Moving to Business Analytics: Re-Designing a Traditional Systems

Information Systems Education Journal (ISEDJ) 19 (6)

ISSN: 1545-679X December 2021

https://isedj.org/; https://iscap.info

Moving to Business Analytics: Re-Designing a

Traditional Systems Analysis and Design Course

James J. Pomykalski

[email protected]

Sigmund Weis School of Business

Susquehanna University

Selinsgrove, PA 17870, USA

Abstract

Many traditional Information Systems (IS) programs are either redesigning current courses to

incorporate business/data analytics or expanding curricular offerings to include business /data analytics;

our IS program chose the former route to meet the demand (from employers) for business/data

analytics. In that transition, a traditional systems analysis and design (SA&D) course was redesigned

to focus on the procedures and skills needed to perform business/data analytics; this required changes

to the focal topical areas. In 2017-18, curricular changes transitioned a traditionally focused database

systems analysis and design course to incorporate business/data analytic skills. This paper focuses on

the course redesign and the new business/data analytic topics, activities, and technologies; we highlight

the important shifts created to the topical areas of this course. The paper emphasizes the rationale and

student outcomes generated.

Keywords: Data/Business Analytics, Pedagogy, Systems Analysis and Design, Structured Data, SQL

1. INTRODUCTION

Over the past decade, Information Systems (IS)

programs have been adjusting to meet the

growing demand by industry to produce business

analytics/data science professionals (Mills,

Chudoba, & Olson, 2016). This has required a

change/addition of courses that meet the need for

the skills of these professionals (Radovilsky,

Hegde, Acharya, & Uma, 2018). In addition,

“there are a growing number of degree programs,

specializations, and certificates in data science

and data analytics at both the graduate and

undergraduate levels” (Davenport & Patil, 2012;

Dumbill, 2013; Aasheim, Williams, Rutner, &

Gardiner, 2015).

Our undergraduate program decided to revamp

the IS curriculum to address the change in skills.

The first change occurred in existing courses in

the business foundation. Our business foundation

is unique, we did not have a traditional three

credit hour Introduction to IS course, but instead

all students complete three IS-related courses.

The first course in this sequence was a formal

Systems Analysis and Design course. This course

culminated in students designing a simple

database in Access.

The sophomore level Systems Analysis and

Design (SA&D) course was re-focused and re-

titled Data Collection and Modeling; this paper

focuses on the changes made this SA&D course.

When designing this course change certain areas

of the SA&D process were preserved due their

importance in capturing the structured data used

in business analytics; namely requirements

analysis and data modeling; we discuss each area

in a later section of the paper. In addition, new

areas—data literacy, data curation/metadata,

data quality and cleansing and SQL—were added.

This paper is structured in the following manner.

In the next section, we describe the primary

learning objectives of this newly designed course;

including the areas of SA&D that remain due to

their importance in the data/business analytics

process. In section three, the importance of

focusing on data literacy is examined. Section

four continues the data literacy discussion with

the introduction of the two major models that

Information Systems Education Journal (ISEDJ) 19 (6)

ISSN: 1545-679X December 2021

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form the basis for this newly developed course;

the data life cycle model of Chisholm (2015) and

the CRISP-DM model for data analytic problem

solving (Chapman, Clinton, Kerber, Khabaza,

Reinartz, Shearer & Wirth, 2000). In section five,

we address the primary pedagogical approach

used to introduce the skills-based modules of the

course. In section six, we describe the major

topics explored throughout the course. These

topics include project management, data curation

and the use of metadata, data cleansing, data

modeling and data structuring, data analysis

using the CRISP-DM model, and data retrieval

using basic SQL commands. In the seventh

section, we examine the major project that is

undertaken (in teams) by the students to gain

hands-on experience. In the final section, we

state some concluding remarks by discussing the

impact and major outcomes of the course for the

students. In addition, we address some future

work that needs to be undertaken.

2. COURSE LEARNING OBJECTIVES

The newly designed Data Collection and Modeling

course required a complete reworking of the

course learning objectives. For this redesign

effort the work of L. Dee Fink (2013) was utilized.

Fink’s taxonomy of significant learning includes

six critical areas to consider when designing a

course for significant learning impact. These

areas are: foundational knowledge, application,

integration, human dimension, caring, and

learning how to learn. Fink believes that a course

that addresses each of these categories will

create a lasting change in the learner that will be

important to the learner’s life. In particular, we,

when designing this course, envisioned that

change to be one of understanding the

importance of leveraging data to solve business

problems.

A primary goal of the course is to begin to

introduce students to the basic skills necessary to

become a data literate member of a data-driven

society. The concept of data literacy is not new

and not limited to data analytics. The primary

definition of data literacy used for the course is

given by Wolff et al. (2016). Data literacy is “the

ability to ask and answer real-world questions

from large and small data sets through an inquiry

process, with consideration of ethical use of data”

(Wolff, Gooch, Cavero Montaner, Rashid, &

Kortuem, 2016, p. 23). Students “need to have

at least a basic understanding of the concept of

data, and they need to be able to understand and

engage with data fitting their role and start

talking the language of data” (Goodhardt,

Lambers, & Madlener, 2018, p. 1).

The new course learning objectives are:

1. Identify the data literacy skills necessary

for your given profession.

2. Utilize Microsoft Project, Microsoft Visio,

Microsoft Access and basic SQL

commands to answer business questions

using data from a project domain.

3. Develop a multi-part report that

describes how your data activities

addressed the selected business

questions

4. Employ and advance your written

communication skills in conveying

technical information.

5. Demonstrate and advance your

teamwork skills in working through a

data-intensive project.

In order to achieve the first learning objective, we

strive to produce students that at least meet the

minimum data literacy requirements of their

chosen major field. This includes acquiring the

basic foundational knowledge necessary to

function productively in their first employment

opportunity. The second learning objective deals

with the application (skills) that the student will

obtain through the course. This learning

objective deals primarily with Fink’s (2013) areas

of providing foundational knowledge, the

application of that knowledge and the ability to

learn how to learn especially in dealing with new

skill development. These areas were chosen

based on the work of authors who had addressed

the specific skills necessary for today’s data

scientist. (Dumbill, 2013; Goodhardt, et al.,

2018; Mills, et al., 2016; Radovilsky, et al.,

2018). The third learning objective deals with the

team project assigned in the course. This project

work will encompass the integration of the

knowledge and skills through application, human

dimension in understanding how to function as a

productive team member, caring, about the work

and the people on your team and learning how to

learn category through the work of the project.

The final two learning objectives are included to

fulfill of both the University’s Central Curriculum

and the business school’s AACSB learning

objectives. These objectives are most concerned

with integration of knowledge and the human

dimension in terms of both written

communication and teamwork skills.

In addition to these learning objectives, we

identified a set of conceptual and constructive

take-aways for each student; these take-aways

are shown in Appendix 2. These take-aways

highlight more specific skills learned in the

course. The learning objectives were developed

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using the current literature on analytics skills

(Dumbill, 2013; Mills, et al., 2016; Radovilsky, et

al., 2018) and in consultation with recent alumni

in the business analytics and related fields. The

skills were deemed appropriate for a first course

in business analytics, taught to all business

students, in the business foundation of the

program.

3. DATA LITERACY

In 1992, Peter Drucker, in an article in the Wall

Street Journal (Drucker, 2005)), explained that

data users—executive or professional—need to be

data-literate and decide what data to use, what

to use it for and how to apply the data to solve

problems. For organizations to prosper in a data

rich environment, “every organization, in every

industry, should adopt a data-literate culture”

(Smith, n.d.).

The development of data literacy requires

individuals to acquire skills in two areas. First,

individuals must be able to understand and

appropriately use tools and technologies for data

analysis and decision-making. Second,

individuals “need to learn to think critically and

analyze data to choose the correct data and the

suitable analytical and presentation methods for

the situation” (Smith, n.d.).

This course module begins with two separate

readings on data literacy to familiarize the

students with the need for data literacy and a

data culture, to provide a basic understanding of

data literacy, and to show how data literacy is

germane to the various majors with the business

curriculum. The essence of this discussion is

summarized by the figure in Appendix 1 which is

taken from Goodhardt et al. (2018).

To further enhance data literacy skills

assignments are used so -students practice using

tools—Microsoft Project to create and update a

project plan, Excel for data cleaning, Microsoft

Access to structure data, and SQL to create

queries—that allow students to fulfill the

technology component of data literacy.

To improve the critical thinking and data analysis

skills of the students, individual assignments in

data cleaning, database development, and SQL

statement development, and project assignments

problem statement development, selection and

matching of potential data sets for analysis, data

cleaning, database development, and SQL

statement creation.

Another aspect of data literacy that is covered in

this module is the life cycle and use of data.

Students are introduced to the two models which

are described in more detail in the next section.

Through data life cycle (Chisholm, 2015), the

students are presented with a model of how data

is typically handled within the business

environment; the model describes seven stages

within the life cycle of data. The CRISP-DM model

(Chapman, et al., 2000) is also introduced—

although to lesser extent. The model illustrates

the application of data within a problem-solving

process. The concurrent examination of these

models exhibits the important business processes

undertaken within overlapping intervals of these

models; as stated earlier the discussion on the

overlap of the models is a work in progress.

One final discussion area in this module is the

development of a data strategy as proposed by

DalleMule and Davenport (2017). The article

reveals the two intertwined sides of a data

strategy: data offense and data defense. Data

offense, which is the primary focus of their project

work, deals with how an enterprise uses data to

“support business objectives such as increasing

revenue, profitability, and customer satisfaction.

Data defense activities ensure compliance with

regulations and minimizing downside risk. Data

defense shows the ethical implications that are

addressed in data literacy (Wolff, et al., 2016)

and includes discussion of data governance

issues.

Data literacy, the data life cycle model, the

CRISP-DM model and data strategy are topics

that will be revisited throughout the course as

they set the foundation for the student’s

understanding of how data “should be” viewed

and handled within and enterprise.

The primary assessment vehicle for the

understanding of data literacy is a short reflective

paper (two to four pages). The students are

asked to identify the activities and topics that

were covered in the course that contribute to

building their data literacy skills foundation. The

students are asked to put themselves, in their

chosen major, along the scale shown in Appendix

1 and discuss the level of proficiency and their

projected data role. The reflective assignment is

used as the final individual learning assessment

to gauge each student’s depth of knowledge

attained through the course and work on the

team project.

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4. DATA AND DATA-DRIVEN DECISION-

MAKING

When examining data and data-driven decision-

making it is often helpful to examine existing

models that describe these processes. In

particular the examination of the data life cycles

within an organization and the data-driven

decision-making process.

In examining the data life cycle, one particular

model that is most useful is the data life cycle

(DLC) model proposed by Chisholm (2015).

Chisholm proposed a seven-stage model of the

data life cycle: capture, maintenance, synthesis,

usage, publication, archival, and purging. The

data life cycle does not necessarily define all the

specific processes involved in handling data, it

does provide “high-level”, i.e., strategic,

understanding of the activities within that stage

regarding enterprise data; the stages are shown

in Figure 1 and discussed in more detail in

Chisholm (2015) and Pomykalski (2020).

Figure 1: Stages in the Data Life Cycle

On the other hand, the analytics problem-

solving process is best described by CRISP-

DM model which was developed through a

partnership led by DaimlerChrysler. The

CRoss-Industry Standard Process for Data

Mining (CRISP-DM) methodology,

developed in 1996, is “based on the

practical, real-word experience of how

people conduct data-mining projects”

(Chapman, et al., 2000, p. 3). The CRISP-

DM methodology (see Figure 2) consists of

six stages: business understanding, data

understanding, data preparation, modeling,

evaluation, and deployment.

These two models, blended together, form the

basis of the course. The primary objective of

which is to introduce students to the use of data

to solve business questions. While a preliminary

understanding of the integration of these models

has been developed the formal description of the

integration is considered future work.

Figure 2: Stages in CRISP-DM

5. GENERAL PEDAGOGICAL APPROACH

As described in the previous sections, the course

covers several topical areas relevant for

understanding and sufficiently comprehending

the business/data analytics field. In the sections

below, we describe the topical areas and justify

their inclusion in the course.

Before examining the course topics, a brief

discussion of the pedagogical approach taken to

enhance the learning of the students is essential.

For the major, skill-based modules within the

course (project management, data cleansing,

data modeling, data structuring and data

retrieval), the course is designed to follow a

similar pattern in the student learning experience.

Each module is designed and introduced using a

similar pattern:

1. The module is introduced through a

reading(s) with an in-class lecture and

discussion to clarify the topic;

2. Students are then engaged in a low-

stakes, in-class assignment as practice

(discussion of the frequent, low-stakes

(FLS) assignments is given below);

3. Students are then given an individual

assignment—with a more complex

problem as a graded element; finally

4. Students then perform the same

assignment, within the context of their

team project.

For the skill-based modules, the method of FLS

graded assignments is used (Warnock, 2012).

This is done, as stated by Warnock (2012) as a

means for student to build confidence that they

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are performing the task correctly and as a means

to communicate and clarify any misunderstands

or missteps apparent in the assignments. This is

important because after the low-stakes

assignments, the individual and team-based

assignments carry larger weight in the overall

grading of the course.

6. TOPICAL AREAS

Within the current course there are two topical

areas that are largely addressed as general topic

areas (data literacy and data curation/metadata)

and five skill-based areas. The five skill-based

areas all follow the pedagogical approach outlined

in the section four. While data curation/metadata

can be viewed as a skill-based area, the

pedagogical approach is focused more on the

work done as a project team and not individually.

Project management

Project management has been deemed as an

important skill for all students within the business

foundation and was made part of the original

SA&D course; it has carried over into the business

analytics curriculum. Knowledge of project

management is vital for students to manage the

significant number of course projects that they

have each semester within their business

foundation courses and for their work in their

career.

Within this module, students gain basic

knowledge through readings, chapters from

Heagney (2016) and FSL assignment in

developing both a work breakdown structure and

the Gantt chart for short case study. In addition,

the students create a work breakdown structure

for their team project, based on the overall

project description, and create a Gantt chart in

Microsoft Project that must be updated

throughout the semester; this is done to track the

project deliverables.

This learning module and work on the project

allows each student to not only understand the

vocabulary and basic activities performed in the

management of project work, but gain

understanding as to how their individual

contribution to that work enhances not only their

individual learning but their team development.

Data curation and metadata

Data curation is a data management activity that

plays an important role in the data life cycle. Data

curation is “the work of organizing and managing

a collection of datasets to meet the needs and

interests of a specific groups of people” (Wells,

2019, p. np). Data curation is performed prior to

the start of the data analysis process; it is the

identification and structuring of the data pertinent

to the data analysis process. The primary

purpose, according to Wells (2019), of data

curation is to make datasets easy to find,

understand and access. Data curation activities

are performed primarily by problem domain

experts and those ensuring metadata quality.

Metadata, or “data about data”, is vital in the use

of data driven solutions in enterprises. Metadata

contains information that not only describes the

data, but includes the type, length, and previous

use information; this is captured in many modern

data catalogs (Villanova University, 2019). Data

curation and metadata development are

important in the metadata management process.

The students develop knowledge of the

importance of metadata to the business problem-

solving process primarily in the team project by

examining data dictionaries to find pertinent data

fields. They must differentiate between

descriptive, structural, and administrative

metadata and apply the three types of metadata

to their project work by reading and utilizing a

data dictionary that is provided (Villanova

University, 2019).

Data quality/data cleaning

“In the case of business analytics, or the study of

data and what information can be gained from the

data, the 80/20 rule becomes: 80% of the time

spent by a data scientist is on gathering,

cleansing, and storing the data, while 20% of the

time is spent on analyzing the data” (Snyder,

2019, p. 23). While gathering and storing are

explicit parts of the data life cycle process, Snyder

(2019) points out that data cleansing has often

been granted a “lower status” in the data quality

activities within business analytics. However,

given the importance of data quality in the

business analytics process it should have a more

prominent role in the overall data life cycle.

In this course we dedicate an entire module to

data quality and data cleansing. Through a

reading and a class discussion the importance of

high-quality data for decision-making is stressed.

The students are then introduced to a number of

basic Excel techniques that can be used in the

data cleansing process with any size data sets.

Excel is used, in this course and subsequent

business foundation courses, as part of a business

school wide plan to incrementally build the

student’s knowledge and proficiency with using

Excel as a tool in solving business problems.

Again, a low-stakes in-class assignment is used

to assess their initial understanding.

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A data cleansing activity highlights the work in

this module and the students are expected to

repeat the data cleansing process within their

data analytics project; see section seven. The

data cleansing activity—a specific tutorial in

Monk, Brady, and Mendelssohn (2017)—focuses

on finding missing data, inconsistent data (based

on data type), duplicate records, formatting

issues, incorrect entries based on spelling

mistakes, and simple logic errors. The students

create an error log which utilizes a different type

of entry for each particular error type. A class

discussion that focuses on the remedies for each

type of error follows the work on the class

activity. An individual assignment with a larger

dataset is given to determine their skill

development in Excel and understanding of the

data cleansing process; a key output is the

development of the well documented error log.

Data modeling

Within a data analytics project, the data model is

most critical model for the understanding of the

structured data necessary for solving the

business problem. Data modeling is a “technique

for organizing and documenting a system’s data.

A method for the representation of organizational

data” (Whitten & Bentley, 2008, p. 270).

The primary model that is used to capture the

entities and the business relationships between

the entities is an entity-relationship diagram

(ERD). The entity-relationship diagram is “a data

model utilizing several notations to depict data in

terms of the entities and relationships described

by that data” (Whitten & Bentley, 2008, p. 271).

The students use traditional SA&D readings to

understand and develop an ERD through the in

class exercise. Coverage of all the elements of

the ERD—entities, attributes, relationships and

cardinality—are addressed. The individual

assignment used is a carryover from the SA&D

course in which the students develop both the

model and a business memo to describe the

major elements of the ERD (Pomykalski, 2006).

The students, within the context of their project

also design and develop a data model specific to

the data selected to address the chosen business

problems and create a new memo, using the

same structure, to discuss the ERD development

process.

Data structuring/analysis

Having the ability to structure data in a simple

database application is important to all students

regardless of the particular business discipline. In

this course, Microsoft Access is used to structure

the project data.

The students are introduced to Microsoft Access

though a set of in-class and individual

assignments taken from the Monk, et al. (2017).

These assignments give the students the basic

skills necessary to create relational database

tables, forms, queries and reports. The students

learn to move Excel data into tables, and then

perform basic data analysis functions.

One of the fundamental Excel skills introduced in

this module is the use of Excel pivot tables as a

means for further exploration and analysis of the

data. This again is covered through tutorial in

Monk, et al. (2017). The basic skills developed in

this module also followed the prescribed

methodology laid in section five.

The CRISP-DM model is also examined in this

module of the course. The students examine the

particular activities that occur in the later stages

of the model dealing with data preparation,

modeling, and evaluation.

Data retrieval—SQL

The final learning module of the course is focused

on the development of an understanding of basic

SQL commands. This is the first introduction to

SQL commands that students receive, and it was

implemented in this course due to feedback from

alumni that were obtaining first jobs in a variety

of different majors. The knowledge of SQL to find

and extract data for their new teams was seen as

an enhancement to the role they could fill directly

after graduation.

This three-week module includes introduction to

a basic SELECT statements and the ability to

perform many simple extraction and

summarization functions. In particular, the

module covers the initial half of the Forta (2013)

text on SQL. In particular, we introduce

commands for: data retrieval, sorting, filtering,

manipulation, summarizing, grouping and table

joins. This module does not cover the

development of a database structure using SQL.

Again, this skills-based module follows the same

pedagogical structure described in section five,

however, since each new function needs to

practice, a series of in-class assignments, each

with newly understood functions, are provided in

a low-stakes assignment format.

7. PROJECT WORK

One of the primary learning assessments in the

course is the work done through the team project.

The team project is a full semester project which

has multiple intermediary deliverables that help

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comprise the overall grade for this component.

Teams of three to four students work through the

various activities that mimic the skill-based

modules in the course. The project idea and the

necessary business questions and data are taken

from the Teradata Analytics challenge. Each of

the two projects that are currently available have

been used without significant overlap due to the

richness of the business questions and the vast

amount of data that is available to the students.

The project, the description of which is given as

Appendix 3, includes a number of activities for the

project team to undertake.

The students begin the project by creating an

overall project management plan for the

undertaking of the project. The first activity is to

review and select two business questions, these

are provided as part of the Teradata Analytics

Challenge; the students are encouraged to review

the data that is associated with the particular

business questions. An in-class review and

discussion of the data sets (along with the

corresponding data dictionary) is undertaken.

The first project deliverable includes (1) a written

description of the selection of the business

questions and their significance, (2) a work

breakdown structure including the rationale for

many of the project management decisions, and

(3) a Gantt chart that schedules each of the

particular activities on a semester long timeline.

In part two of the project, the students turn their

attention to closer examination of the data sets

that they have chosen for their particular

business questions. The students are asked to

explore and cleanse the data sets. They must

provide a detailed account of the activities that

they undertook in the examination and data

cleansing work. The final stage for this work is to

structure the selected data fields into different

data sheets within Excel to facilitate the import of

the data to a Microsoft Access database structure.

In part three, the students are asked to model the

data and create an ERD that shows the data in

clear logical, well organized structure. The

students must define the data entities, attributes,

relationships, and cardinality. The deliverable for

this stage is the memo and ERD model for their

project data similar to the deliverable for the

individual assignment (Pomykalski, 2006).

The final activity that is undertaken within the

project is the development of the SQL queries

needed to examine the data and answer the

business questions. The final deliverable—the

final written report—incorporates the discussion

on the development of the SQL queries as well as

summarizing all of the work undertaken in the

project.

The project gives the students the experience of

providing a final client driven report that

summarizes all of their activities over the course

of the semester. The students also gain

experience creating intermediate, progress

reports that can be provided to the client and the

project team as a means for documenting the

work. The students are allowed to utilize the

previous (corrected) reports to create the final

project deliverable.

8. CONCLUSIONS/FUTURE WORK

The transition of a traditional IS curriculum to a

business analytics curriculum requires a

substantial amount of rework and new thinking as

to the topics and pedagogies used. However, not

all of the traditional IS concepts need to be

removed. As we have shown, both project

management and data modeling have been

revised to focus on the data aspects of the

analytics process.

In this paper, we examined and described this

transitional work—we continue to tinker with

many areas—and this new structure seems to

serve the students well. We have had a number

of students go into both internships and

employment opportunities with these new skills

and that feedback from these students has been

positive.

Analytics is a field that continues to evolve and

therefore the evolution of this course and

subsequent courses in the business foundation

will require close scrutiny and monitoring in the

coming years. The integration of the data life

cycle and the CRISP-DM models are future

projects so that students can investigate the

complexities of the business/data analytics

process.

We are pleased with the progress we have made

thus far and we believe that these courses will

better serve our students are they undertake new

employment opportunities as both interns and

permanent employees.

9. ACKNOWLEDGEMENTS

The author would like to acknowledge the

contributions of the other faculty, both current

and recently retired, in the development of this

course redesign and the establishment of a

recently created major in business data analytics.

As new faculty have recently joined our ranks we

look forward to their contributions to the

continual development of this course and expect

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to be able to provide updates of these

development efforts in the near future. In

addition, we appreciate the comments and

feedback of the reviewers.

10. REFERENCES

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Gardiner, A. (2015). Data Analytics vs. Data

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Chapman, P., Clinton, J., Kerber, R., Khabaza, T.,

Reinartz, T., Shearer, C., & Wirth, R. (2000).

CRISP-DM 1.0: Step-by-step data mining

guide. Somers, NY: IBM. Retrieved June 2017,

from http://www.crisp-dm.org/CRISPWP-

0800.pdf

Chisholm, M. (2015, July 9). Seven Phases of a

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n.p. Retrieved May 28, 2018, from

https://www.information-

management.com/news/7-phases-of-a-data-

life-cycle

DalleMule, L., & Davenport, T. (2017, May-June).

What's Your Data Strategy? Harvard Busines

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APPENDIX 1: Relation between Data Literacy Levels of Proficiency and Data Roles

(Goodhardt, Lambers, & Madlener, 2018)

Level

Definition

Conversational

Basic understanding of the

concepts of data, analytics and

use cases; one who “gets it” but

cannot explain it to others

Literacy

Ability to speak, write and engage

in data and

analytics programs

and use cases

Competency

Competent of designing, developing

and

applying data and analytics

programs

Fluency

Fluent in all three elements of

information

language across

most business domains within

an

industry vertical

Multilingual

Fluency across all three

elements of the

information

language across multiple business

domains, industries and

ecosystems

APPENDIX 2: Course Take-Aways

Conceptual Take-Aways

Constructive Take-Aways

Develop a data literacy mindset

Use Microsoft Project to manage a data analysis

project

Examine data through a forma data life cycle

Compile a large dataset, clean and structure the

data

Relate the roles and responsibilities involved in

data management

Develop/enhance your Microsoft Excel skills

Load data into a Microsoft Access database

Write SQL queries to produce answers to

business questions

Produce a formal written report explaining the

results of your queries in addressing the

business problems.

Data Believer

Data User

Data Scientist

Data Leader

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APPENDIX 3: Course Project Description

Analytics Project

Objective: The goal of this project is to create a data analysis report. To be able to do

this, you will chose two business questions, identify, organize, clean and normalize data

relevant to the business questions, load the data into a database, develop SQL queries,

create charts, and detail your analysis in a written report. You will choose your own teams

of 2-3 people.

Project: The general description of this project is given at:

https://www.teradatauniversitynetwork.com/Community/Student-Competitions/2019/2019-

Data-Challenge

The business questions for this project reside in a file on Blackboard; under the Analytics

Project folder. There are five major categories of questions listed: Client Services,

Volunteer Programs, Development, Employer Partnerships and Opportunities, and

Serving Spouses Program. Your team is to pick a category and within that category, they

will select at least two questions to investigate further.

The General Task (From Teradata Challenge submission template):

1. State and describe your understanding of the business question(s) you are

addressing.

2. Choose the datasets provided by the Hiring Heroes USA organization from

Teradata that are most appropriate to address your business question(s). Similar

to an SQL query:

a. Identify the field names and values that you need to address each business

question.

b. Identify the data sets that have related fields to those that you need to

address the question.

c. Clean the datasets to make them consistent in form and format.

3. Provide an Entity Relationship diagram of your relevant data.

4. Design SQL queries to address each question from the cleaned and structured

data.

5. If you cannot run your queries, describe what you expect to get (estimate your

results)

The Specific Deliverables

I. Team Sign-Up: This is the simplest of the deliverables for this project. Each

person enrolled in the course must get into a team with one or two other people

currently enrolled in the course; preferably in the same section. Once the team

members have agreed to become a team, one member of the team must send

me an EMail that lists the members of the team.

Due Date: Friday, September 20, 2019

II. Plan: There are two deliverables associated with this stage.

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a. A document describing the selection of the category and the two business

questions your team will address and your rationale for your choices. You

should clearly state the questions and then describe, in your own words,

your understanding of those questions.

b. A Microsoft Project file containing the work breakdown structure of tasks,

precedence of tasks, who completed each lowest-level task (resource) and

duration (time) to complete each task. In addition, you need to provide a

document that describes your decision making process in completing this

project management task.

Tentative Due Date: Wednesday, October 9, 2019

III. Microsoft Excel File(s): The data with each worksheet cleaned and

normalized as a table suitable for importing to Microsoft Access. There will be

two deliverables associated with this activity.

a. Your team will produce a progress report, which will outline the selection

of the data sets, field names and values that you have identified, and the

activities you have undertaken, to date, on the files and the work that still

needs to be completed.

Tentative Due Date of Friday, October 18, 2019.

b. Upon completion of the work with the data files your team will create a

document which describes all of the activities you performed on the data

sets to put the data into a consistent form and format.

Tentative Due Date of Monday, November 4, 2019.

IV. Microsoft Visio ERD: There will be two deliverables associated with this

stage.

a. Your team will produce a business memo that explains the entities,

attributes, including primary keys, relationships and cardinality depicted in

your ERD. Make sure that the relations are normalized to at least Third

Normal Form (resolve all many-to-many relationships with associative

entities).

b. Create an entity relationship diagram using Visio. For full credit, make sure

cardinalities are valid and display with crow’s feet for the “many” side.

Tentative Due Date: Wednesday, November 20, 2019

V. Microsoft Word Report: Report summarizing what was done that includes:

a. The questions being addressed and your current understanding of these

questions.

b. A corrected, updated version of your Microsoft Excel file activity report.

c. A report that describes the SQL queries that your team created, the

rationale for each of those queries and the results (or expected results) from

those queries. You are to include a discussion on how your team expected

the queries to address the particular business questions.

Tentative Due Date: Monday, December 9, 2019

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VI. Individual Files: Send both as attachments to an e-mail.

a. Microsoft Excel Peer Evaluation: Download the file from Blackboard. Be

aware, awarding all members all 4s for all criteria will be worth 0 points.

b. Microsoft Word Reflection: The personal statement should include a half-

page (in length) to one page document reflecting on the experience of doing

this project (what parts of the project were your responsibility, what you

learned, what parts of the project would you improve, …).

Tentative Due Date: Wednesday, December 11, 2019

Grading:

Requirement:

Poss.

Points:

I. Team Sign-Up

2

II. Plan (MS Project File)

17.5

III. Cleaned and normalized Data [MS Excel

File(s)]—Including both reports

24.5

IV. ERD (MS Visio File)

15

V. Analysis Report with Charts/Graphs (MS

Word File)

26

TEAM GRADE

85

VI. Individual files (to be e-mailed)

1 Personal Statement (MS Word)

2 Peer Evaluation (MS Excel)

INDIVIDUAL GRADE

15

Please note: that while each team member will likely receive the same score for the

team based portion of this project, individuals grades for the team component are

adjustable (mostly downward) based on an individual’s contribution to the team

deliverables. The impetus for this point deduction will come from discussions with team

members and the two evaluation documents shown above.