Challenges to Implementing Effective Data Governance: A Literature

Review

Carlos Alberto Bassi

1 a

and Solange Nice Alves-Souza

2 b

Information System Post-graduation Program (PPgSI), Escola de Artes, Ci

encias e Humanidades,

Universidade de S

ao Paulo, S

ao Paulo, Brazil

Departamento de Engenharia de Computac¸

ao e Sistemas Digitais, Escola Polit

ecnica,

Universidade de S

ao Paulo, S

ao Paulo, Brazil

Keywords:

Data Governance, Use Cases, Data Governance Projects, Data Governance Challenges.

Abstract:

Implementing an efﬁcient data governance program implies overcoming a series of identiﬁed challenges. Re-

viewing the scope of publications of case studies (CS) of Data Governance (DG) projects allowed identifying

diverse types of challenges directly related to the data and/or the organization while others suffer from external

inﬂuences. The market segment and the country of the operation can also inﬂuence DG projects. Investigating

these challenges to ﬁnd ways to face them can contribute to the successful implementation of a DG program.

From 201 papers initially collected in the process of the literature review, 44 publications presented CS that

implement DG projects. As a result, we identify the most impactful challenges for implementation of DG

projects that should be prioritized.

1 INTRODUCTION

Organizations face challenges and problems in imple-

menting a comprehensive and efﬁcient Data Gover-

nance (DG) program. In many cases, there is a lack

of knowledge on the part of the professionals involved

in conducting DG implementation projects regarding

which activities are necessary, who should be respon-

sible for carrying out these activities, what the rela-

tionship and dependence are between these activities,

as well as the impacts generated by not performing

such activities properly.

DG is the comprehensive management of usabil-

ity, availability, data privacy and security (DPS) and

data quality (DQ) inside and outside the organization

(Abraham et al., 2019). It includes establishing poli-

cies, standards, processes, and structures to ensure

the correct use and effective protection of data (Al-

Dossari and Sumaili, 2021). DG requires policy spec-

iﬁcation dynamics that can deal with problems related

to the collection, storage, processing, sharing and use,

reuse, and disposal of data throughout its life cycle

(Filgueiras and Lui, 2022).

According to a McKinsey report, companies

spend an average of 30% of their time on tasks with-

https://orcid.org/0009-0001-2524-7478

https://orcid.org/0000-0002-6112-3536

out added value due to poor DQ and availability

(Zhang et al., 2022). Several studies have found,

among other reasons, that many of the bad decisions

are mainly due to the poor quality of the information

generated from dirty, erroneous and incomplete data.

This has led important companies worldwide to lose

many thousands of dollars by managing information

of low quality in their organization (Castillo et al.,

2017).

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).

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).

Bassi, C. and Alves-Souza, S.

Challenges to Implementing Effective Data Governance: A Literature Review.

DOI: 10.5220/0012185900003598

In Proceedings of the 15th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2023) - Volume 3: KMIS, pages 17-28

ISBN: 978-989-758-671-2; ISSN: 2184-3228

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 compre-

hensive review of the scientiﬁc and practice-oriented

literature shows a lack of understanding the activi-

ties required for introducing a DG program (Alhassan

et al., 2019b).

Knowing in advance the main challenges and their

impacts can allow organizations to plan and allocate

the necessary resources and efforts to implement DG,

and consequently, reap the beneﬁts of good gover-

nance.

This research consists of a literature review to

identify the main challenges faced when implement-

ing a DG project, investigating the impacts generated

by these challenges and the possible relationship of

the challenges with the market segment or country in

which the organization operates.

2 RESEARCH METHODOLOGY

The challenges that organizations face and their im-

pacts on the implementation of DG were identiﬁed by

adopting a Scoping Review (SR) of publications that

present case studies (CS) on DG projects. As part of

the SR, the challenges faced, and their impacts are an-

alyzed and consolidated.

The literature SR was performed according to the

procedures deﬁned by (Kitchenham, 2004), which

employs the following steps: Planning, Research Ex-

ecution and Publication Analysis.

Unlike a Systematic Literature Review (SLR),

which includes clinical cases, a SR follows the

systematization of an SLR, but all the analyses and

conclusions refer to information extracted solely

from articles selected according to the criteria and

questions established for the research.

Planning / Execution - At Planning stage, the di-

rection of the research was deﬁned, as well as the

research questions, parameters and search bases be-

ing formulated, besides the inclusion and exclusion

criteria for publications (Turnbull et al., 2023). The

research questions addressed were:

1. What are the challenges faced by an organization

when implementing a DG project?

2. How do these challenges impact data and organi-

zations?

The initial exploratory research allowed identify-

ing adequate parameters for selecting publications:

Search databases: Scopus, Web of Science and IEEE

Xplore databases; Period of publications: 2016 to

2022, to analyze the latest case studies; Language:

publications in English only and, Title of publica-

tion and Keywords: should contain the expressions

“Data Governance” and “Case Study” or, “Data Gov-

ernance” and “Project Management”.

At the Execution stage 201 publications were

selected. Initially, 40 duplicated publications were

excluded. Later, after reading the title, abstract,

conclusion and, when necessary, a superﬁcial reading

of its content, we rejected publications that did not

allow answering the 2 research questions formulated

or that did not describe CS in the implementation

of DG projects. As a result, 53 publications were

selected for full reading and a more detailed analysis.

Analysis - At this stage, 9 publications obtained from

exploratory research or citations that met the speciﬁed

research parameters were added and the following

were performed for each selected publications: clas-

siﬁcation of the CS described and collection of rele-

vant information to support addressing the 2 research

questions. From these, 44 publications regarded CS

on organizations that implemented DG projects of the

most varied scopes.

Table 1 presents the evaluation of the publications

where the following topics were used for classiﬁca-

tion: Data Governance – addresses aspects of DG,

Case Study – presents/analyzes a DG implementation

CS, Systematic Review – performs a systematic re-

view of the literature, Framework – presents/analyzes

a DG framework, Maturity Model – analyzes the level

of maturity in DG, Metrics – displays/analyzes met-

rics, Data Quality – addresses aspects of DQ, Gov-

ernance – addresses aspects of corporate governance,

Analysis – addresses aspects of data analysis and Se-

curity - addresses aspects of data/information secu-

rity.

The year of publication was considered to ensure

that the challenges to be identiﬁed represented the

most current moment. Figure 1 presents the distri-

bution of the CS by year. 75% of the publications

occurred in the last 04 years indicating the current

and growing interest in the implementation of DG. A

lower number of articles in 2020 may reﬂect the peak

of the COVID-19 pandemic.

The market segment could impose or bring greater

complexity to DG implementation. Some publica-

tions contained CS in more than one market segment,

as occurred in the CS by (Lis and Otto, 2020) that

evaluated an engineering and construction industry,

a telecommunication services company, and a digital

service provider. Figure 2 presents the distribution of

CS by market segment. The Government and Health-

care segments stand out in the implementation of DG

KMIS 2023 - 15th International Conference on Knowledge Management and Information Systems

Table 1: Evaluation of the publications selected.

Publication DG CS SR FR MM MT DQ GV AN SC EV

01 (Li et al., 2019) X X A

02 (Xiang, 2021) X X R

03 (Varlina, 2017) X X R

04 (Fylan and Fylan, 2021) X X A

05 (Joshi et al., 2021) X X A

06 (Qin et al., 2020) X X A

07 (Gul and Ahsan, 2019) X X R

08 (Park et al., 2019) X X A

09 (Jiang et al., 2022) X X A

10 (Kawtrakul et al., 2021) X X A

11 (Bhardwaj and Singh, 2017) X X X R

12 (Kalkman et al., 2019) X X A

13 (Jiya, 2021) X X A

14 (Leiber, 2022) X X A

15 (Yulﬁtri, 2016) X X A

16 (Earley, 2016) X X A

17 (Kurniawan et al., 2019) X X X A

18 (Pratiwi and Ruldeviyani, 2021) X X A

19 (Wibowo and Sandikapura, 2019) X X A

20 (Saltz et al., 2018) X R

21 (Weng and Hirata, 2022) X R

22 (Prado et al., 2021) X X X A

23 (Garcia et al., 2022) X X A

24 (Permana and Suroso, 2018) X X X A

25 (Meyer et al., 2020) X X A

26 (Carretero et al., 2016) X X A

27 (Robinson et al., 2021) X X A

28 (Fusi et al., 2018) X X A

29 (Jones et al., 2020) X X A

30 (Khan and Johnson, 2020) X X X A

31 (Lis and Otto, 2020) X X A

32 (Benfeldt et al., 2020) X X A

33 (Cerrillo-Mart

ınez and Casades

us-De-mingo, 2021) X X A

34 (Murtagh et al., 2018) X X A

35 (Ryyn

anen and Harisalo, 2018) X X R

36 (Zhang et al., 2022) X X X A

37 (Putro et al., 2016) X X A

38 (Krumay and Rueckel, 2020) X X X A

39 (Mukhrizal et al., 2019) X X A

40 (Lin

aker and Runeson, 2022) X X R

41 (Alhassan et al., 2019b) X X A

42 (Timotijevic et al., 2022) X X A

43 (Wildenauer and Basl, 2021) X X A

44 (Alhassan et al., 2019a) X X A

45 (Whittard et al., 2022) X X X A

46 (Dutta et al., 2022) X X X R

47 (Lis et al., 2022) X X A

48 (Carvalho et al., 2021) X X R

49 (Keller et al., 2021) X X R

50 (Baijens et al., 2022) X X X R

51 (Vaghjiani et al., 2017) X X R

52 (Saputra et al., 2018) X X X X A

53 (Aisyah and Ruldeviyani, 2018) X X X A

54 (Castillo et al., 2017) * X X X A

55 (Riggins and Klamm, 2017) * X X R

56 (Al-Dossari and Sumaili, 2021) * X X X A

57 (Juddoo et al., 2018) * X X R

58 (Zygmuntowski et al., 2021) * X X A

59 (Filgueiras and Lui, 2022) * X X A

60 (Jim and Chang, 2018) ** X X A

61 (Kim and Cho, 2018) ** X X X A

62 (Alhassan et al., 2018) ** X X A

Caption

Columns: ( DG ) - Data Governance, ( CS ) - Case Study, ( SR ) - Systematic Review, ( FR ) - DG Framework,

( MM ) - DG Maturity Model, ( MT ) - Metrics, ( DQ ) - Data Quality, ( GV ) - Corporate Governance,

( AN ) - Analytic, ( SC ) – Security, ( EV ) - Evaluation

Evaluation of publication: ( A ) – Publication accepted, ( R ) – Publication rejected

Source: ( * ) – Exploratory Research, ( ** ) – Citations

Challenges to Implementing Effective Data Governance: A Literature Review

Figure 1: Year of publication of the case studies.

with approximately 40% of the cases. These market

segments are highly regulated, data are shared among

various organizations and considered to be of public

interest, and there is a great concern on the part of cit-

izens and patients regarding the security and privacy

of their personal data. Industry and Agricultural seg-

ments are also affected by regulations and in recent

years citizens have become more concerned with the

origin of what they have been consuming.

Figure 2: Case studies market segment.

3 ANALYSIS OF CHALLENGES

Challenges are related to everything that impacts or-

ganizations when pursuing their strategic objectives,

e.g., decision-making, obtaining value, the provision

of products/services or even regarding their image.

These cause direct impacts when not treated or man-

aged, by absence, non-conduction, existence, non-

existence, precariousness, problems, or maintenance.

The challenges found in the CS were grouped into

11 categories, and it evaluated the impacts generated

in relation to the points: Governance – DG, Quality

– DQ, Use – data use, Collaboration – data collabo-

ration, Security – DPS and Organization – about the

organization.

Table 2 presents the challenges referenced in the

publications related for each category and the Figure

3 presents the occurrences in the CS.

Data - deﬁnitions of data emphasize its role in rep-

resenting facts about the world. Businesses use data

to understand their customers, create new products

and services, and improve operational efﬁciency by

cutting costs and controlling risks (Data Management

Figure 3: Occurrence in the case studies.

Association, 2017).

Figure 4 presents the number of occurrences of the

challenges Data regarding the impacts generated. The

existence of data silos, the lack of identiﬁcation of

responsibility for data and a clear view of master data

impact the implementation of DG. Low DQ will have

a signiﬁcant impact as it affects decision-making.

Figure 4: Impacts of challenges Data.

Data Silos: data stored in different places, with repli-

cation in different individual repositories that are not

interconnected, or integrated, and are often inaccessi-

ble to many in the organization.

Data Quality: data need to be accurate, complete,

consistent, reliable, current, and relevant so that they

can serve a certain purpose and are essential to en-

sure assertive business decision-making and valuable

information. Ensuring that the data were not inten-

tionally or unintentionally manipulated, as well as its

latency, the time elapsed between the generation or

collection of the data and its availability for effective

use, impact decision-making.

Data Sanitization: process to identify, correct and re-

move errors, replications and inconsistencies in the

data processed for improving its quality.

Data Standardization: process that seeks to transform

data to ensure their compatibility, consistency, use

and integration between different applications using

rules and transformations in their format, nomencla-

ture, abbreviations, and units of measurement.

Volume: number of data collected, stored, and pro-

cessed that must be managed in a certain period. This

volume has a direct impact on the organization man-

agement and on the costs involved in processing, stor-

age, security, and distribution.

KMIS 2023 - 15th International Conference on Knowledge Management and Information Systems

Table 2: Reference of the category of challenges in the pub-

lications.

Publi-

cation * DT SC PL PC IF OZ CT FM PJ RG EE

01 X

04 X X

05 X

06 X X

08 X

09 X

10 X

12 X X

18 X

19 X

22 X X X

23 X

24 X X

25 X X X X X X

27 X

28 X X X

31 X X X X X

32 X X X X X X X

33 X X

34 X X

36 X X X

38 X X X X

41 X

42 X X X

45 X X X X

47 X X X X

53 X X X X

54 X

56 X

58 X X

59 X X X

60 X

61 X X X X X X

Number

of occu- 20 9 4 4 4 10 8 2 10 9 2

rences

Caption

( DT ) - Data: data

( SC ) - Security: data security

( PL ) - Policies: speciﬁcation or application of policies for

implementing DG

( PC ) - Process: work processes necessary for implementing DG

( IF ) - Infrastructure: existing/necessary infrastructure to process the data

( OZ ) - Organizational: structure of the organization or the

organization itself

( CT ) - Cultural: cultural or behavioral aspects in relation to data

or its governance

( FM ) - Framework: adoption of frameworks for implementing DG

( PJ ) - Project: conducting the DG implementation project

( RG ) - Regulations: laws or regulations existing in the

country/segment of operation

( EE ) - External Environment: environment in which the organization

is inserted that escape the context of previous challenges

( * ) - The publication number is the same as the publication

number shown in Table 1

Life Cycle: describes the various stages that data go

through since their creation, collection, validation,

processing, analysis, storage, distribution up to their

disposal. Knowledge of this cycle is relevant for bet-

ter management, meeting the organization’s objec-

tives and deﬁning the most appropriate policies at

each stage.

Metadata: relevant information that provides con-

text and meaning to the data, helping to understand

their origin, format, content, relationships, and usage.

They are used to manage, locate, classify, integrate,

and share data throughout their life cycle.

Master Data: set of data identiﬁed as a unique and

accurate reference of essential and critical informa-

tion for an organization. Data are centrally managed,

which helps in ensuring integrity, accuracy and con-

sistency throughout systems and applications.

Data Collection: process of gathering data from var-

ious sources and in different formats for processing,

treatment, analysis and sharing, to guarantee that the

organization has accurate and reliable information to

support its operations and decision-making.

Traceability: ability to follow the history of data from

its origin to its current state, by recording and moni-

toring related events and activities. It is essential to

identify the root cause of a problem and take the nec-

essary actions to solve it, in addition to knowing what

processing and treatments were performed.

Data Integration: process for combining data from

multiple sources to create a single, uniﬁed, consistent

view for access throughout the organization. Data

integration can be accomplished with various tech-

niques and the sources can be internal and/or external

to the organization.

Data Inventory: detailed listing of all the organization

data, including location, format, owner, usage, qual-

ity, and other relevant information. Inventory enables

to understand their data assets and to manage them

effectively.

Responsibility / Interest: obligation of a person or

group of persons to ensure the quality, security,

privacy, compliance, monitoring, and proper use of

the data under their responsibility.

Security - set of practices and measures that aim to

guarantee the protection of information against unau-

thorized access or disclosure, alteration, destruction,

or theft, involving the implementation of technical,

physical, and administrative security measures.

Figure 5 presents the number of occurrences of the

challenges Security regarding the impacts generated.

Conﬁdentiality and privacy, as well as the need to ob-

tain consent, have a major impact on the use and pro-

cessing of data considering the security aspect. DG

will be impacted in countries where there is great con-

cern or regulation about data privacy and conﬁdential-

ity, such as the European Union and the United King-

dom.

Consent: process for obtaining an individual’s ex-

plicit permission for his/her personal data to be col-

lected, processed, and stored by the organization or

by third parties. This person must be informed how,

when, why and by whom their personal data will be

used and, whether he/she agree with how they will be

Challenges to Implementing Effective Data Governance: A Literature Review

Figure 5: Impacts of challenges Security.

treated.

Conﬁdentiality / Privacy: people’s right to decide

what personal data and who may access these. Or-

ganizations are responsible for ensuring the privacy

and conﬁdentiality of the personal data they manage

and must implement the necessary measures to pro-

tect from unauthorized access, theft, loss, or unautho-

rized sharing.

Sharing: process of transferring data between two

or more people or organizations for the most diverse

reasons, such as collaboration, service execution,

decision making, analysis or research. Sharing must

be performed in a safe, transparent, ethical manner

and in accordance with applicable authorizations and

laws.

Policies - set of principles, guidelines, rules, proce-

dures, standards, and practices that establish the ex-

pected behavior and responsibilities that professionals

in an organization must observe (Wies, 1994).

Figure 6 presents the number of occurrences of the

challenges Policies regarding the impacts generated.

The need to establish DG policies and DPS policies

are impacting factors when DG is implemented. The

Government segment has been heavily impacted by

the need to specify DG, DPS and DQ management

policies.

Figure 6: Impacts of challenges Policies.

Data Governance: the DG policy must deal with as-

pects such as identifying owners, deﬁning responsi-

bilities, managing the life cycle, standardizing collec-

tion processes, storage, distribution to ensure the efﬁ-

cient use of data.

Data Privacy and Security: the privacy policy deﬁnes

the rules for how the collection, use, storage and shar-

ing of data should occur in compliance with the reg-

ulations and rights of data subjects. The security pol-

icy establishes the requirements to guarantee the con-

ﬁdentiality, integrity and availability of data and in-

cludes technical, organizational and risk management

measures to prevent, detect and respond to threats and

incidents.

Data Quality Management: set of guidelines, objec-

tives, and standards to guarantee DQ at all stages of its

life cycle, deﬁning responsibilities, procedures, and

criteria to continuously evaluate and guarantee this

quality.

Conﬂicts of Interest: occurs when an individual or

organization has conﬂicting interests regarding the

data it uses or manages.

Process - set of structured and interrelated activities

that transform inputs into outputs, aiming to achieve

a speciﬁc objective. Processes can be sequential, par-

allel, or iterative, and can involve people, technol-

ogy, information, and other resources (Association of

Business Process Management Professionals Interna-

tional, 2019).

Data Quality Control and Monitoring: processes for

verifying, validating, and continuously reviewing data

to ensure their accuracy, integrity, consistency, com-

pliance, and ensure that they are reliable in decision-

making.

Degree of Harmonization: alignment and standard-

ization between processes to ensure consistency and

efﬁciency in conducting the organization’s business

activities.

Variations in Processes: different ways of performing

activities in a business process caused by the most

varied factors such as personal, cultural, or regulatory.

Infrastructure - set of capabilities and technologies

needed to store, manage, and protect data. This in-

cludes the hardware, software, networks, and services

necessary to ensure data is securely and efﬁciently

stored and managed.

Compatibility Across Platforms and Standards: en-

suring interoperability, integration and data security

in different software platforms, systems and devices

following the same standards and protocols.

Use of External Infrastructure: use of infrastructure

external to the organization.

Alignment with Application Architecture: ensuring

that data architecture and applications are designed,

developed, and implemented consistently with the

technology and systems infrastructure.

Fragmented Architecture with Legacy Systems: data

are stored in different Information Technology (IT)

systems that may have been developed with different

KMIS 2023 - 15th International Conference on Knowledge Management and Information Systems

technologies.

Standardized Big Data Systems: use of systems

aimed at handling large volumes of data, ensuring

that all data are collected, stored, processed, and ana-

lyzed according to the same principles and standards.

Organizational - it is about organizing companies in

terms of activities and resources needed to fulﬁll their

strategic objectives.

Figure 7 presents the number of occurrences of

the challenges Organizational regarding the impacts

generated. DG is impacted when there are localized

practices, non-alignment/collaboration between units

and, due to the non-existence of a unit responsible for

conducting DG. Data collaboration is affected when

there is a search for competitive advantages.

Figure 7: Impacts of challenges Organizational.

Unit Responsible for Data Governance: unit with at-

tribution and responsibility for managing and moni-

toring DG. It must be multidisciplinary, that is, com-

posed of professionals from different areas, such as

IT, legal, compliance, risk, and business.

Professional Turnover: professionals who leave the

organization and are replaced by new ones generate

impacts on the retention of business knowledge and

on the need for training in practices, policies, proce-

dures, and processes.

Size: size and capacity of a company in terms of ﬁ-

nancial, organizational, human, and technological re-

sources.

Growth Speed: rate at which an organization expands

its revenue, size, proﬁt, or number of employees. The

pace of growth can be affected by factors such as com-

petition, the market, investments in technology, hu-

man resources, and culture.

Competitive Edge: any characteristic, resource, or

strategy that a company has and that differentiates it

from its competitors, allowing it to obtain a competi-

tive advantage that can be based on the quality of the

product or service, technological innovation, opera-

tional efﬁciency, and customer service.

Expectation of Short-Term Results: goals and objec-

tives related to sales, proﬁt, customer satisfaction, or

market growth that an organization expects to achieve

in a short period of time.

Alignment / Collaboration Between Organizational

Units: ability of different units to work together to-

wards a common goal, sharing information and re-

sources efﬁciently and effectively to meet deﬁned

goals and objectives.

Local Practices: refers to the use of speciﬁc pro-

cedures and policies by a given unit, regardless of

whether they are compatible with practices adopted

globally by the organization. This may arise from

cultural, regulatory or market differences, making it

necessary to adapt policies and procedures.

Cultural - set of values, beliefs and behaviors that

deﬁne how the organization conducts its business and

how it treats its customers and partners.

Figure 8 presents the number of occurrences of the

challenges Cultural regarding the impacts generated.

The lack of perception of the value of data as an as-

set and the lack of understanding and training of those

involved in the concepts, technologies and best prac-

tices will signiﬁcantly impact the implementation of

DG in organizations from different market segments

and countries.

Figure 8: Impacts of challenges Cultural.

Perception of the Value of Data as an Asset: recog-

nition that data are a valuable and strategic resource,

capable of generating competitive advantage and of

adding value to the business.

Understanding / Training in Concepts and Technolo-

gies Involved in Data Governance: mastering and un-

derstanding concepts, technologies, methodologies,

and practices involving DG.

Understanding / Training on Security and Privacy:

mastering and understanding the concepts, technolo-

gies, methodologies, and practices involving DPS.

Knowledge of Frameworks and Best Practices: mas-

tering and understanding the concepts, methodolo-

gies, and practices of models for implementing DG.

Collaboration / Information Sharing with Com-

munities / Third Parties: practices involved in

the exchange of information and data between com-

panies, government institutions or other communities.

Framework - it is developed at different levels of ab-

straction to provide a range or perspectives on how to

Challenges to Implementing Effective Data Governance: A Literature Review

approach data management. These perspectives pro-

vide insight that can be used to clarify strategy, de-

velop road maps, organize teams, and align functions

(Data Management Association, 2017).

Adapted to the Needs of the Organization: use of a

DG framework that can meet the speciﬁc needs of

the organization, considering its business objectives,

structure, culture, and size.

Capacity to Promote Data Sharing: the DG

framework must be able to assist in data sharing,

establishing rules, policies, requirements, and pro-

cesses in a safe and effective way.

Project - a temporary endeavor undertaken to create

a unique product, service, or result. The temporary

nature of projects indicates the beginning and end of

project work or a phase of project work (Project Man-

agement Institute, 2021).

Figure 9 presents the number of occurrences of the

challenges Project regarding the impacts generated.

The success of the DG implementation project is im-

pacted by factors, such as the lack of engagement and

commitment by all those involved, the non-existence

or poor performance of a management committee, not

carrying out a project approach for the entire organi-

zation, as well as a lack of understanding the activities

involved in running a DG program.

Figure 9: Impacts of challenges Project.

Organization-Wide Approach: adoption of an ap-

proach that seeks to involve all units and profession-

als to guarantee success in the implementation of a

project.

Existing and Active Management Committee: impor-

tance of having an active management committee to

ensure that DG policies and guidelines are effectively

implemented, monitored, updated, and followed by

all those involved.

Engagement / Commitment and/or Resistance to the

Project / Changes by Those Involved: need to engage

and obtain commitment from all those involved in ad-

dition to mitigating obstacles due to resistance to the

project and organizational changes that may be gen-

erated by the implementation of a project.

Experience / Knowledge of the Resources Involved:

technical and practical knowledge regarding DG con-

cepts, technologies and best practices by the human

resources involved increases the probability of suc-

cessful adoption of DG.

Understanding the Activities Required for a Data

Governance Program: knowledge of the activities

speciﬁed in the DG program and the responsibilities

of each of those involved are critical to the success of

the project.

Perception of the Beneﬁts of Conducting the Project:

depending on how the project is presented, the orga-

nization does not identify the beneﬁts that can be ob-

tained with the implementation of DG.

Limited Resources and Deadlines: the organization

must be aware of the limited resources and deadlines

it has so that it can plan and deﬁne priorities in the

implementation of DG.

Regulations - they are rules established by govern-

ment agencies or regulatory entities that aim to guar-

antee the safety, quality and efﬁciency of the activi-

ties and resources necessary for the fulﬁllment of the

strategic objectives of the companies.

Figure 10 presents the number of occurrences of

the challenges Regulations regarding the impacts gen-

erated. DPS regulations, as well as regulations that

affect the use and processing of data in the public in-

terest and transparency, impact DG. The Government

and Healthcare segments are eventually impacted by

existing regulations on the treatment and use of data.

Countries that have been applying regulations in DPS,

in the public interest and in data processing impact the

implementation of DG.

Figure 10: Impacts of challenges Regulations.

Policies / Regulations on Data Processing: establish

rules, procedures, and responsibilities to ensure that

data processing is performed in an appropriate, legal,

ethical, and secure manner. They must cover aspects

such as data collection, storage, sharing, access, pro-

tection, and disposal, in addition to deﬁning roles and

responsibilities in data processing.

Policies / Rules Of Control / Regulation of the Seg-

ment in Which the Organization Operates: regula-

tions established by regulatory bodies and govern-

ment authorities for the segment in which the orga-

nization operates.

KMIS 2023 - 15th International Conference on Knowledge Management and Information Systems

Public Interest of Data: concept that certain data are

of importance to society as a whole and, therefore,

must be treated to guarantee their availability and ac-

cessibility to all interested parties, highlighting those

data related to public health, national security, envi-

ronment, and education.

Transparency in the Use of Data: the practice of en-

suring that everyone involved understands how data

are collected, processed, and shared.

Privacy and Data Security: regulation aimed at

protecting individuals’ personal and sensitive data,

as well as ensuring that companies that collect, store,

process and share these data do so in an ethical and

legal manner.

External Environment - external factors that can

inﬂuence the performance and activities of the com-

pany. These factors include political, economic, so-

cial, technological, and environmental aspects.

Political and/or Institutional Instability: frequent and

unpredictable changes in the political or institutional

environment of a country/region in the economic, so-

cial, political, or legal spheres that directly affect or-

ganizations.

Political Support: need for political support to deal

with regulatory or legal issues, as well as to overcome

institutional or bureaucratic obstacles that may arise

in a country or market segment.

4 DISCUSSION

The related challenges were those identiﬁed in the

analysis of the data collected from the CS within the

scope of this research. Other challenges may be faced

by organizations depending on a plethora of factors,

from the way in which the project was performed,

the level of maturity in DG that the organization is at,

the applicable laws and regulations, the ﬁnancial and

technological resources available and, the importance

of data to the business.

Most Impacting Challenges - The analysis indicates

that certain challenges are more relevant and more

referenced in the research CS. These challenges gen-

erate different impacts, are present in different market

segments and do not depend on the country.

Some of the most impacting challenges observed

are the existence of data silos (Aisyah and Rulde-

viyani, 2018), the need for DQ, the need to guarantee

DPS, the perception of the value of data being an asset

(Benfeldt et al., 2020), the limitation of resources and

deadlines in conducting a DG implementation project

and the data being a differential of the organization

before its competitors (Lis et al., 2022).

When conducting a DG implementation project,

these challenges should be treated as a priority in

view of the impacts they generate and the beneﬁts

that can be reaped when solved.

Inﬂuence of the Segment - The market segment in

which the organization operates inﬂuences the chal-

lenges to be overcome when implementing a DG

project. In general, these are highly regulated seg-

ments that require transparency in data processing,

need to cover an extensive and dispersed geographic

area, and involve different proﬁles of people with the

most different socioeconomic levels, including Gov-

ernment and Healthcare.

Some of the challenges inﬂuenced by the segment

of activity are the relevance of the DQ treated, the

DPS guarantee, greater engagement (Whittard et al.,

2022), and commitment of all those involved, estab-

lishment of clear DG policies, understanding and/or

training in the concepts and technologies involved in

DG (Krumay and Rueckel, 2020), the attribution of

well-deﬁned responsibilities (Cerrillo-Mart

ınez and

Casades

us-De-mingo, 2021) and the occurrence of

political and/or institutional instabilities.

Data / Organization / Environment - The chal-

lenges faced when conducting DG implementation

projects can be classiﬁed according to the character-

istics of the data and/or organization or the organiza-

tion’s operating environment. Depending on the com-

plexity, some challenges can even be classiﬁed in both

situations.

Challenges Related to Data/Organization - are those

inherent to the characteristics of the data or the orga-

nization itself, such as data silos, DQ, volume, life

cycle, data inventory, conﬁdentiality/privacy, align-

ment with application architecture, staff turnover,

size, speed of growth, perceived value of data as an

asset, understanding / training on concepts and tech-

nologies involved in DG and, limited resources and

deadlines. These challenges are not inﬂuenced exter-

nally by the market segment and should be prioritized

when implementing a DG project, as it depends only

on the organization.

Challenges Related to the Environment - are those

directly inﬂuenced by factors external to the orga-

nization, such as consent, sharing, use of external

infrastructure, policies / regulations in data process-

ing, policies / regulations for control / regulation of

the organization’s segment of activity, public interest

in data and political and/or institutional instabilities.

These challenges are more complex to be dealt with

because the organization often does not have control

Challenges to Implementing Effective Data Governance: A Literature Review

or power to act.

5 CONCLUSIONS

The research performed from a literature SR allowed

identifying the most common challenges faced by or-

ganizations when implementing DG projects. Many

of these challenges are independent of the environ-

ment in which the organization operates and are in-

ﬂuenced by the characteristics of the data or by the

organization itself.

Previous knowledge of these challenges helps or-

ganizations that need 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.

The results of the review presented here will sup-

port the speciﬁcation of a guide with practical and co-

ordinated actions that help organizations in the im-

plementation of DG, overcoming the main challenges

and, thus, evolving in their maturity and obtaining the

desired beneﬁts for an effective governance of their

data. The follow-up of this work will be presented in

another article as soon as the research is completed.

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