Factors Influencing Adoption of IoT for Data-driven Decision

Making in Asset Management Organizations

Paul Brous, Marijn Janssen, Daan Schraven, Jasper Spiegeler and Baris Can Duzgun

Delft University of Technology, Jaffalaan 5, 2628 BX, Delft, Netherlands

Keywords: IoT, Internet of Things, Asset Management, Data Governance, Decision Making.

Abstract: Organizations tasked with managing large scale, public civil infrastructure are increasingly looking at data to

drive their asset management decision-making processes. The Internet of Things (IoT) enables the creation of

data that can be used to gain further insights into the current and predicted state of the infrastructure and may

help automate the asset management process. Yet, it remains unclear to what extent data from IoT impacts

decision-making in public asset management organizations. The objective of this paper is to explore

implementation factors for adoption of new data sources for decision-making in asset management

organizations. Based on a systematic literature review and case studies in the asset management domain, this

paper derives the current use and expectations of new data sources for decision-making in asset management.

The paper concludes that although recent technological developments have enabled the deployment of IoT

for asset management, the current level of adoption remains low. The inherent complexity of adopting a data-

driven approach to asset management requires an effective data governance strategy to ensure data quality,

manage expectations, build trust and integrate IoT data in decision-making processes.

1 INTRODUCTION

Many organizations tasked with managing civil

infrastructure routinely store large volumes of data.

More and more, new sources provide this data for

producing and collecting real world data that can be

communicated on the internet, such as sensor devices,

social media, and user-generated data (Barnaghi et

al., 2013). When these resources communicate and

are integrated, many physical objects are able to act

in unison, by means of ambient intelligence (Ramos

et al., 2008). The object becomes a part of a complex

system in which the whole is greater than the sum of

its parts (Miller and Page, 2009). The Internet of

Things (IoT) describes a situation whereby physical

objects are connected to the Internet and are able to

communicate with, and identify themselves to, other

devices (Atzori et al., 2010). For example, this may

include GPS-based navigation applications for

smartphones based on real-time traffic information

shared by other drivers, or real-time weather service

based on the information updated by sensors of users’

smartphones or weather radars and other weather

observation tools (Zhang et al., 2015).

This research takes place in the asset management

domain of large scale civil infrastructure. Asset

management (AM) is important for this industry as

the success of an enterprise often depends on its

ability to use and manage its assets efficiently

(Koronios et al., 2005) and effectively (Schraven et

al., 2011). AM is a discipline for optimizing and

applying strategies related to work planning decisions

in order to effectively and efficiently meet the desired

objective (Mohseni, 2003; Mathew et al., 2008;

Hastings, 2010).

IoT is important to AM because an object that can

communicate digitally also becomes connected to

surrounding objects and data infrastructures. For

example, it is possible to determine the position and

length of traffic jams, and to monitor trends,

variations, and relationships in the road network over

time using smartphone data, networked sensors and

cameras to analyze traffic flow (Brous and Janssen,

2015b). But in order for IoT data to be accepted by

asset managers, a variety of barriers such as trust and

acceptance still need to be overcome (Brous and

Janssen, 2015a). The concept of trust is often used in

various contexts and with different meanings. Trust is

a complex notion which is hard to define, although its

importance in data-driven decision-making is widely

recognized (Sicari et al., 2015).

Brous, P., Janssen, M., Schraven, D., Spiegeler, J. and Duzgun, B.

Factors Inﬂuencing Adoption of IoT for Data-driven Decision Making in Asset Management Organizations.

DOI: 10.5220/0006296300700079

In Proceedings of the 2nd International Conference on Internet of Things, Big Data and Security (IoTBDS 2017), pages 70-79

ISBN: 978-989-758-245-5

IoT devices and the communication between these

devices may benefit the management of civil

infrastructures by providing enough quality data to

generate trusted information required to make the

right decisions at the right time (Brous and Janssen,

2015b), helping organizations improve their decision

making capability. However, the quality of this data

has been seen to vary greatly over time (Barnaghi et

al., 2013). Trusted data is regarded as essential to

aiding the decision-making process in asset

management (Haider et al., 2006). Having trusted

data is therefore essential for organizations which

have data driven decision making processes.

IoT data can vary widely in format and

representation, and determining the quality of data is

important to allow asset managers to trust IoT data,

especially in use-case scenarios where the data is

made available by a large number of different

providers (Barnaghi et al., 2013). The satisfaction of

trust requirements is often related to identity

management and access control (Sicari et al., 2015),

and as real world data can be related to people,

privacy and security are also key concerns (Barnaghi

et al., 2013). The challenge is greater when the scale

of the data and the number of different parties that can

access and process the data increase.

It is often assumed that public organizations are

well equipped to handle data, but this is not always

the case (Thompson et al., 2015). The objective of the

paper is therefore to explore conditions and factors

for effective and sustainable adoption of new data

sources for decision-making in asset management

organizations. Data management is complex and it is

difficult to understand and assess the issues

surrounding this process (Grus et al., 2010). It can be

difficult to attribute success or failure of data

management projects to one or more specific factors.

Because data management is complex, there is an

interrelationship between the sociological and

technical dimensions of data management, and it is

difficult to track cause-and-effect relationships

(Brous et al., 2015).

Data management issues often do not arise from

existing business rules or the technology itself, but

from a lack of sound data governance (Thompson et

al., 2015). Data governance has recently received

wide-spread attention from practitioners as

organizations are becoming increasingly serious

about the notion of “data as an asset”. Data

governance is about identifying the fundamental

decisions regarding data that need to be made and

who should be making them (Khatri and Brown,

2010).

The methodology used in this research is

described in section two. In section three a systematic

review of literature derives the current potential of

IoT data. In section four, two explorative case studies

of asset management projects in The Netherlands

describe the current uses and expectations of IoT data.

The results of the literature review and the case

studies is discussed in section five. The results show

that IoT data has a variety of potential uses and that

expectations are high, but asset managers remain

unconvinced, and adoption of IoT data for decision-

making in asset management remains low.

Conclusions are drawn in section six.

2 METHODS

This article follows the literature review method

proposed by Webster and Watson (2002) and

attempts to methodologically analyze and synthesize

literature regarding the potential uses of IoT data in

asset management. It will advance the knowledge

base of data-driven decision-making in asset

management by deriving the current uses of IoT data

for asset management that can be used by researchers

to focus on important data management issues, and by

practitioners to develop an effective data

management strategy and approach.

There is only limited research on the management

of IoT data, and perceived expectations compared

with the actual usage of IoT data for decision-making

in asset management organizations. The keywords:

“infrastructure”, “IoT” or “Internet of Things”,

“data”, and “use” returned 324 hits within the

databases Scopus, Web of Science, IEEE explore, and

JSTOR. 294 hits were journal articles, 29 were

conference papers and 1 hit was a book. We then

filtered these results and performed a forward and

backward search to select relevant articles based on

the criteria whether they included a theoretical

discussion on the use of IoT data in asset management

decision-making. Based on this forward and

backward search, 30 journal articles, and conference

proceedings were selected and relevant principles

from these sources were listed.

The cases under study occur within the asset

management process of the Directorate General of

Public Works and Water Management of The

Netherlands. The Directorate General of Public

Works and Water Management of The Netherlands is

commonly known within The Netherlands as

“Rijkswaterstaat”, often abbreviated to “RWS”, and

is referred to as such within this research. RWS is the

operational branch of the Ministry of Infrastructure

and the Environment in The Netherlands. It functions

more and more as an agency in which, although RWS

Factors Inﬂuencing Adoption of IoT for Data-driven Decision Making in Asset Management Organizations

retains responsibility, the actual management and

maintenance of assets is carried out under contract by

a consortium of engineering companies, construction

companies, banks, etc. The cases have been

anonymized at the request of the participating parties.

Two case studies were chosen. The first was that of

monitoring activities of a large civil structure, in this

case a bridge. The second case was the monitoring

activities of a section of highway in The Netherlands.

The case studies were explorative in method and

descriptive in nature. Unstructured interviews were

held with managers, subject matter experts, and

internal consultants. Internal documentation

concerned with the use and implementation of new

data sources was studied. The expectations and

current use of IoT data sources found in the case

studies were listed. Uses and expectations were then

grouped according to concept and compared with the

evidence from the literature review.

3 LITERATURE REVIEW

Public infrastructure systems consist of many

different types of assets that could have long life

cycles. Civil infrastructure assets need to be

maintained to ensure their optimal value over their

entire (long) life cycles (Hassanain et al., 2003). We

follow Mohseni’s (2003) definition of AM as being a

discipline for optimizing and applying strategies

related to work planning decisions in order to

effectively and efficiently meet the desired objective.

AM helps public organizations realize value from

assets whilst balancing financial, environmental and

social costs, risks, quality of service and performance

related to assets (ISO 55000, 2014).

As early as 2001 there were already many

software tools for asset management, (Vanier, 2001;

Hassanain et al., 2003; Flintsch and Chen, 2004), and

since then many data formats, data sources and pools

of unstructured data have become available over the

years. The explosive growth in data is due to a

number of different enabling and driving

technologies such as the widespread roll-out of fixed

and mobile internet; the development of ubiquitous

computing and the ability to access networks and

computation in many environments (Kitchin, 2014).

It is expected that IoT will be used in a variety of

ways related both to the real-time measurement and

analyses of data as to trend analysis of historical data

over time (Brous and Janssen, 2015b). The variety of

using IoT enables further understanding of the

conditions and factors for effective and sustainable

adoption of new data sources. Following from that,

we focus on the review of theoretical discussions in

the relevant articles on the varied ways in which IoT

is used.

In information technology (IT) research, an

accepted and suitable way to review literature is

through the distinction of three levels:

strategic/political, tactical and operational (Ackoff,

1971; Ivanov, 2010). This distinction is also

recognized in asset management literature via asset

owner, asset manager and service provider

(Woodhouse, 1997; Volker et al., 2012; CROW,

2017).

In correspondence to this distinction, Table 1

summarizes the expected strategic, tactical and

operational uses of IoT found in literature. The review

reveals three expectations of IoT data. First, the

literature expects that it will change performance

measurement of infrastructure services, like applying

statistical learning (Archetti et al., 2015). Second,

IoT data is expected to change the perception of

infrastructure services, like perceiving sudden

changes in temperature by which a fire could be

detected (Hentschel et al., 2016). Finally, IoT data is

expected to change improvement processes, for

example through self-organizing resource planning.

In the next sections, we describe these uses of IoT.

3.1 Expected Strategic Uses of IoT

Data

Decision support services include support for

management at the tactical and strategic levels. IoT

services are knowledge intensive and require

collection of appropriate data contents, data analysis

and reporting (Backman and Helaakoski, 2016). As

such, statistical learning and network science is

expected to play a critical role in converting data

resources into actionable knowledge (Archetti et al.,

2015).

Due to increasing stresses on budgets and

personnel as well as increased utilization of civil

infrastructure, public AM organizations increasingly

need to intelligently manage their infrastructure with

fewer resources (Rathore et al., 2016). By managing

and analyzing various IoT data, it should be possible

to create new services to achieve an efficient and

sustainable civil infrastructure (Hashi et al., 2015;

Backman and Helaakoski, 2016). IoT may bring an

improved understanding of complex processes which

is expected to help improve the efficiency of transport

management and infrastructure services, and help

with effective reporting (Kothari et al., 2015).

IoTBDS 2017 - 2nd International Conference on Internet of Things, Big Data and Security

Table 1: Overview of expected uses of IoT data found in literature.

IoT data expected to change

performance measurement of

infrastructure service

IoT data expected to change

perception of infrastructure

service

IoT data expected to change

improvement processes of

infrastructure service

Strategic use of

IoT data

 Decision support services (trend

analysis) (Aono et al., 2015)

 Reporting (Backman and

Helaakoski, 2016; Kothari et al.,

2015)

 Communication of long term

planning and strategic choices

 Improve perceived optimization of

services (Sadeghi et al., 2015)



Encourage proactive processes

(Aono et al., 2016)

 Encourage self-organization

(Sadeghi et al., 2015)

 Determine strategic changes to

infrastructure

Tactical use of

IoT data

 Cost management (Archetti et al.,

2015 ; Aono et al., 2016)

 Time management (Aono et al.,

2016)

 Planning (Archetti et al., 2015)

 Post-events evaluations (Tao et

al., 2014; Hashi et al., 2015)

 Communication of short term

planning and actions (Archetti et

al., 2015)

 Improve perceived quality of

services (Archetti et al., 2015)

 Public enactment (Tien et al., 2016)



Enable directed procedures

(Aono et al., 2016)

 Enable efficient recovery (Tien

et al., 2016)

 Control event occurrence (Tao et

al., 2014; Parkinson and

Bamford, 2016)

 Improve utilization of existing

infrastructure (Koo et al., 2015;

Hentschel et al., 2016)

Operational use

of IoT data

 Improve efficiency of monitoring

(Ahlborn et al., 2010)

 Improve quality of monitoring

(Phares et al., 2004; Hentschel et

al., 2016)

 Improve operational decision-

making (Neisse et al., 2016)

 Improve productivity (Hentschel

et al., 2016)

 Communication of operational

activities (Hentschel et al., 2016)

 Improve perceived quality of

delivery (Ahlborn et al., 2010)

 Improve efficiency of operations

(Zhang et al., 2015)

 Improve effectiveness of

operations (Neisse et al., 2016)

Rathore et al., (2016) believe that smart

management of traffic systems with the provision of

real-time information to the citizen based on the

current traffic situation should enhance the

management performance of public AM

organizations. Furthermore, improved granularity of

trend analysis resulting from IoT data may help

public AM organizations in being proactive with

maintenance, reducing the chances of catastrophic

failure (Aono et al., 2016).

IoT may also be used to improve service

optimization through self-organization (Sadeghi et

al., 2015). Self-organizing systems that optimize

themselves with regard to resource availability and

consumption may enable optimization according to

usage and de-centralized long-term support (Sadeghi

et al., 2015).

3.2 Expected Tactical Uses of IoT Data

IoT infrastructure could potentially be used to reduce

costs in terms of time and money (Aono et al., 2016),

as traditional methods of inspecting infrastructure,

such as highway structures and bridges, for damage

are often reactive in nature and require significant

amounts of time and use of costly equipment. Aono

et al. (2016) suggest that an infrastructure monitoring

network could be used to quickly assess damage to

infrastructure so that maintenance procedures could

be directed to areas that need immediate attention. In

this way, IoT may play a significant role in the

channeling and transmission of data through efficient

use of technology (Sakhardande et al., 2016).

IoT is expected to be able to provide users with

information on costs, time, environmental impact and

perceived quality of services (Archetti et al., 2015).

When IoT data becomes available regarding a

particular hazard, there may be opportunities to

control hazard occurrence and recover using these

data sources (Tao et al., 2014; Parkinson and

Bamford, 2016) and trigger analysis with events that

affect measurement, such as repair or maintenance

(Koo et al., 2015; Hentschel et al., 2016). By

specifying events (Tao et al., 2014; Hashi et al.,

2015), it should be possible to obtain a set of data

before and after an event to be used for analysis and

evaluations, taking the effect of the event into

consideration.

It is also expected that IoT will improve the

utilization of existing infrastructure (Koo et al., 2015;

Hentschel et al., 2016). For example, Koo et al.,

(2015) suggest that an automated system condition

monitoring based on IoT including leak detection can

Factors Inﬂuencing Adoption of IoT for Data-driven Decision Making in Asset Management Organizations

optimizing water supply, production, and water

consumption (Koo et al., 2015).

IoT may enable more effective and efficient AM

planning according to variations in user preferences

(Archetti et al., 2015) by providing decision support

functionalities which identify and address criticalities

in civil infrastructure. Archetti et al., (2015) give the

example that commuters may use socially aware and

collective intelligence based on functionalities of IoT

to make individually informed mobility decisions.

But for this to be realized, the collected data must

have significance for operations and services such as

inventory, usage, environmental management, and

events. Also, quality of the information must be

considered with regards to multiple aspects and

dimensions. IoT data should be “fit-for-use”

(Backman and Helaakoski, 2016; Cao et al., 2016).

For example, closures of bridges that are part of major

transportation arteries tend to be major events. These

events often result in “tweets” that point to the same

incident (Tien et al., 2016), which if analyzed

correctly may improve service efficiency and enable

more effective recovery.

3.3 Expected Operational Uses of IoT

Data

In order to keep civil infrastructure such as bridges

safe and functioning, regular inspections to determine

the condition of the asset are a necessity (Ahlborn et

al., 2010; Neisse et al., 2016). For example,

traditional inspections of bridges are usually visual

assessments by trained personnel where all the asset’s

component conditions are observed once every three

to six years, and are summarized into one report

(Phares et al., 2004). After the inspection is done,

asset managers must decide what maintenance

interventions are needed based on these inspection

reports. However, as is shown by Kallen and van

Noortwijk (2005), inspection reports of bridges can

be biased by subjective judgements of the experts or

by lack of information. This can eventually result in

inaccurate statements which may lead to the failure to

perform maintenance or unnecessary maintenance

activities (Phares et al., 2004).

IoT data may make it possible to remotely observe

the condition of objects and thereby enhance the

available information on the condition of public

infrastructure (Ahlborn et al., 2010). IoT data is

expected to allow users to monitor current

environmental conditions affecting the asset. Event

processing should be able to support individual,

complex events if these events are defined by

individual users for localized events (Hentschel et al.,

2016). Examples given by Hentschel et al., (2016) are

sudden increases in sound, light and temperature,

which could indicate a fire or an explosion. Hentschel

et al., (2016) expect that when an event is triggered

alarms could be issued.

Environmental factors such as temperature and air

quality can have significant effects on productivity

(Hentschel et al., 2016). Smart assets may be able to

monitor status parameters, analyze this data and reach

some conclusions, considering at the same time

tensions such as cost and efficiency with regards to

environment preservation (Moreno et al., 2014). As

such, IoT data is also expected to play a role in

increasing public safety and security (Neisse et al.,

2016) through, for example, active road safety,

emergency vehicle warning or collision risk warning.

IoT data is expected to be leveraged for increased

efficiency in various public service applications such

as inspection schedules, public facility management,

urban infrastructure maintenance, intelligent

transportation services, and emergency situation

monitoring (Zhang et al., 2015). By enabling

individuals and organizations to share real time data,

IoT may enable appropriate data services to the

consumers (Kothari et al., 2015). The expectation is

that IoT will be used for key decision making in

operational activities.

4 CASE STUDIES

Two cases have been studied to identify how the

adoption of IoT data is done by asset management

organizations. The case studies focus on the AM

process of civil infrastructure in The Netherlands. In

the first case we study the adoption of IoT data by a

consortium for the maintenance of a bridge. In the

second case we study the adoption of IoT data for the

maintenance of the road sections of a highway

between two cities in the east of The Netherlands.

4.1 Case 1: Bridge Inspection with a

Drone

IoT is expected to enable remote sensing of the

condition of bridges and enhance the available

information on their condition if performed correctly

(Ahlborn et al., 2010). Since limited examples of IoT

adoption for bridge maintenance were known, a

consortium of interested asset management

organizations started a pilot to adopt remote sensing

techniques to assess the condition of a bridge and its

need for maintenance.

For this bridge, new methods of remote

sensing have been tested, in the expectation to pilot

with IoT sensors in the succeeding year to improve

IoTBDS 2017 - 2nd International Conference on Internet of Things, Big Data and Security

the quality of monitoring. For this case, the main

driver for IoT and other forms of remote sensing

appeared to be the lack of accessibility of some parts

of the bridge for visual inspections. For example,

locations above and below the bridge there is no space

for setting up equipment (e.g. scaffoldings, boom

lifters or ladders) such that visual inspector can work.

This way, parts of the bridge remain poorly inspected,

making it harder to physically detect local cases of

bridge deterioration.

In combination with the innovation program

of RWS, the maintenance consortium used the pilot

project to perform inspections with help of a drone

that was equipped with a camera to observe the less

reachable parts of the bridge, thereby increasing the

operation’s efficiency. The drone inspection was also

performed at better reachable parts to compare the

inspection results of the drone against the inspection

results of a human inspector. This comparison gave

new data for the usefulness of adopting IoT data,

since the use of drones during inspection was

relatively new for bridge assessments.

In terms of a strategic use of IoT data, the

consortium judged that the obtained information was

good enough to give a reliable overview of the found

damages at the bridge parts, which were harder to

reach. This shows that the decision support services

and performance report could be based on a more

complete view of bridge data.

In terms of a tactical use of IoT data the

consortium found that the bridge inspection with the

drone resulted in less costs than a human inspection

with the needed equipment to access the areas of the

bridge. Therefore, the adoption of drones results in a

reduction of costs with respect to inspecting a bridge.

On the operational use of IoT data, the

adoption of a drone showed practical constraints. The

drone did not receive a GPS signal under the bridge

deck which prohibited it to follow its predetermined

flight route. Therefore, it had to be steered manually

which made the process of documenting and keeping

record of the locations of the taken inspection

photographs more difficult and time consuming than

expected. Secondly, the damages themselves were

clearly visible but the extent and size were hard to

measure from only the digital images. Thirdly, the

drone had to fly at a minimum distance of 1.5 meters

from the bridge components which resulted in the

incapability of observing the bearings of the bridge

and affected the completeness of the drone’s dataset.

Finally, the bridge had to be closed off for traffic due

to safety regulations.

The consortium concluded that the use of

drones is not ideal for assessing bridges. This

conclusion could be overturned when the practical

downsides of the drone flight are solved.

Nevertheless, the interviewed asset managers still

expect that remote sensing will eventually be able to

compare the actual behaviour of the bridge

components with the expected behaviour at much

shorter intervals, giving asset managers a better

opportunity to construct a more qualitative, efficient

and effective maintenance plan.

Another interesting side-note with regards to

this case of adopting drones for bridge inspections, is

that the consortium has made further plans to

implement a pilot project using IoT sensors that

communicate over a Long-Range Low-Power (LoRa)

network to monitor bridge movements. Robust, smart

wireless sensing systems that are suitable for use in

civil engineering have been developed specially for

this project, as well as the software to analyse and

interpret the data. According to the interviewees,

these new sensing methods should speed up and

improve current monitoring methods.

4.2 Case 2: Highway Connection

Between Two Dutch Cities

An asset management organization under contract in

the East of the Netherlands had the task to increase

the safety of a highway between two Dutch cities. In

addition, the organization had to improve the

connection to surrounding villages. The highway in

the contract stretches for 23 km. The asset

management organization worked under a new

maintenance contract adopted by RWS, but this

contract included no requirements for adopting IoT

data. It is worth noting that this type of contract was

representative for other maintenance contracts in The

Netherlands at the time.

Traditional inspection methods were performed

through annual measurements with an Automatic

Road ANalyser (ARAN) vehicle and visual

inspections. These were the only inspection methods

that RWS, the contracting authority, accepted. Since

the data on performance or its perception could not be

changed with IoT data, the added value could only

come from improving the main inspection method.

Therefore, to be better able to inspect the road the

asset management organization adopted various

additional IoT-based techniques. For example, the

organization chose to use cloud-based service lane

technology to monitor the road. They adopted these

techniques with the intent to enable their inspectors to

add inspections quickly while on location and to help

asset managers to see the information with a better

overview. This case shows that adoption of IoT data

to change the operational efficiency was possible,

despite the contractual requirement.

Still, for the tactical use of IoT data some

limitations remained. The asset management

organization could not yet adopt IoT data with respect

Factors Inﬂuencing Adoption of IoT for Data-driven Decision Making in Asset Management Organizations

to making the assessments. This still had to be done

by experts judging the extent to which the

deterioration as inspected and monitored should be

resolved with control measures. As this was still

mostly a manual process these experts defined control

measures while trying to combine the deterioration

overviews of ARAN and the IoT techniques, and by

coupling various road sections to judge the quality

between these. Experts were still needed because

non-condition data had to be included for determining

the appropriate action, eg. scheduled maintenance

tasks, the cost of repairs and cost of a penalties.

At a strategic level, the adoption of IoT data

started to show a conflict of interest between the asset

management organization and the contracting

authority. When a failed requirement was detected,

the contractor was obliged to inform the contracting

authority and notify them of the intended actions. The

contractor ensured there was proof of Quality of

Service, while, at the same time, the contracting

authority also inspected the same section of the road

to check if the used measurements were correct. The

conflict of interest typically surfaced in this case at

the point where assurances were needed to meet

requirements in the contract. On the one hand,

requirements helped the contracting authority

manage the contract, by use of financial

compensations and penalties. On the other hand, the

contractor took the view that requirements could be

handled more flexibly if they were managed

independently. This conflict showed that an asset

management organization under contract did not

choose to adopt IoT technologies because of the

persistence of the contracting authority to use

established methods, thereby missing the expected

benefits of more self-organized and more proactive

highway inspection.

5 DISCUSSION

Civil infrastructures such as transport infrastructure

systems present unique opportunities for developing

new applications aligned with IoT and it is expected

that IoT will play a significant role in AM processes

in the future. Civil infrastructure systems provide

many of the services that are critical to the continued

functioning, and security of society (Tien et al., 2016)

and failure of these infrastructures can be

catastrophic. Detecting these damage or failure events

is critical to minimize the negative impacts of these

events, but many of these infrastructures still lack

continuous monitoring to be able to detect these

events (Tien et al., 2016). Bridges, for example, are

generally subject to only three to five yearly

inspections, and very few are instrumented with

physical sensors that would be able to detect damage

that may occur at any time. The opportunities for IoT

adoption are apparent, and expectations appear to be

high. However, adoption of IoT remains low.

Noticeably, current data sources are still largely

provided by expert judgement in combination with

technical devices in specific measurement points,

although a growing role is being played by human

data generated through, for example, social networks

(Archetti et al., 2015). Table 2 below outlines the

conditions and factors found in the cases and

literature, grouped according to elements of data

infrastructures as suggested by Brous et al., (2014).

Table 2: Conditions and factors for IoT adoption in AM.

Cate

Conditions and Factors

Human High technical knowledge

Good understanding of data

management processes

Good understanding of data

quality issues

Ongoing training and education

Organizational Clear responsibility for

innovation

Availability of best practices to

benchmark

Positive business cases.

Data Governance:

Clear responsibilities for data

managemen

Data Data Governance:

High level of data quality

Alignment of data to AM

requirements

Technical Data Governance:

Adoption of stringent security

measures

Availability and adoption of

interoperabilit

standards

Adoption of IoT in AM is facing challenges to

integrate data from diverse data sources and to design

applications to support the management of

infrastructures (Brous and Janssen, 2015a). Statistical

learning is thus expected to play a critical role in the

design of representation models and computational

engines needed to turn the data resources into

actionable knowledge (Archetti et al., 2015).

According to Aono et al., (2016), IoT is only

practical if the IoT infrastructure matches the useful

life of the physical asset. But Kothari et al., (2015)

believe that there is still some work required on

building IT infrastructures for supporting the IoT

ecosystem. IoT infrastructures require powerful

IoTBDS 2017 - 2nd International Conference on Internet of Things, Big Data and Security

mechanisms for sensor feed discovery, planning of

feed processing workflow, failure resilience and

system management (Kothari et al., 2015). As seen in

the cases, existing IT infrastructures often do not yet

provide these capabilities, thus requiring high-levels

of manual intervention (Kothari et al., 2015).

Furthermore, although quality of sensor feeds is

critically important, little attention is currently paid to

data quality in most existing infrastructures (Kothari

et al., 2015).

The importance of data quality for IoT

infrastructures and the persisting requirement for

manual intervention suggests the need for instituting

strong data governance procedures as data quality

issues are often do not arise from existing business

rules or the technology itself, but from a lack of sound

data governance (Thompson et al., 2015) and data

quality is often seen as an important metric for data

governance (Brous et al., 2016). Data governance is

the exercise of authority, control, and shared decision

making over the management of data assets. It

provides organizations with the ability to ensure that

data and information are managed appropriately,

aligns the data infrastructures with business

requirements, ensures a common understanding of the

data, and ensures compliancy to laws and regulations

(Brous et al., 2016).

Aligning complex data structures such as

semantics or ontology between different IoT eco-

systems is a complex task and interoperability and

convergence with regards to visibility of processed

data at the level of applications remains an issue

(Mihailovic, 2016). This barrier has hampered IoT

data sharing. According to Cao et al., (2016), sharing

of IoT data will only reach its full potential if data can

be collected by multiple sources such as if people are

able to share their data related to different events by

leveraging the sensing capabilities of their

smartphones (Cao et al., 2016). But some of the data

collected by smartphones may contain sensitive

information such as the location data of the owners.

Compliancy to privacy and security regulations is

imperative. In Europe, the new General Data

Protection Regulation (GDPR) defines the conditions

under which personal data can be processed,

specifying that consent must be unambiguous. To

provide informed consent regarding the use of

personal data, the citizen must have a clear

understanding on how his/her personal data will be

used by the ICT systems and applications and

especially in the emerging paradigm of IoT (Neisse et

al., 2016).

In addition to the resolution of data quality issues,

data governance may also assist IoT adoption in other

ways as data governance provides both direct and

indirect benefits (Ladley, 2012). Direct benefits of

data governance for business processes can be linked

to efficiency improvements (Hripcsak et al., 2014),

reductions in privacy violations (Tallon, 2013), and

increased data security (Panian, 2010). Indirectly,

data governance also improves the perception of how

information initiatives perform (Griffin, 2010),

improves the acceptance of spending on information

management projects (Thompson et al., 2015), and

improves trust in information products (Otto and

Weber, 2011).

6 CONCLUSIONS

Currently, organisations are experimenting with new

data sources and there is a general expectation that

IoT will provide significant added value to AM

decision making. Organisations can effectively and

sustainably adopt these new data sources in their AM

decision making if the data that is measured can

monitor the important factors of the asset itself.

Adoption of IoT requires an IT infrastructure that can

facilitate the new data sources and requires a good

understanding of the data collected and its quality

aspects. Adoption of IoT needs appropriate

management of the data to ensure compliancy to laws

and regulations. Sound data governance is required to

ensure that IoT can provide trusted data for AM

decision making.

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