Data Governance to Be a Data-Driven Organization
Carlos Alberto Bassi
1 a
, Solange Nice Alves-Souza
2 b
and Luiz Sergio de Souza
3 c
1
Information System Post-Graduation Program (PPgSI), Escola de Artes, Ci
ˆ
encias e Humanidades,
Universidade de S
˜
ao Paulo, S
˜
ao Paulo, Brazil
2
Departamento de Engenharia de Computac¸
˜
ao e Sistemas Digitais, Escola Polit
´
ecnica, Universidade de S
˜
ao Paulo,
S
˜
ao Paulo, Brazil
3
Faculdade de Tecnologia de S
˜
ao Paulo, S
˜
ao Paulo, Brazil
Keywords:
Data Governance, Data-Driven Organization, Data Governance Challenges, Data-Driven Challenges,
Data Governance Culture, Data-Driven Culture.
Abstract:
Many organizations have been trying to become data-driven which their business decisions, their relationships
with customers / suppliers, the innovation of their products / services, the improvement in their performance
and their growth are based on the collection and analysis of an increasing volume of data. To reach this level,
organizations need to overcome a series of challenges related to the way they govern data and creating a data-
driven culture. The main challenges to be overcome are directly related to data culture and the culture of
the organization itself. These paper presents the results of a survey performed among 67 professionals with
experience in Data Governance (DG) in which was possible to identify the main challenges to establish a
DG program and data-driven culture in organizations, besides priories actions to face these challenges. The
challenges and necessary actions to implement a DG program are shown and discussed. Addressing these
challenges is fundamental to raising the organization culture and maturity in DG and, consequently, becoming
a Data-driven organization.
1 INTRODUCTION
Data-driven Culture (DDC) is interpreted as a pattern
of behaviors and practices by a group of people who
share a belief that having, understanding, using data
and information plays a crucial role in the success of
their organizations (Chaudhuri et al., 2024).
DDC represents a specific form of organizational
culture that is realized through data orientation. A
DDC emphasizes that organizational decisions are
grounded on insights from data, which fosters con-
tinuous knowledge and skills acquisition within the
organization. Organizational culture encompasses a
collection of values, beliefs, and attitudes held by or-
ganizational members. As it is based on organiza-
tional cultures, DDC’s complexity lies in its need for
consistency with decision-making principles (Fattah,
2024).
Becoming data-driven is about building capabili-
ties, tools and most important a culture that is acting
a
https://orcid.org/0009-0001-2524-7478
b
https://orcid.org/0000-0002-6112-3536
c
https://orcid.org/0000-0002-7855-0235
on data (Anderson, 2015).
In DDC data must be shared across the organi-
zation. DDC focus on setting goals, measuring suc-
cess, interaction, feedback, learning and recognition
of Data Literacy (DL) (Anderson, 2015).
Becoming data-driven is stated as one of the top
priorities for organizations for the last 10 years. Num-
bers show clearly benefits of being data-driven. Com-
panies that base their decisions on evidence are on
average 5% more productive and 6% more profitable
than their competitors (Storm and Borgman, 2020).
DDC influences business models and provides
ways through which organizations develop their op-
erations to secure higher profits (Chaudhuri et al.,
2024).
Efficient and fast analysis of huge volumes of
data has helped organizations make accurate deci-
sions which could help influence innovative activi-
ties. Such an approach has also helped organiza-
tions revamp their business processes and develop
smart products in relation to customer needs, eventu-
ally leading to increased profitability. Thus, the im-
provement of the organizational data-driven culture
could lead employees to be more creative and gen-
Bassi, C., Alves-Souza, S. and de Souza, L.
Data Governance to Be a Data-Driven Organization.
DOI: 10.5220/0012900700003838
Paper published under CC license (CC BY-NC-ND 4.0)
In Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2024) - Volume 3: KMIS, pages 175-186
ISBN: 978-989-758-716-0; ISSN: 2184-3228
Proceedings Copyright © 2024 by SCITEPRESS – Science and Technology Publications, Lda.
175

erate novel ideas that could lead to the creation of
new products to cater to the needs of dynamic mar-
kets (Chaudhuri et al., 2024).
Data Governance (DG) is the comprehensive man-
agement of usability, availability, Data Privacy and
Security (DPS) and Data Quality (DQ) inside and out-
side the organization (Abraham et al., 2019).
It includes establishing policies, standards, pro-
cesses, and structures to ensure the correct use and
effective protection of data (Al-Dossari and Sumaili,
2021).
DG requires policy specification dynamics that
can deal with problems related to the collection, stor-
age, processing, sharing and use, reuse, and disposal
of data throughout its life cycle (Filgueiras and Lui,
2022).
Many businesses are currently adapting digital
strategies and new business models. Studies have
shown that organizations of all sizes recognize the
need for DG (Lis and Otto, 2020). Increasing impor-
tance has been given to incorporating DG as a means
of encouraging the strategic use of data, thus promot-
ing data-driven innovation (Lis et al., 2022).
The data-driven innovative capabilities have been
enhanced by the applications of advanced Informa-
tion and Communication Technology (ICT), strong
analytic capabilities, effective data management and
governance mechanisms (Chatterjee et al., 2024).
Beyond this, DG serves as a structured framework
for organizations to manage data, recognizing data as
a valuable corporate asset, thus promoting the opti-
mal use of data. In practical terms, by organizing and
mapping data-related processes within organizations,
DG enables organizations to identify which data
should be analyzed, unlock potential value, and over-
come obstacles in developing DDC (Fattah, 2024).
DG plays a crucial role in encouraging and em-
powering the use of data analysis, such as DL, aligned
with DDC. In this context, DG establishes decision
making rights and accountability to ensure appropri-
ate behavior in assessing, creating, using, and con-
trolling organizational data, analytics, and informa-
tion assets. Integrating DG with the overall business
strategy and aligning it with data and analytical assets
is considered critical for the organization’s stakehold-
ers (Fattah, 2024).
The findings indicate that DG and DL originally
precede DDC. Consequently, it delivers a relevant
theoretical contribution by providing empirical evi-
dence and delving into the role of DG as a precur-
sor to DDC. Interestingly, despite DG’s crucial role in
overseeing data pertinent to organizational decision-
making, existing literature lacks clarity concerning
the nomological network between DG constructs and
other analytical capacities (Fattah, 2024).
This article consists of identifying that an organi-
zation will become data-driven when it creates a real
DDC, and this will be possible through the implemen-
tation of effective DG.
2 CHALLENGES
2.1 Data-Driven Culture
DDC helps organizations to support process and prod-
uct innovation eventually impacting the performance
of the organizations (Chaudhuri et al., 2024).
Culture permeates every aspect of organizational
actions, influencing decisions related to products, em-
ployees, customers, measurements, resource alloca-
tion, etc. While some leaders seek to embrace cul-
tural norms using advanced technology, others may
resist cultural change (Fattah, 2024).
There is necessity for shift the executive mindset
by stimulating DDC and formulating strategies and
mechanisms for governance (DG), as well as renew-
ing the skills of analysts (DL) (Fattah, 2024).
Study conducted to identify the enabling factors
of a DDC identified that (Berndtsson et al., 2018):
• established DG and access to data of good quality
are mandatory for any type of analysis. If these
features are not in place, then trust in business in-
sights generated by various tools will deteriorate
and undermine the move towards a DDC.
• top level management is important and needs to
be actively involved in developing a strategy for
establishing a DDC.
A survey conducted to identify challenges to cre-
ating a DDC pointed out the following (Storm and
Borgman, 2020):
• a big challenge is taking away resistance to adopt
a new technology.
• the struggle of creating insights derived from
analysis.
• the complicated organizational structure.
• the lack of time forms a barrier among employees.
• the lack of knowledge occurs in departments
where data is not originally embedded.
2.2 Data Governance
Organizations face challenges and problems in imple-
menting a comprehensive and efficient DG program.
In many cases, there is a lack of knowledge on the part
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176

of the professionals involved in conducting DG im-
plementation projects, regarding which activities are
necessary, who should be responsible for carrying out
these activities, what the relationship and dependence
are between these activities, as well as the impacts
generated by not performing such activities properly
(Bassi and Alves-Souza, 2023).
Several studies have found, among other reasons,
that many of the bad decisions are due to the poor
quality of the information generated from dirty, erro-
neous and incomplete data. This has led important
companies worldwide to lose many thousands of dol-
lars by managing information of low quality in their
organization (Castillo et al., 2017).
Companies spend an average of 30% of their time
on non-value-added tasks due to poor DQ and avail-
ability (Zhang et al., 2022). Key trends in DG indi-
cate that by 2025, 80% of organizations seeking to
scale digital businesses may fail if they do not adopt a
modern approach to DG and analytics (Fattah, 2024).
Implementing DG is a complex project that re-
quires long-term commitment and continuous en-
gagement and, as such, organizations usually need
to formulate a series of actions towards these goals
(Zhang et al., 2022).
Mobilizing an organization to adopt DG has
proven to be a challenge in practice. Taking stock
of data inventory remains tedious, the potential for
value creation seems abstract, and the importance of
investing in DG is understood only if the company
has already suffered major regulatory pressure or data
breaches (Benfeldt et al., 2020).
There is a general lack for a clear understanding
of what DG is and how it is currently implemented in
companies (Krumay and Rueckel, 2020).
A comprehensive review of the scientific and
practice-oriented literature shows a lack of under-
standing the activities required for introducing a DG
program (Alhassan et al., 2019).
The implementation of a DG program implies
a set of actions as presented by the Data Manage-
ment Framework (Association, 2017) and Data Gov-
ernance Institute (DGI) DG Framework (Thomas,
2024), highlighting the need to carry out:
• an inventory of assets, business processes, regu-
lations regarding data processed by the organiza-
tion.
• the constitution of a governance management
committee.
• creating consistent and effective policies.
• the training of all employees in DG concepts,
technologies and best practices and the creation
of a data culture.
3 RESEARCH
Within the scope of a research to specify a guide with
practical and coordinated actions that help organiza-
tions in the implementation of DG, a survey was car-
ried out among professionals with experience in DG
in two stages, the first through a web form and the
second through an interview.
3.1 First Stage
A web form was created based on the challenges, im-
pacts and solutions in the implementation of DG ob-
tained from a literature review [ reference - (Bassi and
Alves-Souza, 2023) ] to help evaluate the following
points:
• validate that organizations carry out a series of ac-
tions in the context of implementing DG, among
them, the mapping / survey / inventory of data,
processes, regulations, and infrastructure; the cre-
ation and performance of a Management Com-
mittee; the development and application of DG,
DPS and DQ policies, in addition to presentation
/ training in the concepts of DG and the policies.
• identify the importance, level of complexity and
priority of such actions in DG.
• identify the main challenges that prevent organi-
zations from carrying out such actions.
• identify the main challenges faced by organiza-
tions when carrying out such actions.
Table 1 in the APPENDIX displays the web form
used in this stage.
It was estimated that a population of 81 research
participants could represent at least organizations of
different sizes (small, medium and large), in different
stages of implementation (initial, advanced and plan-
ning) and in different market segments.
It is important to determine the size of a sample
to represent the population of a research. Accord-
ing to Krejcie and Morgan, the recommended sample
size would be around 82% of this population (Krejcie,
1970).
200 professionals who work with DG were invited
to fill out a web form. 67 professionals answered the
form and signed the consent form to participate in the
research.
They have been working on the implementation
of DG in organizations from the most different mar-
ket segments and of the most varied sizes as show in
Figure 1, Figure 2, and Figure 3.
Data Governance to Be a Data-Driven Organization
177

Figure 1: Experience in DG.
Figure 2: Size of the organization.
Figure 3: Organization’s market segment.
3.2 Second Stage
38 professionals from the group who responded to the
web form and who had more than two years of ex-
perience in DG were invited to participate in the in-
terview phase (75% of the participating professionals
have more than 3 years of experience in DG).
19 professionals agreed to participate in this sec-
ond phase. A web form based on the results collected
in the previous stage was designed to conduct an on-
line interview, whose objective was to obtain a clas-
sification / prioritization of the main challenges iden-
tified at the first stage to that prevent from carrying
out the actions and faced when carrying out the ac-
tions in order to help in prioritizing these actions in
the implementation of the DG.
Table 2 in the APPENDIX displays the web form
used in this stage.
4 ANALYSIS
Among the various challenges highlighted by profes-
sionals during the first phase of the research, it was
possible to identify that the challenges listed below
are directly related to cultural and organizational
aspects that impact the implementation of effective
DG and alignment with the organization’s strategic
objectives:
Resistance (RST) - professionals’ reluctance to adopt
new processes, policies, procedures, and tools due to
concerns about changes in organizational culture, the
perception of loss of control or impact on the activi-
ties performed or, the lack of awareness / training.
Employee Engagement (EEG) – active and moti-
vated participation of employees in the process of un-
derstanding, adopting, executing, and effectively us-
ing practices, policies, procedures, and tools in their
activities.
Culture / Knowledge / Empowerment (CKE) –
forming a data-driven mindset, promoting under-
standing and effective use of practices and procedures
through training and ongoing education.
Experience (EXP) - practical knowledge and in-
depth understanding of practices, procedures, and
tools by the organization’s professionals.
Alignment / Communication (ALC) - ensure all
stakeholders are coordinated and informed about poli-
cies, procedures, and objectives to promote effective
collaboration and successful implementation.
Perception of Value / Benefits (PVB) – everyone in-
volved recognizes the potential positive impacts, en-
couraging support, adherence and commitment to the
initiatives are being implemented.
Management Support (MSP) – demonstration of
leadership, commitment, and strategic direction to
ensure adequate resources, organizational alignment
and prioritization of initiatives are being implemented
by the organization’s executives.
4.1 Mapping / Survey / Inventory
Challenges
The implementation of a DG program implies the
need to know the data from the organization, the busi-
ness processes that manipulate this data, the regula-
tions, and standards that they must be followed and
the infrastructure that supports the processing of the
data.
To achieve this, the following actions are neces-
sary:
Data Inventory (DI) - comprehensive identification,
cataloging and documentation of all data that the or-
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178

ganization collects, processes and stores, with the aim
of understanding how that data is used, who has ac-
cess to it and where the data is stored.
Process Mapping (PM) - identify, visualize, and doc-
ument in detail all business processes, aiming to un-
derstand how data flows through the organization and
identify points of intervention.
Regulatory Survey (RS) - identify and analyze rele-
vant laws, regulations, internal policies, and external
standards that impact data management, security, and
privacy, ensuring legal compliance and risk mitiga-
tion.
Infrastructure Mapping (IM) - identify and docu-
ment the structure of hardware, software, and data
storage systems, ensuring a comprehensive under-
standing of the technological infrastructure that sup-
ports the management and manipulation of organiza-
tional data.
Figure 4 presents the ‘Challenges that Prevent
from Carrying Out’ and the ‘Challenges Faced when
Carrying Out’ the Mapping / Survey / Inventory
(MSI). The x-axis of the figures shows the number
of citations of the challenge by participants.
Figure 4: Challenges to carry out the mapping / survey /
inventory (MSI).
The MSP, PVB and CKE are challenges that prac-
tically impact the carrying out of these actions as in-
dicated by some research participants.
[P54] - “Culture of the organization, people
trained to implement the process and mainly lack of
support from the organization’s leadership.”
[P66] - “The lack of technical knowledge about
process mapping is one of the main challenges, due to
the complexity of the processes and the level of ma-
turity of the processes. Another point is not knowing
what benefits this mapping will bring to the company
objectively.”
The challenges CKE, EEG and RST impact the
execution of these actions.
[P75] – “Engagement of the organization to map
concepts and metadata, size of the current Data Gov-
ernance structure and understanding of the impor-
tance of the subject by board members.”
[P01] – “Resistance and commitment of those re-
sponsible.”
When professionals were asked to classify / prior-
itize the most relevant ‘Challenges that Prevent from
Carrying Out’ and the ‘Challenges Faced when Car-
rying Out’ for these actions, the following ranking
was obtained as shown in the Figure 5.
The challenges MSP and the PVB are the main
‘Challenges that Prevent from Carrying Out’ and
‘Challenges Faced when Carrying Out’ in the execu-
tion of an MSI.
Figure 5: Ranking of challenges to carry out the mapping /
survey / inventory (MSI).
4.2 Management Committee Challenges
The creation and effective performance of a manage-
ment committee will have a significant impact on the
implementation of DG.
The Data Office establishes vision, strategy and
governance of initiatives, projects, actions of the
council data, ensures the smooth functioning and
management of data systems, guarantees ethic, re-
sponsibility, and lawful use of data. It consists of
a multidisciplinary team, as well as an information
and records manager and experts in efficiency, trans-
parency, accountability, and life cycle management
(Cerrillo-Mart
´
ınez and Casades
´
us-De-mingo, 2021).
Figure 6 presents the ‘Challenges that Prevent in
the Creation’ and the ‘Challenges Faced when in the
Data Governance to Be a Data-Driven Organization
179

Performance’ of the Management Committee (MC).
The x-axis of the figures shows the number of cita-
tions of the challenge by participants.
Figure 6: Challenges that prevent in the creation and faced
when in the performance of the Management Committee
(MC).
The MSP, PVB and CKE are challenges that prac-
tically impact in the creation of the MC while the
challenges CKE, EEG and RST impact the perfor-
mance of the MC.
[P68] – “The lack of a data governance culture in
the organization, which recognizes the strategic im-
portance of data as an asset and a resource for deci-
sion making.”
[P55] – “Management of conflicts of interest re-
lated to corporate vs departmental prioritization, lack
of knowledge and experience, as well as resistance to
change.”
Figure 7 presents the classification / prioritization
carried out by professionals of the ‘Challenges that
Prevent in the Creation’ and the ‘Challenges Faced
when in the Performance’ of the MC.
The creation of the MC is impacted by the chal-
lenges MSP and PVB while your performance is im-
pacted by the challenges PVB and CKE.
Figure 7: Ranking of challenges in the creation and faced
when in the performance of the Management Committee
(MC).
4.3 Policy Challenges
Companies specify guidelines and rules for the cre-
ation, acquisition, storage, security, quality and per-
mitted use of data, developing standard processes and
policies for data, and establishing employee organiza-
tions that support dedicated data governance activities
(Zhang et al., 2022).
The following policies are relevant within the DG
program:
Data Governance Policy (DGP) – set of princi-
ples, guidelines and procedures formally established
to guide the management, quality, security, privacy,
and ethical use of data, aligned with the organization
strategic and regulatory objectives.
Privacy and Data Security Policy (PSP) – set of
formal guidelines that establish the requirements and
procedures to protect the confidentiality, integrity, and
availability of data, ensuring compliance with privacy
and security regulations, such as the General Data
Protection Regulation (GDPR) - European Commu-
nity and the Lei Geral de Protec¸
˜
ao de Dados Pessoais
(LGPD) - Brazil, and mitigating risks related to data
breaches.
Data Quality Policy (DQP) – set of guidelines
that defines standards, processes, responsibilities to
ensure the reliability, accuracy, integrity of data
throughout its life cycle, aiming to effectively support
operations and decision making.
Figure 8 presents the ‘Challenges that Prevent
the Developing and Application’ and the ‘Challenges
Faced when Applying’ these policies. The x-axis of
the figures shows the number of citations of the chal-
lenge by participants.
The challenges MSP, CKE and EEG are CPDA
of the policies while the application of the policies
is impacted by the challenges PVB, ALC, CKE and
EEG.
[P27] – “The preparation of data owners and data
stewards to carry out their role within data quality.”
[P31] – “The company’s culture does not favor the
practical application of a Data Governance policy.”
[P35] – “Changing behavior and adopting a pol-
icy is always delicate and if it is not done correctly,
all the work may have been in vain.”
Figure 9 presents the classification / prioritization
carried out by professionals of the ‘Challenges that
Prevent the Developing and Application’ and ‘Chal-
lenges Faced when Applying’ of the policies.
The challenges MSP, PVB and CKE impact on the
developing and application of the policies.
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180

Figure 8: Challenges to develop and apply the policies.
Figure 9: Ranking of challenges that prevent the developing
and applying and in the application of the policies.
4.4 Presentation / Training Challenges
Different actions / interactions are recommended to
ensure appropriate employee data competencies. The
most important action / interaction is ‘training’, such
as continuous training in dealing with and implement-
ing data policies as well as data processes and proce-
dures and includes internal and external training (Al-
hassan et al., 2019)
Figure 10 presents the ‘Challenges that Prevent
from Carrying Out’ and the ‘Challenges Faced when
Carrying Out’ the presentation / training (PT) on the
concepts involved in DG and the elaborate policies.
The x-axis of the figures shows the number of cita-
tions of the challenge by participants.
The MSP, PSB and CKE and employee engage-
ment are challenges that impact the carrying out of
these actions.
Figure 10: Challenges to carry out the presentation / train-
ing (PT).
[P31] – “Include the training agenda in a broader
literacy program with a focus on governance themes
associated with business challenges.”
[P46] – “The clear definition of a data strategy for
the entire institution from the organization’s manage-
ment team and not from the information technology
area.”
The execution of these actions is impacted by the
challenges ALC, CKE and RST.
[P45] – “Employees who do not participate in
training, not all managers are concerned with Data
Governance.”
[P68] – “Measuring and demonstrating the bene-
fits and results of the Data Governance Policy, which
demonstrate improvements in the quality, security,
privacy and value of data.”
Figure 11 presents the classification / prioritiza-
tion carried out by professionals of the ‘Challenges
that Prevent from Carrying Out’ and the ‘Challenges
Faced when Carrying Out’ for the PT.
The challenges MSP and CKE are the main ‘Chal-
lenges that Prevent from Carrying Out’ while the
EEG and PVB are the ‘Challenges Faced when Car-
rying Out’ for the PT.
Data Governance to Be a Data-Driven Organization
181

Figure 11: Ranking of challenges to carry out the presenta-
tion / training (PT).
5 DISCUSSION
It can be identified that cultural aspects are extremely
relevant to the successful implementation of a DG
program in organizations. These aspects compre-
hend a set of values, beliefs and behaviors that define
how the organization conducts its business and how
it treats its customers and partners (Bassi and Alves-
Souza, 2023).
A Scoping Review (SR) of publications that
present case studies (CS) on DG projects identified
that the challenges related to cultural aspects like the
lack of perception of the value of data as an asset
and the lack of understanding and training of those
involved in the concepts, technologies and best prac-
tices have significantly impact on the implementation
of DG in organizations from different market seg-
ments and countries (Bassi and Alves-Souza, 2023).
The importance of incorporating DG as means of
encouraging the strategic utilization of data, therefore
promoting innovation and culture, is increasing (Lis
et al., 2022).
One of the solutions identified in the literature
for implementing DG is train and improve DL for
all staff across organizations that participate in the
project (Kawtrakul et al., 2021).
Implementing DG is a complex project that re-
quires long-term commitment and continuous en-
gagement and, as such, organizations usually need
to formulate a series of actions towards these goals
(Zhang et al., 2022).
A DDC should be developed, engaging the entire
business, and sparking employee interest and moti-
vation. DDC must be extended in an integrated way
throughout the organization, instead of being segre-
gated (Anton et al., 2023).
Data-driven organizations require strong, top-
down data leadership. They need a leadership that
inspires, promotes a data-driven culture, and actively
drives and supports all aspects of the analytics value
chain, from data collection through to data-driven de-
cision making and institutional learning (Anderson,
2015).
Establishing clear policies for DG helps ensure the
responsible and ethical use of data within the organi-
zation (Anton et al., 2023).
The implementation of a DG will effectively con-
tribute to making an organization data-driven. It turns
out that implementing a DG program involves over-
coming a series of cultural and organizational chal-
lenges.
In the research performed with professionals who
work in DG, it was possible to identify the four main
challenges directly related to cultural and organiza-
tional aspects that prevent or impact the carrying out
of the actions necessary to implement the DG pro-
gram as shown in the Figure 12.
Previous knowledge of these challenges helps or-
ganizations that desire to implement effective gover-
nance of their data to better plan and take the neces-
sary actions to mitigate the impacts that these chal-
lenges generate (Bassi and Alves-Souza, 2023)
The successful implementation of a DG program
will create a DDC and, consequently, make the orga-
nization data-driven.
Figure 12: Cultural challenges of a Data Governance.
6 CONCLUSIONS
Many organizations are on the path to becoming data-
driven. It turns out that there are a series of challenges
that must be overcome in how they manage their data
so that they can enjoy the benefits that data orientation
offers.
The main challenges to be overcome are related
to data culture and the culture of the organization it-
self. Management support will be fundamental in all
actions involved in implementing a DG program.
The perception of value and benefits that data has
and that its governance offers enhances the creation
of this data-driven culture.
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182

Management support and the perception of value
/ benefits will have a direct impact on employee en-
gagement in all actions necessary to implement data
governance and culture.
Carrying out training, awareness, and engage-
ment of all employees in the concepts and activi-
ties involved with data governance, the policies, the
procedures, and methodologies necessary to perform
all necessary actions will facilitate the perception of
value / benefits for everyone in the organization, facil-
itating the allocating of financial resources and human
needed.
The research conducted with professionals al-
lowed us to obtain details related to the challenges in
implementing DG. In addition to the cultural and or-
ganizational challenges discussed in this paper, there
is information related to data, organizational struc-
ture, technology support, policies and DG implemen-
tation projects that will be explored in other papers.
Many of the results presented here were used to
validated the specification of a guide with practical
and coordinated actions that help organizations in the
implementation of DG, overcoming the main chal-
lenges, evolving in their maturity and, thus, creating
a culture to obtain the desired benefits to be a data-
driven organization. This guide will be presented in
another article as soon as the research is completed.
Implementing the actions specified in this guide in
some organizations will allow us to assess how much
the implementation of DG contributes to creating a
data-driven culture and, consequently, making these
organizations data-driven in contrast to organizations
that have not implemented DG.
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APPENDIX
Table 1: Web form used at the First Stage of research.
Begin of the Table 1
Question Alternatives
1 OBJECTIVES
2 IDENTIFICATION OF THE RESEARCH PARTICIPANT
2.1 Do you have experience in DG?
Yes
No
2.2
If you have experience in DG, indicate the
approximate time.
Less than 01 year
01 to 02 years
03 to 04 years
More than 05 years
3 ORGANIZATION IDENTIFICATION
3.1 Size
Small
Medium
Large
3.2 Market segment
Agriculture
Consulting
Education
Financial
Government
Industry
Logistics
Health
Insurance
Services
Social
Technology
Telecommunications
Transportation
Other
4 STAGE IN DATA GOVERNANCE
4.1
How would you classify the organization’s
stage of DG?
Planning
Under implementation
Implemented
Not planning to implement
4.2
How long has the organization been
implementing DG?
Not applicable
06 months
01 year
02 years
More than 02 years
4.3
What are the main factors that motivate or
motivated the organization to implement
DG?
Assist in decision-making
Improve data quality
Gain competitive advantage
Reduce data management costs
Comply with legislation/regulations
Increase data security
Reduce data volume
Improve access to data
Other
5 DATA INVENTORY (DI)
5.1
Regarding the organization’s carrying out
of DI?
Performed
Is currently being performed
Intends to perform
Will not perform
5.2
How important is it to conduct a DI for
DG?
Not at all important
Slightly important
Moderately important
Very important
Extremely important
5.3
What level of priority should be given to
DI?
No priority
Low priority
Neutral
High priority
Maximum priority
5.4
What is the level of complexity for
performing DI?
No complexity
Low complexity
Moderate complexity
High complexity
Extreme complexity
5.5
List the main challenges that prevent the
organization from carrying out the DI
5.6
List the main challenges faced by the
organization when carrying out the DI
6 PROCESS MAPPING (PM)
6.1
Regarding the organization’s carrying out
of PM?
Performed
Is currently being performed
Intends to perform
Will not perform
Continuation of the Table 1
Question Alternatives
6.2 How important is it for DG to PM?
Not at all important
Slightly important
Moderately important
Very important
Extremely important
6.3
What level of priority should be given to
PM?
No priority
Low priority
Neutral
High priority
Maximum priority
6.4
What is the level of complexity involved
in PM?
No complexity
Low complexity
Moderate complexity
High complexity
Extreme complexity
6.5
List the main challenges that prevent the
organization from carrying out PM
6.6
List the main challenges faced by the
organization when carrying out PM
7 REGULATION SURVEY (RS)
7.1
Regarding the organization’s carrying out
of RS?
Performed
Is currently being performed
Intends to perform
Will not perform
7.2
How important is it to conduct RS for
DG?
Not at all important
Slightly important
Moderately important
Very important
Extremely important
7.3
What level of priority should be given to
the RS?
No priority
Low priority
Neutral
High priority
Maximum priority
7.4
What is the level of complexity in
conducting a RS?
No complexity
Low complexity
Moderate complexity
High complexity
Extreme complexity
7.5
List the main challenges that prevent the
organization from conducting a RS
7.6
List the main challenges faced by the
organization when conducting a RS
8 INFRASTRUCTURE MAPPING (IM)
8.1
Regarding the organization’s carrying out
of IM?
Performed
Is currently being performed
Intends to perform
Will not perform
8.2
How important is it to perform IM for
DG?
Not at all important
Slightly important
Moderately important
Very important
Extremely important
8.3
What level of priority should be given to
IM?
No priority
Low priority
Neutral
High priority
Maximum priority
8.4
What is the level of complexity in
performing IM?
No complexity
Low complexity
Moderate complexity
High complexity
Extreme complexity
8.5
List the main challenges that prevent the
organization from performing IM
8.6
List the main challenges faced by the
organization when performing IM
9 MANAGEMENT COMMITTEE (MC)
9.1
Regarding the definition / performance of
a DG MC in the organization?
Existing and active committee
Committee in the process of being created
Intends to establish a Committee
Does not intend to establish a Committee
9.2 How important is it to establish a DG MC?
Not at all important
Slightly important
Moderately important
Very important
Extremely important
9.3
What level of priority should be given to
the creation of a DG MC?
No priority
Low priority
Neutral
High priority
Maximum priority
9.4
What is the level of complexity in defining
a DG MC?
No complexity
Low complexity
Moderate complexity
High complexity
Extreme complexity
9.5
What is the level of complexity in the
performance of a DG MC?
No complexity
Low complexity
Moderate complexity
High complexity
Extreme complexity
9.6
List the main challenges that prevent the
creation of a DG MC
9.7
List the main challenges encountered in
the performance of the DG MC
KMIS 2024 - 16th International Conference on Knowledge Management and Information Systems
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Continuation of the Table 1
Question Alternatives
10 DATA GOVERNANCE TRAINING
10.1
Regarding the presentation of DG
concepts to the organization’s employees?
Performed
Is currently being performed
Intends to perform
Will not perform
10.2
How important is it to present DG
concepts to the organization’s employees?
Not at all important
Slightly important
Moderately important
Very important
Extremely important
10.3
What level of priority should be given to
presenting DG concepts to the
organization’s employees?
No priority
Low priority
Neutral
High priority
Maximum priority
10.4
What is the level of complexity in training
employees in DG concepts?
No complexity
Low complexity
Moderate complexity
High complexity
Extreme complexity
10.5
List the main challenges that prevent
employees from being trained in DG
concepts
10.5
List the main challenges encountered
during employee training in DG concepts
11 DATA GOVERNANCE POLICY (DGP)
11.1
Regarding the definition of a DGP for the
organization?
Prepared and applied
It is in the development phase
Intends to prepare
Won’t elaborate
11.2
How important is it to develop and
implement a DGP?
Not at all important
Slightly important
Moderately important
Very important
Extremely important
11.3
What level of priority should be given to
developing and implementing a DGP?
No priority
a Low priority
Neutral
High priority
Maximum priority
11.4
What is the level of complexity in
developing a DGP?
No complexity
Low complexity
Moderate complexity
High complexity
Extreme complexity
11.5
What is the level of complexity in
implementing a DGP?
No complexity
Low complexity
Moderate complexity
High complexity
Extreme complexity
11.6
List the main challenges that prevent the
development and implementation of a
DGP
11.7
List the main challenges encountered
during the implementation of the DGP
11.8
Regarding the presentation of DGP to the
organization’s employees?
Performed
Is currently being performed
Intends to perform
Will not perform
11.9
How important is it to present DGP to the
organization’s employees?
Not at all important
Slightly important
Moderately important
Very important
Extremely important
11.10
What level of priority should be given to
presenting DGP to the organization’s
employees?
No priority
Low priority
Neutral
High priority
Maximum priority
11.11
What is the level of complexity in
presenting the DGP to the organization’s
employees?
No complexity
Low complexity
Moderate complexity
High complexity
Extreme complexity
11.12
List the main challenges that prevent the
presentation of the DGP to the
organization’s employees
11.13
List the main challenges encountered
when presenting the DGP to the
organization’s employees
12 PRIVACY AND DATA SECURITY POLICY (PDSP)
12.1
Regarding the definition of a PDSP for the
organization?
Prepared and applied
It is in the development phase
Intends to prepare
Won’t elaborate
12.2
How important is it to develop and
implement a PDSP?
Not at all important
Slightly important
Moderately important
Very important
Extremely important
12.3
What level of priority should be given to
developing and implementing a PDSP?
No priority
Low priority
Neutral
High priority
Maximum priority
Continuation of the Table 1
Question Alternatives
12.4
What is the level of complexity in
developing a PDSP?
No complexity
Low complexity
Moderate complexity
High complexity
Extreme complexity
12.5
What is the level of complexity in
implementing a PDSP?
No complexity
Low complexity
Moderate complexity
High complexity
Extreme complexity
12.6
List the main challenges that prevent the
development and implementation of a
PDSP
12.7
List the main challenges encountered
during the implementation of the PDSP
12.8
Regarding the presentation of PDSP to the
organization’s employees?
Performed
Is currently being performed
Intends to perform
Will not perform
12.9
How important is it to present the PDSP to
the organization’s employees?
Not at all important
Slightly important
Moderately important
Very important
Extremely important
12.10
What level of priority should be given to
presenting the PDSP to the organization’s
employees?
No priority
Low priority
Neutral
High priority
Maximum priority
12.11
What is the level of complexity in
presenting the PDSP to the organization’s
employees?
No complexity
Low complexity
Moderate complexity
High complexity
Extreme complexity
12.12
List the main challenges that prevent the
presentation of the PDSP to the
organization’s employees
12.13
List the main challenges encountered
when presenting the PDSP to the
organization’s employees
13 DATA QUALITY POLICY (DQP)
13.1
Regarding the definition of a DQP for the
organization?
Prepared and applied
It is in the development phase
Intends to prepare
Won’t elaborate
13.2
How important is it to develop and
implement a DQP?
Not at all important
Slightly important
Moderately important
Very important
Extremely important
13.3
What level of priority should be given to
developing and implementing a DQP?
No priority
Low priority
Neutral
High priority
Maximum priority
13.4
What is the level of complexity in
developing a DQP?
No complexity
Low complexity
Moderate complexity
High complexity
Extreme complexity
13.5
What is the level of complexity in
implementing a DQP?
No complexity
Low complexity
Moderate complexity
High complexity
Extreme complexity
13.6
List the main challenges that prevent the
development and implementation of a
DQP
13.7
List the main challenges encountered
during the implementation of the DQP
13.8
Regarding the presentation of DQP to the
organization’s employees?
Performed
Is currently being performed
Intends to perform
Will not perform
13.9
How important is it to present DQP to the
organization’s employees?
Not at all important
Slightly important
Moderately important
Very important
Extremely important
13.10
What level of priority should be given to
presenting DQP to the organization’s
employees?
No priority
Low priority
Neutral
High priority
Maximum priority
13.11
What is the level of complexity in
presenting the DQP to the organization’s
employees?
No complexity
Low complexity
Moderate complexity
High complexity
Extreme complexity
13.12
List the main challenges that prevent the
presentation of the DQP to the
organization’s employees
13.13
List the main challenges encountered
when presenting the DQP to the
organization’s employees
End of the Table 1
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185

Table 2: Web form used at the Second Stage of research.
Question Alternatives to be ranked
I ACTIONS RELATED TO INVENTORY / INFORMATION MAPPING
01
How would you rate the
IMPLEMENTATION of these Inventories
/ Information mappings?
- Data Inventory
- Process Mapping
- Regulatory Survey
- Infrastructure Mapping
02
How would you rate these challenges that
PREVENT the PERFORMANCE of
Inventory / Information Mapping?
- Planning / prioritization
- Diversity of technologies / environments systems
- Support of tools
- Existing / updated documentation
- Employee engagement
- Culture / knowledge / training
- Perception of value / benefits
- Management support
- Well-defined / clear strategy
03
How would you rate these challenges that
IMPACT the EXECUTION of Inventory /
Information Mapping?
- Planning / prioritization
- Diversity of technologies / environments systems
- Support of tools
- Existing / updated documentation
- Employee engagement
- Culture / knowledge / training
- Perception of value / benefits
- Management support
- Well-defined / clear strategy
II ACTIONS RELATED TO POLICIES
04
How would you rate the PREPARATION
of these Policies?
- Data Governance Policy
- Data Privacy and Security Policy
- Data Quality Policy
05
How would you rate these challenges that
PREVENT the DEVELOPMENT of
Policies?
- Planning / prioritization
- Diversity of technologies / environments systems
- Support of tools
- Employee engagement
- Culture / knowledge / training
- Perception of value / benefits
- Management support
- Well-defined / clear policies
- Well-defined / clear processes
06
How would you rate these challenges that
IMPACT on POLICY MAKING?
- Planning / prioritization
- Diversity of technologies / environments systems
- Support of tools
- Employee engagement
- Culture / knowledge / training
- Perception of value / benefits
- Management support
- Well-defined / clear policies
- Well-defined / clear processes
07
How would you rate the
IMPLEMENTATION of these Policies?
- Data Governance Policy
- Data Privacy and Security Policy
- Data Quality Policy
08
How would you rate these challenges that
IMPACT the IMPLEMENTATION of
Policies?
- Planning / prioritization
- Diversity of technologies / environments systems
- Support of tools
- Employee engagement
- Culture / knowledge / training
- Perception of value / benefits
- Management support
- Well-defined / clear policies
- Well-defined / clear processes
III ACTIONS RELATED TO TRAINING / TRAINING / PRESENTATION
09
How would you rate the PRESENTATION
of these Training / Presentations?
- Data Governance Concepts
- Data Governance Policy
- Data Privacy and Security Policy
- Data Quality Policy
10
How would you rate these challenges that
PREVENT the PRESENTATION of
Training / Presentations?
- Planning / prioritization
- Clear / defined policies
- Alignment / communication
- Employee engagement
- Culture / knowledge / training
- Perception of value / benefits
- Management support
- Well-defined / clear strategy
11
How would you rate these challenges that
IMPACT the PRESENTATION of
Training / Presentations?
- Planning / prioritization
- Clear / defined policies
- Alignment / communication
- Employee engagement
- Culture / knowledge / training
- Perception of value / benefits
- Management support
- Well-defined / clear strategy
IV ACTIONS RELATED TO THE MANAGEMENT COMMITTEE
12
How would you rate these challenges that
PREVENT the CREATION of a
Management Committee?
- Planning / prioritization
- Clear / defined policies
- Employee engagement
- Culture / knowledge / training
- Perception of value / benefits
- Management support
- Well-defined / clear strategy
13
How would you rate these challenges that
IMPACT the PERFORMANCE of the
Management Committee?
- Planning / prioritization
- Clear / defined policies
- Employee engagement
- Culture / knowledge / training
- Perception of value / benefits
- Management support
- Well-defined / clear strategy
V GROUP OF ACTIONS
14
How would you rate the
IMPLEMENTATION of these Group of
Actions?
- Carrying out the Inventory / Mapping informa-
tion
- Policy Development
- Implementation of elaborated Policies
- Carrying out the Training / Training / Presenta-
tion
- Creation of a Management Committee
- Performance of established Management Com-
mittee
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