Bayat, Ali

Kawalek, Peter

English

Aberdeen University Research

Data As InfrastructurePeter Kawalek1 and Ali Bayat21School of Business and Economics, Loughborough University, Loughborough LE11 3TU. p.kawalek@lboro.ac.uk2Alliance Manchester Business School, Manchester University, M13 9SS. ali.bayat@manchester.ac.uk14 December 2017It is now widely accepted that the digitization of theeconomy has taken root in a way that means it isnot confined to one sector. All sectors are aﬀected insome common ways. Brynjolfsson and McAfee 1 areamong those who have described this. The economicdrivers behind digitization are successfully isolatedand described by Goldfarb and Tucker2, and thereare important contributions to understanding given byeconomists including Levin3 and Nordhaus 4. It is pro-portionate to describe the economic and social ramifi-cations within the frame of ‘Creative Destruction’, orig-inally described by Schumpeter in 19425. In this light,the importance of data can be expected to grow acrossmost or all industry sectors. Its eﬀective managementwill become ever more critical to the economy and tosociety more widely.1 Data As A Public GoodThe deep technological reformation of current societyhas meant that the potential of data is gathering atten-tion alongside other celebrated advances in the hard-ware and infrastructure of society. New mechanismsfor the assembly, management and processing of dataprovide a new impetus for rethinking how the data1McAfee, A. and Brynjolfsson, E., 2017. Machine, Platform,Crowd: Harnessing Our Digital Future.2Goldfarb, A. and Tucker, C., 2017. Digital Economics (No.w23684). National Bureau of Economic Research.3Levin, J.D., 2011. The economics of internet markets (No.w16852). National Bureau of Economic Research.4Nordhaus, W.D., 2015. Are We Approaching an Economic Sin-gularity? Information Technology and the Future of EconomicGrowth (No. w21547). National Bureau of Economic Research.5Schumpeter, J., 1942. Creative destruction. Capitalism, social-ism and democracy, 825is best managed so that society can best utilize itsresources, solve the most problems, and provide themost social good for the most people.Data itself has become an important part of the in-frastructure of the nation and must be managed forthe best eﬀect. For this reason, data can be widely de-scribed as constituting ‘a public good.’ Its availabilityand use is a necessary part of the public realm. It ismore subtle and yet profound to identify that withinthis, data is taking on new roles in the real-time per-formance of infrastructure, and even in the design andmaintenance of that infrastructure. Yet, this data oftencomes from diverse sources and must be managed innew ways. We reserve the term ‘Data as Infrastructure’for these new circumstances where the incorporationof data into the performance, design and maintenanceof infrastructures reaches a new level of sophistication.The realisation of the value of data across the publicrealm depends not only on the technological exploita-tion of massive amounts of data, but also upon thegovernance strategy of that data. A data governancestrategy gains value when it is placed in the broadernational context andwhen it is alignedwith the overallvision of the nation. We synthesise these fragmentedconcerns into a more coherent framework. The frame-work provides a systematic way of defining the concept‘data as infrastructure’ and of seeing its place along-side other important data governance initiatives.2 Technological DriversThe eﬀective governance of data relies on the imple-mentation of a number of ‘Big Data’ technologies. Thisterm, ‘Big Data’ is now commonplace. It has becomerelevant because a cluster of innovations made it fea-Page ii of vsible to utilize larger and larger data sets, and thatoften these data sets are only semi-structured or evenunstructured. These technologies that enable Big Datainclude the Cloud, ever faster chips, the Internet ofThings, and Machine Learning. Together these inno-vations help in the collection, integration, validation,real-time analysis and reporting of massive amountsof data. Table 1 outlines an overview of these tech-nologies and the type of problems that they are ableto solve.In practice, the technologies listed and described inTable 1 are utilized in bundles. To solve a specificproblem, a combination of technologies are used to-gether. These bundling eﬀects amongst technologiesmakes prioritisation for investment very complex andthe choices depend substantially on the data gover-nance mode that we describe next.3 Data Governance StrategiesWe formalise the data governance modes around fourmajor themes based on the role that the governmentcan take. This role of government is very important inall the governance modes but can vary greatly amongthem. ‘Data as Infrastructure’ comprises the ultimateposition where Government is wholly or partially ‘aSmart System.’In practice, all government institutions will seek hy-brid arrangements across these four themes accordingto their unique, current and expected challenges.i) Government as a ProviderThe first and most common form of provision is thedesignation and release of government data as publicdata. Part of this is data that is generated in relation topublic infrastructure. The rationale behind such initia-tives (commonly referred to as Open Data Initiatives)is the idea that releasing such data brings about ac-countability and transparency, and that it empowers aform of participatory governance. Data is consideredas a public good and access to this data is a potentialright for every member of society. It is argued thatsuch initiatives promote participation, increase inno-vation and facilitate evidence-based decision making.In this conception, government is only the providerof the data. The data itself can be used by citi-zens, bodies such as non-governmental organizations(NGOs), and the private sector; all for diﬀerent pur-poses. Although the provision of data is subject topublic request and scrutiny, a hierarchical approachis used to maintain control over the type of contentthat is released and to manage issues relating to dataprivacy. This unidirectional system of control is lim-ited in scope. In order to ensure the maximum poten-tial value is developed from the data, an open datainitiative will seek to impose only a minimal criticalset of controls over the usage of the data. The ‘gov-ernment as a provider’ category can be considered asanalogous to the provision of a park wherein the gov-ernment decides on its location and content but hasa minimal framework of control over how the park isused. Such a ‘park’ is encapsulated by government act-ing as a provider in Open Data UK.ii) Government as an EnablerAn alternative approach is for the government toprovide a unified data marketplace. This approach hasbeen less common than the simpler role of ‘Govern-ment as a Provider’, but has recently gained momen-tum. ‘Government as an Enabler’ is developed in recog-nition that government is not the only entity that hasdata that is important to the needs of society. Muchof the data about infrastructure and its use is in thecontrol of the private sector (e.g. telecommunicationscompanies, IT companies, logistics companies), semi-state organizations (e.g. public transport franchisees),universities and research organizations, and individu-als themselves. This approach of ‘Government as anEnabler’ also potentially satisfies the common call thatdata held by the government should not be used bybusinesses free-of-charge. This is justified on the basisthat the data has been gathered at taxpayers’ expenseand that is has economic value. A governance strategybased on enablement helps governments to control ac-cess and use. Enablement implies that the role of gov-ernment is to design a data marketplace so that thereis exchange among suppliers and users, and the opti-mal value of the data is realized by participants in themarket. In this category, data is considered as a com-modity and exchanged via the medium of the markettherein. This facilitates more eﬃcient use of the databy a greater multiplicity of interested parties and po-tentially leads to more data-driven innovations and toeconomic growth. Examples of this approach includeData For London and the Copenhagen City Data Ex-change.iii) Government as a LabIn this approach government will seek to developa network of diﬀerent providers and users through aspecific initiative. It does so in order to manage a re-search agenda within the network. The research ques-tions and their answers might be attained through akind of closed or semi-closed environment that reliesupon only certain parties and data. Typically, this willbe known as a ‘lab.’ A lab allows government to bringdata providers together in order to answer specificpolicy issues or questions and hence is pro-active andmanaged through formal research governance.This formal research governance is concerned withissues of quality in terms of inputs, outputs and pro-cess, and is also concerned with the interface to pol-icy mechanisms themselves. Designing an appropriatePage iii of vPurpose Technologies Solution to6Data Collection. Integrating and Uni-fying Diﬀerent Sources of dataSensing (including radar, lidar, sonar, satellite imaging, thermal imaging,quantum sensing and the use of drones), Cloud Technologies and the Inter-net of ThingsSelection ProblemDimensionality Reduction of MassiveDatasets and Real-Time PredictiveModellingMachine Learning (e.g. Deep Learning) Prediction ProblemTransaction Verification, Data Accu-racyBlockchain and Distributed Ledger Technologies Verification ProblemPrototyping, Design Diagnostic andOperation MonitoringVirtual and Augmented Reality, Digital Twinning Replication ProblemTable 1: Technological Driversecosystem and providing the right incentives demandsa high level of government involvement (at least in thedesign and maintenance phase) and enriched collab-orations and partnerships between public sector, pri-vate sectors and citizens. In this, the data itself can beconsidered as embodying a concept. The co-creationprocesses turn the concept into valuable innovations,enabling more eﬃcient use of infrastructure and help-ing in the delivery of public services. A well-knownexample of a lab is that of the Bristol Living Lab.iv) Government as a Smart SystemA fourth approach is to develop highly automatedand intelligent closed systems that support both thereal-time working of the environment and an ongoingprocess of analysis/learning about the optimisation ofthis environment. This ‘smart’ environmental model ismost obviously illustrated in the ‘Smart City’ conceptbut is more general. Eﬀectively the concept applieswhenever the Internet of Things and other monitor-ing systems are brought into the wholescale and real-time management of a facility or a geographical area.Given that such a system is controlled by or on behalfof the government, it can be described as ‘Governmentas a Smart System.’ Regulated algorithms will instru-ment space and will determine many things that po-tentially have political or economic ramifications, e.g.who has access to physical space, road-space, naturalenvironments or services. Smart systems will learnthrough Artificial Intelligence about any issue withintheir scope, e.g. the best movement of emergency vehi-cles, crop interventions, patterns of lighting or refusecollection. As data generates the behaviour of infras-tructure, it can be said that data is in a sense also ahard infrastructure and that it needs to be maintainedand managed through a formal approach, analogousto the way that physical infrastructure itself is man-aged. This kind of system is necessarily closed for thereason that data quality is key, but the system will alsosupport learning and can be integrated into an overallgovernance framework alongside other roles of gov-ernment (1-3 above).The management of infrastructural data is possiblethrough centralised silos drawing from each aspectof physical infrastructure. New algorithmic and stor-age advances support the collecting, merging, visu-alising and analysing massive amounts of data. TheSmart City architecture promoted by IT corporationsnormally relies upon this type of hierarchical arrange-ment. The closed governance structure is able to pro-vide real-time monitoring of the all infrastructure towhich it is linked. This security comes at the ex-pense of issues such as privacy, ownership and flexi-bility. An example is The Dubai Smart City but themeteorological project Radar Meteorológico of the Riode Janeiro Centre of Operations exhibits the samecharacteristics. There are significant existing initia-tives in rising economies including India. The King-dom of Saudi Arabia’s plans to develop Neom willconstitute a new global benchmark in the scale ofuse of intelligent, algorithmically-driven, infrastruc-tural data. The redevelopment of Toronto Quaysideis a further benchmark example. Its plans incorpo-rate closed, hierarchical management of data along-side a city lab project in hybrid governance arrange-ments. The ambient intelligence that will characterisesuch environments will increasingly support ecologi-cal management of urban areas, whilst also facilitat-ing the same sort of ‘smart’ management of non-urbanenvironments. Hence, whether the setting is urban orcountryside, an ecological paradigm will be dominant.‘Smart System’ characteristics are growing throughthe same combinatorial innovation eﬀects as de-scribed earlier as ‘Technological Drivers.’ Digital Twin-ning, for example, is an important initiative that al-lows a key infrastructure, or a key part of an infras-tructure, to be managed through a twin. This twin iseﬀectively a data representation of the infrastructurethat takes real-time and other data into the manage-ment processes of that real-infrastructural component.To illustrate, a gas turbine, a jet engine or a sluicemight have a twin that supports its real-time moni-toring and management. In turn, this implies thata higher level of automation will follow as many ofthe decisions will not need human intervention. Thetwin can take care of an increasing percentage of de-Page iv of vGovernment as aProviderGovernment as an En-ablerGovernment as a Lab Government as a SmartSystemData Considered as Public Good Commodity Concept FeedbackGovernment Involve-mentLow Medium High HighStructural Mode Hierarchy Market Network HierarchyMotivation Transparency, Participa-tory governanceMonetising the value ofthe dataCo-Creation Closed Governance withhighly eﬃcient executionExamples of Initiatives Open Data Data Marketplace Living Lab City DashboardTable 2: Data Governance Strategiescisions. Moreover, it also implies the greater use ofdata through the lifecycle of the infrastructural item,as the digital twin can be created at the design stageof the component and then used in the governanceof its manufacture, installation, maintenance and de-commissioning. As stated, there are examples of this‘digital twinning’ approach available for complex man-ufactured artefacts like jet engines, but it is clear thatthe concept applies much more widely, e.g. to build-ings or to roads. Once diﬀerent elements of an in-frastructure are combined together then it becomespossible to conceive of a range of infrastructural com-ponents all collaborating through their digital twins(e.g. a stadium with a road system and footbridge).This begets a sophisticated level of process automa-tion across components of the environment7. As wellas advantages in eﬃciency across the lifecycle of com-ponents, issues also arise, for example in the examplegiven, the twin of a stadium might, take “algorithmicauthority" over when people can leave a football gameor concert because that twin is responsive to traﬃcflows across a nearby footbridge. Again, such scenar-ios imply the need for proper governance.4 A Conceptual Model of “Dataas Infrastructure”It is the public infrastructure of a nation that enablesthe delivery of public services and which provides theplatform for markets and culture. Nations vary interms of their institutional arrangements over theman-agement of their diﬀerent infrastructures. As these na-tions then head towards the exponential changes of adigital era, the use of data in conjunction with phys-ical infrastructure will lead to an environment thatis ever more eﬃcient and evermore intelligent. Ulti-mately, the strategy of any given nation is shaped bothby its historical context and its vision toward the fu-ture. What kinds of infrastructure does it have, what7This is the Software Engineering concept of an ‘active model’e.g. Snowdon, B. and Kawalek, P., 2003. Active meta-process mod-els: a conceptual exposition. Information and software Technology,45(15), pp.1021-1029.will it have, and how will these fit into the broadestsocio-economic context? The eﬃcient use of publicdata starts with the evaluation of the existing physi-cal infrastructure and how the diﬀerent parts of it willbe impacted by the vision of the future. Identificationof the problems and complexities associated with suchinfrastructure will help government decide upon itsposition and pursue the most appropriate form of datagovernance strategy (a hybrid data governancemode).Upon selecting the right form of governance, the gov-ernment is then able to invest on the right portfolio oftechnology bundles.Figure 1: Data As Infrastructure Conceptual ModelReturning to the grand scheme of digitization, thereis no doubt that the management of infrastructure issubject to the same pressures of digitization as areseen in other sectors. An ambient intelligence will beavailable across all kinds of environment to ensure thebest utility and care of those diﬀerent environments.Benefits and issues await. Within this trajectory ofprogress, data and its management become increas-ingly critical, partially because its eﬀective use enablesPage v of vimproved learning and improved policy, and partiallybecause in increasingly complex ways it becomes partof the actual performance of that infrastructure itself.It ultimately follows that data is infrastructure and hasto be managed as infrastructure.About the AuthorsPeter Kawalek is a Professor of Information Man-agement at Loughborough University. Peter waspreviously Head of Business Economics and StrategyGroup at Manchester Business School, University ofManchester. He has also held academic posts at theDepartment of Computer Science, Manchester Univer-sity, and Warwick Business School at the Universityof Warwick.Ali Bayat is a researcher at Alliance Manchester Busi-ness School. He is working on a wide range of topicsrelated to digital economy, corporate governance, andstrategy.

Data as Infrastructure

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Data as Infrastructure

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