Building a Business Data Analytics Graduate Certificate

Journal of

Information

Systems

Education

Volume 34

Issue 2

Spring 2023

Building a Business Data Analytics Graduate Certificate

Dinko Bačić, Nenad Jukić, Mary Malliaris, Svetlozar Nestorov, and

Arup Varma

Recommended Citation: Bačić, D., Jukić, N., Malliaris, M., Nestorov, S., & Varma,

A. (2023). Building a Business Data Analytics Graduate Certificate. Journal of

Information Systems Education, 34(2), 216-230.

Article Link: https://jise.org/Volume34/n2/JISE2023v34n2pp216-230.html

Received: March 31, 2022

Revised: May 12, 2022

Accepted: August 12, 2022

Published: June 15, 2023

Find archived papers, submission instructions, terms of use, and much more at the JISE website:

https://jise.org

ISSN: 2574-3872 (Online) 1055-3096 (Print)

Journal of Information Systems Education, 34(2), 216-230, Spring 2023

216

Building a Business Data Analytics Graduate Certificate

Dinko Bačić

Nenad Jukić

Mary Malliaris

Svetlozar Nestorov

Arup Varma

Quinlan School of Business

Loyola University Chicago

Chicago, IL 60611, USA

dbacic@luc.edu, [email protected], [email protected], sne[email protected], avarma@luc.edu

ABSTRACT

In this paper we present the evolution of the Business Data Analytics Graduate Certificate (BDA Certificate) at our institution,

Loyola University Chicago. This certificate is a successful and expanding program that attracts a diverse group of dynamic

professionals from local, national, and international populations. The program evolution described in this paper involves multiple

revisions of the curriculum, additions, and subtractions of individual courses, expansions of delivery methods, and program name

changes. The core principles of acknowledging the centrality of data, mandating the modeling-based course sequencing, and

recognizing the proper role of software tools, are outlined and recognized as the foundation of the program’s success.

Keywords: Business analytics, Graduate certificate, Curriculum design & development, Data analytics, Graduate education,

Business analytics curriculum

1. INTRODUCTION

This paper will present the evolution of the Business Data

Analytics Graduate Certificate (BDA Certificate) at our

institution, Loyola University Chicago. Currently, this

certificate is a successful and expanding program that attracts a

diverse group of dynamic professionals from local, national,

and international populations. The program has undergone

several changes involving multiple revisions of the curriculum,

additions, and subtractions of individual courses, expansions of

delivery methods, and name changes of the program.

Throughout this evolution, we have learned several valuable

lessons, which we outline in this paper. In contextualizing our

BDA Certificate, our research also presents the most up-to-date

summary of the business analytics (BA) curriculum

development and implementation research stream, a valuable

resource for our community as we continue to evolve and

improve BA-focused education.

There are many different approaches to creating a

successful business data analytics program, and we do not

present our program as a singular template to be followed.

Instead, we intend to make our experiences accessible to the

wider community of academics who create and maintain

programs with similar objectives and goals.

This paper is organized as follows: Section 2 presents a

comprehensive literature review of the past and current state of

data analytics and business analytics curriculum design and

implementation. In Section 3, we define and discuss the

founding principles of the BDA Certificate that drove the

inception and development of this program at our institution. In

Section 4, we outline the evolution of the BDA Certificate. In

Section 5, we present and discuss the future of the BDA

Certificate. Finally, in Section 6, we make our concluding

remarks.

2. LITERATURE REVIEW

2.1 Data Analytics Overview

Data Analytics has become a widely accepted area in both

academia and business. Expertise in data analytics is highly

sought by employers in most industries. Students strive to add

data analytics-related skills to their resumes and have a strong

interest in learning how to find useful and actionable

information in large amounts of data. The last decade has seen

this discipline transition from a niche area to a broad area of

interest accepted as extremely valuable by disciplines within

and outside business.

Today’s ability to analyze large amounts of data is possible

only because of computerized data storage. With E. F. Codd’s

creation of relational databases in the 1980s and the

development of SQL (Codd, 2002; Elmasri et al., 2000),

businesses could store and retrieve data in efficient ways that

were previously unfeasible. The area of Business Intelligence

grew from this data availability. In the late 1980s, focus shifted

from data collection and storage to finding information and

knowledge within this stored data. We began to use the phrases

“knowledge discovery” and “data-driven decision-making”

(Frawley et al., 1992).

The emergence of data warehousing (Chaudhuri & Dayal,

1997) led to greater amounts of clean data being available to

Journal of Information Systems Education, 34(2), 216-230, Spring 2023

217

answer business questions. Once this source of clean and

consistent data became available to analysts and decision-

makers, they asked, “What else can I discover?” The focus

moved from building a data warehouse that could answer

specific, pre-defined questions to collecting clean, integrated

data to find answers to ad-hoc questions never asked before. For

example, finding the best-selling products in different

categories is a typical question a sales data warehouse is

designed to answer. On the other hand, finding products that

consumers tend to purchase together is a question that arose

from the availability of historical transaction data in the data

warehouse (Agrawal & Srikant, 1994; Nestorov & Jukic, 2003).

With the new opportunity, companies began looking for

students who could analyze and integrate information, think

analytically, and explain the results of their analyses to a

general audience. The emphasis moved from the ability to use

technology to improving company decisions (Elliot, 2012).

There were concerns that the conventional statistics

methods, which have been the basis of data analysis for a long

time, had limitations in their structure and the kinds of problems

they could handle. The necessity of first generating a hypothesis

and then testing it meant they might be missing great insights

for which no hypothesis was formulated. In addition, statistics

originated from a world where data was difficult to compile and

often had to be entered manually, so techniques were not

designed to scale to large amounts of data.

With the rapid growth of massive amounts of data stored

and electronically accessible, analysts needed to develop new

ways to optimize its use for analysis. This led to the

development and application of numerous data mining

techniques: association rule mining, cluster analysis, decision

trees, neural networks, and support vector machines, to name a

few. This ability to collect data rapidly, in many forms, and

from a variety of sources could then be integrated, all of which

led to a growing awareness in businesses that data is a valuable

resource (Chiang et al., 2018). However, without skilled

analysts to find the information contained in these datasets, they

would turn out to be just very expensive storage. Companies

now needed employees who could understand these new

methods of analysis, apply them to their data, and employees

who could better understand the customers and the supply

chain, and gain this understanding speedily (Attaran et al.,

2018). They turned to universities to supply people with such

talents and skills. Thus, along with these new techniques,

universities began adding courses that exposed students to this

new way of approaching data analysis. Initially called Statistics

II, the field gradually grew into its own, and is now referred to

as Business Analytics in many schools. As John Tukey had

argued in 1980 (Tukey, 1980), we need both types of analysis:

that which confirms, and that which explores.

In the early 2000s, Data Science began to be used

(Cleveland, 2001) as a broad term encompassing statistics,

analytics, and machine learning. In 2011, McKinsey Global

Institute predicted a major shortage of skilled data scientists,

managers, and analysts dealing with data by 2018 (Manyika et

al., 2011). This prediction was confirmed by reports of large

shortages of hirable people in this area, with strong job

prospects for the future (Piatetsky-Shapiro & Gandhi, 2018).

Universities with business schools find these job prospects for

students to be a motivating factor in the courses they offer, so it

is not unexpected to find that the number of certificates and

degrees in Business Analytics have grown, too. With an

increasing shortage of people trained in analytics, more and

more universities are moving into this area (Parks et al., 2018).

The Institute for Advanced Analytics (2019) has an interactive

map showing the locations within the U.S. of approximately

250 schools that now offer programs in analytics. They also

show that the number of master’s degrees in analytics and data

science has grown from a negligible number in 2008 to over

20,000 degrees awarded in 2018. Entry-level positions average

in the low $80,000s and Indeed shows over 65,000 entry-level

positions available as of March 2022 (Indeed.com, 2022).

Increasingly, studies are being undertaken linking specific

industry needs to specific coursework (Paul & MacDonald,

2020). Companies are moving from simply recruiting

applicants with a specific degree to looking for an analytics

workforce based on needed skills (Stanton & Stanton, 2020).

Businesses are now also looking beyond basic training in

analytics to analysts and scientists with experience or training

in their specific industry (Matthews, 2019). That is, they want

their hires to have seen and worked with not just data, but data

from their particular domain area, and to be able to

communicate the results of their analyses to non-technical team

and company members.

2.2 Business Analytics (BA) Curriculum Literature

Our research is contextualized in Business Analytics

curriculum-focused literature. The goals of this literature

review section are to (i) identify key research articles, their

publication outlets, key themes and issues raised, and provide a

general landscape of BA program presence in business schools,

(ii) evaluate whether there is a literature gap that our research

can address, and (iii) inform and assist us in assessing the

process we deployed in our institution. We present the most up-

to-date summary of that research stream (see the Appendix),

outlining the main contribution, sample size/method used, and

program type for each manuscript. In the literature summary,

we strictly focused on analytics in the business context (vs. data

science) and curriculum assessment, design, and

implementations (vs. BA course teaching cases).

This literature stream consists of 41 peer-reviewed

manuscripts between 2009 and early 2022 (Figure 1). After first

considerations of creating BI & Analytics focused programs in

2009 (Sircar, 2009), we saw an initial assessment of BA value

and its potential between 2012 and 2014 (Chen et al., 2012;

Chiang et al., 2012; Gorman & Klimberg, 2014; Wixom et al.,

2014). Significant BA curriculum assessments and

considerations were published in the 2015-2017 period (a total

of 19), mainly focused on Big Data implications, classification

of content domains and coursework, clarifying the difference

between data science and business analytics, and recognizing

gaps in the current IS curriculum standards (Topi et al., 2010).

In the last five years, we see the literature focusing on the latest

market trends such as certification integration (Shim et al.,

2021) and changing competencies (Dong & Triche, 2020;

Johnson et al., 2020; Ozturk & Hartzel, 2020; Stanton &

Stanton, 2020). There is a recognition of the changing nature of

BA in terms of job expectations, tools and their applications,

and IS discipline identity (Ceccucci et al., 2020; Urbaczewski

& Keeling, 2019) as a whole.

Journal of Information Systems Education, 34(2), 216-230, Spring 2023

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Figure 1. BA Curriculum Manuscript Count by Years

(2009 - Present)

Selected manuscripts were published in 13 journal outlets

and 6 different conferences (Figure 2). Leading Association for

Information Systems (AIS) publications, systems-, and decision

science-focused education journals are the primary targets for

publishing BA curriculum research.

Figure 2. BA Curriculum Manuscript Count by Outlets

(2009 - Present)

Thematically, BA curriculum literature assesses the state of

the BA curriculum across a wide array of topics. These include

BA value recognition and amplification (Chen et al., 2012;

Chiang et al., 2012; Wixom et al., 2014), BA’s impact on

Information Systems discipline (Ceccucci et al., 2020; Chiang

et al., 2012; Jafar et al., 2017; Urbaczewski & Keeling, 2019),

BA program type classifications and their growth (Hameed et

al., 2021; Mills et al., 2016; Stephens & McGowan, 2018), BA

curriculum component categorization (Gupta et al., 2015; Kang

et al., 2015; Mitri & Palocsay, 2015) and required coursework

(Burns & Sherman, 2019; Ceccucci et al., 2020; Choi et al.,

2017), skills and competencies (Deng et al., 2016; Dong &

Triche, 2020; Johnson et al., 2020; Mamonov et al., 2015;

Stanton & Stanton, 2020), common technology use (Johnson et

al., 2020; McLeod et al., 2017), accreditation (Clayton &

Clopton, 2019) and existing IS curriculum standards

(Matthews, 2019; Mitri & Palocsay, 2015; Rodammer et al.,

2015; Topi et al., 2010). The research commonly reports best

practices and lessons learned in the process of implementation

(Burns & Sherman, 2022; Clayton & Clopton, 2019; Hameed

et al., 2021; Shim et al., 2021; Stanton & Stanton, 2020).

Most BA programs are rooted in the reality that the

marketplace requires a workforce with solid analytics skills

(Deng et al., 2016), suggesting a growing trend of offering

business analytics-infused curriculum and BA programs in the

forms of concentration within IS major or standalone majors

(Mitri & Palocsay, 2015), minors, certificates (both

undergraduate and graduate), MS degrees, and MBA

concentrations (Gorman & Klimberg, 2014). Soon after initial

research suggested the value of BA and its potential impact

(Chen et al., 2012), the opportunity for business analytics

academic programs was recognized (Chiang et al., 2012) and

resulted in the rapid growth in programs (Wixom et al., 2014).

A majority (60%) of AACSB IS programs added analytics-

related courses between 2011 and 2016. By 2016, there was a

dramatic increase in courses offered in big data analytics

(583%), visualization (300%), business data analysis (260%),

and business intelligence (236%) (Mills et al., 2016). A study

in 2017 (using a random sample of 94 schools) found that about

64% of universities have developed business analytics

programs at the undergraduate or graduate level, while about

40% offered BA certificates at the undergraduate or graduate

level (Choi et al., 2017). Another study found that about 35%

of schools offered BA program and about 26% offered BA

minors or certificates at the undergraduate or graduate level

(Phelps & Szabat, 2017), while a study in 2018 focused on peer

private institutions found that about 50% offer any number of

analytics-focused programs (Stephens & McGowan, 2018).

More recently, over 65% of the AACSB schools (using a

sample of 535 schools) offer a degree in business analytics

concentrations at either or both undergraduate and graduate

levels (Hameed et al., 2021). The emergence of the BA

curriculum and student interest in BA reveal the trend of IS

departments shifting their focus to data and data analytics, as IS

represents the top subject domain of instructors teaching

business analytics (Hameed et al., 2021). Consequently, many

adopt the name “Analytics” into their traditional IS departments

and degree names (Ceccucci et al., 2020; Urbaczewski &

Keeling, 2019).

A significant portion of the early literature focuses on

identifying core curriculum components. Several

categorizations were proposed, such as: (1) business

information intelligence, (2) business statistical intelligence,

and (3) business modeling intelligence (Mitri & Palocsay,

2015); (1) analytical skills, (2) IT knowledge and skills, and (3)

business knowledge (Chiang et al., 2012); four pillars of

analytics (1) data preprocessing, storage, and retrieval, (2) data

exploration, (3) analytical models & algorithms, and (4) data

product (Kang et al., 2015); (1) business expertise, (2) applied

statistical analysis, and (3) technical skills (Mamonov et al.,

2015); (1) data management, (2) statistics, and (3) core data

analytics (Jafar et al., 2017); and eighteen topic areas (Gupta et

al., 2015).

These categorizations were often followed by identifying

required courses and their mapping to the aforementioned

categories and program types. For example, literature focused

on BA minors found that, on average, business analytics minor

programs have two prerequisite courses, three required courses,

and two electives (Burns & Sherman, 2019). On the other hand,

BA certificates require between three and six courses (Choi et

al., 2017). The same literature stream suggests that most BA

programs’ top three required courses include a database,

predictive analytics, and introduction to BA course (Ceccucci

et al., 2020).

Graduate-level BA education is particularly critical as

roughly 35% of the entry-level BA jobs on the market prefer

professionals with graduate degrees (Johnson et al., 2020).

Many schools responded to that call and initially implemented

graduate BA-focused programs (Mitri & Palocsay, 2015). At

the graduate program level, significant variations in the

program structure in terms of program length (10 to 18 months)

and flexibility (electives comprise 0 to 37% of the course work)

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were reported (Mamonov et al., 2015). In a sample of six

universities, most graduate BA programs (i) allocated courses

across three areas: business expertise, applied statistical

analysis, and technical skills, (ii) focused on areas that leverage

institutional strengths, and (iii) started integrating practical

training and internships as part of their BA curriculum

(Mamonov et al., 2015). Others suggested a graduate-level

analytics curriculum consisting of five areas: data management,

statistics, core data analytics, capstone, and electives (Jafar et

al., 2017), with a focus on competencies (Mamonov et al.,

2015) and skills demanded by the marketplace, such as tools,

big data infrastructure, technical concepts, and soft skills

(Johnson et al., 2020). Distinct curriculum needs and market

expectations are reported for those completing MBA versus

those completing MS in IS or BA (Dunaway & McCarthy,

2015; Gupta et al., 2015; Jafar et al., 2017; Parks et al., 2018;

Warner, 2013).

Regarding technology used, the literature suggests that the

programs vary greatly in covering both traditional analytics and

the new emergent technologies and analytical methods. SQL,

R, Python, Excel, and Tableau are among the BA field’s most

desired programming languages and tools (Johnson et al.,

2020). The programs that focus on Big Data and its

infrastructure reported common use of platforms such as Hive,

AWS, Azure, Hadoop (Johnson et al., 2020), and SAP HANA

(McLeod et al., 2017), elevating the need for continued faculty

retooling and aligned hiring practices (Hameed et al., 2021).

Calls have been made for guidelines in creating and

assessing the BA curriculum and programs (Wixom et al.,

2014). A wish list of guidelines for “dream” BA program

development has been introduced (Wang, 2015) and applied

(Wymbs, 2016). Those guidelines include (1) developing

interdisciplinary courses, (2) aligning BA course offerings with

the needs of practice, (3) considering using real-world projects,

(4) capturing the union of relevant disciplines, and (5)

strengthening the faculty members’ BA expertise (Wang,

2015).

In summary, our literature review captured critical research

articles suggesting continued interest by the IS community in

the building, improving, and, increasingly, adopting BA

programs as the discipline’s key identity. The research on this

topic has been relatively steady in the last seven years, after its

peak in 2015, and is primarily published in leading IS and

Decision Science education journals and leading IS

conferences. The literature covers a wide array of topics,

focusing on the growth and impact of BA on the IS discipline,

curriculum components clarifications, and best practices,

understanding market-driven skill expectations, and evaluating

the use of technology and programming languages. The

literature summary also revealed a general lack of in-depth

description, reflection, and understanding of BDA graduate

certificate programs, their opportunities, challenges, and

required modifications to remain relevant. Given the shortage

of data analytics skills, these programs are valuable and

essential in addressing the deficit, retooling the workforce, and

providing a viable path to a full graduate-level degree.

Therefore, sharing our story of the BDA Certificate has the

potential to enrich the existing literature and inform the

academic community considering implementing or modifying

their own BA programs.

3. BDA CERTIFICATE FOUNDING PRINCIPLES

Before we describe, in chronological order, the evolution of the

BDA Certificate at our institution, we will first outline certain

basic founding principles that guided and continue to guide, our

decisions from the inception to the previous, current, and future

versions of the program. As we will describe later in this paper,

we have been through several changes since the foundations for

this program were laid out over twenty years ago. Throughout

this evolution, we have had both successes and failures. We also

had to make, and continue to make, some compromises that

were not always ideal. However, we consistently recognized

that adhering to the core principles presented in this section was

the correct course of action that ultimately enabled this program

to prosper. In particular, the three principles that continually

guided the development of this program are: the centrality of

data, modeling-based course sequencing, and the proper role of

software tools.

3.1 Centrality of Data (Principle #1)

When it comes to teaching curriculum that deals with data

utilization, the consensus among faculty at our department is

that the data itself should be at the core of learning. In other

words, the point is to first focus on the data itself, before we

study the methods of analysis and explore the actions and

opportunities that the data can afford us. In a practical sense,

this means to put the modeling and structuring of the data as the

foundation and a prerequisite for all other courses.

In other words, data organization and modeling are

paramount and central to everything else we do in our data-

related curriculum. A strong data foundation is necessary for

storage and retrieval, modeling and transformations, and

analysis and visualization. We also find support for this

principle in our literature. From a market expectation

perspective, data preprocessing (closely linked to our principle

#1) is still considered a dominant skill. A study evaluating top

skills in analytic job posts found that the top required skill was

“database management” (65.8% of ads), while the sixth most

required skill was SQL (56.6%). Both skills are enabled by and

require understanding data, its structure, and modeling (Dong

& Triche, 2020). A similar finding comes from another study

suggesting that one of the top two most desirable hard skills for

entry-level analytics positions includes data modeling (Stanton

& Stanton, 2020). Our formal and informal interactions with

analytics professionals and alumni confirm these findings and

the centrality of data.

3.2 Modeling-Based Course Sequencing (Principle #2)

In many instances, business data analytics is promoted as

enabling actions that can lead to positive change. While that is

true, it also often leads to teaching business data analytics by

immediately jumping into the methods of analysis and showing

a variety of possible actionable outcomes. Our approach differs

from this common scenario. Starting a BA program with a

course that focuses on how data is structured (i.e., Database

Course) before learning how data is analyzed is not the norm in

many BA curriculums. For example, we looked at dozens of

similar programs (Data Analytics certificates at reputable US-

based universities) and found that only two of them offer a

course in databases or a similar course on how data is organized.

In conjunction with the basic principle outlined above in

sub-section 3.1, we adhere to a specific guideline for

Journal of Information Systems Education, 34(2), 216-230, Spring 2023

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sequencing of courses, where a course on database modeling

will be a prerequisite for covering topics that deal with data

analysis. We feel very strongly that students, in any course

dealing with data analytics, must have knowledge about how

data is organized. Therefore, skills such as entity-relationship

modeling and relational modeling must be mastered before

actual analytical skills are learned. Understanding metadata, if

data is organized and prepared for analysis, or data preparation,

if data is not prepared for analysis, is mandatory for any serious

data analytics effort. Therefore, learning the methods that

enable understanding of metadata and data preparation

(including structuring, re-structuring, and cleaning of data)

before learning methods for analyzing data is critical.

3.3 Proper Role of Software Tools (Principles #3)

We acknowledge the need to include state-of-the-art software

in a business data analytics curriculum. However, when it

comes to software tools, we subscribe to the view that tools are

a means to an end, where the end is more important than the

means. In other words, the focus should be on the concepts the

software tools implement rather than on the proprietary details

of how those concepts are employed in particular packages. Our

intent is not to showcase students’ fluency and comfort level

with a particular software or platform. Instead, they should be

recognized for their knowledge of principles, methods, and

problem-solving skills.

We expect our students and graduates to be able to

seamlessly and quickly switch between using competing

software, such as various data modeling tools, DMBS

platforms, data mining software, data visualization packages,

and tools of the future, based on their knowledge of methods

and principles encapsulated in these tools.

Regarding the data centrality principle (principle #1), we

recognize that if data analysts do not have a professional

understanding of how data is organized, they cannot optimally

use their analytical skills; i.e., it is often difficult to analyze data

if one does not understand how it is organized and why it is

organized the way it is organized. Once data centrality is

acknowledged, the modeling sequencing principle (principle

#2) becomes essential for achieving it. Lastly, if we do not

institute principle #3, we render the program a narrow, vendor-

proprietary educational effort rather than a true, universally

applicable degree.

4. BUSINESS DATA ANALYTICS CERTIFICATE

EVOLUTION

In this section, we will chronologically outline the evolution of

the BDA Certificate program at our institution. This outline will

serve to underscore how both internal and external factors and

events influenced and shaped our program. We will outline both

the initiatives that resulted in progressive success as well as the

ones that outright failed or had to be abandoned. All of them

were important in providing us with the experience and

expertise to continually improve the program.

4.1 The DWHBI Certificate (Prior to 2007)

The first precursor to the BDA Certificate at our institution was

the Graduate Certificate in Data Warehousing and Business

Intelligence (DWHBI Certificate), launched in 2001 by the

information systems group at the School of Business at our

institution. This certificate was put together to supplement the

MBA degree and the other graduate degrees while

simultaneously showcasing two newly created graduate

business courses: Data Warehousing, and Data Mining. In the

1990s and early 2000s, both Data Warehousing and Data

Mining were relatively new, growing areas enjoying strong

industry demand accompanied by the lack of professionals with

proper education in these areas. The DWHBI Certificate was

created to capitalize on these trends.

The certificate was created as a 5-course program, where

students began by taking the Database Systems course and then

proceeding to take the Data Warehousing and Data Mining

courses. Two electives followed these. At that time, the

Database Systems course was already mature with an in-depth

coverage of database requirements, database conceptual

modeling (ER modeling), database logical modeling (relational

modeling) and normalization, SQL, and issues related to

database implementation and administration. For database

modeling, we used our own home-grown application called

FatFreeERD, which students could install for free on their

computers. This application’s capability for drawing ER

diagrams and relational schemas was used in the modeling part

of the class. The DBMS platform that was used for SQL was

Oracle, which was acquired through participation in the Oracle

Academic Initiative. This program allowed academic

institutions access to Oracle technology and MS Access.

The newly created Data Warehousing class was designed to

be taken after the Database Systems class. As in the Database

Systems class, this class heavily emphasized data-modeling.

The main focus was, of course, on modeling analytical data

repositories, namely data warehouses and data marts. In

particular, the class heavily emphasized dimensional modeling

with star schemas. The FatFreeERD application was utilized, as

it can also draw star schemas. Other data-warehouse related

topics, such as the extract, transform, and load process (ETL),

and OLAP/BI were also covered, but in a strictly theoretical

manner.

The Data Mining class also had the Database Systems class

as its prerequisite. This class began in the mid-90s and built on

the skills developed by the students in their database class. Data

mining was introduced as the process of discovering

meaningful patterns in large amounts of data, though the

meaning of “large” has evolved since the mid-90s. Originally,

we used a software tool, Clementine, which had been acquired

by SPSS and Excel for data analysis. Students were required to

build a database, export the data into an Excel spreadsheet, and

load it into Clementine to run data mining models. Emphasis

was placed on data understanding, model understanding,

analysis of the model output, and application to business

problems. All students were required to complete a project

following the CRISP (CRoss-Industry Standard Process)

process (Wirth, 2000).

These three courses (Database Systems, Data Warehousing,

and Data Mining) formed the program’s core and were

mandatory for all students enrolled in the certificate. Per

administrative rules of the university, this certificate was

designated as a five-course program. Therefore, the program

had to include two more courses. The decision was made to list

several Marketing courses as possible electives. To acquire the

DWHBI certificate, students had to take two of these applied

elective courses. These included courses on Business to

Business Marketing, Integrated Marketing Communication,

Digital Marketing, Database Marketing Strategy, Customer

Journal of Information Systems Education, 34(2), 216-230, Spring 2023

221

Relationship Management, and Internet Marketing Strategy.

The structure of the program is illustrated in Figure 3.

Figure 3. DWHBI Certificate Curriculum Prior to 2007

At that time, marketing was (at least internally in the School

of Business) regarded as the main client of analytical data and

as such, an appropriate supplier of elective courses for the

certificate. Administration at that time was satisfied that the

Information Systems group was able to fulfill the requirement

for a five-course sequence in the requirements and that the

Marketing group was able to increase the enrollment in their

courses by adding the DWHBI Certificate students.

The certificate students were generally satisfied with both

the core and elective classes. They reported that there was very

little connection between the two groups of classes, but they

liked acquiring the data management and analysis skills they

viewed as supplementary to their main area of study. This was

not understood to be a critical issue since the certificate was

awarded mostly to the students already enrolled in one of the

master’s programs at the graduate school of business, as all of

the courses in the certificate also counted towards their master’s

degree. In fact, no separate enrollment figures for the DWH/BI

certificate were kept during this period.

The program during this period is a combination of

convenience and compromise. There was little communication

and coordination between the Information Systems and

Marketing faculty. Both groups viewed the certificate as a side

initiative that enabled graduate business students that took

certain courses from both areas to acquire an additional

credential.

4.2 DWHBI Certificate (2007 – 2015)

By 2007 the curriculum context at our institution was starting

to change as the data-related issues were perceived as

increasingly valuable, not only by the Information Systems

department but also by other departments. One of the

consequences of this new focus was the decision to start

viewing the DWHBI certificate as its own program as well,

rather than just as a supplement to the existing master’s

programs at the School of Business. The five-course certificate

was still offered as an elective component of the master’s

programs at the School of Business (a relationship that

continues to this day) but an effort was undertaken to promote

it also as a credential that could be acquired by students joining

our graduate school of business just for the certificate.

The leadership of the DWHBI certificate undertook an

audit of all graduate classes at the School of Business and

identified the courses that had greater relevance and connection

to the DWHBI Certificate, and as such, added them to the list

of electives, as shown in Figure 4.

Figure 4. DWHBI Certificate Curriculum 2007-2015

The list included courses on Managerial Accounting and

Integrated Decision Making from the accounting department as

well as Project Management and Forecasting Methods courses

from the operations management area. In addition, another

Information Systems course was identified as an elective:

Strategic Uses of IT. Several Marketing classes from the

previous list of electives were dropped (as they were perceived

as not sufficiently relevant for the DWHBI certificate), while

three remained: Integrated Marketing Communications,

Database Marketing Strategy, and CRM. At this time, the

Information Systems faculty engaged with all of the faculty

teaching elective courses, and all agreed that elective courses

should be integrated in a way that aligned with the mission of

the DWHBI certificate. At the same time, all the core courses

were undergoing changes and additions.

The Database Systems course started using the newly

launched, free, web-based open-suite ERDPlus (erdplus.com)

for the data modeling aspect of the course. In addition to

continuing to use Oracle for the SQL portion of the class,

students were also provided with the access to Teradata DBMS

through a membership in the Teradata University Network

initiative. The Data Warehousing class maintained its heavy

focus on modeling and switched to using the ERDPlus to create

star schemas. Other topics were now covered in a much more

applied way, including using Informatica software for ETL and

Tableau for OLAP. Big Data topics were introduced, and

Greenplum MPP and MapReduce Hadoop platforms were

included in the coursework.

By 2007, Data Mining had evolved, as had the tools

available for its application. SPSS has continued to expand and

improve its product. The software name was changed to

Predictive Analytics Software (PASW), and the number of data

mining tools had expanded to reflect the software’s new

orientation. The ability to load data from various sources was

also made possible. The course continued to include association

rule mining, cluster analysis, decision trees, and neural

networks as focus techniques. The course used several cases

from different business fields, and students were still required

to complete a project that followed the CRISP methodology.

Since the DWHBI certificate was now viewed as more than

just a supplement to the existing master’s degrees, enrollment

into the certificate started to be actively monitored and tracked.

The enrollment numbers for the DWHBI certificate during this

period oscillated widely, as shown in Figure 5. Since this period

coincided with the great recession that started in 2008, it is not

easy to evaluate the effect of the quality of the curriculum and

Journal of Information Systems Education, 34(2), 216-230, Spring 2023

222

courses to the enrollment itself. But, certainly, the numbers

have mostly grown, as compared to 2007.

Figure 5. DWHBI/BDA Certificates Awarded

(Compared to 2007)

Note that enrollment numbers in this paper are given as

ratios. Due to privacy and competitiveness reasons, we do not

have permission from our institution to give exact numbers or

demographic breakdowns. The ratios still provide an

illustration of progress and trends.

4.3 Business Data Analytics Certificate (2015 – 2021)

With the attention that the broad field of Business Data

Analytics was gaining, and considering that all three core

classes in the DWHBI Certificate were fundamental and

integral parts of Business Data Analytics, the decision was

made to revamp the certificate and rebrand it as the Business

Data Analytics (BDA) Certificate. It became apparent that the

terms Data Warehousing and Business Intelligence were being

perceived as somewhat narrow and technical, especially

compared to the term Data Analytics, which was becoming very

popular and had a much broader appeal.

Serious analysis of the DWHIBI Certificate curriculum and

the entire graduate school of business curriculum was

undertaken. For the newly named BDA Certificate, the decision

was made to consider only courses where the data and/or data

analysis was one of the central themes. The intent was to

identify courses that either contain elements and methods that

are useful and instrumental for business data analytics or

integrate business data analytics with the application topics that

they cover.

The decision was made to keep all three core courses in

place as they were as fundamental to the mission of the new

certificate as they were for the old one. Next, the evaluation of

the electives from the 2007-2015 period was undertaken. Due

to various reasons, ranging from not having data and analytics

as the main focus (for example, Strategic Use of IT, Project

Management) to changes in the course syllabus and/or

turnovers of faculty teaching the courses resulting in the change

of course focus (which occurred every couple of semesters and

in too many cases instructors were allowed to change classes

completely, often minimizing the data analytics aspect), all

previous electives, except the Forecasting course, were

eliminated from the certificate. This is illustrated by Figure 6.

At this point, the emergence and prominence of data

analytics as a broad business phenomenon was reflected in the

fact that several departments at our School of Business created

one or more course in their field with a focus on data analytics.

Those courses used knowledge from our base analytics trio of

classes but with applications in their specific areas. Those

courses included Human Resources Data Decision Making,

Supply Chain Analytics, Marketing Metrics, and Customer

Analytics. Unlike the electives of previous iterations of the

DWHBI certificate, these domain-specific courses were created

and taught by faculty with a solid background in data

organization and analysis topics, in addition to their expertise

in their respective domains. Also illustrated by Figure 6 is the

number of new courses added to the list of electives. The

existing Applied Econometrics class in the economics

department was identified as having the analysis of data as its

main focus and, as such, appropriate for inclusion in the list.

Figure 6. DWHBI/BDA Curriculum 2015-2021

In addition, several new courses were created in the

Information Systems department. One of those was a new

course on Data Visualization. This course consists of three main

parts. The first part of the course covers data visualization

theory, including categorical and quantitative variables, basic

chart types, visual distortion and the “Lie Factor” formula, and

chart evaluation. Students learn these theoretical concepts and

apply them to many chart examples. Group exercises in class

are one of the key components of the first part of the course.

The second part of the course focuses on using Tableau, one of

the most widely used software tools for data visualization.

Students learn the fundamental building blocks of charts and

how to implement them in Tableau. Some unique features

include the custom-made top-K chart module, parameterized

and flexible histogram module, and custom rank module. The

last part of the class focuses on dashboarding and storytelling.

The course also involves a quarter-long group project that

requires the analysis of a real-world dataset. In lieu of a final,

students present their project findings using dashboards and

stories in Tableau in a formal setting.

Another information systems course included was a

programming course titled Programming for Decision Making.

For several years, we relied on the Computer Science

department for courses related to coding. Over time, our

students began asking for a coding course with specific

applications in business. We began with Visual Basic, moved

to C#, then to VBA with a strong emphasis on coding in Excel,

then to R, and finally to Python. We currently offer VBA, R,

and Python courses. The programming language focus change

is partially driven by the evolution of languages and their

acceptance by the industry and is in line with trends identified

in our literature review (Dong & Triche, 2020; Johnson et al.,

Journal of Information Systems Education, 34(2), 216-230, Spring 2023

223

2020; Ozturk & Hartzel, 2020; Stanton & Stanton, 2020). Also,

we expose our students to a variety of coding approaches. We

continually monitor real-world developments, especially

regarding skills expected of entrants to the job market.

We strongly believe that coding is a good skill in which to

develop competence for several reasons: First, it strengthens a

student’s logical thought process, and second, it takes the

mystery out of what is often perceived as an inaccessible area

(as many students think of mathematics or statistics). We also

believe that the language of choice will continue to change, but

the skills will remain constant. We have accepted that the

language we offer will be a moving target.

Another new course, titled Business Analytics, was added

to the list of electives. This course was designated as a BSAD

(Business Administration) course, indicating that it did not

belong to one particular department at the school of business,

even though the information systems department managed it.

This particular course was envisioned as a course that would

provide students with the basic instruction of the R language

while at the same time being a broad course that would give

students a high-level overview of data mining, data

visualization, and other information systems tools and methods

for data analysis, in a way that can serve as a standalone course

for the members of our MBA population who only want to take

one course (instead of the entire BDA Certificate). At the same

time, the course serves as an introduction to and building block

of the certificate for students interested in completing the whole

certificate. While the BSAD course touches on various BA

approaches and methodologies, including an introductory

discussion of Data Mining, standalone Data Mining was still

needed in our BDA curriculum, as it delves much deeper into

data mining topics.

In 2020, we added one more elective Information Systems

course (online/asynchronous), which proved to be extremely

popular (proven by large enrollment in the inaugural version of

the class and a high student evaluation score for the course

(4.8/5) and instructor (4.9/5) effectiveness) by both graduate

certificate students and those in other MS and MBA programs

(as elective). The course, Applications of Visualization is

envisioned to introduce the UX side of data visualization to our

students. In this experiential and hands-on course (using

Tableau), students develop a vocabulary and framework for

discussing, critiquing, assessing, and designing visual displays

of quantitative data. This class focuses on the awareness and

application of human perception and cognition (gestalt laws,

preattentive attributes, color, memory, cognitive effort, etc.),

best design practices in visualizing quantitative data,

dashboarding, and interacting and storytelling with data. As the

analytics curricula mature, it is clear that the UX side of

analytics is becoming a critical skill set for our graduates. Their

interest in this course, student satisfaction rates (previously

noted evaluation scores and a high Net Promoter Score of 80),

and resulting professional impact confirm the value of this type

of content. Here are just sample statements of the impact this

newly created course has had on our students:

• “I printed out one of the homework assignments to bring

with me to my interview… I think this made me stand

out in my interview which led to an immediate offer

after.”

• “I’ve started getting projects at work revolving around

class content - it has helped tremendously!”

• “This course has helped me in creating visualization,

sometimes even simple charts were drastically

improved with minimizing cognitive effort. My

supervisor’s supervisor even commented on how well I

improved a monthly report.”

• “The skills learned in this class will set you apart as a

professional and possibly open up opportunities into

data analytics.”

• “I think this is one of the best courses I have taken. The

content is highly relevant and applicable in the

workplace...”

The content choices for our courses and their dynamic

modifications over time are a result of several factors: our

understanding of market needs, alignment of relevant standards

and available literature, and faculty content and research

expertise, collaboration and leadership. We maintain contact

with the marketplace and its needs through close contact with

our alumni (many now with leadership roles in large

corporations and consulting firms) and regular university,

department, and program-sponsored formal and informal

events. Furthermore, we rely on the expertise of our faculty

members who (i) are on boards of local companies, (ii)

published leading database, data warehousing, and

programming textbooks, (iii) run consulting practices focused

on data analytics, and (iv) operate research labs such as UX and

biometrics lab.

As shown in Figure 5, the switch from the DWHIBI

certificate to the BDA Certificate coincides with a significant

increase in enrollments. While it is entirely possible that some

or even all growth was driven by the increase in data analytics

within business schools, the enrolled students often verbalized

that the branding change allowed them to view the core courses

in the certificate as both approachable and widely applicable,

while the choice of electives proved to be both plentiful and

satisfactory. Evidence of our success includes very positive

teaching evaluations (where students’ comments repeatedly

refer to the “approachable” and “applicable” nature of the

courses), as well as results of conversations with students

during the application for graduation process. These are

voluntary, but nearly half of the students participated. During

these conversations, core principles are routinely discussed

with students who confirm their positive effect on curriculum

and job search. It was common to hear from our BDA “alumni”

that our BDA courses were instrumental to securing a new job

or completely pivoting their careers.

4.3.1 Accelerated and Online Versions of the BDA

Certificate. As the BDA Certificate enrollment continued to

grow, we decided to offer our students different ways of

completing the certificate. Traditionally, all courses at our

graduate school of business are offered in person at our campus

location. The courses are offered on a quarterly basis, where

each course meets once a week (typically 6-9 pm in the

evening) for ten weeks. All our graduate programs, including

the BDA Certificate, can be started during any of our quarters:

Fall, Winter, Spring, or Summer. Students complete the BDA

Certificate in as quickly as two quarters or as slowly as five

quarters (if they are taking only one course per quarter).

In the Summer of 2017, we decided to offer an accelerated

version of the BDA Certificate, with each course meeting every

day for two weeks at our downtown location. The students in

Journal of Information Systems Education, 34(2), 216-230, Spring 2023

224

this accelerated intensive program spent 3 hours daily in the

classroom. A single class could be completed in two weeks.

Courses were offered in sequence, enabling students to

complete the 5-course certificate in 10 weeks. In addition to the

three core courses, two electives were chosen for inclusion in

the accelerated version: Data Visualization and Business

Analytics. The program succeeded with good course enrollment

and generally favorable student feedback. The same accelerated

program was repeated in the summer of 2018.

At that time, the decision was made to create an

asynchronous online version of the five courses in the

accelerated program. The accelerated online asynchronous

certificate was offered to a limited group of students in 10

weeks during the winter of 2018-19. We had concerns about

how the students would fare in such an intensive program done

in a relatively isolated fashion, but to our surprise, the five

courses in this accelerated online version received positive

feedback from the students. We compared students’ feedback

using course evaluations across two formats (accelerated vs. 10-

week) and found either no difference or, surprisingly,

preference for the accelerated version in one instance (taught by

the same instructor) as measured through students’ assessment

of Material Learned (+0.1 difference), Overall Course

Effectiveness (no difference), and Instructor Overall

Effectiveness (+0.2 difference).

Based on this experience, in the summer of 2019 we

delivered the accelerated version of the BDA Certificate in the

asynchronous online format. The program generated a lot of

interest and we had to cap the enrollment for it. The attendees

very positively evaluated the program, and we had another

iteration in the same format for the summer of 2020, which was

equally successful. At our institution, Information Systems and

all business courses have high course evaluation averages and

are viewed as exemplary by the university administration. In

this environment, the BDA Certificate did even better. For

example, in student evaluations, the BDA Certificate courses

were evaluated in the Overall Course Effectiveness on a 5-point

scale, 0.1 points higher than all graduate Information Systems

courses and 0.3 points higher than all graduate business courses.

In Material Learned, the difference was 0.3 and 0.5 points

higher, respectively. The format remained popular with our

students and contributed significantly to the continued growth

of the certificate.

4.3.2 Influence of the BDA Certificate on the

Undergraduate Program. As the BDA Certificate (and its

predecessors) evolved at our institution on the graduate level,

we were able to create continually and include information

systems courses with equivalent topics in our undergraduate

program. For example, as we introduced a standalone Data

Visualization graduate class for the BDA Certificate, we also

introduced an equivalent standalone Data Visualization class

for the undergraduate program. Similar scenarios occurred with

other classes. In each case, the initial impetus for a new course

was the intent to improve the BDA Certificate, and then it was

logical to expand the undergraduate program with an equivalent

course as well.

5. BDA CERTIFICATE FUTURE STATE

At this time, our BDA Certificate program is thriving, and we

are satisfied with all of its aspects, including the course

offering, enrollments, and the professional outcomes of our

students. For example, in the current setup, our students can get

instruction in SQL, R and Python, which is aligned with the

current situation wherein SQL, Python, and R are the primary

programming tools used by analytics practitioners in the

industry (Johnson et al., 2020).

However, we are under no illusions that we have reached a

stable state that will remain unchanged for years to come. At

this stage, when it comes to a business data analytics

curriculum, change is a certainty, and maturity is not probable

(at least not in the near term). Technologies and methodologies,

job market, student needs, methods of delivery, competitors,

and other factors are all subject to continuous changes.

The future will undoubtedly bring challenges,

opportunities, and changes. For example, we recently

developed a course covering data storytelling and user

experience. We are already working on developing additional

courses that we may include as future elective (or even core)

courses for the certificate. The new courses we are considering

would cover business requirements for analytics, machine

learning life cycle, AutoML tools (such as DataRobot),

advanced R, etc. At the same time, we are reluctant to require

more than five courses for a certificate degree. Therefore, our

challenge will be incorporating many diverse paths into a

singular BDA Certificate.

One trend that may positively impact future graduate

offerings, including the BDA Certificate, is the growth patterns

in our undergraduate program. The patterns are quite similar to

the growth in the BDA Certificate. Figure 7 shows the increase

in enrollment in our undergraduate program in the 2007-2021

period. A comparison of Figure 7 with Figure 5 reveals

similarities.

Figure 7. Information Systems (Undergraduate)

Majors Graduating (compared to 2007)

Perhaps not surprisingly, we are seeing an uptick in former

undergraduate IS students pursuing both BDA Certificates and

our MS IS-focused programs. We are responding to this pool of

students by giving them credits for some classes. For others, we

provide guidance in selecting advanced topics and newer

courses that exist only at the graduate level.

Another emerging issue is the need to offer the certificate

in a variety of formats, including in-class, online, and hybrid,

providing the students with the flexibility to take their five

courses in whatever mixture of these formats they prefer. This

will require an increase in our resources and innovative

scheduling options. This also may require adopting new

measures for assessing the quality of teaching and learning

(MacLeod et al., 2019).

Journal of Information Systems Education, 34(2), 216-230, Spring 2023

225

Lastly, prompted by the success of the BDA program, in the

Fall of 2020, we launched Masters in Information Systems and

Analytics Program. This program builds on the foundation of

the certificate and enables a deeper dive into information

systems and analytics topics.

6. CONCLUSION

In conclusion, in addition to our three founding principles, we

adopt the guidelines (“dream” BA program) introduced in our

literature review (Wang, 2015) to assess the process used in our

BDA evolution. First, we developed and incorporated

interdisciplinary courses in every iteration of our program

evolution. In our case, this was primarily implemented through

elective courses. However, our program’s list of electives was

initially extensive and lacked data focus. Over time, we

narrowed the list of interdisciplinary courses. Second, we

closely aligned BA course offerings and their content with the

practice’s needs. Our faculty expertise and their close

interaction with practice informed our three founding principles

to ensure alignment with market needs when it comes to critical

thinking, data, analysis methods, and tool skills. Third, our

courses include a mix of theoretical knowledge and experiential

learning using real-world projects and resulting data and project

management challenges. The inclusion of additional real-world

projects continues to be an area of opportunity. Fourth, the

program captures the union of relevant disciplines, which, in

our case, currently include statistics and econometrics,

programming, IS, and SCM/Forecasting. Lastly, we strengthen

the faculty members’ BA expertise through continuous training,

strategic hiring, insistence on adopting all instructors’ founding

principles, and talent realignment.

In this paper, we have presented the process of building the

BDA Certificate at our institution and the evolution that

included multiple curriculum revisions, additions and

subtractions of individual courses, expansions of delivery

methods, and program name changes. Ultimately, the success

of our program was based on the core principles of

acknowledging the centrality of data, mandating the modeling-

based course sequencing, and recognizing the proper role of

software tools.

We do not assert that how we built our BDA Certificate

program is the only way to go about business data analytics

academic programs (which may include vendor-specific

certificates, focusing on one component of BA, and various

forms of domain-specific BDA certificates, to name a few).

Instead, we have described in this paper how that worked for

our circumstances and our constituency in an ever-changing

area. We believe that sharing our story can help the readers

understand the steps in this fluid process as they embark on their

own BA curriculum initiatives.

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Journal of Information Systems Education, 34(2), 216-230, Spring 2023

228

AUTHOR BIOGRAPHIES

Dinko Bačić is an assistant professor of information systems in

Loyola University Chicago’s

Quinlan School of Business, and the

founder of the UX & Biometrics

(UXB) lab. His research interests

include data visualization, human-

computer interaction, biometrics,

cognition, neuro IS, business

intelligence & analytics, and

pedagogy. He has papers published

in premier journals such as Decision Support Systems,

Communications of the Association for Information Systems,

AIS Transactions on Human-Computer Interaction, Springer

Computer Science Lecture Notes and Leonardo, among others.

He has over fifteen years of corporate and consulting

experience in business intelligence, finance, project

management, and human resources.

Nenad Jukić is a professor of information systems at the

Quinlan School of Business at

Loyola University Chicago. Dr.

Jukić conducts research in various

information management–related

areas, including database modeling

and management, data warehousing,

business intelligence, data mining,

business analytics, Big Data,

information systems education, and

IT strategy. His work has been published in numerous

management information systems and computer science

academic journals and conference publications. Dr. Jukić is the

author of the textbook “Database Systems: Introduction to

Databases and Data Warehouses,” whose Edition 2.0 has been

published in July of 2020, and the creator of the free web-based

database modeling tool ERDPlus.

Mary Malliaris is a professor of information systems in Loyola

University Chicago’s Quinlan

School of Business, and Chair of

the Information Systems & Supply

Chain Management Department.

Her research and teaching interests

are in statistics, databases, data

mining and analytics. She has

published articles in the Review of

Quantitative Finance and

Accounting, The International

Journal of Computational Intelligence and Organizations, the

Journal of Banking and Finance, Neural Networks in Finance

and Investing, Neural Computing and Applications, and

Applied Intelligence among others and is currently an associate

editor for The Journal of Economic Asymmetries.

Svetlozar Nestorov is an associate professor of information

systems in Loyola University

Chicago’s Quinlan School of

Business. His research and teaching

interests are in databases, data mining

and analytics, data visualization,

education, and e-commerce. He has

published articles in international

journals and conferences including

Decision Support Systems,

Information Systems Management, Journal of Database

Systems, ACM SIGMOD Record, PLoS Computational Biology,

and Computing in Science and Engineering.

Arup Varma is a Distinguished University Research Professor

and Frank W. Considine Chair in

Applied Ethics at the Quinlan School

of Business, Loyola University

Chicago. He holds a PhD from

Rutgers University, New Jersey

(USA), an M.S. in Personnel

Management & Industrial Relations

from XLRI, Jamshedpur (India), and

a BSc (Hons) from St. Xavier's

College, Kolkata (India). Dr. Varma’s research interests include

performance appraisal, expatriate issues, and HRM issues in

India. He has published over 100 articles (and book chapters)

in leading journals. He has won multiple awards for teaching,

research, and service, including the 2017 Alumnus Award for

Academics from his alma mater, XLRI. In 2018, he spent 6

months in India, as a Fulbright Scholar.

Journal of Information Systems Education, 34(2), 216-230, Spring 2023

229

APPENDIX

BA Curriculum Manuscript Summary

Year

Manuscript

Contribution

Data Source(s)

Type

2022

(Burns &

Sherman, 2022)

Lessons through a reflection on the process of BA

minor creation

Single school

focus

Undergraduate

2021

(Hameed et al.,

2021)

Overview of BA program offering and faculty

background.

535 AACSB

schools (US)

Undergraduate and

graduate

2021

(Shim et al.,

2021)

Integrating professional certification with four

courses in the undergraduate BA track.

Single school

focus

Undergraduate

2020

(Ceccucci et al.,

2020)

The overlap between the BA and the Information

Technology degree programs.

225 AACSB

schools

Undergraduate

2020

(Dong & Triche,

2020)

Common set of skills required across BA jobs and

highlighting gaps in undergrad. curricula.

29 universities

Undergraduate

2020

(Johnson et al.,

2020)

A framework for MSBA curriculum by identifying

BA practitioners’ concepts, skills, and tools.

15 MSBA

programs

Graduate

2020

(Paul &

MacDonald,

2020)

Comprehensive analysis linking specific industry

needs to specific coursework that allows any

university to create a well-rounded BA program.

Industry,

literature, and 18

universities

Undergraduate and

graduate

2020

(Stanton &

Stanton, 2020)

Identification of job titles, credential and skills

required for BA jobs along with program changes to

prepare students for BA career requirements.

Literature

Undergraduate

2019

(Burns &

Sherman, 2019)

Assessment of BA minor structures

65 randomly

selected schools

Undergraduate

2019

(Clayton &

Clopton, 2019)

AACSB accreditation concerns and BA certificate

developmental process.

Single school

focus

Undergraduate

2019

(Urbaczewski &

Keeling, 2019)

Critical discussion of MIS departments transition to

Analytics departments

Literature

Undergraduate and

graduate

2018

(Parks et al.,

2018)

Explore MSBA programs from top business schools

and investigates their content.

62 programs

Graduate

2018

(Rienzo & Chen,

2018)

Analytics curriculum development roadmap is

offered by examination of evolving analytic needs

through the lens of disruption theories.

Literature

Undergraduate and

graduate

2018

(Stephens &

McGowan, 2018)

Identify four overarching issues in BA curricula

literature and provide BA curricula development

recommendations.

24 private

universities and

literature

Undergraduate

2017

(Choi et al.,

2017)

Overview of how U.S. universities have designed

and implemented BA programs/courses in terms of

the degree (major, minor) and certificate program,

and the number of courses.

94 AACSB

schools

Undergraduate and

Graduate programs

2017

(Jafar et al.,

2017)

Made the case (and a curriculum template) that a

Master’s degree in data analytics is the right place

for IS educators start the assimilation of the data

analytics phenomenon.

13 graduate

programs and

single school

focus

Graduate

2017

(McLeod et al.,

2017)

Compare curricula usage by SAP member schools to

evaluate changing curriculum.

SAP member

schools’ requests

Not specified

2017

(Phelps &

Szabat, 2017)

Statistics perspective on the content and who is

teaching business analytics courses

17 universities

Undergraduate

2016

(Mills et al.,

2016)

Explored the growth of analytics, BI, and big data

content in AACSB IS programs between 2011 and

2016. The growth is analyzed through the mapping

of courses to four pillars of analytics.

118 AACSB

programs,

literature

Undergraduate and

graduate

2016

(Turel & Kapoor,

2016)

The analysis of BA-related course offerings and

subsequent BA-maturity ranking of institutions

124 schools (414

courses)

Undergraduate and

graduate

Journal of Information Systems Education, 34(2), 216-230, Spring 2023

230

2016

(Wymbs, 2016)

Developing and managing BA curriculum using

innovation theory. Two-phased curriculum

approach: program mission & accreditation and

course design.

Literature, Single

school focus

Undergraduate

2015

(Aasheim et al.,

2015)

Assessment of similarities and differences between

undergraduate BA and data science programs.

13 universities

Undergraduate

2015

(Dunaway &

McCarthy, 2015)

Lessons in the development and implementation of

MSBA

Single school

focus

graduate

2015

(Gupta et al.,

2015)

BI&A model curriculum guidelines across program

types (undergraduate, MS, and MBA)

Lit., interviews,

and surveys

Undergraduate and

graduate

2015

(Henry &

Venkatraman,

2015)

Identified a need for data analytics integration into

business skill sets and curriculum designs. Provided

a framework to design and teach data analytics

skills.

Literature

Unspecified

2015

(Hilgers et al.,

2015)

Recognition that traditional business and IT degrees

lack important data and analytics skills. A mapping

of BA skills to three newly courses is documented.

Single school

focus

Undergraduate

2015

(Kang et al.,

2015)

Four pillars of analytics; trace the skills and courses

needed to support each pillar.

Single school

focus

Graduate

2015

(Mamonov et al.,

2015)

Present competency-based BA curriculum

6 institutions

Graduate

2015

(Mitri &

Palocsay, 2015)

Explore two curricular: a BI/BA concentration in a

typical IS major and a comprehensive, integrated

BI/BA undergraduate major. Assess 2010 IS Model

curriculum in relation to BI/BA content,

Literature

Undergraduate

2015

(Rodammer et

al., 2015)

Document the redesign of business curriculum to

meet the demand for BA skills development

Single school

focus

Undergraduate

2015

(Schiller et al.,

2015)

BA curriculum strategies and best practices.

Conf. panel, 2

universities

Graduate

2015

(Wilder &

Ozgur, 2015)

BA undergraduate curriculum designed around five

knowledge domains: PLC, DM, analytical

techniques, deployment, and a functional area.

Literature, single

university focus

Undergraduate

2014

(Gorman &

Klimberg, 2014)

Early analytics program benchmarking focused on

job outlook and academic response and curricula

(programs, concentrations, certificate + graduate)

32 institutions

offering BA

programs

Undergraduate and

graduate programs

2014

(Jacobi et al.,

2014)

Formalized interdisciplinary Big Data Analytics

(BDA) curriculum development process.

Literature

Undergraduate

2014

(Wixom et al.,

2014)

Panel report describing the current state and best

practices in BI and BA curricula. Focus on the

emergence of Big Data and its implications.

96 universities +

practitioners

Undergraduate and

graduate

2013

(Warner, 2013)

Overview of the efforts made to develop a

curriculum for business analytics that meets

the needs of all MBA students

Single school

focus, literature

Graduate

2012

(Chiang et al.,

2012)

Evaluate the role of BI&A education in business

schools, the challenges facing IS depts, IS curricula

and program development in BI&A education.

8 schools

Undergraduate and

graduate

2009

(Sircar, 2009)

BI importance in the leading business programs and

a description of BI/BA minor curriculum

50 universities,

single school

Undergraduate

Information Systems & Computing Academic Professionals

Education Special Interest Group

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initial editor screening and double-blind refereeing by three or more expert referees.

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ISSN: 2574-3872 (Online) 1055-3096 (Print)