 Flowers and the mayor formalized the team andcreated the Mayor's Office of Data Analytics (MODA) as the city's "civic intelligence center," where data fromacross agencies is aggregated, analyzed and turned into actionable solutions.

Headed by the current chief analytics and opendata officer, Amen Ra Mashariki, MODA is now situatedinside the Office of Operations, where its functionality and responsibility has expanded. The officials inOperations have a deep, handson knowledge of the daytoday processes that make the city work; add theteam of data scientists and the result is a formidable pair. What began with Flowers as a niche innovation hasnow blossomed into a mainstream culture of datadriven governance.

MODA's tools have also allowed for more agile and sophisticated performance management. Headed byMindy Tarlow, the Office of Operations is as interested in measuring outcomes as it is in monitoring activities.MODA leverages citywide data to better comprehend the metabolism of the city, searching for correlationsand emerging trends. This produces highlevel, predictive analytics that result in a more responsive andefficient allocation of city resources.

In addition to efficiency gains, MODA's placement in Operations has contributed to a wider shift in citygovernment culture. Operations now also includes the Center for Economic Opportunity, which uses metricsto strengthen the city's antipoverty initiatives; NYC Open Data, which makes data from various city agenciesavailable to the public; and several other data savvy project and performance management teams. Data is nolonger just a part of the work that the Office of Operations does: it is becoming the very core of Operations.

MODA's team of internal consultants remains an internal dataresource center for other agencies. Somecome to MODA needing help on one issue or requiring a single dataset; others need a whole suite of theoffice's services. The inhouse expertise in MODA is intentionally flexible, adjusting to a wide variety of areasacross the city that can benefit from data analytics. One of its standout projects is Databridge, a citywidedatasharing platform that integrates 311 and geographic data with more than 50 realtime data feeds fromsome 20 agencies and external organizations. MODA works on a casebycase basis with agencies to takefull advantage of the deep, crossagency analyses that the platform facilitates.

There has been a widespread recognition that data doesn't only enhance what is done in government butthat it has the capacity to transform what is done. New York City government has yet to perfect this model no city has it entirely figured out but the growth of its data intelligence from my time there to now may be a

http://www.governing.com/authors/Stephen-Goldsmith.html

http://www.nyc.gov/html/analytics/html/home/home.shtml

http://www.nyc.gov/html/ceo/html/home/home.shtml

http://www1.nyc.gov/site/operations/research/nyc-open-data.page

http://www.nyc.gov/html/analytics/html/initiatives/data_sharing.shtml

2/11/2016 www.governing.com/templates/gov_print_article?id=327218711

http://www.governing.com/templates/gov_print_article?id=327218711 2/2

good indicator of what lies ahead for the burgeoning movement of datadriven governance.

This article was printed from: http://www.governing.com/blogs/bfc/govnewyorkcitydatadrivengovernance.html

SMART CITIES READINESS GUIDEThe planning manual for building tomorrow’s cities today

SMART CITIES READINESS GUIDE

The planning manual for building tomorrow’s cities today

Authors and Key Contributors:This Guide was written by Smart Cities Council Chairman Jesse Berst,

Editorial Director Liz Enbysk and Research Director Christopher Williams. It was conceptualized by Chris Caine, President and CEO of Mercator XXI;

researched by Christopher Williams; illustrated by Scott Dunn, and designed and produced by Tahne Davis and Anne Schoenecker of Davis Design, Seattle.

SmartCitiesCouncil

© 2013 SMART CITIES COUNCIL

Acknowledgments

the expertise, the energy and the meticulous diligence of dozens of enthusiastic smart city advocates – subject matter experts from around the world, municipal government leaders and their staffs, technology advocates and business leaders among them.

We extend our sincere thanks and congratulations to the Lead and Associate Partners of the Smart Cities Council for their involvement and passion for this project: Alstom Grid, AT&T, Bechtel, Cisco, EDF, GE, IBM, Itron, MasterCard, Microsoft, National Grid, Qualcomm, S&C Electric Company, ABB, Alphinat, GRID2020, Invensys, MaxWest, Opower, Sungard and Zipcar.

The Readiness Guide would also not have been possible without the help of the Council’s Advisory Board, a collection of the world’s foremost smart city thinkers, doers and visionaries. Our 50-plus Advisors are listed on page 21 of this Guide’s Appendix.

Finally, we would like to thank the city mayors and their hardworking staffs who gave us real-world insights and provided us with critical early feedback. Their contributions represent the bridge between political infrastructure realities and 21st Century innovation. Special thanks to: Mayor Elizabeth Kautz, Burnsville, MN; Mayor Rawlings-Blake, Baltimore, MD; Mayor Jim Schmitt, Green Bay, WI; Mayor Kim McMillan, Clarksville, TN; Mayor Pedro Segarra, Hartford, CT; Mayor Jim Hovland, Edina, MN; Mayor Buddy Dyer, Orlando, FL; and Mayor Mike Rawlings, Dallas, TX.

TABLE OF CONTENTS

1. Introduction to Smart Cities

2. How to Use the Readiness Guide

3. Universal

4. Built Environment

5. Energy

6. Telecommunications

7. Transportation

8. Water and Wastewater

9. Health and Human Services

10. Public Safety

11. Payments

12. Smart People

13. Ideas to Action

Appendix

CHAPTER 1: INTRODUCTION TO SMART CITIES | Smart Cities Council Readiness Guide

INTRODUCTION TO SMART CITIES

Welcome to the Readiness Guide. This document was assembled with input from many of the world’s leading smart city practitioners – the members and advisors of the Smart Cities Council. Its mission is two-fold.

1

CHAPTER 1

First is to give you a “vision” of a smart city, to help you understand how technology will trans-form the cities of tomorrow.

Second is to help you construct your own road-map to that future. It suggests the goals to which you should aspire, the features and func-tions you should specify, the best practices that

-mum cost, at reduced risk.

The Readiness Guide is intended for mayors, city managers, city planners and their staff. It helps cities help themselves by providing objective, vendor-neutral information to make confident, educated choices about the tech-nologies that can transform a city.

Cities around the world are already making tremendous progress in achieving economic, environmental and social sustainability, in export-based initiatives and in the creation of 21st century jobs. All of these are excellent ways to improve city living standards and econ-omies. The concept of smart cities doesn’t compete with these efforts. Instead, smart city technologies can support and enhance work already underway. You’ll see how in the chap-ters to come.

In this chapter, we will define the smart city, explore its benefits and introduce the frame-work that underlies this Readiness Guide.

Taking a holistic

view of ‘city’

and explores the trends that are driving this global phenomenon. It also discusses some of the barriers cities may face and strategies to overcome them.

Before we define the “smart” piece, however,

world smart city examples are rarely a city in the strictest term. Many are more than a single city, such as a metropolitan region, a cluster of cities, counties and groups of coun-ties, a collection of nearby towns or a regional coalition. Other examples are less than a full-scale city, such as districts, neighborhoods, townships, villages, campuses and military bases. Indeed, many municipalities are taking a neighborhood-by-neighborhood approach to modernization. This Guide is designed to address all of these human ecosystems.

Because it is in common use, we will continue to use “city” throughout this Guide. But we use it to mean all relevant examples big and small. Regardless of size, we are taking a compre-hensive, holistic view that includes the entirety of human activity in an area, including city governments, schools, hospitals, infrastruc-

ture, resources, businesses and people. As you’ll read, smart technologies have matured to the point that cities of all sizes can afford and benefit from their implementation. For example, new cloud computing offerings allow even the smallest city to affordably tap into enormous computing power. So the lessons of this Guide apply regardless of size – and you’ll see real-world examples in the case studies featured throughout.

of a smart city

A smart city uses information and communi-cations technology (ICT) to enhance its livability, workability and sustainability. In simplest terms, there are three parts to that job: collect-ing, communicating and “crunching.” First, a smart city collects information about itself through sensors, other devices and existing systems. Next, it communicates that data using wired or wireless networks. Third, it “crunches” (analyzes) that data to understand what’s happening now and what’s likely to happen next.

Collecting data. Smart devices are logically located throughout the city to measure and monitor conditions. For instance, smart meters can measure electricity, gas and water usage

2CHAPTER 1: INTRODUCTION TO SMART CITIES | Smart Cities Council Readiness Guide

with great accuracy. Smart traffic sensors can report on road conditions and congestion. Smart GPS gear can pinpoint the exact locations of the city’s buses or the whereabouts of emergency crews. Automated weather stations can report conditions. And the smartphones carried by many city dwellers are also sensors that can –

so – collect their position, speed, where they clus-ter at different times of the day and the environ-mental conditions around them.

A smart city, then, is one that knows about itself and makes itself more known to its populace. No longer do we have to wonder if a street is congested – the street reports its condition. No longer do we have to wonder if we’re losing water to leaks – the smart water network detects and reports leaks as soon as they occur. No longer do we have to guess the progress of the city’s garbage trucks – the trucks report where they’ve been already and where they are headed next.

Communicating data. Once you’ve collected the data, you need to send it along. Smart cities typi-cally mix and match a variety of wired and wire-

to cellular to cable. The ultimate goal is to have connectivity everywhere, to every person and every device.

Crunching data. After collecting and communi-cating the data, you analyze it for one of three purposes: 1) presenting, 2) perfecting or 3)

predicting. If you’ve read about “analytics” or “Big Data,” then you may already know about the astonishing things that become possible by crunching large amounts of data. Importantly, crunching data turns information into intelligence that helps people and machines to act and make better decisions. This begins a virtuous cycle wherein data is made useful, people make use of that data to improve decisions and behavior, which in turn means more and better data is collected, thereby further improving decisions and behavior.

Presenting information tells us what’s going on right now. In the aerospace and defense indus-tries, they call this “situational awareness.” Software monitors the huge flow of incoming data, then summarizes and visualizes it in a way that makes it easy for human operators to under-stand. For instance, a smart operations center can monitor all aspects of an emergency situa-tion, including the actions and locations of police, fire, ambulances, traffic, downed power lines, closed streets and much more.

Perfecting operations uses the power of computers to optimize complex systems. For instance, balancing the supply and demand on an electricity network; or synchronizing traffic signals to minimize congestion; or selecting the ideal routes for a delivery fleet to minimize time and fuel costs; or optimizing the energy usage of an entire high-rise to achieve maximum comfort at minimum cost.

Figure 1.1

THE THREE CORE FUNCTIONS OF A SMART CITY

Collectinformation about current conditions across all responsibility

weather, buildings, etc.).

Communicateinformation, sometimes to other devices, sometimes to a control center and sometimes to servers running powerful software.

Crunchdata, analyzing it to present information, to perfect (optimize) operations and to predict what might happen next.

1

2

3

Collect Communicate “Crunch”


Predicting what’s next is perhaps the most exciting part of analytics. Cities such as Singapore already crunch data to predict traf-fic jams while there is still time to minimize their effects. Cities such as Rio de Janeiro already predict just where flooding will occur from a particular storm, so emergency crews and evacuation teams know just where to go.

-nicating and crunching information from a single department. But the greatest benefits come when data is connected with multiple departments and third parties. Many cities

about population growth and business expan-sion to know when and where to add or subtract bus and train routes. Other cities correlate multiple data sources to predict crime the way we predict weather. Or predict which expensive transformers are about to fail on the power grid and which will still be good for years to come.

As we’ll see in more detail, a smart city is a system of systems – water, power, transpor-tation, emergency response, built environ-ment, etc.– with each one affecting all the

ability to merge multiple data streams and mine them for amazing insights. It is those insights – presenting, perfecting and predict-ing – that enhance the livability, workability and sustainability of a smart city.

Resolution: 72ppi

Smart cities collect, communicate and crunch data. The city of Rio de Janeiro collects information from 30 different city departments about transportation, water, energy, weather and other conditions. Then it communicates those conditions to powerful

center the city developed with IBM. Not only does the city gain full situational awareness, it can even predict some conditions in advance, such as where floods will occur during severe storms. It can also develop actionable tasks based on modeled patterns, creating a competitive advantage for smart cities.

Figure 1.2


OTHER SMART CITY DEFINITIONS

-mation and communications technology (ICT) to enhance its livability, workability and sustainability.” Other

For instance, Forrester Research emphasizes the use of computing to monitor infrastructure and improve servic-es: “The use of smart computing technologies to make the critical infrastructure components and services of a city – which include city administration, education, healthcare, public safety, real estate, transportation and

stresses infrastructure, explaining that “a city that monitors and integrates conditions of all of its critical infrastructures – including roads, bridges, tunnels, rails, subways, airports,

seaports, communications, water, power, even major build-ings – can better optimize its resources, plan its preventive maintenance activities, and monitor security aspects while maximizing services to its citizens.”

Meanwhile, in 2010 IBM’s Journal of Research and Development paid particular attention to the wide range of smart devices that collect information, calling it “an instru-mented, interconnected and intelligent city.”

These and other definitions are valid and helpful under-standings of what smart cities are. The Council stands

others so that cities that have planned and invested under these and other models will understand that we share complementary, not competitive, views of the smart city

Livability, workability and sustainability are the goals. Smart cities use information and communications technologies to achieve them. Seoul, South Korea – pictured here – is often cited as one of the world’s most vibrant, sustainable cities.

Figure 1.3


The drivers of

smart cities

Powerful forces are converging to make smart cities a growing trend all around the world. It is valuable for city leaders to understand what’s behind this momentum and how it will play out in their region. Chances are some of the pain points described below will hit close to home.

Growing urbanization. Cities deliver many

greater access to healthcare and education, and greater access to entertainment, culture and the arts. As a result, people are moving to cities at an unprecedented rate. Over 700 million people will be added to urban popula-tions over the next 10 years. The United Nations projects that the world’s cities will need to accommodate an additional 3 billion residents by the middle of the century. A recent UN report suggests that 40,000 new cities will be needed worldwide.

Growing stress.challenges – increasing populations, environ-mental and regulatory requirements, declining tax bases and budgets and increased costs –

-cult growing pains ranging from pollution,

crowding and sprawl to inadequate housing, high unemployment and rising crime rates.

Inadequate infrastructure. Urbanization is putting significant strain on city infrastruc-tures that were, in most cases, built for popu-lations a fraction of their current size. Much of the developed world has infrastructure that is near or past its design life, requiring massive upgrades. For instance, in 2013 the American Society of Civil Engineers gave the United States an overall grade of D+ for its infrastruc-ture. Meanwhile, much of the developing world has missing or inadequate infrastructure, requiring massive build-outs. The 2012 black-out in India that left more than 600 million people without electricity is a prime example; the country has inadequate power generation to meet ever-increasing demand. The bottom line? McKinsey & Company estimates that cities will need to double their capital invest-ment by 2025, to $20 trillion from today’s $10 trillion per year.

Growing economic competition. The world has seen a rapid rise in competition between cities to secure the investments, jobs, busi-nesses and talent for economic success. Increasingly, both businesses and individuals evaluate a city’s “technology quotient” in decid-ing where to locate. A real challenge for cities with economies based on heavy industry is

Growing urbanization drives change. Over 700 million people will be added to urban populations over the next 10 years. The United Nations projects that the world’s cities will need to accommodate an additional 3 billion residents by the middle of the century. A recent UN report suggests that 40,000 new cities will be needed worldwide.

Figure 1.4


creating job opportunities that appeal to recent university graduates so they will stay and help build the kind of high-quality work-force that new industries, for instance those in technology, demand.

Growing expectations. Citizens are increas-ingly getting instant, anywhere, anytime, personalized access to information and servic-es via mobile devices and computers. And they increasingly expect that same kind of access to city services. In fact, a May 2013 United Nations survey of over 560,000 citizens from 194 countries revealed their top priorities are a good education, better healthcare and an honest and responsive government. We also know that people want to live in cities that can

communications and healthy job markets.

Growing environmental challenges. Cities house half of the world’s population but use two-thirds of the world’s energy and generate three-fourths of the world’s CO2 emissions. If we are going to mitigate climate change, it will have to happen in cities. Many regions and cities have aggressive climate and environ-mental goals – goals that cannot be reached without the help of smart technologies.

Rapidly improving technology capabilities. Many of the smart city drivers listed above are negatives – problems that demand solutions.

One-stop shopping for city services. Citizens increasingly expect instant, anywhere, anytime personalized access to information. The web portal pictured here is from the province of Quebec, Canada and uses technology from Council member Alphinat. Its goal is to give businesses “one-stop shopping” for virtually all of their needs – permits, licensing, taxes, etc. In many cities taking care of business needs requires dealing individually with numerous different city departments.

Figure 1.5


There are positive drivers as well, especially the rapid progress in technology. The costs of collecting, communicating and crunching data have plunged. What’s more, much of the need-ed technology is already in place:

begun to modernize their electric power grids and, to a lesser extent, their water and gas networks. Hundreds of millions of smart meters and smart sensors are now in place, producing data of value to a smart city.

building management systems, there are now millions of buildings with some of the pieces needed to be smart, on the cusp of being able to ‘talk’ and ‘listen.’

we’re seeing better access to healthcare with in-home consultations via computer. Meanwhile most agencies are switching to electronic records and many are using analytics to improve results.

smarter thanks to intelligent transportation management software, roadway sensors and smart parking apps. Navigation apps and equipment display real-time traffic so

pointed to – less congested alternatives.

And we are seeing more electric vehicles on our roads which help reduce pollution.

high-bandwidth networks worldwide that connect one billion computers and four billion cell phones. These networks are already in place in almost all major cities and can be leveraged for smart city applications.

Communication (NFC) equipped point of sales with the roll-out of contactless cards technology. It means hundreds of merchants are already capable of accepting mobile payments and wallets for seamless consumer experience and value added services, but also cashless cities are able to reduce frauds and benefiting from better insights on their citizen purchasing journeys.

It’s important to realize that today’s ubiquitous smartphones are becoming both a “delivery platform” and a “sensor network” for smart city applications. The delivery platform is obvious – a smartphone is a great place for a resident to receive alerts and access city services. But today’s smartphones can also be leveraged to collect information when the user agrees to share data. For instance, one launched in 2013 has the following sensors: a GPS locator, a microphone, a gyroscope, a light sensor, a camera, an accelerometer, a barometer, a ther-mometer, a magnetometer and a hygrometer.

“By the end of the decade, many infrastructure

systems, building energy management – will be deployed across North America and Europe and, increasingly, in the rest of the world,” says Navigant Research analyst Eric Woods. Once

Rapid progress in technology. An increasing number of

from technology that allows merchants to accept payments via smartphones and wallets.

Figure 1.6


in place, that technology provides the basis for a wide range of innovative smart city applica-tions and services.

Rapidly declining technology costs. Even as capabilities are climbing, technology costs are plummeting. Hardware costs are declining at a steady pace. But it is software costs that have plunged the most, thanks to four trends.

The first trend is the advent of inexpensive mobile apps and information services view-able by mobile phones. Those phones are so popular that millions of developers have turned their attention to building applications, many of which cost only a few dollars.

The second trend is the arrival of social media. Applications such as Facebook and Twitter act as free “platforms” to deliver alerts, updates or even small-scale apps. They also act as “listening posts” that help cities monitor citizen needs and preferences. In fact, compa-nies such as IBM and Microsoft now have the capability to use machine intelligence to moni-tor social media and derive trends.

The third trend is the maturation of cloud computing. Cloud computing delivers power-ful solutions via the Internet. Suppliers save money because they can build one solution and sell it to many different users, gaining tremendous economies of scale. Users save money because they don’t have to buy and

maintain giant data centers or hire and train large IT staffs. Only a few years ago, advanced applications were available only to the very biggest agencies and corporations. Today – thanks to cloud computing – they are not out of reach for even the smallest township. And they are available without a giant upfront investment, simply by paying a monthly fee.

The fourth trend is about the data. From an analytics perspective, we can now cost effec-tively handle the high volume, velocity and variety of data – e.g. Big Data.

And there’s much more to come. The smart city is part of an even larger trend – the “Internet of Things.” Technology provider Cisco estimates there were 200 million devices connected to the Internet in the year 2000. By 2012, that number had increased to 10 billion. There are approximately 200 connectable “things” per person today, or 1.5 trillion things globally, Cisco estimates.

Clearly we are entering a remarkable new phase. Research firm IDC predicted in 2012 that the smart city market would grow by 27% year over year. Meanwhile, Navigant Research said it would hit $20 billion in worldwide sales by 2020. And Cisco in early 2013 predicted the overall Internet of Things market will create an

for the world’s industries in the next decade.

No surprise the world’s biggest corporations and brightest entrepreneurs are racing to bring their best ideas to this market. And the fierce competition is raising capabilities, increasing choice and lowering costs at a rapid pace, making smart cities more viable every day.

The “Internet of Things.” Cisco estimates there were 200 million devices connected to the Internet in the year 2000. By 2012, that number had increased to 10 billion.

Figure 1.7


The barriers

to smart cities

Despite the powerful drivers in favor, the path to smart cities has obstacles along the way. Members of the Smart Cities Council have worked on thousands of smart city projects all over the world. As they’ve collaborated with local governments certain barriers have emerged frequently.

Siloed, piecemeal implementations. Cities often tackle challenges in a piecemeal fash-ion, due to short-term financial constraints

and long-term traditions that divide city func-tions into separate, “siloed” departments with little interaction. As a result, many projects are built to solve a single problem in a single department, creating “islands of automation” that duplicate expenses while making it diffi-cult to share systems or data.

Building a smart city requires a system-wide view and an integrated approach. The bad news: holistic thinking and collaborative work are hard. The good news: done right, they can save time and enable new services that were not possible in an isolated, siloed model. For instance, a city department can drastically cut the development time for a new application by

re-using data and software modules already created by other departments. A municipal water utility can drastically cut the cost of a communications network by using one already built out for an electric utility. And a city can sometimes reduce overall information technology (IT) costs by as much as 25% just by implementing a master IT architecture and technology roadmap. This is not to suggest that cities must finance and implement dozens of investments at one time. In fact, it is entirely fine to begin with just one or two projects. What is critical is that these projects all fall into a larger, integrated plan so that city investments are not redundant.

THE PROBLEM WITH “SILOED” CITIESExpensive redundancies. Despite the fact that modern IT architectures make it possible to connect city departments and solutions today,, far too many cities still use a “siloed” approach to smart city applications. Individual depart-ments build individual applications, with little regard to sharing costs, infrastructure and data. The result is expensive redundancies and

those isolated applications.

Figure 1.8


Most experts agree that technology will not be the gating factor for the smart city transfor-mation. Instead, we will be limited by our human ability to coordinate and collaborate between departmental and technology silos.

Tax revenues are shrinking in many cities, making infrastructure projects increasingly difficult to finance. In fact, some cities have been forced to implement austerity measures – such as furloughing employees one day a month or cutting back on travel and discretionary expenses. Yet if those cities remain old-fashioned while others modernize, they will suffer even more, since cities must

-cial models are emerging. And payment inno-vations like e-Procurement or electronic bene-fits can help cities reduce costs and free up money to invest in infrastructure and other improvements. Some of them require little or no upfront capital from the city. Instead, the city “rents” its solution as it goes. And perfor-mance contracts and shared revenue models between the city and solution vendors provide cities with attractive financing solutions. What’s more, many smart city solutions have a rapid payback so that they save money over the long run. In many cases, the technology can actually improve the city’s economic return.

A new view of city apps. Early city applications were inward-facing and intended just for city employees. Today, more and more cities are producing outward-facing apps. For example, to get citizens involved in cleaning up London before the 2012 Summer Olympics, the city worked with Council member Microsoft on the Love Clean London portal (above) and companion mobile

by texting or uploading images. It’s still being used today.

Figure 1.9


Lack of ICT know-how. Although industry has developed highly sophisticated ICT skills, few city governments have had the budget or the vision to push the state of the art. Since smart cities are essentially the injection of ICT into every phase of operations, this lack of ICT skills puts cities at a disadvantage. Fortunately, more and more applications are offered as a service. That is, they are hosted in the cloud (out on the Internet) where they have access to tremendous computing power, virtually unlimited storage and innovative soft-ware. Another plus is that the smart city sector has developed a large cadre of experi-enced global, regional and local consultants and service providers who are partnering with cities to deploy ICT solutions.

Lack of integrated services. To the extent cities applied ICT in the past, they applied it to their internal, siloed operations. The result has been a grab-bag of aging applications that only city employees can use. Although this was an acceptable practice in the last century, today we can and must allow citizen access and self-service. There is no reason that citi-zens who want, for instance, to open a restau-rant should have to make multiple applica-tions to multiple city departments. In a smart city, a single portal can gather all the data and parcel it out to the appropriate departments. Likewise, residents should have instant access to up-to-the-minute information about their energy and water usage, their taxes and

fees, their social services programs and more. And ideas like Open Data not only improve transparency, they enforce a people-first perspective that is critical in smart cities Lack of citizen engagement. The smart cities movement is often held back by a lack of clari-ty about what a smart city is and what it can do for citizens. As a result, many stakeholders are unaware of the smart city options that have found success already. Often, there is a communications issue. Cities should be wary of being too abstract with their smart city initiatives, recognize that citizens care about services that make their lives better, and adjust their engagement accordingly. Cities need to recognize when they need citizen and business awareness versus complete ‘buy in.’

Remedying the citizen engagement challenge will require visionary leadership that paints a

the U.S. in the late 2000s, several electric power utilities learned this lesson the hard way. They rolled out smart meters without explaining how customers would benefit. They suffered consumer backlash and resistance as a result.

Lack of a smart city visionary. Every parade needs a leader. Sometimes that leadership comes from an elected official – a mayor or council person who acts as the smart city champion. Smart city leadership can also come from elsewhere in the administration –

The barriers to smart cities. Despite the powerful drivers in favor, the path to smart cities has obstacles along the way. Sometimes it comes down to lack of a smart city visionary. Cities need a smart city champion – a mayor, a city manager, a planning director. Or it can come from outside city hall – civic or business leaders or a public-private partnership, for example.

Figure 1.10


a city manager or a planning director, for instance. Or it can come from outside city hall altogether with involvement from business lead-ers, civic organizations or public-private partnerships.

of smart cities

Now let’s look at why it is so worthwhile to over-come those barriers and take advantage of the technology advances described earlier that allow you to re-imagine your city. With the right plan-ning and investment, government leaders can make our ciites more livable, more workable and more sustainable – both economically and envi-ronmentally. Let’s examine those overall goals, which are the very purpose of becoming smart.

Enhanced livability means a better quality of life for city residents. In the smart city, people have access to a comfortable, clean, engaged, healthy and safe lifestyle. Some of the most highly valued aspects include inexpensive ener-gy, convenient mass transit, good schools, fast-er emergency responses, clean water and air, low crime and access to diverse entertainment and cultural options.

Enhanced workability means accelerated economic development. Put another way, it means more jobs and better jobs and increased

local GDP. In the smart city, people have access to the foundations of prosperity – the fundamental infrastructure services that let them compete in the world economy. Those services include broadband connectivity; clean, reliable, inexpen-sive energy; educational opportunities; affordable housing and commercial space; and efficient transportation.

Enhanced sustainability means giving people access to the resources they need without compromising the ability of future generations to meet their own needs. Merriam-Webster defines sustainability as a method of using a resource so that it is not depleted or permanent-ly damaged. When the Council uses the term, it refers not only to the environment, but also to

-cient use of natural, human and economic resources and promote cost saving in times of austerity, and they are careful stewards of taxpayer dollars. It isn’t about investing huge sums of money into new infrastructure, it’s about making infrastructure do more and last longer for less.

Life is better in a smart city – better for people and better for businesses. In the chapters to come, we will discuss dozens of specific bene-

city vision. But let’s take a moment to summa-rize them by imagining a day in the life of a citi-zen in our smart city.

Smart cities enhance livability. Residents have access to a comfortable, clean, engaged, healthy and safe lifestyle.

Figure 1.11


A DAY IN YOUR NEW URBAN LIFE:

HOW SMART CITIES MAKE FOR HAPPIER CITIZENS

It’s Monday morning, a rare day off for Josie. But when the alarm on her smart wristphone chirps, she doesn’t reach for the snooze button. “Too much to do today,” she reminds herself. Peeking around her bedroom’s solar curtain, she’s pleased to see the sun shining brightly.

“Perfect,” she decides. “I can bike over to the mall, drop off the bike and pick up a car when I’m done.”

Josie doesn’t actually own a bike or a car; living in a city with abundant share programs means she doesn’t have to. And since the café she runs is only 10 blocks from her condo, she typically walks to work – or if the weather is really lousy hops on a bus. She’s proud that her city has a smart transportation system that uses advanced tech-

Wandering into the kitchen, Josie pours herself a cup of coffee that started brewing when her alarm went off.

Between her smart wristphone and her smart thermostat, pretty much every creature comfort in her condo is auto-mated. She told the system her preferences, of course, but from then on it took care of the details. If it notices her overriding the original settings, it quickly adapts to her new wishes. Her shower is programmed to run at the same temperature every day and her refrigerator sends an alert to her phone when she’s running low on items she typically has on hand. She just brings up the list when she’s at the grocery store.

She knows she’ll miss her condo when she and Miguel move into the loft they found. But the condo is on the small side for two people. Though the loft is small too, it has trans-formable spaces thanks to “robot walls” that can be moved to create different spaces for different needs. Josie is espe-

screen will let Miguel telecommute much of the time and she plans to use it for the online courses she’s taking.

Continued on next page

Sensors and digitization will change lives. Day-to-day living will become much more convenient in tomorrow’s smart cities, thanks to the digitization of just about everything.

Figure 1.12


After a quick trip to the roof to check on the garden she shares with the building’s other tenants, she grabs her backpack and looks at a phone app to see the closest available bike-share. Turns out, there is one just around the corner. But if Josie had been running late or faced with rainy weather, she had only to enter her destination into her city transit app to get a route plan optimized for her preferences.

Jumping on her bike, she picks her destination from her favorites list and transfers her phone display to an overlay in her glasses. She instantly sees

threatens to jam up her usual route. She picks an alternate route calculat-ed by the system and follows the directions as they appear in her glasses.

The purpose of her trip to the mall is to find something to wear to a party. But as she walks past the virtual city hall that occupies a small storefront near the mall entrance, she realizes she can take care of another item on her to-do list.

“This is pretty sweet,” she says as she sits down in a private “closet” -

act with a remote city agent. She tells him she needs a permit for a street fair her cafe is going to participate in but doesn’t know what it’s called. The agent quickly finds the form she needs, transmits it to the

within minutes. Before she leaves the agent mentions a new waste management system the city is testing at restaurants. It’s “pay as you

throw” – meaning the less they throw away and the more they recycle the lower their monthly bill. Josie likes the sound of that and signs up on the spot. She asks for daily updates. Since her bins are monitored by smart sensors, the city knows moment by moment how much trash Josie’s cafe has accumulated. It can warn her when it looks like she’ll exceed the goal she set for herself, while there is still time to improve.

She spends another hour trying on dresses suggested by the store’s shopping service, which taps into a history of past purchases that Josie has rated and posted for just this purpose. Then glancing at her wrist, she realizes she has to get moving. She promised to take her grand-mother to a medical appointment and doesn’t want to be late. As she walks toward the mall exit, she passes a car-share wall display that has

nearest electric car– and sees there’s one just two blocks away that’s fully charged and ready to go.

During the medical appointment, Josie is relieved to see the specialist her grandmother is seeing for the first time pull up electronic records that provide a complete view of her medical history. She’s heard stories about elderly patients suffering harmful drug interactions because one doctor doesn’t know what the other is prescribing.

When she finally gets home that evening, it is dinnertime and Josie’s hoping a robot will appear with a gourmet meal – but then she sees

Figure 1.13


The Problem The Smart City Solution

Planning

Infrastructure

System operators problems

ICT investments

Citizen engagement

intelligent city data

Sharing data

collaborate on initiatives correlated with other data services

AT A GLANCE: TRADITIONAL CITIES VS SMART CITIES

Figure 1.14


2/11/2016 How South Bend, Indiana Saved $100 Million By Tracking Its Sewers | Fast Company | Business + Innovation

http://www.fastcompany.com/3014805/howsouthbendindianasaved100millionbytrackingitssewers 1/7

3 M I N U T E R E A D

How South Bend,Indiana Saved $100Million By Tracking ItsSewers

A S T O L D T O J . J . M C C O R V E Y 0 8 . 0 5 . 1 3

Back when the Big Three was the Big Four, SouthBend, Indiana, was a thriving industrial city. Untilit wasn’t. Not wanting to go the way of Detroit,citizens turned to Peter Buttigieg, a 31-year-oldMcKinseyian, to shake things up. Is there really anysurprise that he’s rocking it?

S U B S C R I B E

http://www.fastcompany.com/3014805/how-south-bend-indiana-saved-100-million-by-tracking-its-sewers

http://www.facebook.com/sharer.php?u=http%3A%2F%2Fwww.fastcompany.com%2F3014805%2Fhow-south-bend-indiana-saved-100-million-by-tracking-its-sewers&app_id=178479832213933&display=popup

http://twitter.com/share?url=http%3A%2F%2Fwww.fastcompany.com%2F3014805%2Fhow-south-bend-indiana-saved-100-million-by-tracking-its-sewers&text=How%20South%20Bend%2C%20Indiana%20Saved%20%24100%20Million%20By%20Tracking%20Its%20Sewers

https://magazine.fastcompany.com/loc/FST/navcharger



6 : 0 0 A M

A Midwestern municipal governmentisn’t the first thing that leaps to mind whenyou think of innovation, but it ought to be.Cities face more demand than ever to deliverwith the same amount of resources. In localgovernment, it’s very clear to your customers—your citizens—whether or not you’redelivering. Either that pothole gets filled in orit doesn’t. The results are very much ondisplay, and that creates a very healthypressure to innovate.

Businesses always have competitors nippingat their heels. Historically, cities have notviewed themselves as subject to that sametype of competition. But that’s wrong. Thereality, especially for our modest-sized,middle-American community, is that peoplecan choose. Labor is mobile. Individuals aredeciding where they want to work, live, andset up their businesses. Our job is to takecertain worries off the table. They shouldn’thave to worry about whether there’s going tobe clean, safe drinking water coming out ofthe faucet. Getting through school, holdingdown a job, and raising a family? That’s hardenough.

I’m the youngest mayor in America of a city

T R E N D I N GHow The Most Successful PeopleAsk Questions

•

One Of The World's Top AgingResearchers Has A Pill To KeepYou Feeling Young

•

This House Costs Just $20,000—But It’s Nicer Than Yours

•

http://www.linkedin.com/shareArticle?mini=true&url=http%3A%2F%2Fwww.fastcompany.com%2F3014805%2Fhow-south-bend-indiana-saved-100-million-by-tracking-its-sewers&media=How%20South%20Bend%2C%20Indiana%20Saved%20%24100%20Million%20By%20Tracking%20Its%20Sewers

mailto:?body=http%3A%2F%2Fwww.fastcompany.com%2F3014805%2Fhow-south-bend-indiana-saved-100-million-by-tracking-its-sewers&subject=How%20South%20Bend%2C%20Indiana%20Saved%20%24100%20Million%20By%20Tracking%20Its%20Sewers

http://www.fastcompany.com/3056318/hit-the-ground-running/how-the-most-successful-people-ask-questions

http://www.fastcoexist.com/3041800/one-of-the-worlds-top-aging-researchers-has-a-pill-to-keep-you-feeling-young

http://www.fastcoexist.com/3056129/this-house-costs-just-20000-but-its-nicer-than-yours



with a population of more than 100,000. Partof the message the community sends whenthey put a rookie in a job like this is, We wantsomething different. As a consultant atMcKinsey, I learned the value of data and theability to shape that information into ananswer. Last year, South Bend became thefirst city in the world to migrate its sewersystem to the cloud, which prevented pollutedwater from going into the river and saved$100 million in new pipes. It all started with alocal startup called .

Now, we’re looking for ways to enhancepeople’s relationships with theirneighborhoods. When the Studebaker carcompany left South Bend in the '60s, wegradually lost about a quarter of ourpopulation. The consequence of that is, wehave a lot of vacant houses that we’ve got todo something about. So I set a target of 1,000houses in 1,000 days—we’ll save them if wecan, and turn them down if we can’t. But weneeded something that would galvanizepeople to help.

That’s why I reached out to .I thought there was something appealingabout the kind of person who would become aCode for America fellow. It’s still counter-cultural to be working on civic issues. They

EmNet

Code for America

http://www.emnet.net/

http://www.fastcompany.com/1714303/hacker-driven-code-america-kicks-today



could be doing any number of prestigious andlucrative things, yet they chose this. InJanuary, we partnered with three fellows tocreate a web app for citizens to findinformation about properties in thecommunity. What happened to that house atthe corner of the block? Can a few neighborsget together and buy it for $8,000? If it’s beentorn down, who do we need to call to turn itinto a garden? It does no good for me to tellour citizens that we’re transparent, but inorder to find something out, you have to showup to our code enforcement department andwade through thousands of pages of files andfolders.

At the end of the 11-month program, we wantto have a number of apps that will improvepeople’s lives—even those who never log on touse them. It’s part of an ongoingtransformation here. We’ve cleared out thisvast tract of land that was covered withdecaying factories and turned it into a landingpad, called Ignition Park, for startups comingout of Notre Dame. The first company set upthere does data hosting, which symbolizes abit of geographical luck for us. Because SouthBend was built at a nexus of highways andrailway lines, we have an abundance of fiber-optic cables going through our city. That, inaddition to our cold climate, creates the ideal



situation for us to take that old 800,000-sq.-ft.Studebaker factory, which I had alwaysthought we would have to blow up, and bringit back as a data center. That’s what SouthBend has always been good at—adaptingsomething into a new and differentopportunity. We just need to take it to the nextlevel.

Photo by Daniel Shea

Fast Talk: "I'm From The Government..."

A version of this article appeared in the issue of Fast Company

magazine.

• Rachel Sterne Haot, Chief Digital Officer,New York City

• Caitria O’Neill and Alvin Liang,Recovers.org

• Lily Liu, PublicStuff

• Smart Talk: Lisa Gans, Fuse Corps; JayNath, San Francisco Chief InnovationOfficer

September 2013

http://www.fastcompany.com/3014804/rachel-sterne-haot-chief-digital-officer

http://www.fastcompany.com/3014806/caitria-oneill-and-alvin-liang-cofounders-recoversorg

http://www.fastcompany.com/3014807/lessons-from-a-civic-minded-startup-lily-liu-founder-and-ceo-publicstuff

http://www.fastcompany.com/3014808/smart-talk-lisa-gans-fuse-corps-jay-nath-san-francisco-chief-innovation-officer

http://www.fastcompany.com/magazine/178

http://www.facebook.com/sharer.php?u=http%3A%2F%2Fwww.fastcompany.com%2F3014805%2Fhow-south-bend-indiana-saved-100-million-by-tracking-its-sewers&app_id=178479832213933&display=popup

http://twitter.com/share?url=http%3A%2F%2Fwww.fastcompany.com%2F3014805%2Fhow-south-bend-indiana-saved-100-million-by-tracking-its-sewers&text=How%20South%20Bend%2C%20Indiana%20Saved%20%24100%20Million%20By%20Tracking%20Its%20Sewers

http://www.linkedin.com/shareArticle?mini=true&url=http%3A%2F%2Fwww.fastcompany.com%2F3014805%2Fhow-south-bend-indiana-saved-100-million-by-tracking-its-sewers&media=How%20South%20Bend%2C%20Indiana%20Saved%20%24100%20Million%20By%20Tracking%20Its%20Sewers

mailto:?body=http%3A%2F%2Fwww.fastcompany.com%2F3014805%2Fhow-south-bend-indiana-saved-100-million-by-tracking-its-sewers&subject=How%20South%20Bend%2C%20Indiana%20Saved%20%24100%20Million%20By%20Tracking%20Its%20Sewers

Everyone wants to go digital. The first step is truly understanding what that is.

Companies today are rushing headlong to become more digital. But what does digital really mean?

For some executives, it’s about technology. For others, digital is a new way of engaging with customers. And for others still, it represents an entirely new way of doing business. None of these definitions is necessarily incorrect. But such diverse perspectives often trip up leadership teams because they reflect a lack of alignment and common vision about where the business needs to go. This often results in piecemeal initiatives or misguided efforts that lead to missed opportunities, sluggish performance, or false starts.

Even as CEOs push forward with their digital agendas, it’s worth pausing to clarify vocabulary and sharpen language. Business leaders must have a clear and common understanding of exactly what digital means to them and, as a result, what it means to their business (for a deeper look at how companies can develop meaningful digital strategies and drive business performance, see “Raising your Digital Quotient,” on mckinsey.com).

It’s tempting to look for simple definitions, but to be meaningful and sustainable, we believe that digital should be seen less as a thing and more a way of doing things. To help make this definition more concrete, we’ve broken it down into three attributes: creating value at the new frontiers of the business world, creating value in the processes that execute a vision of customer experiences, and building foundational capabilities that support the entire structure.

Creating value at new frontiersBeing digital requires being open to reexamining your entire way of doing business and understanding where the new frontiers of value are. For some companies, capturing new frontiers may be about developing entirely new businesses in adjacent categories; for others, it may be about identifying and going after new value pools in existing sectors.

Unlocking value from emerging growth sectors requires a commitment to understanding the implications of developments in the marketplace and evaluating how they may present opportunities or threats. The Internet of Things, for example, is starting to open opportunities for disrupters to use unprecedented levels of data precision to identify flaws in existing value chains. In the automotive industry, cars connected to the outside world have expanded the

What ‘digital’ really meansMcKinsey Digital July 2015

Karel Dörner and David Edelman

2

frontiers for self-navigation and in-car entertainment. In the logistics industry, the use of sensors, big data, and analytics has enabled companies to improve the efficiency of their supply-chain operations.

At the same time, being digital means being closely attuned to how customer decision journeys are evolving in the broadest sense. That means understanding how customer behaviors and expectations are developing inside and outside your business, as well as outside your sector, which is crucial to getting ahead of trends that can deliver or destroy value.

Creating value in core businessesDigital’s next element is rethinking how to use new capabilities to improve how customers are served. This is grounded in an obsession with understanding each step of a customer’s purchasing journey—regardless of channel—and thinking about how digital capabilities can design and deliver the best possible experience, across all parts of the business. For example, the supply chain is critical to developing the flexibility, efficiency, and speed to deliver the right product efficiently in a way the customer wants. By the same token, data and metrics can focus on delivering insights about customers that in turn drive marketing and sales decisions.

Critically, digital isn’t about just working to deliver a one-off customer journey. It’s about implementing a cyclical dynamic where processes and capabilities are constantly evolving based on inputs from the customer, fostering ongoing product or service loyalty. Making this happen requires an interconnected set of four core capabilities:

Proactive decision making. Relevance is the currency of the digital age. This requires making decisions, based on intelligence, that deliver content and experiences that are personalized and relevant to the customer. Remembering customer preferences is a basic example of this capability, but it also extends to personalizing and optimizing the next step in the customer’s journey. Data providers such as ClickFox, for example, blend data from multiple channels into one view of what customers are doing and what happens as a result. In the back office, analytics and intelligence provide near-real-time insights into customer needs and behaviors that then determine the types of messages and offers to deliver to the customer.

Contextual interactivity. This means analyzing how a consumer is interacting with a brand and modifying those interactions to improve the customer experience. For example, the content and experience may adapt as a customer shifts from a mobile phone to a laptop or from evaluating a brand to making a purchasing decision. The rising number of customer interactions generates a stream of intelligence that allows brands to make better decisions about what their customers want. And the rapid rise of wearable technology and the Internet of Things represents the latest wave of touchpoints that will enable companies to blend digital and physical experiences even more.

2

3

Real-time automation. To support this cyclical give-and-take dynamic with customers and help them complete a task now requires extensive automation. Automation of customer interactions can boost the number of self-service options that help resolve problems quickly, personalize communications to be more relevant, and deliver consistent customer journeys no matter the channel, time, or device. Automating the supply chain and core business processes can drive down costs, but it’s also crucial to providing companies with more flexibility to respond to and anticipate customer demand.

Journey-focused innovation. Serving customers well gives companies permission to be innovative in how they interact with and sell to them. That may include, for example, expanding existing customer journeys into new businesses and services that extend the relationship with the customer, ideally to the benefit of both parties. These innovations in turn fuel more interactions, create more information, and increase the value of the customer-brand relationship.

Building foundational capabilitiesThe final element of our definition of digital is about the technological and organizational processes that allow an enterprise to be agile and fast. This foundation is made up of two elements:

Mind-sets. Being digital is about using data to make better and faster decisions, devolving decision making to smaller teams, and developing much more iterative and rapid ways of doing things. Thinking in this way shouldn’t be limited to just a handful of functions. It should incorporate a broad swath of how companies operate, including creatively partnering with external companies to extend necessary capabilities. A digital mind-set institutionalizes cross-functional collaboration, flattens hierarchies, and builds environments to encourage the generation of new ideas. Incentives and metrics are developed to support such decision-making agility.

System and data architecture. Digital in the context of IT is focused on creating a two-part environment that decouples legacy systems—which support critical functions and run at a slower pace—from those that support fast-moving, often customer-facing interactions. A key feature of digitized IT is the commitment to building networks that connect devices, objects, and people. This approach is embodied in a continuous-delivery model where cross-functional IT teams automate systems and optimize processes to be able to release and iterate on software quickly.

Digital is about unlocking growth now. How companies might interpret or act on that definition will vary, but having a clear understanding of what digital means allows business leaders to develop a shared vision of how it can be used to capture value.

Karel Dörner is a principal in McKinsey’s Munich office, and David Edelman is a principal in the Boston office.

3

Copyright © 2015 McKinsey & Company. All rights reserved.

smart city as a service - granicus

Documents