Roadmap for Implementing Business Intelligence Systems in Higher

Education Institutions: Exploratory Work

Nuno Sequeira

, Arsénio Reis

1,3 b

, Frederico Branco

1,3 c

and Paulo Alves

School of Science and Technology, University of Trás-os-Montes and Alto Douro, Quinta dos Prados, Vila Real, Portugal

Research Centre in Digitalization and Intelligent Robotics (CeDRI),

Instituto Politécnico de Bragança, Bragança, Portugal

INESC TEC - Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal

Keywords: Decision Making, Business Intelligence, Higher Education Institutions.

Abstract: Higher Education Institutions must define and monitor strategies and policies essential for decision-making

in their various areas and levels, in which Business Intelligence plays a leading role. This research addresses

the problem of Business Intelligence system adoption in Higher Education Institutions, with a view, in the

first instance, to identify and characterise the strategic objectives that underpin decision-making, activities,

processes, indicators and information in Higher Education Institutions. After a literature review, it was found

that the absence of a roadmap that can serve as a reference to implement a Business Intelligence system in

Higher Education Institutions may limit the adoption of this type of solution. Therefore, this research intends

to present the methodology of a proposed roadmap for the implementation of Business Intelligence systems

in Higher Education Institutions, that allows for increasing its capacity for analysis and evaluation of the data

and information available in the various systems and platforms.

1 INTRODUCTION

Higher Education Institutions make decisions in

several domains, namely strategic and internal

management, without using systematised data that

support these decisions, which may jeopardise the

success of their actions or even their efficiency. The

HEIs are characterized by having different

specificities in their mission and management

strategies (Nieto et al., 2019). Nevertheless, they all

need clear and concise strategies that allow them to

create more value and follow through on their vision

(Valdez et al., 2017). On the other hand, HEIs need

relevant information to monitor their performance,

according to the goals established in their strategic

plans (Calitz et al., 2018).

The challenges Higher Education Institutions

(HEI) face today are notorious, especially with

increased competition in the higher education sector.

Thus, there is a need on the part of HEIs to achieve

https://orcid.org/0000-0002-9733-1097

https://orcid.org/0000-0002-9818-7090

https://orcid.org/0000-0001-8434-4887

https://orcid.org/0000-0002-0100-8691

better decision-making in all their areas and levels

(Sanchez-Puchol et al., 2017; Scholtz et al., 2018;

Valdez et al., 2017). In this way, HEIs are

increasingly committed to ensuring a better quality of

their services and an increase in the degree of

satisfaction of their students, as these are two

important differentiation factors that can be decisive

for their sustainability (Calitz et al., 2018). Thus,

HEIs assume today an essential role in promoting

their internal change towards sustainable

development models, involving the main areas of

activity of HEIs, such as teaching, research,

operational management and extension. On the other

hand, another major challenge that should not be

disregarded is to holistically involve all activities of

HEIs that are inherent to their sustainability (Yáñez et

al., 2019). The decision-making activity can be

defined as a management process to frame a specific

situation in which it is necessary to make a decision.

By defining a decision model consisting of a set of

162

Sequeira, N., Reis, A., Branco, F. and Alves, P.

Roadmap for Implementing Business Intelligence Systems in Higher Education Institutions: Exploratory Work.

DOI: 10.5220/0012118000003552

In Proceedings of the 20th International Conference on Smart Business Technologies (ICSBT 2023), pages 162-169

ISBN: 978-989-758-667-5; ISSN: 2184-772X

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

actions and evaluation criteria, it is possible to select

the most appropriate action flow and acquire learning

from the decision-making process (Mora et al., 2017).

The organisational culture of an HEI is a vital

aspect to consider, as it influences decision-making

and the processes implemented to ensure the

efficiency of the HEI's activity, taking into account

the established strategy or goals (Deja, 2019). HEIs

make their decisions according to their institutional

priorities, strategies, goals and allocation of

resources, ensured by different officials with

governance functions (Nieto et al., 2019).

Information Systems (IS) play a key role in the

management of HEIs, supporting their activities and

decision-making processes. Although, the IS must be

developed according to the HEIs' needs, and there

should be an integration of the various IS and

Information Technologies (IT), to enable better

access and processing of information. To Bessa et al.

(2016), the IS have a class called Decision Support

Systems (DSS) that is oriented to this purpose,

particularly concerning the tactical and strategic

levels, with analytical specificities that allow the

creation of knowledge and organizational

intelligence. Although the DSS capacity to support

decision-making is recognized, the degree of

complexity required, with the need to involve all the

HEI's IS, leads to the adoption of Business

Intelligence (BI) systems. A BI solution consists of a

data-driven DSS that supports a set of operations such

as historical data querying, summary reporting,

executive reporting, Online Analytical Processing

(OLAP) and BI systems (Tripathi et al., 2020).

This paper is organised into sections as follows:

the first section presents the problem and highlights

the contribution and motivation for this work. The

second section describes the related work and then

shows the preliminary view of the roadmap to aim

for. Finally, current and future work is mentioned,

followed by acknowledgements and the list of all

bibliographical references used for this research.

1.1 Contribution

This position paper presents the methodology of a

roadmap that can serve as a reference to implement a

BI system in HEIs, to support decision-making in

their various areas and levels, taking into account the

following dimensions of the HEIs' activities:

teaching, research, internationalisation and extension.

The roadmap will include a set of dashboards for

decision support and a BI system reference

architecture, scientifically validated through a review

of state of art and an expert panel.

The research methodology to be used is Design

Science Research (DSR) with recurring cycles of

design, demonstration and evaluation, intending to

improve the artefact's usefulness concerning the

identified problem (Paul et al., 2021).

Finally, the results of this research will be

communicated to determine if the proposed

methodology allows aims a roadmap that enables the

improvement of the HEI decision quality.

1.2 Motivation

Often, decision-making in HEIs is carried out without

specific data or analysis (Nieto et al., 2019). Thus, our

starting point begins with the systematised

presentation of how HEIs are using the data generated

by their various IS in decision-making, as well as

analysing to what extent modern IS that simplify an

entire process of exchange, access and use of data,

information and knowledge, could be decisive for

good institutional performance.

An HEI can collect relevant performance

indicators through the data generated by its IT

structures, which may enable the ascertainment of

certain events in its internal management (Al-Rahmi

et al., 2019). Early dropout identification is one of

these cases, consisting, inclusively, of a phenomenon

quite common and worrying for HEIs (Maldonado et

al., 2021).

Decision-making in planning the activities of an

HEI is a rather complex process since the data they

are based on are in large quantities and are scattered

across several sources, making it difficult to analyse

them (Perez-Castillo et al., 2019). Nowadays,

accurate information and data represent a substantial

competitive advantage. When appropriate, the

implementation and exploitation of IS may be the

basis for the success of a sustainable strategy at the

management level (Stojkic et al., 2020).

The advances verified in the integration of IT,

namely BI, have been determinant in the evolution of

HEIs, allowing decision-making based on data

analysis to be a constant reality today (Ain et al.,

2019; Scholtz et al., 2018; Viberg et al., 2018). BI is

dependent on processes related to data quality

management and classification and the definition of

processes (Zavale et al., 2017). BI tools and

technologies have followed this evolution, as a way

to respond to the greater complexity of organisational

requirements and decision-making. In addition, the

lack of specific data mining tools to deal with

unstructured data has leveraged the use of BI (Calitz

et al., 2018). Following this, it can be considered that

BI can take advantage of the various information

Roadmap for Implementing Business Intelligence Systems in Higher Education Institutions: Exploratory Work

163

generated to provide more efficient answers (Jalil &

Hwang, 2019), improving the capacity to handle the

data generated and the quality of the information

obtained, thus significantly supporting HEIs in their

decision-making (Bordeleau et al., 2018; Calitz et al.,

2018; Sorour et al., 2020a). However, the

implementation of a BI system requires an adequate

infrastructure, as well as the identification of

operational indicators to measure it, to ascertain the

conditions under which the HEI is to implement a BI

system (Jahantigh et al., 2019). Thus, HEIs need to

find the best way to implement BI systems and

maximise their benefits (Ain et al., 2019; Jalil &

Hwang, 2019). Although, these benefits can only be

achieved if a BI system is implemented properly

(Musa et al., 2019).

The literature review that we are conducting,

allowed us to conclude that there is space for

developing research in this area since it was possible

to identify several studies on the use of BI in HEIs,

although there is a need to define a roadmap for the

implementation of BI systems that can serve as a

reference for HEIs.

2 RELATED RESEARCH WORK

This section presents a set of analyses that result from

the exploration of several studies that address the use

of BI solutions in HEIs. Although multiple cases were

found, the following studies provide the best support

for the work in progress.

The authors Combita Niño et al. (2020) created a

BI governance framework which aims to take

advantage of data generated by the HEI to obtain

patterns and forecasts that are important in

formulating strategies. An HEI was used as a case

study to be easily replicated in other HEIs, and a

diagnosis was carried out to identify the level of

maturity in analytics. On this basis, a decision-

making model was designed to strengthen

organisational culture, infrastructure, data

management, data analysis and governance, which

includes defining a governance framework, guiding

principles, strategies, policies, processes, decision-

making bodies and functions. The framework aims to

implement adequate controls that ensure the success

of BI projects, as well as enable an alignment of the

objectives of the development plan with the analytical

vision of the HEI.

Several frameworks enable the monitoring of

quality assurance in HEIs. Sorour et al. (2020b)

identified five frameworks with differentiated

orientations and perspectives. However, all of their

support uses data to measure the performance of

HEIs. There is a consensus that BI tools, such as

dashboards, can help provide real-time information

about the quality assurance performance in HEIs.

Morais and Lopes (2019) describe the

implementation of a BI solution in a HEI, which aims

to support its quality system and improve its future

strategy based on the HEI's area of activity: Teaching

and Learning. The project contemplated several

stages: mission, strategy and process analysis, Key

Performance Indicator (KPI) identification, KPI

validation by the process managers, identification of

the IS in use at the HEI, identification of the existence

of necessary information in these systems, the

definition of the access profiles, according to the

different process users and the BI system selection

and implementation. As a result, it was possible to

obtain valuable dashboards for management and

identify potential improvements for the quality

assurance mechanisms, which should simplify the

continuous improvement process of HEIs (Morais &

Lopes, 2019).

HEIs need tools for effective academic analysis,

which requires a systematic and balanced process for

collecting, synthesising and assessing relevant data.

To support the accreditation process of HEIs, Ortiz

and Hallo (2019) presented an analytical data mart to

obtain adequate information for its rapid

interpretation and management and to avoid the

dispersion of the data required for the respective

accreditation. The solution was developed through a

BI tool, taking into account the student criterion

indicators according to an institutional evaluation

model, allowing the efficient monitoring of indicators

before university accreditation, reducing response

time and resources in the report generation process.

Al Rashdi and Nair (2017) created a BI

framework implemented in an HEI to collect helpful

information through the big data generated by the

HEI. The framework was tested within the critical

business activities, teaching and learning, and the

results show that the aggregation of these activities

and KPIs contributes to the overall performance of

the HEI, even allowing a better perception of the

operationality of the HEI.

3 BUSINESS INTELLIGENCE

SYSTEMS

IS have a central role in HEI management, supporting

their activities and decision-making processes. IS

have a class called Decision Support Systems (DSS)

ICSBT 2023 - 20th International Conference on Smart Business Technologies

164

that is oriented to this objective, namely at the tactical

and strategic levels, with analytical features that

allow the creation of knowledge and organizational

intelligence. Although the DSS capacity to support

decision-making is recognized, the degree of

complexity required, with the need to involve all the

HEI's IS, leads to the adoption of BI systems (Bessa

et al., 2016). Given the fact that HEIs have difficulty

leveraging their data and that their success depends

on accurate, fact-based decisions, BI can simplify the

decision-making process through the information that

is commonly produced by HEIs daily. (Jalil &

Hwang, 2019; Dadkhaha et al., 2019).

To enable the implementation of a BI system, it

becomes essential to design a plan consisting of a

roadmap, an architecture and some guidelines

(Mishra & Pani, 2021). Several studies similarly

present the architecture of a BI system. In general,

and as can be seen in Figure 1, for Sorour et al.

(2020a) and Boulila et al. (2018) a BI architecture in

HEIs comprises three main layers or components: 1)

data source layer: in which data are collected from

different sources; 2) Extract Transform Load (ETL)

process layer: extraction, transformation and loading,

in which there is the collection of relevant data for

analysis, and then they are loaded into a data

warehouse, which stores the data for analysis; 3) data

presentation layer: dashboards are used to present

analysis in a synthesised form, although they can be

detailed to enable better decision-making assistance

when reviewing objectives and monitoring HEI KPIs.

Figure 1: Business Intelligence Architecture. Adapted from

Boulila et al. (2018) and Sorour et al. (2020a).

In this sense, the architecture is a guideline for

developing the roadmap (Ma et al., 2018). A roadmap

can be considered an established concept regarding

knowledge management, aiming at collecting

knowledge and obtaining solutions to problems in a

structured way (Johannsen, 2020). On the other hand,

a roadmap can be used as a guide for developing a

strategy (Mishra & Pani, 2021), in this case,

responsible for presenting the processes required for

implementing a BI system in HEIs (Chofreh et al.,

2018). According to Bhushan and Rai, cited by

Ebrahimi et al. (2018), the strategic decision-making

process can be categorised into seven steps, as

presented in Figure 2.

Figure 2: Strategic decision-making process. Adapted from

Ebrahimi et al. (2018).

Although some studies are carried out on BI in

HEIs, BI has progressed along two paths, theoretical

and practical. Most studies on BI aim to describe the

advantages of its usage, and there is little research on

BI implementation (Jahantigh et al., 2019). Ain et al.

(2019) reinforce this position by finding in their

research that previous studies do not

comprehensively discuss the issues and challenges

related to the adoption, use, and success of BI

systems. In our research, it was not possible to find

any reference regarding creating a roadmap for

implementing BI systems in HEIs. Despite this, it is

perceptible that the development of a BI system

consists of a progressive process in which it is

possible to identify the various stages that should

constitute a roadmap, aiming at the use of BI to

support the decision-making process of HEIs

(Gastaldi et al., 2018).

We found that it is essential to take some steps

before implementing a BI system. In the first place, it

is necessary to assess whether the HEI is prepared

from the technical point of view, as well as from the

management point of view, and it is vital to have clear

support from its management. According to Jahantigh

Determination of

current and target state

of competition, threat

or opportunities,

problem

Design a plan to

achieve them and

propose the alternative

approaches

Identification of

criteria to evaluate

alternative approaches

Identification of the

team members and

individual roles

Evaluation of various

alternatives and

keeping the possible

solutions

Ranking them based

on selected criteria by

using various a

decision-making tool

Deploy the best

alternative

Data Sources

ETL

Data

Warehouse

Dashboards

Roadmap for Implementing Business Intelligence Systems in Higher Education Institutions: Exploratory Work

165

et al. (2019), there are two predominant factors to

determine the quality of BI support management: the

familiarity of those responsible with IT and the

relevance of the information presented. In addition,

the compatibility of the systems with the BI

application must be ensured (Jalil & Hwang, 2019),

and the BI solution should have the ability to manage

technological assets, people and processes (Moscoso-

Zea et al., 2019). Before a BI architecture is

implemented, all the data stored by HEIs from their

various sources must be integrated into a data

warehouse by applying the ETL process.

After the development of the data warehouse,

different servers can efficiently access their data

through front-end applications (Dadkhaha et al.,

2019). Thus, it is essential to identify all data sources

that should be subject to analysis. Only then is the

respective data selection performed to ensure that the

BI process assures reliable results. This process

involves a data dictionary, which consists of a

technical description for one of the HEI repositories,

in which the data fields, origin, availability and

responsible party are recorded. Through the

information collected from the data sources and

dictionaries, it will be possible to identify the

variables that will guide the operation of the BI

system (Villegas-Ch et al., 2020). Given the amount

and diversity of this data, it is necessary to extract the

related data before proceeding with the execution of

the respective query.

Finally, the BI solution must be able to present the

data appropriately and within an appropriate period,

for which dashboards should be used, their

integration being ensured through the data warehouse

that has quality data. A generic BI system should

integrate data from different sources to be

subsequently transferred. Another option is

integrating data presentation applications with the

data mining tool (Villegas-Ch et al., 2020). In

addition to the data warehouse, a diverse range of

tools and techniques can support HEIs in developing

BI capabilities, such as Enterprise Resource Planning

(ERP) systems, document management systems, and

knowledge repositories, among others. The data

considered are grouped in data marts, from which the

data is accessed through applications that create

customised visualisations. Thus, it becomes essential

to mention that HEIs should seek the right balance

between standardisation and customisation of BI.

Being necessary to standardise the memory of HEIs

and the integration of information, although, as

previously referenced, one can customise the creation

and presentation of insights (Calitz et al., 2018).

4 FUTURE WORK

Our research aims to verify whether, through the

proposed methodology, it will be possible to define a

roadmap to facilitate data processing and the

detection of trends and patterns, and thus obtain an

adequate visual representation that allows HEIs to

make decisions based on concrete data. Thus, our

research will consist of the following steps: to review

BI systems in HEIs; to identify and characterize the

strategic objectives that underlie decision-making,

activities, processes, and information in HEIs; to

identify and characterize the computer systems that

support HEIs; to propose a set of dashboards to

support decision-making in HEIs; to propose a BI

system architecture of reference, and, finally, to

propose the implementation roadmap.

We are currently developing a literature review,

according to the protocol proposed by Kitchenham

and Charters (2007), aimed at identifying other

academic contributions which have scientific

validation regarding the main areas of interest

addressed in this research, mainly, regarding the

application of BI in HEIs. Thus, this literature review

aims to identify and characterise the strategic

objectives that underlie decision-making, the

activities, the processes and the information of the

HEIs, to obtain a perspective of the use of data and

information that HEIs produce, as well as their use in

obtaining knowledge and intelligence, to support

decision-making processes. As far as decision-

making processes are concerned, the aim is to identify

and characterise the HEIs' processes in general, in

terms of areas, levels, actors and decision-making

points. It is also intended to identify and characterise

the typologies of computer systems used in the HEIs,

the processes they support and the information they

own, as well as to identify a BI system reference

architecture. In the next phase, it is intended to carry

out a set of individual semi-structured interviews with

a range of a Portuguese HEI managers to find out how

this HEI is using the data generated by its various IS

in decision-making, intending to identify the

characteristic processes of an HEI, mainly the tactical

planning processes, as well as identify the

information required for each process. It is also

intended that these interviews can complement the

literature review to identify and describe the

characteristic processes of an HEI, as well as the

respective performance indicators, which can serve as

a basis for the definition and development of the

roadmap proposal.

Next, a set of dashboards for decision support will

be defined, based on the processes, decision points,

ICSBT 2023 - 20th International Conference on Smart Business Technologies

166

and actors, among others, previously identified and

characterised. The next phase consists of the proposal

of a BI system reference architecture, showing the

various elements of the system, the relationships

among them and the information flow and processing,

from the sources to the dashboards, to then design the

final design of the reference roadmap, in the form of

project template, for the implementation of a BI in an

HEI.

In the last stage, our roadmap proposal will be

scientifically validated through interviews, which

will be performed with a set of specialists of that HEI

to verify if the roadmap satisfies the proposed

requirements and helps to solve the specified

problem. In this sense, a questionnaire will be

developed to perform the validation based on

previous studies and recommendations of Pestana et

al. (2018) and Apandi and Arshah (2016). The

interviews will be divided into three phases:

presentation of the research and demonstration of the

dashboards; testing of the dashboards by the experts;

and finally, answers to the questions by the experts.

The results obtained be communicated to

ascertain whether the proposed dashboards and

architecture allow the improvement of the quality of

the HEI's decision, the follow-up of their strategic

axes, and the prediction of abnormal situations,

among other actions. In summary, it will be verified

if the proposed roadmap can serve as a reference for

HEIs, and some considerations can be presented so

that HEIs can adopt it in their strategies.

ACKNOWLEDGEMENTS

This work has received funding from FEDER Funds

through COMPETE program and from National

Funds through Portugal 2020 under the project

"SATDAP - Capacitação da Administração Pública

operation BI@UTAD", grant number POCI-05-

5762-FSE-000264. The authors acknowledge the

work facilities and equipment provided by CeDRI

(UIDB/05757/2020 and UIDP/05757/2020) to

the project team.

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