2020 8th International Conference on Information Technology

and Multimedia (ICIMU)

Data Governance and Data Stewardship: A Success

Procedure
Doris Hooi-Ten Wong, Nurazean Maarop and Ganthan Narayana Samy
Razak Faculty of Technology and Informatics
Universiti Teknologi Malaysia
Jalan Sultan Yahya Petra, 54100, Kuala Lumpur
Corresponding email: doriswong@utm.my

Abstract – Data governance and data stewardship have a close repository, documenting data sources, and setting the rules,
relationship with each other. In order to start the governance and names and definitions of data[2].
stewardship program, a better understanding of the concept and
key elements is essential. In this paper, the overall of data For organizations that already have the basic data
governance and stewardship is discussed including the definition, governance framework in place, data governance can be
goal, elements, focus area, framework, role, and responsibilities, defined as the formal enforcement of authority over data
how to implement and also how to measure its maturity state.
management and formalizing the accountabilities and behavior
Thus, this will give an insight into how to establish data
governance and data stewardship in an organization. over the production, definition, and use of data assets[3].

Keywords -- Data Stewardship, Data Governance Data governance which ensures data is formally managed
should not be confused with data management. To clarify,
I. INTRODUCTION
Fig.1, the “Governance V” is introduced [9]. The left side
signifies the governance part, where the input, content, rules,
Thousands of data are being created every second. If not policies, and activities to ensure data management is provided.
properly utilized, definitely they are going to be a waste, not to The right side signifies the management part, undertaken by the
mention consuming a lot of space and money for storage. Most management team. The intersection of the two lines indicates
importantly in organizations, in which their main purpose is the activities that operate an organization such as maintaining
either to make profit or provide services, the usage of data is data, information and content life cycles. This shows that the
extremely important since data that is captured signify many duties or functions of data governance should not be mixed up
aspects of an organization for example customers, human with data management[4].
resources, finance, marketing, and many others. All the
available data that is not properly managed will hinder
decision-making process that may affect the business
performance and end-user satisfaction. These issues have made
many to realize the importance of data governance and data
stewardship thus have started to apply it to manage their
valuable data assets.

II. OVERVIEW

A. Data Governance and Data Stewardship

Nowadays, many organizations believe that data is a
source and can be defined as a valuable asset to their business
with a proper measurement and guideline[1]. Data governance
can be defined as the practice of organizing and implementing
policies, standards, and procedure to ensure the usage of data is Fig.1 the Governance V
effectively managed[2]. In general, data governance is all about
making data management better by ensuring the right people A good quality of information is depending on the
get the right data at the right time, reducing data redundancy, characteristic of the data itself whereby good data is often
integrating data, setting a standardized business performance processed by a skilful person who is known as the role of data
measure, determining appropriate use, sensitivity and steward, they are the backbone of the organization in ensuring
confidentiality of the data, managing and documenting data that data governance processes are followed, guideline is

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enforced and recommend improvement for the data governance Examples from the marketing and sales department, by
processes. practicing data governance, customer data can be analysed and
used to plan a suitable marketing strategy using available
In the enterprise-wide organization data governance resources. In addition, potential customers can be targeted,
program, they will be many data stewards involved in various putting the company in a more advantageous position. Besides,
data processing, different data stewards are assigned in customer service, finance, and operational functions will be
different segments, areas or domain with different roles and improved thus resulting in more satisfied customers[7].
responsibility for the data. Therefore, this is where stewardship
is needed to act, manage and supervise the organization’s data. In any organization, the goal of implementing data
In a nutshell, data stewardship can be defined as the role of governance program is to enable better decision making, to
responsibility and accountability for data and processes that minimize operational friction, to protect data stakeholders’
ensure effective control and the use of the data assets. needs, to train the staff to adopt common approaches to issues
related to data, to standardize rules and processes, to reduce cost
In general, data governance refers to a discipline in and to ensure transparency of processes[8].
formalizing to organize and manage the data information and
asset throughout the entire organization across people, process With respect to the principle in organizing and managing
and technology with the concern of establishing information of the data and information across the people, process and
quality and data accountability optimizing for better decision technology, it is initiate that a good data stewardship
making. Meanwhile, data stewardship is more focused on data implementation will lead success to a data governance program
management, process, and activities involved in order to whereby data governance generally focuses on establishing in
support the goals of data governance. An example is rules and policies to ensure effectiveness and reliable of the data
demonstrated in Fig.2 by showing the difference between data in the organization with the help of data stewardship to manage
governance and data stewardship[5]. the operational, tactical roles, responsibilities and activities that
adhere and support the governance plan[7]. Without data
stewardship, data governance is just a framework. In Fig.3
illustrates the parts involved in the governance program and
data stewardship should be in place across all functions to
support the entire program.

Fig.2 Difference between data governance and data stewardship

B. Importance of Data Governance and Data Stewardship

Data governance has an effect on the achievement of
competitive advantage of an enterprise. Companies have
invested millions of dollars to capture data. However, problems Fig.3 Data governance program
arise when a large amount of data is not properly looked after.
These problems include data that is not clean, data that is
redundant, data that is not consistent, data that is not available,
data is difficult to integrate, poor performance, and little
accountability [6]. Statistics have shown that $700 billion is
lost a year due to bad data, 40% business initiatives fail to
achieve targeted benefit due to poor data quality and 66%
companies lack coherent, centralized approach to data
quality[7]. If this continues, this may reduce employee
productivity, affect customer engagement, increase rivalry,
erode trust and affect the business functions.

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To develop a good data stewardship, several goals can be

defined as follow:

▪ A Data Stewardship Council that is fully functional.

▪ Ensure the establishment and enforcement of procedure
and policies.
▪ Define clear role and responsibility for all data stewards.
▪ Define business and technical data steward.
▪ The accountability of data management.
▪ Ensure training to all people that involve in data steward.
▪ To maintain data quality.
▪ Ensure written process and procedures are approved and
can be used.

In summary, the data assets if treated and managed in the

right way will bring benefit and advantage to an organization
because it allows precise understanding, adaptation, focus and
execution[7]. This can be done by executing data governance Fig. 4 EIM overview
and data stewardship within the organization.

C. The concept of Data Governance III. FINDINGS

To understand data governance, what and where governing A. Triggering Factors of Data Governance
is happening; its concepts comprising of data management,
enterprise information management, and data architecture[9] The three areas that usually trigger the needs for data
will be discussed in depth. governance are master data management, data quality and
business intelligence[9].
Data management is a business function that implements
Master data management is a discipline that ensures
and performs the plans and practices to enhance the value of
shared master data assets within an enterprise is uniform,
data and information. It can also be defined as the profession or
accurate, consistent and accountable[4]. It makes sure data is
duties of individuals or organizations that perform the data
kept up to date and well-coordinated across the enterprise. Data
management disciplines. Within the context of data
governance supports master data management by ensuring
governance, the following terms of business function, data and
standards on data is defined, enforced and maintained, it aligns
discipline must be understood.
with the business needs and activities related to master data
As the name implies, Enterprise information management management is adapted by the organization[9].
is not carried out in a department, but on a bigger enterprise
Data quality is involved in the majority of problems
level. It represents the direction and mindset to manage the
related to data and therefore ensuring data quality is one of the
enterprise information assets, to ensure the business is
main drivers of data governance. Seven common issues related
supported and value is enhanced. It manages the policies,
to data include ease of retrieval, accuracy, completeness, data
framework, people, technologies, and processes in an enterprise
definition, privacy, security, consistency, redundancy, and
in order to maximize the investment in data. Fig.4 illustrates
confusion on which data is important and needed[6]. In another
EIM overview.
source, data quality attributes include age or time elapsed since
Data architecture is a picture that explains the components last modified, completeness of data, usage, accuracy,
and interactions of information management, interrelating consistency, and duplication[7]. Data governance supports data
various aspects such as people, process, policies, procedures, quality by ensuring the standards and rules related to data
and technologies to manage the valuable enterprise information quality is defined and integrated, data quality is frequently
assets. The elements include models of how data is managed, evaluated, and any issues related to change processes are
standards of formats, descriptions the organization, value addressed[9].
statement of the architecture and explanation of what is
Business intelligence is an area, concept or tools which
governed.
utilize information to achieve organizational goals by
performing query, analysis, reporting, and visualization of data.
Data governance may help to enhance business intelligence by
ensuring it is aligned with business activity, data quality and
data profiling are defined for business intelligence purpose,
consistency in data standards and definition and promoting data

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governance as an important aspect to enforce business tools chosen should satisfy the needs of the organizations not
intelligence[9]. just because it is the newest tools in the market. Otherwise, the
application could remain unused because of its incompatibility
B. Elements of Data Governance
to the system or lack of knowledge to use it. Some features of
From reference[9], the elements of data governance that the tools that can be taken into considerations are the
organizations should put the focus on include organization, administration of principle and policy, administration of
principles, policies, functions, metrics and technology and business rules and standards, organization management, data
tools. dictionary, document management, workflow of issues and
audit, enterprise search, metrics scorecard, and collaboration
Data governance organization explains one of the most features
important aspects in data governance which is the concept of
data accountability and responsibility where roles are formally C. Data Stewardship Types, Roles and Responsibilities
assigned within the organization to manage the data assets. To start a data stewardship program, there are types of
Usually, the terms "stewards" or "custodians" are used to stewardship to be defined and recognized. Data stewardship
designate the role. In an organization, a hierarchy is required comprises two types of data steward which is Business Data
to allow monitoring and guiding in which most of the time, the Steward and Technical Data Steward[10]. Fig.5 demonstrates
hierarchy is made up of a combination of people with business how these two stewards interact whereas the business data
and IT backgrounds. Therefore, data governance can be steward represents business domain and technical data steward
considered as a decision-making authority since it specifies will be more focus on technical areas.
decision rights and accountabilities to ensure data is treated
properly[6].

Another crucial aspect of data governance is principles,

which act as a foundation of a set of rules that control the
conduct and application of the data assets. As data governance
is being executed, there will be needs to revisit the enterprise
principle to ensure all actions taken are in line with the
foundation.

Policies are a set of processes that support the principles.

They include standards that become important guidelines when
data governance takes place. Both the principle and policy
prevent any processes that take place without any supervision
or importance.
Fig.5 Interaction among data stewards
Functions describe the right things to do in implementing
data governance in all the activities within all areas. Functions Business data steward represents the specific area of business
are not necessarily processes, but can also be day-to-day where they use quality and meaningful data in the organization
activities. The functions carry out two roles, which are listing and making an appropriate suggestion to the higher level of
what action has to be taken, and determining who or what to be people in the organization (also referred as Data Governors). In
responsible and accountable. In defining the functions, the an organization, position as Data Analyst or Business Analyst
business areas that require collaboration need to be considered. will fulfill the business data stewardship roles and
This includes risk management, legal and human resources. To responsibilities. A good skill of business data steward is
ensure data governance effectiveness by time, the program recommended to have the three major skill which is excellent
needs to be evaluated. Otherwise, the data governance program writing skill, devoted to data change and improvement and
will become useless. Here comes the use of metrics to measure good communication skill. This is because, with these abilities,
the program. Some common metrics include maturity a business data steward able to publish a good writing result,
assessment for information management and data governance, collaborate, communicate with others and fulfilling the
data stewardship progress report, data stewardship organization needs.
effectiveness, data quality profiling measures, and business
value. Meanwhile, the technical data steward is generally
referring to it IT person who plays the important role at the
The use of appropriate tools and technology is important backbone in supporting data governance. They work closely
to support the data governance program. Although there is no with Business data steward and stakeholders to complete the
specific tools or technology that is outlined for this purpose, influence of the whole flow of the information chain in the
many organizations use applications like SharePoint, Word, organization as shown in Fig.6. Meanwhile in Fig.7 shows the
Excel, data modeler and data dictionary. The capabilities of the relationship between business and technical data steward.

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The responsibilities of Data Stewardship Council are[10]:

▪ To find ways to improve data quality so that the

organization can leverage and obtained values from their
data.
▪ To advise or recommend an enterprise level of data
policies, standard, and guideline.
▪ To work together in solving issues regards rules,
requirement, and modification of a data.
▪ To propose and discuss the decision with the Data
Governance Board.
▪ To ensure all procedure and processes activities are
aligned with data governance protocols.
▪ Participation and contribution to data governance
Fig.6 Work relation of data management between all stewards processes.
▪ To ensure the vision and objective of data governance are
conveyed across all people in their respective business
functions.
▪ To establish the rules of data usage.
▪ To measure and review the effective performance of data
governance.
▪ To give input based on the measurement and reviews to
data governance for goals and scorecard reviews.
▪ To establish a forum among data steward in discussing
regards various topics such as policies, procedures and
data issue.
▪ To ensure the same concept of business terminology in
Fig.7 The relationship between business and technical steward avoiding confusion among business functions.

Technical data steward will look after the technicality

involved in organizing and managing the data which commonly
associate to the adaptation various technology tools across from
the system, data storage (warehouse) up to the application level.
As the concern of data stewardship to maintain and manage the
data quality, an enforcement of data quality rules and policy is
quintessential for the technical data steward. Thus, this will
ease the technical data steward activities to create, manipulate,
stored and move the data in the information chain.

However, in some situations, there is another kind of data

steward introduce with specific roles and responsibilities as the
extension or a helping hand to business data steward, they are
domain data steward, project data steward, and operational data
steward. Nevertheless, Fig.8 is the summary of the type of data
steward and their responsibility involved in the organization.

In some enterprise organization, a single data steward may

own many tasks at once, but they are also can have the
responsibility as a group by establishing the Data Stewardship
Council, however this the assignment of the data steward will
be based on the complexity of the business function in the
organization. Commonly, in the Data Steward Council is led by
Enterprise Data Steward which is initially occupied by the data
governance. Fig.9 illustrates an example of a simple Data
Stewardship Council in a company, where each business
function will represent a business data steward. Fig.8 Data steward types and responsibilities

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Table 1 [8] summarizes the focus area with its purpose,

concentration, and accountability of data governance and
stewardship participants.

IV. APPLICATION
A. Implementing Data Stewardship
After an insight of what is data governance and stewardship
all about, and their role and responsibilities, to initiate data
Fig.9 Data Stewardship Organization (by business function) stewardship program, several factors are taken into count. From
the view of SAS Best Practices white paper, to start a data
D. Data Governance Framework stewardship program, the organization need to consider these
six factors[11].
Data Governance framework translates the concepts
into a form that can be easily understood or clarified across the ▪ Existing data-centric skills
organization. It helps the organization to create a clear mission, ▪ Company culture
realize the value from the program, maintain its focus, establish ▪ Reputations of data
accountabilities and define how success can be measured. The ▪ Current view of data ownership
10 components of a data governance framework are mission ▪ Understanding of data measurement
and vision, goals, governance metrics, success measure and ▪ Reuse of data
funding strategies, data rules and definition, decision rights,
accountabilities, controls, data stakeholders, a data governance SAS proposed five types of model of data stewardship
office, data stewards and lastly proactive, reactive, ongoing which are:
data governance processes. The DGI data governance
framework is illustrated in Fig.10 [8]. ▪ Data steward by subject area
▪ Data steward by function
▪ Data steward by business process
▪ Data steward by system
▪ Data Steward by project

Each model has their own advantage and disadvantages.

Table 2 shows the comparison of all five models.

In ECAR working group initiate a collaboration project in

creating a solution in IT with EDUCAUSE[12]. Data
stewardship in EDUCAUSE is developed to ensure the data is
available to the institution use and to improve data
understanding, increase effectiveness and better decision
making. The first step of implementing data stewardship is the
institution had a clear understanding of the main key
component of their data stewardship program which is:
Fig.10 The DGI data governance framework
▪ The purpose of the data stewardship program.
▪ The people involved and use of the data.
E. Data Governance Focus Area ▪ The policies and practices used for the data stewardship
program.
Data governance may be executed in different focus areas
depending on the needs of the organization. The common
In EDUCAUSE they follow four essential steps in defining
ground for all areas is that they address the mission to create a
their data stewardship.
rule, to resolve conflicts and to provide services, they use all
components on a data governance program and they describe ▪ Assign responsibility for collecting, create and manage
universal governance processes and services. However, they the university's data.
may differ in term of the issues, rules, and level of involvement ▪ Define the role of data steward by clarifying policy or
of stakeholders. The different focus areas are policy, data procedures.
quality, privacy, architecture, data warehouse and management ▪ Identify the data steward to consult the operation or
support[6]. business area.
▪ Specify the stewardship coordination that enables data
stewards to work together for data enhancement.

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The same interest is also shared by America’s education

system where they apply data stewardship in State-wide TABLE 2
SAS DATA STEWARD MODEL COMPARISON
Longitudinal Data Systems (SLDS) in managing data
collection, stored, process and use of the data[13].
Model Names Advantages Disadvantages

TABLE 1 Data steward by ▪ Clear data ownership ▪ The increase of the

DATA GOVERNANCE FOCUS AREA boundaries. potential data in the
subject area
▪ The data steward is more particular business
Focus Area Purpose Accountabilities focus only on the subject scope.
area, therefore the ▪ Difficult to tie the data
Policy, In need of support ▪ Review, approve, monitor knowledge to the steward across other
policy particular area will business initiatives since
standards, from a leadership
▪ Collect, choose, review, increase over time. it only supports their
strategy body to convince own business.
approve, monitor standard
the teams to carry Data steward by ▪ Easier for data steward ▪ With peers across the
▪ Align sets of policies and
out the plan by standards function to establish rules. functional, it will create
providing structured ▪ Contribute to business rules and ▪ Data steward in the data conflict.
guidelines called a data strategies organization will be ▪ Multiple data steward in
formal data ▪ Establish decision rights for more business savvy and different department
stakeholders familiar with the managing and
governance policy
metadata usage. manipulating the same
data, and it creates
Data quality Presence of issues ▪ Set direction for data quality conflict.
related to data and monitor it
Data steward by ▪ Easy to explain data ▪ Difficult to assign data
quality, usability, ▪ Report the status of the
initiatives business process quality and benefits the ownership.
and integrity data steward. ▪ Data steward can be
▪ Establish decision rights and
▪ Easy to justify data only effective with a
accountability for stakeholders
steward to another clear definition of the
Privacy, Presence of issues ▪ Protect sensitive data business process.
process.
compliance, related to data ▪ Align the framework and
initiatives Data steward by ▪ Benefit the IT who can ▪ Business people may
security privacy, lead the technical not able to take the data
▪ Assess risk and control it system
information security perspective the data ownership because of
▪ Enforce compliance
control, access requirements improvement. lack IT knowledge.
management, and ▪ Establish decision rights and ▪ Allow IT to educated the
compliance with accountability for stakeholders. business with the
description of IT
standards
technical needs in order
to suit the rules and
Architecture Comes into ▪ Ensure consistent definition regulations of business.
and existence when ▪ Support architectural policies Data steward by ▪ Data stewardship can be ▪ The data steward is
integration there is major and standards tailored according to the based on project and
project
▪ Support metadata programs, project. finishes when the
system acquisition,
service oriented architecture, project is complete. In
development effort, master data management, and
and updates that resulting no
enterprise data management continuation.
require cross- ▪ Bring the attention of
functional decision integration challenges
making. ▪ Establish decision rights and
accountability for stakeholders B. Data Stewardship Maturity
Warehouse It takes place when ▪ Establish rules for data usage In data governance, a data maturity is a used as diagnostic
and business an organization and data definitions.
▪ Clarify data value
tool to measure the level of data maturity for an organization.
intelligence needs to make data-
▪ Establish decision rights and The same concept applies in data stewardship, where it consists
related decision.
accountability for stakeholders of five levels of maturity which are initial, tactical, well-
Management This occurs when ▪ Measure data value defined, strategic and optimized[10].
support the organization ▪ Align the framework and
finds difficulty in initiatives At the initial state is where the data quality is still in the
▪ Monitor and report on data poor state, no data stewardship is assigned due to the lack of
making data related
related project business need to the data where the business looks IT to manage
collaborative ▪ Promote data related message
decision as a result and projects. the data. The second stage is tactical, where the IT or localized
of the inability to ▪ Establish decision rights and team becomes the champion in data stewardship and data
assemble the accountability for stakeholders management practice in the organization. Nevertheless, there
accountable will be an effort to educate and gain awareness from the bottom
stakeholders. to the upper level of the organization. In the third stage is where
data stewardship is well defined and get recognized by the

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business and IT in taking responsibility for their data. At the V. CONCLUSION

fourth stage is defined as the strategic stage where data steward
attempt to involve in improving data quality and data issue is In a nutshell, to begin the data governance and data
monitored and measured. The last stage is optimized, this is the stewardship program, it starts with when the organization
stage where data stewardship has been implemented from the begins to realize the data is an important asset and able to
bottom to top or corporate level, a formalized data management benefit them to generate values in return. By establishing data
is initiated and has become as part of the corporate culture to governance in the organization, this will help the organization
support and achieve data governance goal. Table 3 shows that to set a clear goal with discipline in formalizing to organize and
the details of data stewardship maturity level and their manage the data information and asset throughout the entire
descriptions. organization across people, process and technology. Therefore,
in order to make the data governance program a success, data
In Dell, the data stewardship level is measured based on stewardship came to this part whereby to ensure the standard
data management in the company, from the level of maturity, operational and activities throughout the process across all level
the company able diagnose according to their business value of people, business and technology are carried out according to
and at the same time to achieve their data governance goal[14]. the standard policy or goal of data governance, at the same time
Fig.11 illustrates the Business value with dependence to data to ensure the quality data information with a proper data
management maturity level with the beginning level of management. A data stewardship program can be measured
integration until data governance state. according to their maturity stage and can potentially improve
until it reaches to data governance state. Thus, with the proper
TABLE 3 DESCRIPTION OF DATA STEWARDSHIP MATURITY data management, data responsibility and accountability across
LEVEL the organization, it eventually will lead to better decision
making, customers’ satisfaction and increase business revenue
and opportunities.

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Fig.11 Data management maturity curve

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