A Conceptual Framework for Data Governance in IoT-enabled
Digital IS Ecosystems
Avirup Dasgupta
, Asif Gill
and Farookh Hussain
School of Software, University of Technology Sydney, Australia
Keywords: IoT, Data Governance, Framework, Data Management.
Abstract: There is a growing interest in the use of Internet of Things (IoT) in information systems (IS). Data or
information governance is a critical component of IoT enabled digital IS ecosystem. There is insufficient
guidance available on how to effectively establish data governance for IoT enabled digital IS ecosystem. The
introduction of new regulations related to privacy such as General Data Protection Regulation (GDPR) as
well as existing regulations such as Health Insurance Portability and Accountability Act (HIPPA) has added
complexity to this issue of data governance. This could possibly hinder the effective IoT adoption in
healthcare digital IS ecosystem. This paper enhances the 4I framework, which is iteratively developed and
updated using the design science research (DSR) method to address this pressing need for organizations to
have a robust governance model to provide the coverage across the entire data lifecycle in IoT-enabled digital
IS ecosystem. The 4I framework has four major phases: Identify, Insulate, Inspect and Improve. The
application of this framework is demonstrated with the help of a Healthcare case study. It is anticipated that
the proposed framework can help the practitioners to identify, insulate, inspect and improve governance of
data in IoT enabled digital IS ecosystem.
IoT is an emerging concept in the Healthcare industry
with new applications and devices being
manufactured using the Internet of Things (IoT).
Sensor fitted wearable devices or implantable devices
are increasingly being used to monitor the well-being
of a patient. These devices automatically monitor
health conditions, notify abnormal situations and
propose protective actions such as informing doctors,
family and friends (Karahoca et al., 2018, Gill et al.,
The complexity of gathering, storing and
processing data, has given rise to many data related
problems in particular the governance of data. The
General Data Protection Regulation (GDPR) law
(Cha et al., 2018) has introduced additional aspects to
this issue. Hence, it is vital that we understand the
dynamics of data governance and related regulations
in the IoT enabled digital IS ecosystem. This includes
comprehending data ownership, the process of
gathering consent before processing the data as well
as understanding data lineage in the IoT enabled IS
ecosystem. Thus, data governance issues pertaining
to data security, data confidentiality, and data
ownership stand as obstacles to the exchange of data
among distributed IoT network and applications.
Therefore, it is important that organizations address
these challenges from a data governance (DG)
perspective (Gartner, 2016a).
Data governance is not a new concept and is in
use in the financial sector for more than two decades
(Kontzer, 2006). However, in an IoT-enabled IS
context, it is still at a nascent stage. The evolution of
decentralized IoT architectures like Fog, Cloudlets
and Edge implies that centralized approaches to
governance are not viable (Gartner, 2016a). Several
authors have highlighted the unethical use of data
(Dastjerdi and Buyya, 2016), reprogramming device
to function beyond its intended purpose and lack of
network-intrusion-detection mechanism (security) as
a major challenge associated with deploying IoT
Dasgupta, A., Gill, A. and Hussain, F.
A Conceptual Framework for Data Governance in IoT-enabled Digital IS Ecosystems.
DOI: 10.5220/0007924302090216
In Proceedings of the 8th International Conference on Data Science, Technology and Applications (DATA 2019), pages 209-216
ISBN: 978-989-758-377-3
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
based applications (Dhillon et al., 2016, Dasgupta and
Gill, 2017). This paper focuses on the following
research question (RQ): How to establish the data
governance in digital IS ecosystem? In order to
address the above question, this paper presents the
application of the 4I framework in the Healthcare
This paper is organized as follows. Section 2
discusses the data and IoT governance concepts.
Section 3 discusses the research method. Section 4
presents the existing frameworks related to data
management and governance in the context of IoT.
Section 5 summaries the 4I framework. Section 6
demonstrates the applicability of the 4I framework
with the help of a Healthcare case study before
concluding the paper in section 7.
This section describes the key concepts of data in the
context of IoT enabled digital IS ecosystem in order
to provide the research background and context
2.1 Data Management
Data Management is concerned with the use of data
to make good business decisions. It focusses on
defining data, its storage, structure and data flow. The
Data Management Association (DAMA), an
association of technical and business data
management professionals, defines data management
as the development and execution of architectures
,policies, procedures and practices to manage entire
data lifecycle as well as planning, executing and
managing the activities which acquire, control,
protect, deliver and enrich data assets (Mosley et al.,
2010, Stryk, 2015). Data Management Body of
Knowledge (DAMA-DMBOK) identified 10
functions, which constitute Data management
(Mosley et al., 2010) as shown in the figure below.
Figure 1: Data Management Functions (adapted from
(Mosley et al., 2010)).
2.2 Data Governance
As corporations recognize the importance of data and
the challenges they face in integrating the data from
various disparate source systems, an increasing
number of companies have started exploring data
governance. Data governance enables corporate-wide
accountabilities and decision rights for data quality
management (Weber et al., 2009) and is essential for
the existence of an organization (Stryk, 2015). It is
defined as an organizational approach to data and
information management (Janne J. Korhonen et al.,
2013) that formalizes a set of policies and procedures
to include the full life cycle of data, from acquisition
to use and to disposal. Gartner defines it as the
procedure of setting decision rights and answerability
for an asset, establishing policies aligned to business
objectives, investing in assets that aid business
objectives, establishing measures to ensure
compliance to corporate policies, and ensuring
adequate corporate risk management (Gartner,
While governance refers to the decisions that are
taken to ensure effective use and management of
resources, management is focused on executing the
decisions. Thus, management is influenced by
governance (Ibrahim Alhassan, 2016). Data
governance defines standards and procedures to
ensure the proactive and effective handling and
guidance of data management practices such as data
replication, data archival, security, data backup ,meta
data management (MDM), data traceability and
lineage, business glossary mapping, governance
council, release and change management, master data
and business (Infotech, 2016). Effective data
governance results in profitable data use in an
organization (Panian, 2010). With appropriate data
governance, businesses can make insightful decisions
by putting context to the data and transforming the
information into knowledge and intelligence. This
includes ensuring data has the necessary quality,
availability, integrity and security throughout its
lifecycle (Al-Ruithe et al., 2018).
2.3 IT Governance
IT governance is different from data governance and
is concerned with the overseeing of IT resources such
as computer networks, servers and applications
through risk monitoring and control (Peterson, 2004)
in alignment with the aims and strategies (Tallon et
al., 2013) of an organization. Traditionally, financial
assets and services (Gill et al., 2015) were
administered using Governance, however, in last few
DATA 2019 - 8th International Conference on Data Science, Technology and Applications
decades it has been extended to data and IT
assets(Robert C. Rickards, 2012). There are several
IT governance frameworks such as ISO 27001,
Information Technology Infrastructure Library
(ITIL) and Control Objectives for Information and
Related Technology (COBIT) (Gehrmann, 2012).
While ITIL was established to provide best practices
for the IT services to its customers, COBIT
framework supports governance of IT assets with a
distinctive focus on ensuring IT procedures and
activities align with the strategic goals of an
enterprise (Egelstaff and Wells, 2013); (Juiz and
Toomey, 2015).
2.4 IoT Governance
IoT governance is an extension to IT governance,
where IoT governance is specifically focused on the
lifecycle of IoT devices, data managed by the IoT
solutions, and IoT applications in an organization's IT
landscape (Gantait et al., 2018). IoT governance is
can be considered a part of the existing IT governance
landscape. It comprises of organizations such as
Internet Engineering Task Force (IETF),Regional
Internet Registry (RIRs), Information Security
Operations Centre (ISOC), IEEE, The Internet
Corporation for Assigned Names and Numbers
(ICANN),Internet Governance Forum (IGF), and
W3C and should leverage or tailor IT governance
frameworks available to govern IoT(Virgilio A.F.
Almeida 2015).
There is a need to have a clear distinction between
the IT governance and data governance. While data
governance deals with the data assets to improve
business outcomes for business stakeholders, where
IT governance is primarily focused on the IT assets.
Further, these two concepts can be linked to strategy
and enterprise architecture (Korhonen et al., 2016) )
in modern adaptive enterprises. These are two
different but related concepts and thus there are some
Table 1: Difference between IT and Data Governance
(adapted from (Dimick, 2013)).
IT driven led by (Chief
Information Officer)
Business Driven
Oversee implementation of
IT policy process and
extract business benefits
Operational Focus
Policy and process ensuring
effective evaluation,
selection, prioritization,
funding of competing IT
assets and investment
Policy, process and
practice that address
accuracy, validity,
timeliness, data
overlapping areas between IT and Data Governance
as shown in table below. Thus, the scope of this paper
is limited to data governance in IoT (a kind of IT)
enabled IS.
This research aims to address the data governance
challenges in IoT and proposes the development and
evaluation of the 4I framework using the Design
Science Research (DSR) (Prat et al., 2014). DSR is
problem focused (Kuechler and Vaishnavi, 2008) and
seeks to design and evaluate an innovative product, or
artefact, that provides a potential solution to a real-
life problem within an organization as shown in
figure 2 below. In DSR, the artefacts usually can be
technical elements such as concepts, models, methods,
frameworks or instantiations (March and Smith,
1995) as well as social elements such as humans,
roles, work processes, teams, groups of organizations
(Drechsler, 2015).
Figure 2: Design Science Research.
We applied the guidelines of DSR (Hevner and
Chatterjee, 2010) to conduct this research. This paper
focuses on the evaluation aspect of the proposed 4I
The traditional data governance practices comprising
of people, process and technology(Merkus, 2015) are
going through a fundamental shift or transformation
phase. This can be attributed to the changes in
regulations (Wachter, 2018) as well as advancement
in technologies such as Big Data, Blockchain, Cloud,
IoT and Mobile (Copie et al., 2013). Thus, there is a
need to tailor the data governance practices in an IoT
context (Al-Ruithe et al., 2016),
(Porambage et al.,
2016), (IERC, 2015), (Banerjee and Sheth, 2017)).,
(IOTAlliance, 2017)
in order to address IoT specific
issues as indicated in figure 3 above. Few studies on
A Conceptual Framework for Data Governance in IoT-enabled Digital IS Ecosystems
Figure 3: IoT introduced Data Governance challenges.
governance of data have been conducted in
thedomain of healthcare such as HeathFog in (Verma
and Sood, 2018) and attribute based smart health in
(Fuentes et al., 2018). In (Sajid and Abbas, 2016),
authors discussed encryption based data privacy in
cloud based healthcare systems. In another study
(Banerjee and Sheth, 2017), the authors put forward
an evaluation model to contextually evaluate the data
quality based on two use cases.
To the best of our knowledge, currently there is no
concrete IoT framework, which is available and can
provide a blueprint to establish a gata governance
environment, particularly from a regulatory
perspective. However, it is evident from the recent
commercial works and analyst reports such as Gartner
(Gartner, 2016a) and Frost and Sullivan(Sullivan,
2018), that there is an urgent need for more research
and development in this area of governance.
This research developed a 4I framework (Dasgupta et
al., 2019) (Identify, Insulate, Inspect and Improve)
for managing and governing the data assets in the
IoT-enabled Digital IS ecosystem. The 4I framework
(Version 2) depicted in figure 4 is an updated version
of the framework introduced in (Dasgupta et al.,
It is based on the extensive review of existing data
governance literature from academia and industry
such as DAMA-DMBOK2 Framework (Mosley et
al., 2010), The Data Governance Institute Data
Governance Framework (Proença and Borbinha,
2016), The IBM Data Governance Council Maturity
Model (A. Wróbel, 2017), The Gartner Enterprise
Information Management Framework, EDM Council
Figure 4: The 4I framework.
DATA 2019 - 8th International Conference on Data Science, Technology and Applications
Data management Capability Maturity Model
(Council, 2015), Generic Framework (Al-Ruithe et
al., 2016), and DGMM Framework (Merkus, 2015).
It is intended for use by data governance
personnel as a guide to ensure appropriate data
collection (“what to use”), processing (“how to use”),
and retention (“until when to use”) mechanisms as
well as significance (“why to use”) of data. It is
composed of four stages or phases and explained in
detail with the Fitbit case study in Section 6.
Wearables are the main fitness trend for
2019(Thompson, 2018) according to American
College of Sports Medicine (ACSM). Wearables rely
on the collection of the consumer’s private and
personal data. Personal Identifiable Information (PII)
can include First name, Last Name, Data of Birth,
Address, Phone number, Financial and health of an
IoT Fitbit user. This also constitutes sensitive
personal information (SPI).
Figure 5: Wearable smart IoT-enabled ecosystem.
The “Fitbit” data is transmitted using Bluetooth
technology to the consumer’s mobile or desktop
application before it is transferred to the cloud.
As shown in figure 5, the data exchange involves
stakeholders such as App manufacturer, Cloud
Providers, Health Service Provider and several
systems to provide the end user with health service.
From the Fitbit providers perspective, ensuring data
is secured and compliant is of highest priority.
Regrettably, the consumer has a lack of
understanding of the risks (Skierka, 2018, Banerjee et
al., 2018) linked with some of the wearable devices
or products. For example, some wearable devices
have default passwords that can be found on public
websites and cannot be altered (Government, 2019).
In this section, we evaluate the applicability of the
4I and demonstrate how the 4I framework can be
applied by the Fitbit service providers to avoid
unethical usage of data.
I in the 4I Framework:
The Identify phase of the 4I Framework ascertains
the key actions that the Fitbit service provider needs
to perform to ensure that the users data is not
compromised. It includes
1)Reviewing the laws such as Health Insurance
Portability and Accountability Act (HIPPA) and
GDPR to understand rights of smart health device
user (Sharma et al., 2018) with regards to the data
Figure 6: Healthcare data related Laws.
2) Identifying potential threats or risks around data
management. For example, exploitation of security
vulnerabilities to obtain user data is a common
phenomenon. This is particularly important in Fitbit
app context where apps running on Android are
impacted by vulnerabilities from time to
time(Linares-Vásquez et al., 2017), As a part of
“identify” stage, Security advisories on vulnerability
published periodically by Android can be
documented and included in the patch management
3) Classifying the sensitivity of data collected and
determining the impacts of
Sharing of health data publicly
Sharing of data (health, PII, PCI and location)
to a 3rd party such as medical providers(
hospital, doctors), healthcare service provider,
cloud or fog hosting service provider, network
Tracking of movement of individuals
(including elderly patients) using motion
sensors, camera, GPS tracker.
Retention of data after customer stops using the
device and its services
Inferring customer’s traits based on data.
A Conceptual Framework for Data Governance in IoT-enabled Digital IS Ecosystems
4) Establishing policies related to data retention, data
protection, patch management, Fitbit device
procurement, data sharing and management
techniques such as data anonymization, obfuscation
are identified in this phase of the framework.
I in the 4I Framework:
The second phase Insulate includes the preventive
actions taken to mitigate the risks identified in the
previous stage
Technology can be used to implement the data
protection policies related to healthcare devices. This
can include preventive measures such as ensuring
software is patched to the current version in
accordance with the patch management policy. It can
also include implementing the data management
processes formulated in “Insulate” phase. For
example, an agent can be installed at wearable user’s
gateway or mobile application to ensure that data is
passed to Cloud only if
Latest firmware version is present in the IoT
Intended address to push data matches the
hardware endpoint requirements such as Host
IP address or Mac address
Explicit Consent is recorded from customer
Encrypted data is sent
party software used is patched is upgraded.
I in the 4I Framework:
The inspection phase is a combination of
sophisticated real-time monitoring, auditing and
reporting as performed by the software agent.
A robust asset management software can map
each Fitbit device all the way to the database
where each record of data is stored in database
or application server.
For each Fitbit, security information and event
management (SIEM) agent can scan the data
records stored in the files or databases. The
agent can flag a risk through automated alerts
to the data governance team in case it finds
non-encrypted records or inappropriate data
With respect to data stored beyond the data
retention requirement, the agent can check if
any PII data is stored in fileservers or database
in unencrypted form and may take remedial
4th I in the 4I Framework:
In the Improve phase, continuous enrichment of the
process is done to ensure that the operational process
is continuously monitored and enhanced. For
example, conducting a Third Party Vendor
assessment to check cloud storage vulnerability and
updating the Vendor Selection or SLA policy can be
an improvement and outcome of this final phase of
the 4I framework. Additionally, non-technical
changes such as reworking the contracts to pass
liability of data breach to the third party can be
another consequence of the improvement phase. In
nutshell, this case of Fitbit demonstrates the
applicability of the 4I framework for data governance
in IoT-enabled Digital IS ecosystem.
The effective and informed governance of data in
IoT-enabled applications is a complex undertaking.
Currently, there is no holistic framework exists to
address the important research question: how to
ensure data governance in IoT-enabled Digital IS
ecosystems? This paper discusses the newly
developed 4I framework that can provide the
Governance coverage across the data lifecycle in IoT-
enabled Digital IS ecosystem. The 4I framework is
developed through analysis and review of existing
scientific and practice-oriented literature related to
IT, Data and Enterprise Governance within the
context of IoT and Digital IS ecosystem. Data
stewards can use the proposed framework to manage
and define enterprise-wide guidelines, company
rules, and data assets to deliver the essential data
governance and quality. The initial applicability of
the proposed framework is demonstrated with the
help of a healthcare case study. We intend to conduct
further detailed studies to enhance the 4I framework.
This research is supported by the Australian
Government Research Training Program Scholarship
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