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The 9th International Conference on Emerging Ubiquitous Systems and Pervasive Networks
(EUSPN 2018)
Exploring Big Data Governance Frameworks
Ali Al-Badia *, Ali Tarhinia, Asharul Islam Khana
a Department of Information Systems,, Sultan Qaboos University, Muscat, P.O. Box 20, Al-Khodh 123, Oman
Abstract
The recent explosion in ICT and digital data has led organizations, both private and public, to efficient decision-making.
Nowadays organizations can store huge amounts of data, which can be accessible at any time. Big Data governance refers to the
management of huge volumes of an organization’s data, exploiting it in the organization’s decision-making using different
analytical tools. Big Data emergence provides great convenience, but it also brings challenges. Nevertheless, for Big Data
governance, data has to be prepared in a timely manner, keeping in view the consistency and reliability of the data, and being
able to trust its source and the meaningfulness of the result. Hence, a framework for Big Data governance would have many
advantages. There are Big Data governance frameworks, which guide the management of Big Data. However, there are also
limitations associated with these frameworks. Therefore, this study aims to explore the existing Big Data governance frameworks
and their shortcomings, and propose a new framework. The proposed framework consists of eight components. As a framework
validation, the proposed framework has been compared with the ISO 8000 data governance framework.
© 2018 The Authors. Published by Elsevier Ltd.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).
Keywords: Big Data; Big Data model; Big Data governance; Data management; Big Data governance framework; Big Data analytic;
1. Introduction
Nowadays, Organizations and social entities generate large amounts of structured and unstructured data referred
to as Big Data. Data growth is very fast and some may have increased over 100% such as in healthcare,
pharmaceuticals, energy, telecommunications, and transportation [1]. Big Data is a collection of large data sets that
contain massive and complex data. Big Data holds huge volumes of sets of data, measured in zettabytes and is
derived from a variety of sources [2]. Big Data refers to “data that exceeds the processing capacity of conventional
* Corresponding author. Tel.: +968-99245674
E-mail address: [email protected]

272 Ali Al-Badi et al. / Procedia Computer Science 141 (2018) 271–277
Author name / Procedia Computer Science 00 (2018) 000–000 2
database systems. The data is too big, moves too fast, or doesn’t fit the structures of your database architectures”
[3].The Big Data can be structured, un-structured or semi-structured. The examination and analysis of Big Data is
termed ‘Big Data analytics’ [4]. Big Data consists of phases: Big Data generation, Big Data acquisition, Big Data
storage, and Big Data analytics [5]. In the generation phase, several data sources generate huge volumes of data. The
Big Data acquisition includes data collection, data transmission, and data pre-processing. ‘Big Data storage’ refers
to the storage and management of large-scale datasets while achieving availability and reliability of data accessing.
Finally, Big Data analytics involves the analysis and forecasting of market trends, products, and services. Big Data
can be used in many applications in different fields such as healthcare, education, management, and logistics, etc.
The Big Data is used for crime prevention and counterterrorism in the United States, while in Korea it has been
implemented to support smart government operations and strategic planning [6]. Big Data systems enable the
dynamic analysis of data. Big Data has many issues and challenges due to the huge data sets. Companies that are
dealing with Big Data face challenges such as management, processing, and security because of using Big Data.
Moreover, there are issues of capturing, analysis, storage, searching, sharing, visualization, transferring and privacy
violations [4].
Data governance refers to the policies and procedures adopted in order to manage data in an organization [7]. A
‘data governance framework’ is defined as “a set of processes that ensures that important data assets are formally
managed throughout the enterprise” [8]. Data governance provides the right sets of data to the right people whenever
the need arises, so that the right decisions can be made [9]. Data governance needs a framework for managing
information [10]. The data governance helps an organization with data standardization, efficient business policy
formulation, and defining the roles of stakeholders [6]. The Big Data managers face problems in cleaning,
governing, and managing the data [11]. The Big Data problem needs to be managed [12]. The enterprises
implementing Big Data need a new set of governance policies [13]. A strong Big Data governance program is
required, this being the foundation of information management [14]. The Big Data governance must account for
stewardship, information governance, data definition and usage standards, master data management, metadata
management, data lifecycle management, risk, and cost containment [15]. Big Data governance encompasses the
data governance program consisting of optimization policies, privacy, and monetization of the Big Data according to
the set objectives [14]. The data governance principles need to be defined [7], and these principles are essential in
data governance communication and authorization [16]. A Big Data governance framework is important in framing
policies, processes and standards for effectively managing and ensuring the availability, usability, integrity,
consistency, auditability and security of Big Data [17]. The governance of Big Data is crucial to the success of an
organization where huge amounts of data are processed. A Big Data governance framework is required to manage
the Big Data in an organization. There are Big Data governance frameworks which guide the management of Big
data. However, there are also limitations associated with these frameworks. Therefore, this study aims to identify the
existing Big Data governance frameworks and their shortcomings, and to propose a new framework.
The article is divided into five sections. Section 2 is about the research method. Section 3 corresponds to
motivation, existing frameworks and their shortcomings. Section 4 proposes the conceptual framework. The last
section concludes with the outcomes.
2. Research method and protocol followed
In this study, the research method used consists of four steps. The first step mainly involved a literature review
with the objective of generating ideas and identifying problems. In the second step, the articles were filtered, and
selected articles were analyzed thoroughly in terms of components and characteristics of the Big Data governance
frameworks. In this step, the initial search, by applying the simple and advanced query on the search terms “Big
Data governance”, “Big Data governance framework”, “Big Data governance model” in online databases (Google
scholar, Scopus, Science direct, Springer, and IEE explore), 200 articles published between 2008 and 2018 were
produced. The number was reduced to 110 after removing duplicates and non-English language articles. The articles
were further filtered based on relevant title and abstract. About 40 articles were then left for study. Finally, out of
the 40 articles, only 12 articles were actually talking about the Big Data governance frameworks. The articles were
filtered based on the set criterion of uniqueness, completeness, accuracy, time framed study, and accessibility
Ali Al-Badi et al. / Procedia Computer Science 141 (2018) 271–277 273
Author name / Procedia Computer Science 00 (2018) 000–000 3
(several articles were inaccessible as full articles, such as [18]). The filtered articles were thoroughly analyzed and
interpreted.
The third step was the building of a new Big Data governance framework to overcome the
shortcomings/limitations of the existing ones. The last step was a comparative analysis and framework validation
(identifying the relationship between the components of the proposed framework and the components of the
existing frameworks).
3. Motivation and relevant studies on Big Data governance frameworks
The concept of Big Data has been strongly leveraged and has become a driver for innovation [19]. Big Data
governance is a matter of critical importance for every organization that relies on data to drive business value.
Successful companies capitalize on their organizational data assets through effective understanding. Working with
Big Data raises new challenges and risks, such as ensuring secure access to data, and policies to govern the
uninterrupted upstream and downstream of data flow. The data governance is a part of data management, which
includes numerous other concepts and practices as well [20]. The data governance helps enterprises to improve and
maintain data quality and their use [21]. Data governance has rapidly gained in popularity [22; 23] and is now
considered to be an emerging area [24]. It is an emerging subject in the information systems (IS) field. In recent
years, the volume of data used within organizations has increased dramatically, playing a critical role in business
operations [25]. The structured data is easily accessed by tools like SQL, while for unstructured and semi structured
data, profiling tools are used [26; 22]. The existing data governance works on structured data while Big Data
governance incorporates both structured and unstructured data [6]. The huge volume of data forces the use of IT
methods in analysis and interpretation of Big Data [27]. The Big Data has introduced many challenges into
organizations [28]; for example, privacy and security in terms of personal information leakage and the monitoring of
customers’ private lives [6].
According to Soares (2013b), using a Big Data governance forecasting and decision in the business is quickly
made irrespective of source, type, and speed of data or information received [29]. In particular, the data influences
both operational and strategic decisions. How to govern these data has become critical [22]. The data governance is
a serious concern for successful implementation and risk reduction, but most of the time it is ignored by
organizations [22; 30]. Not only the corporate data integrity and data quality is at risk but also IT professionals are
facing problems in the transition from existing data sets to Big Data because of this lack of a Big Data governance
framework [9]. The existing data governance faces challenges in the light of Big Data cases [31]. Too little attention
is paid to Big Data governance [32]. There is a need to establish Big Data governance frameworks in enterprises for
decision making [33]. Thus, a strong Big Data governance framework is crucial to the success of any Big Data
initiative and the management of that data. Unfortunately, most of the Big Data technologies do not offer data
governance functionality.
Each industry can be driven by the Big Data analytic such as marketing, customer service, information security,
or information technology. Big Data analytics support strategic and competitive decisions [34]. There are very few
studies on regulatory issues and the governance of Big data; most of the studies are on Big Data and analytics,
cloud, the Internet of things, mobility or social media, algorithms, and architecture [27]. The data governance
framework contains five inter decision domains such as data principles, data quality, metadata, data access,
and data life cycle [35]. In [22] the researcher has provided a framework for data governance based on five
inter domains. The search query produced many articles, and the studies relevant to Big Data governance
have been investigated thoroughly. There are 12 important articles on the Big Data governance framework as shown
in Table 1.
Table1. Big Data governance frameworks

Frameworks	Year	Components/ characteristics
BGF1-[36]	2018	Objective, strategy (personal information protection strategy, data quality, and the data disclosure/accountability strategy), components (organization, standards and guidelines, policies and process), IT infrastructure (audit and control, Big Data infrastructure).
BGF2-[37]	2017	Data consumers, self-provisioning data portal, optimize and compute, data infrastructure or tired storage
BGF3-[38]	2017	Data analytic, data querying, distributed data processing, distributed data storing, data acquisition.

274 Ali Al-Badi et al. / Procedia Computer Science 141 (2018) 271–277
Author name / Procedia Computer Science 00 (2018) 000–000 4

BGF4-[39]	2017	Governance objectives, the top-level design, governance objects, governance methods, the internal and external environments and contributing factors.
BGF5-[40]	2016	Quality and consistency, policies and standards, security and privacy, compliance, retention and archiving.
BGF6-[26]	2016	Organization, metadata, privacy, data quality, business process integration, master data integration, information lifecycle management.
BGF7-[18]	2015	Big Data governance framework (content in accessible).
BGF8-[31]	2015	Discover, define, apply, measure and monitor.
BGF9-[29]	2013	(a) a maturity assessment to determine readiness for data governance, (b) a business case to justify implementing data governance, and (c) a roadmap to guide the data governance implementation.
BGF10-[41]	2013	Establish difference between traditional data and Big Data governance, establish basic rules for where new data governance can be applied, establish processes for graduating the products of data science to governance, and establish a set of tools to make governing Big Data feasible.
BGF11-[17]	2012	Strategy, organizations, policies processes and standards, measurement and monitoring, technology, communication.
BGF12-[42]	2012	Big Data types (i.e. web and social media, machine-to-machine (M2M), big transaction data, biometrics, and human-generated), Information governance disciplines (i.e. organization, metadata, privacy, data quality, business process integration, master data integration, and information lifecycle management), industries and functions (i.e. marketing, customer service, information security, or information technology).

Morabito in his study describes Big Data maturation models, organizational benefits and challenges without any
detail of a framework [18]. The Gartner promotes the creation of a Big Data governance framework consisting of
top management, IT tools and infrastructure, accounting to ensure controls, and business end users [9]. A Big Data
framework for handling urban governance issues and data analysis algorithms has been provided in [43]. In [14] the
researcher highlights the data governance frameworks for risk reduction (i.e. Big Data implementation), and
postulates a similar governance framework for Big Data [14]. Many researchers propagated the idea of using
traditional data governance attributes for Big Data (i.e. organization, metadata, business process integration, and
information lifecycle management).
4. Conceptual framework and discussions
It is important to have a Big Data governance framework. For smooth transitions to Big data, a data governance
framework can play a significant role and contribute to success [44]. The data generated are often in the range of
petabytes and many organizations have a lack of expertise to manage and operate them [45]. Big Data governance
solutions have an immediate impact across all enterprises and business operations. Big Data management without a
Big Data governance framework is difficult [17].
The proposed conceptual Big Data governance framework consists of eight components (Fig. 1). These
components are: identify organization structure, identify relevant stakeholders, identify the scope of Big data, set the
policies and standards, optimize and compute, measure and monitor quality, store the data, communicate and
manage the data. The information governance principles have been used in the proposed framework. The seven core
principles of information governance guidelines (i.e. organization, metadata, privacy, data quality, business process
integration, master data integration, and information lifecycle management) are also applicable to Big Data
governance [26].
Fig 1. The proposed Big Data governance framework
The organization and its structure influence the Big Data governance decisions. The organization’s structure
requires enhanced study. Big Data governance should be aligned with the objectives and vision of the organization
[6]. Therefore, organization structure has been taken as a component. Another key element is the identification of
Big Data governance
framework
Identify organizations
structure
Stakeholders selection
Big Data scope determination
Measure and monitor quality Optimize and compute Policies and standards setting
Data storage
Communictaion and data
management
Ali Al-Badi et al. / Procedia Computer Science 141 (2018) 271–277 275
Author name / Procedia Computer Science 00 (2018) 000–000 5
relevant stakeholders in Big Data governance such as data scientists, data analysts, business steward leads, data
stewards, steering committee, etc. The next step is to understand the scope of Big Data under consideration, and
check to see if the scope for it that applies to the concerned organization has been defined. Many problems arise
with Big Data due to inadequate technologies to process it effectively [46]. The policies, rules, and standards
corresponding to data capture, management, consumption, privacy, security, risk, retention, regulatory compliance
and data classification requirements need to be framed. Big Data governance includes the policy of data
optimization, privacy protection and data realization to Big Data [26]. Whether or not the framed policies are
consistent with those for traditional systems needs to be checked. The optimization and computation consists of data
acquisition and data transformation. The organizations can benefit from analysing the data [47; 48]. The measuring
and monitoring of Big Data quality should be the top priority. The Big Data managers should check and remediate
issues of inconsistent or invalid data in the Big Data analytics pipeline. They should track every change from the
original conception to the final visualization. The Big Data governance produces quality output [18]. The data must
be cleaned before analysis in order to answer the questions [49]. Preparation and analysis is very important for
quality data [50]. The data is stored in a secured location, while at the same time being accessible when required.
Finally, the outputs are communicated and delivered to the clients. The selected studies (i.e. BGF1 to BGF12) on
Big Data frameworks have been compared with the ISO 8000 data governance framework [51] standard to identify
the gaps in the existing frameworks. Table 2 shows the comparison with ISO 8000 standard, where FR: full
representation, PR: partial representation. BGF7 framework is not accessible, so comparison is not possible.
Table 2. Big Data governance frameworks comparison with ISO 8000 standard

ISO 8000 data governance framework	Planning	Process identification	Data identification	Stakeholders identification	Technology identification	Implementation and evaluation	Conformance to the quality	Summary (FR %)
BGF1	FR	FR	FR	FR	FR	PR	PR	71 %
BGF2	PR	PR	FR	FR	PR	FR	PR	43%
BGF3	PR	PR	FR	PR	PR	PR	PR	14%
BGF4	FR	PR	PR	PR	PR	PR	PR	14%
BGF5	PR	PR	PR	PR	PR	PR	FR	14%
BGF6	PR	FR	FR	PR	PR	FR	FR	57%
BGF7	NA	NA	NA	NA	NA	NA	NA	NA
BGF8	PR	FR	FR	PR	PR	FR	FR	57%
BGF9	FR	FR	PR	PR	PR	PR	PR	29%
BGF10	FR	FR	FR	PR	PR	PR	PR	43%
BGF11	FR	FR	FR	FR	PR	PR	PR	57%
BGF12	PR	FR	FR	FR	PR	PR	PR	43%
Proposed framework	FR	FR	FR	FR	FR	PR	FR	87%

5. Conclusion
Big Data management has become a challenge in the IT and business fields due to a massive growth in the data,
which are often structured, un-structured, and semi structured in nature. The Big Data governance has recently
attained importance in organizations’ decisions and policymaking. This article describes a comprehensive study on
the existing Big Data governance frameworks and their limitations. There are very few studies on Big Data
governance frameworks. In this study, a conceptual framework for Big Data governance has been proposed. The
framework has been derived through analysis of the existing governance problems in Big Data. There are eight
major components in the proposed frameworks. These components are the organizations’ structure identification,
stakeholders’ identification, identifying the scope of Big Data, setting the policies and standards, optimizing and
computing, measuring and monitoring quality, data storage, communication and data management. The proposed
framework and the existing frameworks have been compared with the ISO 8000 standard. The proposed framework
satisfies 87% of the ISO 8000 standard criteria. The framework is expected to be implemented in an organization for
validation in future.
276 Ali Al-Badi et al. / Procedia Computer Science 141 (2018) 271–277
Author name / Procedia Computer Science 00 (2018) 000–000 6
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• Click on the “Place Your Order” tab at the top menu or “Order Now” icon at the bottom and a new page will appear with an order form to be filled.
• Fill in your paper’s requirements in the “PAPER DETAILS” section.
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• Click “CREATE ACCOUNT & SIGN IN” to enter your registration details and get an account with us for record-keeping and then, click on “PROCEED TO CHECKOUT” at the bottom of the page.
• From there, the payment sections will show, follow the guided payment process and your order will be available for our writing team to work on it.

About AcademicWritersBay.com

AcademicWritersBay.com is an easy-to-use and reliable service that is ready to assist you with your papers 24/7/ 365days a year. 99% of our customers are happy with their papers. Our team is efficient and will always tackle your essay needs comprehensively assuring you of excellent results. Feel free to ask them anything concerning your essay demands or Order.

AcademicWritersBay.com is a private company that offers academic support and assistance to students at all levels. Our mission is to provide proficient and high quality academic services to our highly esteemed clients. AcademicWritersBay.com is equipped with competent and proficient writers to tackle all types of your academic needs, and provide you with excellent results. Most of our writers are holders of master’s degrees or PhDs, which is an surety of excellent results to our clients. We provide assistance to students all over the world.
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