Assessing the Impact of Data Governance on Decision Making in

Saudi Arabia

Bashayer A. Alotaibi

, Zahyah H. Alharbi

and Tahani Alqurashi

Management Information Systems Department, King Saud University, Riyadh 12372, Saudi Arabia

Data Science Department, Umm Al-Qura University, Makkah 21421, Saudi Arabia

Keywords: Data Governance, Data Governance Framework, Data Quality, Data Management, Decision Making,

Complexity Management.

Abstract: There is currently a huge amount of data stored by Saudi Arabian organizations that requires work to make it

useful. This has led to the concept of ‘data governance’ as a means of organizing data and managing its use

in organizations. This research evaluates how decision making has been influenced by data governance in

Saudi Arabia. Twelve interviews were conducted on two aspects of data management, data governance and

data analytics, to explore how each approach affects decision making. Both interviewee groups indicated that

these approaches had multiple direct and indirect effects on decision making. The interviewees mostly agreed

that data governance increased confidence and trust in data and improved its quality. They viewed data

governance as being likely to develop a more consistent business terminology and a set of rules and

responsibilities for managing data, and that decision making would be made timelier. Despite the potential

benefits of data governance for decision making, the lack of awareness about its potential makes it difficult

for many Saudi Arabian organizations to benefit from its use. This study provides valuable insights for

businesses considering the implementation of data governance practices to optimize their decision-making

process.

1 INTRODUCTION

Data has become a key resource for organizations, but

technological progress has created a huge variety of

data types. Analysis of this data is necessary to help

organizations remain competitive, but this analysis

requires sophisticated techniques (Bento, Neto, &

Côrte-Real, 2022; Alsaad, 2023). Data governance

(DG) is crucial for organizations’ strategic and

operational decision making (Master & Management,

2007). It is needed to facilitate data flow, store and

utilize information, enhance accountability, and

develop strategies, all of which are dependent on, and

improve, effective decision making (Bento, Neto, &

Côrte-Real, 2022). Organizations are becoming

increasingly aware of the need for the sound DG of

customer information to maintain data’s

confidentiality, coherence, overall quality, and

accessibility. DG is considered by many analysts as a

https://orcid.org/0000-0006-4227-0529

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https://orcid.org/0000-0002-1750-4462

means of improving data quality and increasing its

value to organizations (Otto, 2011; Wende, 2007).

DG is also likely to lead to more reliable and effective

decision making (Janssen et al., 2020). The Kingdom

of Saudi Arabia (KSA) is at the forefront of a new

information age, captured in its National Vision 2030.

The wealth of data that the Kingdom creates and

gathers can be used to foster economic growth and

improve the living standards of its citizens (SDAIA,

2021).

The KSA has recently begun to standardize DG

by implementing a set of protocols for data handling

to enable a central body to make systematic use of the

large volume of data across government departments

(Sdaia.gov.sa, n.d.). DG is important for

organizations and countries but the academic research

highlights that it is still a newly emerging concept and

needs more development for the advantages it offers

114

Alotaibi, B., Alharbi, Z. and Alqurashi, T.

Assessing the Impact of Data Governance on Decision Making in Saudi Arabia.

DOI: 10.5220/0012700600003708

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 9th Inter national Conference on Complexity, Future Information Systems and Risk (COMPLEXIS 2024), pages 114-123

ISBN: 978-989-758-698-9; ISSN: 2184-5034

to be available to decision makers in the KSA (Begg

& Caira, 2012; Wende, 2007).

This paper addresses the need for more research

by providing a systematic assessment of DG and how

it affects decision making in the KSA. It examines

how decision making is influenced by DG according

to those directly involved in DG, as well as those

affected by DG, such as analysts, artificial

intelligence researchers, and other actors who use

data to create products and new ideas. Section 2

discusses the background to DG and the gaps in

research that this paper addresses. Section 3 describes

the methodology, section 4 presents the analysis

results, and section 5 draws conclusions.

2 THEORETICAL

BACKGROUND AND RELATED

LITERATURE

2.1 Data Governance Overview

Currently there is no universally accepted definition

of DG (Benfeldt Nielsen, 2017), and most are simply

based on a particular person’s or organization’s

interests. The Data Governance Institute (n.d.)

defines DG as a set of regulatory principles about the

correct handling and use of data based on established

models. DAMA International (2017) offers a

definition more explicitly about who has authority to

use and manipulate data, and how such usage is

monitored and regulations enforced. DG is concerned

with bringing data use into a formal organizational

structure where policies and other regulatory

standards (privacy protection, collection and storage,

terminology etc.) are followed (Informatica, 2019). It

thereby hopes to make the handling of data principled

and properly managed. DG is frequently used to refer

incorrectly to data management (Otto, 2011).

According to the Data Management Association

(DAMA) (International, 2017), data management “is

the development, execution and supervision of plans,

policies, programs and practices that control, protect,

deliver and enhance the value of data and information

assets.” Data management is primarily concerned

with how data elements are defined, manipulated,

stored, moved about, accessed, and structured. This

positions DG at a higher level than data management,

as it refers to how the latter is regulated and controlled

and its use planned (Al-Ruithe & Benkhelifa, 2017).

DG is closely related to the quality of data and

most organizations give one individual responsibility

for both (Otto, 2011; Pierce, Dismute, & Yonke,

2008). DG and data quality are often discussed

together (Otto, 2011; Weber, Otto, & Österle, 2009;

Wende & Otto, 2007). One of the main goals of DG

is often emphasized as being the improvement of the

quality of data (Otto, 2011; Soares, 2010). In order to

achieve trustworthy decision making, many

organizations employ DG to enable them to control

their data and ensure that its use meets ethical and

legal standards (Janssen et al., 2020). DG is still a new

field and requires more research to develop its

potential in Saudi Arabia – a framework for DG was

put forward in 2007 (Poor, 2011; Wende, 2007)

2.2 The Impact of Data Governance on

Decision Making

DG is increasingly important to organizations

because of its influence on both strategic and

operational decision making (Master & Management,

2007). Despite its potential, organizations have yet to

fully utilize data and harness its potential for business

growth and profit (Ransbotham, Kiron, & Prentice,

2016). Similarly, its value to government decision

making on important public issues has not been

maximized (Ransbotham, Kiron, & Prentice, 2016).

The value of data to organizations is impeded by

problems with quality, accuracy, and accessibility,

and these problems can translate into business

problems. In the public sector, despite the volume of

data about citizens and its potential to improve

decision making, it is not used effectively to address

citizens’ needs because of a lack of DG (Benfeldt

Nielsen, 2017). Furthermore, solutions to data

problems are often short term and tackled in isolation,

which further impedes effective data use (Brous,

Janssen, & Vilminko-Heikkinen, 2016). To address

such issues organizational involvement is required,

not solely a dedicated team of IT specialists (Lee et

al., 2014).

Data science is becoming ever more important to

organizations and many are initiating programs to

develop its use. Two case studies on asset

management (Brous and Janssen, 2020) were

examined to explore how DG can help make decision

making trustworthy and to produce acceptable

proposals. Both case studies revealed that decisions

made by organizations with a DG scheme were more

likely to be accepted. While it is well known that data

science is a useful decision-making tool, it is

dependent on the quality of the data it manipulates,

and the theoretical model used to guide its methods

(Brous and Janssen, 2020). Quality issues with data

frequently prevent organizations from making full

use of data science for decision-making purposes

Assessing the Impact of Data Governance on Decision Making in Saudi Arabia

115

(Lin, Gao, & Koronios, 2006), and they have led

many organizations involved with asset management

to adopt DG to control how it is used (Brous and

Janssen, 2020).

The poor understanding of DG has meant that its

ability to improve data science is underdeveloped and

needs more research (Brous and Janssen, 2020). The

emergence of ‘big data’ has brought with it a rise in

the use of artificial intelligence (AI) approaches to

processing this rich and inter-linked source of

information (Janssen et al., 2020). These neural

networks employ various machine learning

algorithms to manipulate and process large data sets.

Where such systems are used to inform decisions that

affect individual citizens and the communities they

belong to, any errors can result in harm and must be

eliminated. This has led to the adoption of strict

ethical standards and regulations. The size of current

databases makes their management extremely

difficult, increasing the importance of DG. In this

way the quality of data can be maintained, ethical

guidelines, as well as legislation, can be adhered to,

and trustworthy decision making ensured (Janssen et

al., 2020).

Technological advances have led to the concept of

a smart city, and DG is playing an increasingly

important role in such cities’ management and

decision making, as huge quantities of data need to be

utilized to run the applications required to serve city

functions (Choenni et al., 2022). The data comes from

many sources, including mobiles, drones, IoT

products, and robots. It also includes public sector

and organizational databases and registries, and is not

of uniform quality. To operate a smart city, the vast

quantity of data needs to be efficiently processed and

analyzed. As an example, the recording of entry and

exit times on public transport was originally intended

to be used to calculate the cost of travel based on the

distance, but the data also contains information of

great value for creating efficient public transport

services. The type and quantity of vehicles can be

adjusted to meet the different demands at specific

times, and high citizen movement areas can be

highlighted as potential crime spots for the police to

monitor. The data is also highly valuable to

businesses and can be used to develop applications

and public services, such as sophisticated route

planners that take account of safety as well as journey

duration (Choenni et al., 2022).

Other applications of such data could be more

environmental, and they can enable greener

development policies, including tree planting to

mitigate air pollution (Choenni et al., 2022). Eke and

Ebohon (2020) conceive DG as the use of all

available data in a way that takes account of

stakeholder interests and concerns, particularly the

overall wishes of the residents for their city. This

perspective emphasizes how data-driven decision

making affects the lives of ordinary citizens. Such a

view moves DG from a means of extracting value

from data legally and responsibly, to one that also

includes stakeholders in the evaluation of decisions

(Eke & Ebohon, 2020). For data to be used to manage

smart cities in order to properly further residents’

needs, DG needs to be in place to ensure that the data

used is objective, unbiased, and accurate. The

algorithms used to analyze the data and inform

decisions need to fairly weight the importance of

equality and a fair distribution of goods and services,

and the people operating the governance systems

should be suitably qualified. Policy should thus be

informed by accurate data, use transparent methods,

be accountable, and adhere to acceptable ethical and

legal standards (Eke & Ebohon, 2020).

The trustworthiness of decisions, and their impact

on both operational and strategic plans, depends on

good DG. There is, however, very little research on

how DG affects decision making in the KSA, a

shortfall that the current research aims to address.

3 RESEARCH METHODOLOGY

AND DESIGN

Semi-structured interviews were conducted to

understand how DG affects decision making in the

KSA. The study comprised two groups of six experts

working for organizations in either data analytics or

DG roles. The DG interviewees were responsible for

the implementation of DG in their organization as part

of their professional roles, while the data analysts were

involved in analysis and related data processing work.

Individual semi-structured interviews and focus groups

were conducted to discover how each of the two expert

groups viewed the impact of DG on organizational

decision making. Both groups had experience of

decision making within their organizations.

Qualitative research using semi-structured

interviews and content analysis typically continues

with interviews until data saturation has been attained

(Francis et al., 2010). In this present study, saturation

occurred after twelve interviews had been conducted.

Most of the interviewees were IT graduates and the

details of the group members and their organizations

are displayed in Table 1. This research focuses on

governmental/semi-governmental and private sectors.

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Table 1: This table includes the demographic information of the interviewees.

Interviewee

Number

Interviewees Information

Organization Type Interviewee Group Name Interviewee Position

#1 Private consultanc

com

Data Governance Lea

data

overnance consultant

#2 Semi-Government Data Governance Hea

of data an

ital solutions

#3 Semi-Government Data Governance Data

overnance

ect mana

#4 Private

–

consultancy compan

Data Governance Data strategy manage

#5 Government Data Governance Data management & governance

section hea

#6 Government Data Governance Data governance department

manage

#7 Private

–

ankin

secto

Data Anal

tics Di

ital anal

tics mana

#8 Government Data Analytics Data scientist

#9 Government Data Analytics AI advisor

#10 Private – consultancy company Data Analytics Data & business intelligence senior

specialist

#11 Private

–

consultancy compan

Data Analytics Senio

analyst

#12 Private

–

consultanc

com

Data Anal

tics Business intelli

ence anal

Standard ethical procedures were followed, and

none of the data collected could be used to identify

individuals, thereby fulfilling the anonymity

requirements. Participation was voluntary, and all

participants gave their informed consent before the

start of the interviews or focus groups. The interviews

comprised a series of pre-written questions, but both

the interviewer and interviewee(s) had the

opportunity to ask questions and respond during the

interview, allowing topics to be explored in greater

depth. The pre-written questions were carefully

constructed to be objective and unbiased. Before

beginning interviews with the full participant group,

we carried out an initial pilot interview to ensure that

the questions we planned to use were clear,

comprehensive, and capable of eliciting in-depth

information. The results from this pilot interview

were encouraging, indicating that our questions

adequately covered the areas we aimed to explore

(Ritchie, Spencer, & O’Connor, 2003). Furthermore,

the structure of our main questions proved effective

in guiding more detailed discussions about topics

raised by participants. It is worth noting that the

individual interviewed during this pilot phase was

chosen from the same pool of participants as those in

our main study. Interviews are an appropriate

qualitative research method when the aim is to obtain

detailed information about people’s views and

attitudes related to a field of enquiry (Gaillet & Eble,

2021).

After the interviews were conducted, they were

transcribed and subjected to thematic analysis. This

approach was considered suitable as it allows themes

and sub-themes to be extracted from the

interview/focus group transcriptions (Gaillet & Eble,

2021). The thematic analysis began with the

researcher examining the data and identifying codes,

which were then grouped into separate categories

based on similarity. Themes and sub-themes were

then extracted from the data. Interview transcription

and data analysis were carried out using MS-Word

and Sonix software. As the interviews were

conducted in Arabic, native Arabic speakers

translated the transcription data into English. The data

thus obtained enabled a comprehensive evaluation of

organizational decision making in the KSA and how

this has been influenced by DG.

To ensure the reliability and validity of our

research methods, we implemented two key

strategies: triangulation and member checking. These

techniques are crucial for reinforcing the

trustworthiness of the findings in qualitative research

(Varpio et al., 42).

Triangulation in our study meant comparing

various data points to identify consistencies,

differences, and complementary elements. This

approach is based on the idea of using diverse sources

or methods to evaluate research findings, which

increases confidence in these results. As outlined by

Varpio et al. (42), triangulation can include different

dimensions like data, investigator, theory, and

methodology. Our focus was primarily on data

triangulation, involving a thorough comparison of

responses from different participants to discover

Assessing the Impact of Data Governance on Decision Making in Saudi Arabia

117

common threads, discrepancies, or supplementary

information.

Member checking, also known as informant

feedback, respondent validation, or dependability

checking (Varpio et al., 42), was another vital

technique we used. This method entails sharing the

data transcripts or interpretations with participants for

their feedback. The aim is to validate the data analysis

and increase participant involvement in the research.

Typically, this process happens at two stages. First,

participants review their transcripts to verify that their

words accurately convey their intended meanings.

Later, they assess the initial or final data analyses to

confirm or critique the researchers’ interpretations.

This phase often involves asking participants for their

insights on identified patterns or contextual factors,

which enhances the interpretive process and enriches

the study’s findings.Top of Form

4 RESULTS & DISCUSSION

DG was found to have had a significant effect on

decision making in the KSA. Both the data analytics

and DG groups were of this opinion. Two example

quotes from the interviewees were:

“Data governance has a high impact on decision

making” (interviewee #3)

And: “The impact on decision making should be

high” (interviewee #11).

The following sections present the results organized

by the themes that emerged.

4.1 Trusted Data

Interviewees from the DG and data analytics groups

mostly agreed that the adoption of DG would improve

both confidence and trust in data, thereby also

improving decision making. Example quotes from the

interviewees on this matter were:

“Data governance will increase confidence to make

decisions as the data will be trustworthy”

(interviewee #9).

“….we tell our employee that this data represents

reality and is actually true. They must have effective

governance that creates security and confidence for

the bank’s employees” (interviewee #4).

As DAMA International (2017) observes,

organizations are keen to make full and effective use

of the huge volume of data available to them. To

ensure that this data is reliable and of high quality,

and that deep insights can be drawn from it, DG has

now become a priority (International, 2017).

Organizations are also beginning to establish ‘data

warehouses’ to manage the data so that their decision

making and business analysis are well informed, and

the insights gleaned from the data can be used in

innovation (International, 2017). Certain

interviewees described how the use of a centralized,

secure, and trusted database functioned as a trusted

source of information for their organization. This

centralized data source integrated the data from

multiple external sources (including other

intraorganizational sources) and it was then used to

provide reliable data for business reports and data

analytics. Interviewee #4, for example, stated:

“We had a data warehouse project that took all the

existing data from all data sources in the bank and

integrated them into one reliable source (single

source of facts). This place actually produces reliable

data that can be used to build reports and gain

insights”

In the data analytics group two interviewees

reported that one of the advantages of a single,

centralized data source was that more consistent data

was used to inform all the departments – there was a

single and trusted source of factual information. This

avoided having different departments within the

organization making decisions based on different data

that could sometimes be conflicting:

“Why data governance is important .., you’ll have a

standard, so you won’t have conflicting figures

coming out of your different units or departments. So

you'll have a single source of facts for everything”

(interviewee #11).

In the data governance group three interviewees

reported similar views and explained how the lack of

a single source of accurate facts had resulted in a

decision maker receiving different data for the same

business report:

“One of the most important principles implemented

by data governance team is having one single source

of facts. Imagine if I had more than one source! One

of the problems faced by the decision maker is when

he says: “I got the same report from two different

departments ... and later, the same report produces

different output! This happens in most entities”

(interviewee #1).

These comments illustrate why organizations

need DG. It ensures that the data used to inform

decisions is accurate, clear, and trusted, and this leads

to greater confidence in the organizational data (Begg

& Caira, 2012). DG also increases the trust and

confidence in information products as a result of it

directly improving the reliability of the data used to

make them and indirectly as a result of the wider trust

levels across the organization (Otto,2011; Wende &

Otto, 2007).

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4.2 Data Quality

Most interviewees considered DG to be associated

with greater data quality: “The impact of data

governance on decision making is very high as it

produces better, higher quality data, which will be

reflected in improvements in the quality of

recommendations we provide as analysts. It means

that the decisions made based on these

recommendations will be more accurate”

(interviewee #7).

“As a data governance team, we aim to raise the

quality of data …. I work with the business team to

identify the key data elements that they rely on to

make decisions. I then set rules based on business

inputs which are implemented by the information

technology team to help me establish the dimensions

of data quality” (interviewee #1). These views are

consistent with the literature, where data quality is

also considered to have a strong correlation with DG

(Otto, 2011; Pierce, Dismute, and Yonke, 2008.).

This has led to data quality being used as a

measure for evaluating the performance of DG

(Khatri & Brown, 2010; Otto, 2011; Wende and Otto,

2007). In the data analytic group, nearly all of the

interviewees considered data quality to be crucial and

problematic when not attained. Rather than analyzing

data, many analysts found themselves addressing data

quality issues:

“Most of the challenges that I face relate to data

quality and processing ... we work more on the steps

to improve data quality” (interviewee #8).

“Sometimes, or actually almost all of the time, the

data that comes in is of very poor quality. So we have

to do quality checks” (interviewee #10).

Decision making throughout an organization is

seriously affected by poor data quality:

“If you have data that’s ambiguous or inaccurate or

duplicated, it can be misleading … these things affect

many of the tasks that someone who analyzes data

performs. It's what affects the decision making

indirectly” (interviewee #10).

The efficient use of data is hindered by poor-

quality data and can even result in erroneous decision

making that has serious consequences. High-quality

data is required to obtain the benefits of big data

through analysis, and to extract value from the

information contained in the data (Cai & Zhu, 2015).

DG needs to ensure that the quality of data is of a

suitably high standard for the areas of business in

which it is used (Brous, Janssen and Vilminko-

Heikkinen, 2016). The data used by an organization

needs to be ‘fit for purpose’, and DG needs to ensure

that it is maintained to sufficiently high standards;

this requires that binding policies and usage

guidelines are in place for data management (Otto,

2011).

In the DG group, two interviewees described how

it was necessary for the quality of data to be improved

in conjunction with other business departments, as the

DG team played a part in forming the business rules

for their organization:

“Actually, yes we were involved, I speak their

business language … we convert our understanding

of data to business rules so we can check data later

on” (interviewee #4).

4.3 Roles and Responsibilities

The DG team were all of the opinion that the proper

management of data required having clearly defined

roles and responsibilities: “When you’re governing

data, you must define roles and responsibilities…how

do we define roles and responsibilities? … by

policies” (interviewee #3).

The interviewees were also in broad agreement

with the fact that DG should involve a dedicated data

steward and that owners should be properly assigned

to specific data. Data stewards have comprehensive

knowledge of the data and the business requirements

for that data (Cheong & Chang, 2007). They also need

excellent IT skills with which to produce data in a

form that meets specific business requirements. Data

stewards understand the terminology and definitions

used by a business, and how the data will be used by

the business. They can form part of the team

responsible for producing business rules, definitions

and terms, and quality standards (Wende, 2007).

In contrast, data owners are responsible and

accountable for the data they own, and they have the

authority to approve decisions about data within their

field (International, 2017). One interviewee in the

data analytics groups reported that the absence of

clearly defined roles and responsibilities, and proper

assigned authority can be detrimental to data quality.

“There is a real problem happening today in our bank

… we told branch managers that when a client comes

to you, try to let him\her use a digital channel – and

this is what they do. The problem is that after the

client uses bank services through a digital channel,

the branch manager calls the IT team to change the

channel from digital to branch. They are doing this to

meet branch KPIs (key performance indicators) …

they have no idea of the damage to data quality that

they caused!!! I lost my customer because of what

they did” (interviewee #7).

They added: “We raised this with the data

governance team and they made the excellent

Assessing the Impact of Data Governance on Decision Making in Saudi Arabia

119

decisions to assert that not everyone has the authority

to change data. The role of the data governance team

in this case was a savior” (interviewee #7).

As noted by International (2017), decision making is

affected by data quality. Therefore, the role of data

owners and data stewards is essential for maintaining

this quality and addressing any shortcomings

(International, 2017).

4.4 Common Understanding

For data to be governed properly the DG team need

to understand the value and meaning of the data to the

organization (Smith, 2007). When managing big data,

reliable metadata must be formed to enable

organizations to realize what data exists in the

databases, where it came from, what it represents,

who can access it, what quality standards must be

maintained, as well as how the data moves through,

and is integrated into, the organization’s systems

(International, 2017). The DG team must be

accountable for the metadata (Al-Badi, Tarhini, &

Khan, 2018).

4.4.1 Business Glossary

The different uses of terminology can be confusing

and lead to errors. A business glossary addresses this

need for clear and well-defined terminology

(International, 2017). By ensuring that consistent and

accurate data description terms are used and

incorporated into the business glossary,

communication is improved throughout the

organization and ambiguities resolved (International,

2017). This view of the importance of a data glossary

was confirmed by most interviewees:

“As an organization, how do I benefit from the data

that I have? … I must classify data … I should have

metadata and a business glossary to define data

attributes” (interviewee #5).

“Data governance helps decision makers in certain

things such as standardization. For example, when

we say the word ‘employee’ what is its definition? Is

it a part-time or full-time employee?” (interviewee

#2).

If data is not referred to and manipulated

consistently, and with accurate terminology, errors

can filter through into the reports used by decision

makers. One of the data analytics interviewees stated:

“I once saw a scenario where a leader requested their

teams to calculate the numbers, for example, for a

sale. And two different teams provided different

numbers … and it was a huge embarrassment to the

organization. And when they investigated, it turned

out both of the numbers were right. They were just

using different measures” (interviewee #10).

Furthermore, the DG group shared this view:

“…when the finance department shares a report, it is

not reflecting the same information as the other

sectors. The reason is that the finance department

looks at it from a different viewpoint, and the method

of calculating data was also different …, so there was

a conflict. This misleads the decision makers, how did

he make the appropriate decision?” (interviewee #6).

These remarks highlight the importance of a business

glossary to good DG (International, 2017).

4.5 Decision Time

Most interviewees considered that poor DG results in

organizations not realizing the full value and potential

of the data they have, which can impact decision

making: “The impact of data governance on the

decisions, is cost, it’s as simple as that, it would be

costly. When a decision maker makes a particular

decision, usually the reason behind it is investment –

it is either profit or loss” (interviewee #5).

DG that is effective and well structured also

enables decision makers to acquire information of

high quality within their time constraints: “Today we

are living in a very fast-paced world. In the private

sector, I would not even wait for a day or two to make

a decision. It is possible for the competitor to get the

opportunity and be ahead of us. In the government

sector, certain decisions are supposed to be taken

very quickly based on specific data, so that the

country develops. So the question is how will the

decisions be affected if there is no data governance?

This decision is supposed to be instant – within hours,

for example. If there was no data governance, time

would be wasted in figuring out how to solve data

quality problems, or where to get data from, or how

to make sure data is reliable? And missed

opportunities are costs” (interviewee #4).

Most interviewees did not refer to DG increasing

the decision time, with only two remarking on it. One

of the data analytics interviewees commented:

“Data governance ensures the quality of the data, but

slows down the decision-making process”

(interviewee #8). However, organizations are aware

that high-quality data is more valuable than low-

quality data (International, 2017). Reliable data is an

advantage to employees as they can answer questions

faster and with greater consistency. Employees can

also use their time more effectively for the

organization, by addressing customer needs, making

decisions, and finding insights from the data, rather

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than expending valuable time on data issues

(International, 2017).

4.6 Change Management

Data in the KSA is regulated by the National Data

Management Office (NDMO). This office is

responsible for setting standards in national DG, data

management, and personal data protection, and it set

up a regulatory framework three years ago to apply

these standards (Sdaia.gov.sa, n.d.). All government

departments, entities, and any assigned business

partners who use government data must comply with

the standards set by the NDMO (SDAIA, 2021). DG

remains a recent development in KSA, particularly in

the government domain, and this was reflected in the

interviews with the DG group.

All of these interviewees raised concerns about

the lack of awareness about DG and the challenges

that remain, posing barriers to its effective

implementation: “In general, the challenges that face

data management offices in the government sector

are always culture and change in management”

(interviewee #5).

“The regulator has to play a bigger role, and

understand that there are challenges, and try to apply

changes in management because this is a change

happening at the level of their entity or at the level of

Saudi Arabia. And this change will affect many things

and everyone should be aware of it” (interviewee #4).

Two of the DG interviewees commented on the

way it was being implemented. The employees

responsible for implementing DG were inadequately

informed about their roles, and some also considered

it to be nothing more than an added workload:

“Business people do not know what roles and tasks

they’re supposed to do. For example, when a data

governance officer tells them: ‘According to the

policy, you must classify your data before you share

it’, or ‘You must define data fields’, they reply: ‘You

are asking me for something that is not one of my

duties and responsibilities’. So they consider it extra

work. How do we overcome this issue? The leader of

the entity should believe in data governance

practices, he can help the data management office by

assigning a data steward to ensure that data

governance is implemented and that it is a part of

their tasks” (interviewee #5).

The interviewees made certain suggestions to

tackle these barriers to the effective implementation

of DG. These included raising awareness of its

function and importance through workshops and

other focus-group-style meetings. Good DG practices

should be filtered down from top level management,

and good leadership is required for these practices to

be established successfully. Their implementation

should also be done in stages, as gradual changes in

data handling practices meet less resistance to

change.

Finally, once established, good DG needs to be

maintained through periodic checks of adherence to

standards. Examples of the interviewees’ views on

this matter are as follows:

“Raising awareness about data governance should be

through interactions, sending emails is not enough

and we should leverage the data steward to transfer

knowledge. Therefore, selecting a data steward is

important” (interviewee #1).

“Data governance should be flexible, especially in the

early stages. The entire framework should not be

applied at once, but rather in stages for easier

implementation, also to measure its progress”

(interviewee #2).

“I recommend that before establishing a data

governance office, the regulator should conduct

sessions and workshops from the top down, starting

with ministries and deputies, to explain the main

objectives of data governance that they want the

office to achieve, and then the office will apply it

gradually, then meet with the regulator next year –

not to check compliance, but rather to share any

lessons learned and to come up with

recommendations. Then in the second or third year

after going through this process with the various

departments, the regulator can conduct compliance

checks” (interviewee #4).

5 CONCLUSION AND FUTURE

WORKS

In summary, this study presented a thorough and in-

depth evaluation of DG in KSA and its impact on

decision making. Qualitative data was obtained from

interviews with six experts working in DG and six in

data analytics. The former group were responsible for

implementing DG in their organizations, while the

latter were involved with analysis and data modelling.

Both groups of interviewees considered effective

DG to improve the decision making of an

organization, as it increases trust and confidence in

organizational data and improves the clarity of

useable information. Decision making is further

improved by the increases in data quality brought

about by good DG. Having clearly defined roles and

responsibilities, particularly data stewardship and

ownership, is essential for good DG. These roles

Assessing the Impact of Data Governance on Decision Making in Saudi Arabia

121

assign accountability for data quality and authority

over data usage, which are both crucial components

of good DG. They also help to ensure its positive

impact on organizational decision making. DG also

includes standardizing data and data metrics, as well

as the terminology used in data analysis and

reporting. The improvements in the

intraorganizational consistency and clarity in

reporting that come from this standardization are

essential for effective decision making. A properly

implemented DG framework speeds up data

processing, enabling high-quality data to be produced

or accessed when most needed for important

decisions.

The interviewees make it clear that there are many

direct and indirect benefits of good DG. However, a

lack of awareness and experience has meant that KSA

institutions and governmental actors face barriers

when attempting to instill good DG practices. To

mitigate these problems, organizations should

introduce workshops to increase awareness, ensure

excellent leadership provides top-down support, and

implement DG gradually. This will enable

organizations to obtain the considerable value that

good DG can offer.

Lastly, this study's findings present several

limitations that warrant further research. First, there

have been limited studies on the impact of data

governance in KSA, given that data governance is a

relatively new field both globally (Benmoussa,

Khoulji, Laaziri, and Larbi, 2018), and particularly in

KSA. As a result, further research on data governance

in KSA is recommended.

Second,

this study's conclusions are not supported

by quantitative data. Although valuable insights were

gained from qualitative analysis, the inclusion of

quantitative measures would significantly enhance

the empirical foundation. Future studies, therefore,

will seek to incorporate quantitative data for a more

comprehensive and balanced analysis.

Third, the sample was drawn from a variety of

sectors, including government, semi-government,

and private sectors, spanning diverse business

domains such as health, energy, and banking.

According to interview results, the energy sector in

KSA is seen as mature in terms of data governance

and quality, potentially facing fewer challenges

compared to other sectors. Therefore, future research

should focus on specific sectors (e.g., government or

private) or domains (such as health, energy,

education, etc.) to gain deeper insights and more

comprehensive results. To enhance the findings'

generalizability, subsequent studies should aim to

increase the participant pool and extend the scope

across different geographical areas.

ACKNOWLEDGEMENTS

The authors extend their thanks to the interviewees

who participated in this study.

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