Benefits of the Enterprise Data Governance in Industry:
A Qualitative Research
Rodrigo Prado
, Edmir P. V. Prado
, Alexandre Grotta
and Andre Montoia Barata
EPAM – Enterprise Software Development, Design and Conculting , Krakow, Poland
IS Post-graduation Program (PPgSI), University of São Paulo (USP), São Paulo, Brazil
CitiBank, Data Governance Department, São Paulo, Brazil
Keywords: Data Governance, Consulting Services, Case Study.
Abstract: Data governance policies and procedures (DGPP) ensure proactive and efficient data management within the
Enterprise context. Thus, DGPP corporate projects may result in positive impacts we name benefits to
these companies. However, we found few studies reporting these benefits in-developing countries. Another
gap is no evidence of a DGPP benefits model. Given this context, we first created a DGPP benefits model
(DGB-M) via a Systematic Literature Review; we planned and conducted case studies at four different
Brazilian industry sectors: agribusiness, fertilizers, automotive, and logistics. As main results, we have: (i)
The DGB-M itself; (ii) evidence that 62% of the processes described by DGB-M were implemented by these
four cases; (iii) evidence that 68% of the DGB-M benefits expected were achieved by these cases; and (iv)
cases lessons learned. These results are highly relevant to forecast the benefits and challenges of future DGPP
Contemporary organizations are fast adopting Data
Governance (DG) policies and procedures (DGPP)
given data is a fundamental company asset, which
supports from daily operations and up to strategy
decisions. Enterprise data is been considered one of
the most critical assets of a company (Yebenes and
Zorrilla, 2019). Data is a vital backbone that may be
scaffolded by the DG and its DGPP, towards
proactive and efficient data management (Dasgupta,
Gill, & Hussain, 2019).
DGPP are implemented via frameworks, in which
the expected results are positive impact what we
name benefits – to these organizations (Haider &
Haider, 2013). However, were found a gap given that
few studies investigated the benefits right after the
adoption of these DGPP. Even further, we found no
consolidate model to bound DGPP and their expected
Anyhow, towards the adoption of these
frameworks, companies are expected to face many
challenges, such as partial framework adoptions,
based usually on customers’ priorities and
requirements. As a natural consequence, frameworks
should consider partial adoptions as a possible result,
thus it should also consider what to prioritize the
implementation of DGPP (Otto, 2011a). Within this
context, the main goal of this study is to analyze four
DGPP projects occurred in Brazil as well as its
benefits. To achieve the research's main goals, we
created the DG Benefits Model (DGB-M).
Thus, there are two mains objectives: (1) describe
the DG processes implemented by Brazilian
organizations and the benefits obtained, and (2)
analyze the benefits obtained with partially or fully
implementation of DG processes.
Prado, R., Prado, E., Grotta, A. and Barata, A.
Benefits of the Enterprise Data Governance in Industry: A Qualitative Research.
DOI: 10.5220/0010418606990706
In Proceedings of the 23rd International Conference on Enterprise Information Systems (ICEIS 2021) - Volume 2, pages 699-706
ISBN: 978-989-758-509-8
2021 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
The DGB-M emerged as a result of a systematic
literature review (SLR). We first describe the SLR
base concepts and the SLR methodology. We then
collected all the DG Frameworks from the SLR
results. Then, we clustered the SLR results into two
main groups: DGPP and DG benefits. DGPP and DG
benefits are detailed in Subsection 2.3.
2.1 SLR Base Concepts and Method
A base concept is the DG itself, which might be
defined as a system of accountabilities and decision
rights for processes related to information within an
organization (DGI, 2015). Two other base concepts
used by this SLR are Data Governance Office
(DGO) and data stewardship. DGO is defined as a
group of people that provide general support to
various fields of information and data-driven tasks
within the corporate operations. Data stewards is a
person that specializes in the data fields. He/she
follows after data policies, practice oversight, and
process (Thammaboosadee and Dumthanasarn,
2019; Rosenbaum 2010).
DG is the main subject of the Data Management
Association (DAMA, 2009), an organization that
promotes best practices of information and data
management across the Globe. DAMA is most
known by its framework, the Data Management
Body of Knowledge (DMBoK). The DMBoK
provides an overview of data management through
the definition of standards, terminologies, and best
Based on these concepts, this SLR was carried
out as described by Kitchenham et al. (2009). The
study addressed the following research questions:
(1) Which DG frameworks are available in the
scientific literature? (2) What are the benefits of DG
processes and practices? In sum, we consolidate DG
processes and practices and their expected benefits
into a model we named DGB-M as seen in Figure 1.
This SLR included English researches only
published between 2005 and 2019 found at the IEE,
ACM, and SCOPUS databases. We limited
researches to academic journals and conferences in
the information systems (IS) field only that we
peer/expert reviewed. The SLR was finished on Feb.
2020. Our final list of 31 articles found the
frameworks described as follows.
2.2 DG Frameworks
In addition to the DMBOK framework, other DG
frameworks to meet specific contexts have been
reported. Some of them are enhancements of DMBoK
such as (Aisyah & Ruldeviyani, 2018). The SLR
identified sixteen studies reporting DG frameworks.
We grouped them by context as follows: Big Data,
Cloud, Public Sector, and Other Sectors.
2.2.1 Big Data Governance Frameworks
A big data governance framework for healthcare data
was reported regarding health information yet useful
for industries non-healthcare industries. For instance,
it can be used to analyze the risk factors in advance,
preventing prevent issues and problems, and actuate
at these frameworks' limitations level (Al-Badi,
Tarhini and Khan, 2018; Li et al., 2019).
2.2.2 Cloud Data Governance Frameworks
Monolithic Enterprise Applications have been
transformed integrated, thus they are been connect
into what has been named Enterprise Service
Ecosystems. These ecosystems modularize business
rules and expose these rules as services. These
services at then hosted via Cloud Infrastructure, and
event Internet of Things devices should be thus
subjected to a Cloud Data Governance Framework. A
framework proposed by (Shrivastava and Pal, 2017)
spans across various Data Centers and Cloud
Infrastructure resources for instance. A common
agreement among these studies is that the gap
regarding Cloud Data Governance Framework still is
an open question.
2.2.3 Data Governance Framework in the
Public Sector
In in-development countries, personal data privacy
is the role and responsibility of government
agencies. As the first sample, there is a legal gap in
Thailand government data (Thammaboosadee and
Dumthanasarn, 2019) proposed a new framework
for Thailand open government data. Another
emerging problem in in-developing countries Public
Sector is the large data amount. Aisyah and
Ruldeviyani (2018) reported that Indonesia
Insurance Institute requires much data from their
users. They also reported data governance and
management structure based on the guidance of
DMBoK to accommodate these challenges.
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
Figure 1: The Data Governance Benefits Model (DGB-M).
2.2.4 Other Frameworks
There have been reported other frameworks for DG
general context, such as: DG maturity (Haider &
Haider, 2013); DG frameworks unification (Liaw, et
al., 2014); Framework for data governance which can
be used to focus on DG relevant issues, (Khatri, and
Brown, 2010); and literature and practice topics
complementation (Otto, 2011a).
There have been reported also DG-specific
context frameworks, such as elderly citizens
(Dahlberg, 2014), new-generations platforms
(Yebenes & Zorrilla, 2019), and more. Finally, it
has been reported frameworks intended to
implement the Enterprise Information Management
maturity model, such as Gartner® DG (Newman &
Logan, 2008).
2.3 DGPP and DG Benefits
As seen, we identified and classified the DG
frameworks. At the next SLR step, we found that the
10 key processes advocated by the DMBOK
(numbered them from 1 to 10, on Figure 1 first box)
and also the DGPP process number 11 was added
based on Mosley et al. (2009) report. We also found
28 DGPP as described in Figure 1 second box.
The case study is an exploratory and qualitative
research method (Yin, 2015). The four case studies at
Brazilian organizations described the DGPP and
analyse the resulting benefits via the DGB-M. The
research design and case protocol are described as
follows. The case data were collected in the 2
semester of 2020.
3.1 Research Design
This research design was: First (1) we performed the
SLR; then (2) we then defined the research method
and conducted the field research (presented in this
section); finally we (3) presented the results in
Sections 4 (single cases) and 5 (cross-cases),
followed by discussions, as seen on Figure 2.
3.2 Cases Selection Criteria
The case selection criteria were based on Yin (2015):
First, the research could have comprehensive access
to the entire project, including its processes,
participants, and documentation. Second, selected
cases that refer to different periods in time. Third,
cases that came from different industries.
(1.1) Standards linked to business need
(1.2) Well-defined and structured DG policies
(1.3) Improvement in the control of business processes
(2.1) Improvement in performance of data analysis
(2.2) Data elements with the same semantics
(3.1) Improvement in performance of data maintenance
(3.2) Guarantee of integrity, security, usability and
(3.3) Consolidated data model
(4.1) Data preserved and effectively archived
(4.2) Database performance optimization
(5.1) Reliability in data security and privacy
(5.2) Access to data with security and integrity
(5.3) Data and information risks mitigation
(6.1) Control of costs related to data problems
(6.2) Centralized access to organization’s master data
(7.1) Improvement in performance and efficiency of reports
(7.2) Improvement in strategic decision-making
(8.1) Increased productivity in the use of data
(8.2) Standardization, storage and use of well-defined data
(8.3) Control of costs related to document storage
(9.1) Effective data interpretation and use of information
(9.2) Common understanding of data elements
(10.1) Data with accuracy, timeliness, consistency
completeness and integrity
(10.2) Reduced cost of data duplication
(10.3) Increased client satisfaction with the use of data
(11.1) Adherence to legal rules
(11.2) Risk mitigation of fines and lawsuits
(11.3) Data fraud control
DG benefits
Benefits described in literature
DG benefits
(1) Data Governance
(2) Data Architecture Management
(3) Data Development
(4) Data Operations Management
DG benefits
(5) Data Security Management
(6) Reference and Master Data Management
(7) Data Warehousing and BI Management
(8) Document and Content Management
DG benefits
(9) Data Quality Management
(10) Metadata Management
(11) Data Auditing, and Compliance
DG processes and practices
Benefits of the Enterprise Data Governance in Industry: A Qualitative Research
Figure 2: Research phases.
We finally select four organizations that met the
criteria as shown in Table 1. The first organization
comes from the agribusiness industry, thus we shall
name it AGRO, and it has implemented DGPP on:
master data for customers, suppliers, and bill of
Table 1: Enterprise characteristics.
Characteristics Enterprise
Employees 7,500 10,000 5,000 17,000
Annual revenue
(US$ millions)
700 2,100 600 3,000
IT team 25 50 42 100
The second organization is the automotive
industry, thus from now on refer as AUTO, has
implemented the GDPP to its bill of material master
data. The third organization is a logistics industry,
thus from now on refer as LOGI, has implemented
DGPP to on the following data: customers, suppliers,
and bill of materials. Finally, a fertilizer company,
thus from now on refer as FERT, has implemented the
DGPP to its bill of material master data.
3.3 Data Acquisition
We adopted individual interviews as an instrument
for data collection due to the qualitative nature of the
research (Yin, 2015). We adopted the semi-structured
interview (Selltiz, Wrigthman & Cook, 1987), given
it has a pre-establish script that makes it easier to
compare information among participants. The
interview script had both open and closed questions.
The answer regarded projects that were already
closed between 2016 and 2019.
We interviewed different groups of people with
different perspectives: staff, project managers, and
data analysts. First, we conducted interviews with
eight professionals from a multinational consulting
company that operates in Brazil. These eight staffs
were members of the DG implementation projects at
the mentioned companies. We then interviewed two
project-managers that were in charge of these project
implementations. And finally, a data analyst who has
supported DG processes and practices. We also
collected project data.
3.4 Data Processing
Data treatment and analysis were performed using the
semantic content analysis technique (Neuendorf,
2001), according to the following steps: (1) the
interviews were recorded based on participant
consent; (2) we generated the transcribed; (3) the data
was classified according to the previously explained
categories; and (4) The final list of benefits was then
double-checked back with the participants.
3.5 Research Limitations
The main research limitations are as follows:
(1) Data analysis technique. The data collected in
the interviews were analyzed using the content
analysis technique. The interpretation of this
data was made by the author, which attributes
subjectivity to the results.
(2) Results generalization. All interviewees
belong to a single consulting service company,
which does not allow generalization of the
research results to other companies and
The final within-case analysis is as shown in Table 2.
Light-gray lines are special cases as follows. Zeroed
lines: the process was not implemented by the current
project. Line with 100(%): both processes 4 and 7 are
were already implemented by earlier projects.
Literature review
Research method
Results and
Systematic literature review
Multiple case study
Data acquisition: - through interviews and documents
- two professional per case
Data processing: - SLR for literature articles
- content analysis for interviews
Descriptive statistic
Triangulation of data
DG processes and frameworks
Benefits of DG processes
Four cases
Two interviews per case
Project’s documents
Comparation between benefits
described and achieve
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
Table 2: The DGPP per Sector.
Processes Enterprise (%)
1 Data governance 75 65 75 75
2 Data architecture 88 75 68 50
3 Data development 50 7 73 65
4 Data operations 100 100 100 100
5 Data security 50 75 50 0
6 Reference and master
data management
100 58 100 100
7 Data warehousing and
BI management
100 100 100 100
8 Document and content
63 0 58 50
9 Data quality
0 0 0 0
10 Metadata management 100 78 68 85
11 Data auditing 63 65 20 20
Total implemented
processes (%)
74 54 64 56
4.1 AGRO Case
The AGRO DG project finished by the fall of 2015
after a 22 months project. The project budget was
circa US$ 700,000 distributed as follows: 20% to
executives - partners, directors, and managers; 20%
to senior consultants; and 60% to junior consultants
and trainees. The project was implemented in the
headquarter office and it was sponsored by the Supply
Department. The departments involved in the project
were engineering, accounting, supplies, and
Information Technology (IT). The project team had
five employees from the interviewed consulting
company. The PMBOK and the SCRUM
methodologies supported the project management
methodologies, while the DMBOK was used as a
reference for DGPP.
4.2 AUTO Case
The AUTO DG project finished by the fall of 2016
after 17 months. The project team had five
employees. The project budget was circa US$
875,000 distributed as follows: 37% to executives -
partners, directors, and managers; 30% to senior
consultants; and 33% to junior consultants and
trainees. The project was implemented in a branch
office and it was sponsored by the Accounting
Department. The departments involved in the project
were accounting, supplies, and IT. The project
management was supported by the SCRUM
methodology, and DMBOK was used as a reference
for DG processes. The AUTO company implemented
54% only of DGPP, due to the specific
characteristics, such as the one company with the
lowest productivity rate.
4.3 LOGI Case
The LOGI DG finished by the fall of 2016 after a 12
months project. The project team had six employees.
The project budget was circa US$ 500,000 distributed
as follows: 7% with executives - partners, directors,
and managers; 44% to senior consultants; and 49% to
junior consultants and trainees. The project was
implemented in both a headquarter office and branch
offices. The departments involved in the project
billing, warehousing, supplies, and IT. The project
used SCRUM as the project management
methodology, while the DMBOK was used as a
reference for DGPP. As seen in Table 2, circa 62% of
DGPP identified by
DGB-M were implement. Given
these projects was the first one of this kind, supported
by a consulting company, the results were considered
4.4 FERT Case
The FERT DG project finished in 2019 after a 9
months project. The project budget was circa US$
350,000, distributed as follows: 10% with executives
partners, directors, and managers; 40% to senior
consultants; and 50% to junior consultants and
trainees. The project was implemented in the head
office and was sponsored by the Finance Department.
The departments involved in the project were in the
areas of finance, and IT. The project used SCRUM as
the project management methodology, while the
DMBOK was used as a reference for DGPP.
This section analyses the interrelation among cases.
5.1 DGPP Costs
As a relevant outcome, the cost to implement 1% of
DGPP per sector was, from lowest to higher: FERT:
US$ 6,292; LOGI: US$ 7,813 AGRO: US$ US$ 9,507;
AUTO: US$ 16,355. The higher AUTO project is
indeed a relevant subject to analyze. This project had
high management costs. While in the other companies
the projects spent an average of 12.3% with it, AUTO
spent 37.0%. Even a longer project (AGRO) and the
same size team project (FERT) had much lower costs.
Except for AUTO, other projects had a US$ 7,871
Benefits of the Enterprise Data Governance in Industry: A Qualitative Research
average cost for the implementation of 1% DGPP,
which was in line with the 2015-2019 consulting
company historical database.
5.2 DGPP Benefits
Table 3 presents the DGPP benefits according to the
DGB-M model. This information was acquired with
the project team members, from both the consulting
company and the customer side. The final benefits
and percentages were agreed upon among the
For instance, The Metadata Management process
at AGRO was fully implemented (100%) but it
resulted in 38% only of the benefits expected benefits.
On the opposite, 7% only of the Data Development
process in AUTO generated a gain of 33% of the
expected benefits. Indeed, AUTO was the case that
had the highest relation (1.25 as seen in Table 3)
DGPP vs benefits, yet it had the highest costs as seem.
Table 3: DGPP projects Benefits.
Processes Enterprise
1 Data governance 70 88 88 70
2 Data architecture 100 100 83 50
3 Data development 50 33 88 60
5 Data security 50 75 100 0
6 Reference and master
data management
100 75 83 90
8 Document and content
100 0 67 50
10 Metadata management 38 83 83 80
11 Data auditing 83 83 33 15
74 67 78 53
Benefit / proc. implemented
1,01 1,25 1,22 0,95
To make it simpler to cross analyze cases, we have
created Figure 3 where it is possible to identify a
directly proportional relationship between the DGPP
and benefits. In fact, 78% of the processes (i.e., 25/32
of the implemented DGPP) are in the #4 quadrant.
This quadrant has a higher implementation degree
versus higher benefits. As expected, there was a
certain exception for this rule. AGRO processes 8 and
10 and for LOGI Process 5, detailed as follows:
AGRO GDPP two exceptions: processes 8 and 10.
In the first process, the perception of benefits was
total (100%), even without the implementation of all
processes described in the literature. However,
AGRO intended to improve this process after project
implementation. In process 10, the benefits achieved
were low (below 50%), this is because sometimes the
benefits related to data quality are perceived only
later after the implementation of the project.
LOGI exception: Process 5. With only 50% of the
implementation of this process, the client had a
perception of having obtained 100% of the benefits.
This happened because LOGI had a deficient DG
maturity level, and with the implementation of part of
the DG processes, the benefits obtained are
expressive and generate significant value for the
Finally, it is possible to state that the average
GDPP was 62% and the average benefits were 68%.
These values were considered in line with the other
projects managed by the same consulting company.
In complement to the cross-analysis section, this
section discusses the customer motivations,
challenges and lessons learned behind these DG
6.1 Motivation for DG Projects
Cost-saving and process standardization were the
main motivation behind these four cases.
AGRO specific motivation was to improve their
decision-making process as well as enhance their data
quality. AUTO specific motivation was to avoid
losing tax benefits due to their inconsistent data. In
fact, Brazil has one of the most complex tax systems
in the world, which thus elevates costs, the number of
employees their overheads. LOGI specific motivation
was their Enterprise Resource Planning System new
release, which was intended to centralize and
organize their resources and data.
6.2 Challenges to Carry Out the
As expected, all research participants reported
challenges. Resistance to changes is a common issue
among the four cases, such as the difficulty of
obtaining knowledge from people who owned it.
Stakeholders also expected maximum return with
minimum changes, which indeed is most probably not
prone to occur. Bellow, we present specific
challenges for each sector:
AGRO. Employees’ turnover made it difficult to
obtain stakeholders’ involvement. This required more
effort from the consulting service company
professionals in managing change and gaining
business knowledge.
ICEIS 2021 - 23rd International Conference on Enterprise Information Systems
Figure 3: The degree of DGPP implementation and its related benefits.
AUTO. The enterprise size results in a higher
number of processes and challenges across the
enterprise to the addressed. The problem to obtain
knowledge when considering many departments
highly increase these challenges.
LOGI. Managing stakeholders. There was
reported a lack of alignment and interest among
stakeholders, especially between the project sponsors
and other stakeholders. Besides, there were
difficulties to identify the data owners.
6.3 Lessons Learned
According to the research participants, these were the
main lesson learned:
(1) Clear Identification of Project
Stakeholders, such as a formal project board
with decision making power, and a formal and
empowered sponsor. Additionally, it is
mandatory to have well-prepared staff in the
data registration processes. If the company has
local branches, make a physical visit to each
of them to identify both stakeholders and data
(2) Change Management Process. Relevante
tasks are formalized the changes will occur
and thus create DG policy, especially for large
organizations, and implement data
standardization process whenever when
(3) Make Technologies and Infrastructure
Available to the Project, given there are
fundamental assets on which these projects
rely, such as the ERP for LOGI project.
This was exploratory and qualitative research, that
proposed a model, named
DGB-M, for investigating the
DGPP benefits at four different sectors: agribusiness,
fertilizers, automotive, and logistics. As result, we
found that on average 62% of the process was
implemented and 68% of the benefits were achieved.
Even these processes were implemented
partially they added value to these organizations,
given many in-developing countries still lack DGPP
projects, this research is highly relevant to identify the
key-process DGPP, in which we created the DGB-M
model. We also validated the DGB-M process and
benefits, thus contributing to enhancing the success
rate of future similar projects. Additional material
such as DGPP costs, benefits, challenges, and other
lessons learned we also collect from this research. For
0 10 20 30 40 50 60 70 80 90 100
Percentage of DG processes implemented
Percentage of benefits achieved
# - number of DG process:
Far form
Benefits of the Enterprise Data Governance in Industry: A Qualitative Research
future researches, the DGB-M can be extended to
contexts, such as companies that actuate at these
reported sectors and in in-developing countries.
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ICEIS 2021 - 23rd International Conference on Enterprise Information Systems