Module Manual - hs-neu-ulm.de · PDF filerequirements appropriate to its solution ... Project management in consulting ... Strategic management concepts including the core directions

Module Manual Master in Business Intelligence and

Business Analytics (BIA)

Master in Business Intelligence & Business Analytics

1. Semester

Module: Enterprise Information Systems No 1

Responsibility

for Module:

HNU

Type of course: compulsory

Language: English Semester: 1. semester

Course Type: Class, Project Extend: 10 weekly teaching hours

ECTS: 15 Duration: 1 term

Frequency: every winter semester

Requirements: none

Module

Description:

The Module Enterprise Information Systems compounds of the

courses Enterprise Application and IT-Management, Enterprise

Application Engineering, Consulting and IS Research.

The course Enterprise Application and IT-Management provides a

comprehensive overview of enterprise information management

concepts and practices and their use in achieving business objectives.

Furthermore students are introduced to IT management and the

managements of information systems on a companywide level.

On successful completion of the course, students will be able to:

define and understand essential terms and concepts of enterprise applications

understand benefits, functions and features of enterprise applications supporting business processes

understand the importance of the manager’s role in implementing information technology

list the different types of IT systems and the characteristics of each one

analyse a problem in IT and identify and define the computing requirements appropriate to its solution

In the course Enterprise Application Engineering students will get

familiar with the planning, design and development of information

systems as well as the necessary concepts, methods and tools.


understand and apply the software development process for enterprise applications

understand the functionality and application domains of modern application architectures, software components and technologies


relevant for information system development and use them appropriately to perform the design and implementation of enterprise applications

understand the process, methods and tools of enterprise application design and development, and being able to work in different roles within a corresponding project

In the course Consulting students will be introduced to the consulting

market and the consulting process.


differentiate consulting companies

understand the consulting business and apply the related methods and techniques during a consulting project

create high-quality business presentations for certain audiences

understand the difficulties of consulting as a people business and learn and apply techniques to manage these

understand the purpose, objectives, challenges and concepts of project and quality management and apply the related methods and activities

In the course IS Research students are introduced to scientific work in

general and in particular to the domain of IS research and the related

questions and activities.


understand IS as a research disciplines and be able to perform own research work in this field

understand scientific publications and the review

write, review and present a scientific paper

Content: Enterprise Application and IT-Management

Overview about the purpose, features and functions of enterprise IS

Essential terms an concepts of enterprise applications

Benefits, functions and features of enterprise applications supporting business processes

Common approaches, concepts and methods in the field of IS management

Core concepts of IT management

IT systems

Enterprise Application Engineering

Processes, methods and tools of enterprise application design and development

Software development process models and their properties


Functionality and application domains of modern application architecture, software components and technologies relevant for information system development

Consulting

Consulting market in Europe

Consulting methods and techniques

Project management in consulting

Consulting process

Consulting project

IS Research

Objectives, methods, tools and typical research question of information system research

Scientific publications

Exam: 1 written paper, 1 presentation

Literature: Baan P.: Enterprise Information Management: When Information

becomes Inspiration, Springer, 2012.

Thiadens T.: Manage IT!: Organizing IT Demand and IT Supply,

Springer, 2005.

Faircloth J.: Enterprise Application Administration: The Definitive

Guide to Implementation and Operations, Morgan Kaufmann,

2014.

Stair R., Reynolds G.: Fundamentals of Information Systems, 7th

edition, Course Technology/Cengage Learning, 2014.

Zelkowitz M.: Advances in Computer Software: Software

Development, Academic Pr Inc., 2008.

Huskey J.: Software Development, World Technologies, 2012.

O’Mahoney J., Markham C.: Management Consultancy, Oxford

University Press, 2013.

Wickham L.: Management Consulting: Delivering an Effective

Project, Pearson Education UK, 2007.

Heinzl A.: Theory-guided Modeling and Empiricism in Information

System Research, Springer, 2011.


Module Business Information Management No 2

Responsibility for

Module:

HNU



Course Type: Class, Project Extend: 12 weekly teaching hours


Frequency: every winter semester

Requirements: none

Module

Description:

The Module Business Information Management compounds of the

courses Strategic Management, Corporate Performance

Management, BI Strategy, Data Management and BI Platforms and

Tools.

The course Strategic Management introduces the participants to the

core concepts, frameworks, and techniques of strategic management,

which will allow the students to understand what the manager tasks are

to achieve performance.


formulate and execute effective organization strategies

develop the necessary resources and capabilities to achieve sustainable competitive advantage

examine strategic issues from the perspective of a CEO or general manager

develop leadership perspectives and core process management skills

During the course Corporate Performance Management students

learn to steer an enterprise in the intended direction with the support of

corporate performance management, as measures and measurement

systems are the foundation of returning values to investors and owners

of enterprises.


explain CPM theories and understand how they can help companies to measure the success of their strategies by using information management systems


develop key performance indicators to measure and control

organization strategies.

The course BI Strategy gives the participants an understanding of

strategic business initiatives and the core concepts of a successful BI

strategy.


define the scope of a BI strategy, and follow the BI implementation and deployment with respect to the business strategy

draw and/or recommend a BI strategy roadmap which will cover the business and technology implementation process

outline the scope of investment in BI tools, human resources and technology setup for the BI implementation

establish and run the BICC

In the course Data Management students will be trained to

understand the practices and processes of data quality assessment

and improvement as well as to manage the increasing amount of data

and use it as competitive advantage.


develop a common definition for business rules for data elements across an enterprise

explain the processes and tasks in data management

design data models

define special requirements of data management

ensure data integrity throughout different databases and applications

In BI Platforms and Tools the students will get familiar with BI

architecture and the technical, organizational and entrepreneurial

requirements for a successful implementation.


understand the difference between operational and analytical information systems (OLAP vs. OLTP)

understand and apply the main concepts of relational and multi-dimensional data modeling and database systems

understand and handle tools, methods, technologies and interfaces to work with ETL processes, databases and data warehouses and Management Cockpits

examine the entire BI software market and different market segments of BI solutions


develop a framework for software selection and define selection criteria

evaluate the BI platform and tools

Content: Strategic Management

Strategic management concepts including the core directions and goals of an organization, the environment of an enterprise (social, political, technological, economic and global factors), industry and market structure and organizational strengths and weaknesses

Frameworks of strategic management (global economy, corporate responsibility, marketing, human relations)

Techniques of strategic management (human resource management, strategic resource allocation, corporate sustainable strategy, crisis management)

Corporate Performance Management

Main theories of CPM

Strategic planning of CPM projects

Performance management application (formulation of a CPM-strategy, Balances Scorecard, …)

Measures and measurement systems

BI Strategy

Organisations needs for BI and key benefits of implementing a BI strategy

Process of BI strategy development

Components of a BI strategy and key steps in formulation a BI strategy

Function and tasks of a BICC

Data Management

Development, execution and supervision of plans, policies, programs and practices that control, protect and deliver the value of data and information assets

Definition of data elements, the structure, storage and exchange

Information management

Definition of data quality

Causes of data quality problems

Roles, responsibilities and accountabilities in data management

Processes and techniques of data management assessment and data quality improvement

BI Platforms and Tools

OLAP and OLTP

Foundations and trends of database management, ETL and data warehousing

Management dashboards


Trends in the BI market

BI architecture and scenarios for the implementation

BI front-end solutions

BI software providers

Exam: written exam, 180 minutes

Literature: Sherman R.: Business Intelligence Guidebook: From Data

Integration to Analytics, Morgan Kaufmann, 2014.

Rausch P., Sheta A., Ayesh A.: Business Intelligence and

Performance Management, Springer, 2013.

Eckerson W.: Performance Dashboards: Measuring, Monitoring

and Managing your Business, Wiley, 2011.

McKnight W.: Information Management Strategies for gaining

competitive advantage with data, Elsevier, 2014.

Sebastian-Coleman L.: Measuring Data Quality for ongoing

Improvement, Morgan Kaufmann 2011.

Vaismann A., Zimányi E.: Data Warehouse Systems: Design and

Implementation, Springer, 2014.

Barney J., Hesterly W.: Strategic Management and Competitive

Advantage, 5th edition, Pearson Education Limited, 2014.

Jones G., Hill C.: Theory of Strategic Management, 10. ed.,

South-Western Cengage Learning, Mason (Ohio) 2013.

Bourne M., Bourne P.: Handbook of Corporate Performance

Management, Wiley, 2011.

Ireland D., Hoskinson R., Hitt M.: The Management of Strategy,

Concepts and Cases, Cengage Learning Enea, 2010.

Paladino, B.: Five key principles of corporate performance

management, Hoboken, 2007.


2. Semester

Module: Quantitative Methods No 4

Responsibility

for Module:

UTN



Course Type: Class, Lab Extend: 8 weekly teaching hours


Frequency: every summer semester

Requirements: none

Module

Description:

The Module Quantitative Methods compounds of the courses Applied

Statistics, Big Data and Social Network Analysis and Predictive

Analytics and Data Mining.

The course Applied Statistics introduces the participants to the core

concepts of applied statistics.


understand and apply common statistical methods

test hypothesis using empirical research methods

describe and analyse statistical research data

In the course Big Data and Social Network Analyses students will

learn how to analyse huge data amounts and how to collect data from

social networks and use social network analysis.


understand the concept and importance of big data

retrieve information from huge and fast changing data sets

understand the fundamental big data platforms and tools

understand how to handle a large amount of data with search engines

use social network analysis to analyze data collected from social mediating technologies such as Facebook, Twitter, Wiki and e-mail for sales and marketing, engaging customers and promotion initiatives


The course Predictive Analytics and Data Mining provides an

integrative foundation in the field of predictive analytics and data mining,

which becomes increasingly important for today’s companies.


understand the art and science of predictive analytics to define clear actions that result in improved decisions and business results

select, prepare, construct, integrate, structure and format data to be most effective and to ensure the predictive model meets the business goals

understand the basic concepts and principles in data mining

learn commonly used algorithms for data mining, for structured and unstructured data

gain experience applying some of the algorithms to solve real world data mining problems

Content: Applied Statistics

Statistical basics

Describing, exploring and comparing data

Probability distribution

Correlation and regression analysis

Hypothesis testing

Estimates and sample size

Reliability analysis

Big Data and Social Network Analysis

Big data technologies

Fundamentals in big data platforms and tools

Introduction to social media and social networks analysis

Visualizing and analysing e-mail

Predictive Analytics and Data Mining

Data validation and cleaning

Preparing a Data Mining analysis

Tools for Data Mining

Predicting Methods, e.g. Classification, Regression

Describing Methods, e.g. Cluster Analysis, Assoziations

Exam: P(K/StA/RE)

Literature: Witten I., Hall M., Frank E.: Data Mining, 3rd edition, 2011.

David Doane: Applied Statistics in Business and Economics,2012

Professor D. R. Cox and Professor Christl A. Donnelly: Principles

of Applied Statistics, 2011


Robson Leonardo Ferreira Cordeiro and Christos Faloutsos: Data

Mining in Large Sets of Complex Data,2013

Matthew A. Russell: Mining the Social Web: Data Mining

Facebook, Twitter, LinkedIn, Google+, GitHub, and More, 2013

Thomas Miller: Web and Network Data Science: Modeling

Techniques in Predictive Analytics (FT Press Analytics), 2014

Eric Siegel and Thomas H. Davenport: Predictive Analytics: The

Power to Predict Who Will Click, Buy, Lie, or Die, 2013

Steven Finlay: Predictive Analytics, Data Mining and Big Data:

Myths, Misconceptions and Methods, 2014

Dean Abbott: Applied Predictive Analytics: Principles and

Techniques for the Professional Data Analyst, 2014

Foster Provost and Tom Fawcett: Data Science for Business:

What you need to know about data mining and data-analytic

thinking, 2013.

Vijay Kotu and Bala Deshpande: Predictive Analytics and Data

Mining: Concepts and Practice with RapidMiner, 2014.

Lawrence Maisel and Gary Cokins: Predictive Business Analytics:

Forward Looking Capabilities to Improve Business Performance,

2014.


Module: Analytical Applications No 5

Responsibility

for Module:

UTN



Course Type: Class Extend: 8 weekly teaching hours



Requirements: none

Module

Description:

The Module Analytical Application compounds of the courses

Analytical Processes in Supply Chain Management and Analytical

Processes in CRM and Marketing.

The course Analytical Processes in Supply Chain Management

provides a comprehensive review of the concepts and methods involved

in analytical processes in supply chain management, based on the SCOR

reference model.


analyse and define supply chain business processes and understand their role in the company operations

identify potential bottlenecks and opportunities for cost savings or other improvements

develop and specify an improved process and detect implementation risks

develop BI dashboards to support supply chain business process performance measurement

The course Analytical Processes in CRM and Marketing teaches

students how to collect and analyse customer and marketing specific

data.


evaluate the success of marketing initiatives

personalize marketing on the data collected about a customer

work with what-if scenarios and predict how likely a specific customer will buy a specific product

Content: Analytical Processes in Supply Chain Management

Supply chain concepts

SCOR reference model


Supply chain management tools and techniques

BI dashboards

SCM case studies

Analytical Processes in CRM and Marketing

Marketing business metrics

Customer segmentation grouping

Profitability analysis

Event monitoring

Predictive modelling

Exam: written paper, presentation

Literature: Michael H. Hugos: Essentials of Supply Chain Management,

Third Edition, 2011.

Paul Myerson: Lean Supply Chain and Logistics Management

2012.

Shoshanah Cohen and Joseph Roussel: Strategic Supply Chain

Management: The Five Core Disciplines for Top Performance,

Second Editon, 2013.

David Blanchard: Supply Chain Management Best Practices,

2010.

Sunil Chopra and Peter Meindl: Supply Chain Management,

2012.

Francis Buttle and Stan Maklan: Customer Relationship

Management: Concepts and Technologies, 2015.

V. Kumar and Werner Reinartz: Customer Relationship

Management: Concept, Strategy, and Tools, 2012.

Andrew D. Banasiewicz: Marketing Database Analytics:

Transforming Data for Competitive Advantage, 2013.

Judah Phillips: Building a Digital Analytics Organization: Create

Value by Integrating Analytical Processes, Technology, and

People, 2013.

Philip Kotler: Principles of Marketing, 2013.


Module: Communication Management No 6

Responsibility

for Module:

UTN






Requirements: none

Module

Description:

The Module Communication Management compounds of the courses

Information Visualization and Professional Communication.

In the course Information Visualization participants will be enabled to

present IT-concepts, ideas, and results target-group-specific and

descriptive.


understand the principals involved in information visualization

visualize data expressively and effectively

evaluate visualization systems

The course Professional Communication teaches the meaning of

intern and extern communication in the daily business of an enterprise.


plan and conceptualize the intern and extern company communication

use diverse presentation techniques

use mediation techniques

professionally react in difficult negotiation situations

Content: Information Visualization

Variety of existing techniques and systems in information visualization

Visual representations of abstract data to reinforce human cognition

Descriptive and target-group-specific information preparation for the efficient and effective use of the information

Visualization of CSF and KPI’s with appropriate design elements

Professional Communication

Professional and audience-related communication and presentation


Internal business communication

Negotiation methods

Cooperation, mediation and motivation techniques

Decision making process and communication of intern and extern changes


Literature: Isabel Meirelles: Design for Information: An Introduction to the

Histories, Theories, and Best Practices behind Effective

Information Visualizations, 2013.

Colin Ware: Information Visualization, Third Edition: Perception

for Design (Interactive Technologies), 2012.

Alberto Cairo: The Functional Art: An introduction to information

graphics and visualization (Voices That Matter), 2012.

Stephen Few: Information Dashboard Design: Displaying Data for

At-a-Glance Monitoring, 2013.

Randy Krum: Cool Infographics: Effective Communication with

Data Visualization and Design, 2013.

James R. DiSanza and Nancy J. Legge: Business & Professional

Communication: Plans, Processes, and Performance, 2011.

Barbara G. Shwom and Lisa G. Snyder: Business

Communication: Polishing Your Professional Presence, 2013.

Jennifer H. Waldeck and Patricia Kearney: Business and

Professional Communication in a Digital Age, 2012.

Steven A. Beebe and Timothy P. Mottet: Business & Professional

Communication: Principles and Skills for Leadership, 2012.

James R. DiSanza and Nancy J. Legge: Business & Professional

Communication: Plans, Processes, and Performance. 2011.


Module: Cultural Exchange No 7

Responsibility

for Module:

UTN, HNU


Language: English, German Semester: 1. semester and

2. semester


ECTS: 5 Duration: 2 terms

Workload: Total: Attendance: Pre-reading: Independent

study:

Requirements: None

Module

Description:

The Module Cultural Exchange compounds of the courses Culture and

History in Germany and Culture and History in South Africa.

The course Culture and History in Germany imparts fundamentals

about political, social and cultural structures of Germany.


name the corner stones in German history

understand the basics of Germany’s political system

name important German writers and artists

act according to German business etiquette

The course Culture and History of South Africa teaches

fundamentals of S.A. politics as well as social and cultural structures.


understand the basics of SA’s politics

name the cornerstones of SA history

name important SA writers and artists

act according to SA business etiquette

Content: Culture and History of Germany

Introduction to German culture (architecture, literature, arts)

German customs

Fundamentals in political and social structures of Germany

Cornerstones in German history

Culture and History of SA


Introduction to SA culture (architecture, literature, arts)

SA customs

Fundamentals in political and social structures of SA

Cornerstones in SA history


Literature: Fulbrook M.: A concise History of Germany, Cambridge University

Press, 2004.

Noyle N.: German literature A very short introduction, Oxford

University Press, 2008.

Lord R.: Germany a survival guide to customs and etiquette,

Marshall Cavendish, 2011.

David Rock: Argentina, 1516-1987: From Spanish Colonization to

Alfonsín, 1987.

Jill Hedges: Argentina: A Modern History, 2011.

Vicente López y Planes and Vicente Fidel López: Argentina,

Legend and History, 2013.


3. Semester

Module: Research Methods No 8

Responsibility

for Module:

UTN, HNU


Language: English Semester: 2. semester and

3. semester

Course Type: Class, ELearning Extend: 6 weekly teaching hours


Requirements: none

Module

Description:

The Module Research Methods compounds of the courses Research

Methods and Applied Research Project.

In the course Research Methods students get to know the types of

research they can use for issuing a research paper or dissertation.


understand and describe various research methods

use research approaches and instruments

issue a research paper

The Applied Research Project allows students to apply a qualitative or

quantitative research method to a specific lead theme and deepen their

knowledge regarding advanced research methods.


arrange and critically address sources as well as to apply statistic research methods

write a thesis, present it, and host a critical discussion

Content: Research Methods

Literature research

Interviews

Case studies

Observation research

Experiments

Correlation research

Applied Research Project


Introduction to the lead theme of the course

Advanced research methods

Creation of a paper

Presentation and critical discussion


Literature: Cresswell W. J.: Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, SAGE Publicashion Inc., 4th Edition, 2013.

Walliman N.: Research Methods: The Basics, Routledge, 2010.

Sekaran U., Bougie R.: Research Methods for Business: A Skill-Building Approach, Wiley, 6th Edition, 2013.

Yin. K. R.: Case Study Research: Design and Methods, SAGE Publications Inc., 5th Edition, 2013.

Machi L., McEvoy B.: The Literature Review: Six Steps to Success, Corwin, 2nd Edition, 2012.


Module: Research and Thesis No 9

Responsibility

for Module:

UTN, HNU



Course Type: Master thesis,

colloquium

Extend: 5 months


Requirements: successful completion of the modules 1-8

Module

Description:

This module compounds the Master Thesis and the Thesis

Colloquium.

The Master Thesis shall exhibit the student’s competencies and

abilities to research, solve and critically discuss a current topic of the

field Business Intelligence and Business Analytics. The students have to

meet formal and content standards and have to organize their work load

to finish in a specific time frame. A reference to the practice is desirable.

In the Thesis Colloquium the students defends his or her thesis with a

presentation and following discussion.

Documents

Module Manual - hs-neu-ulm.de · PDF filerequirements appropriate to its solution ... Project management in consulting ... Strategic management concepts including the core directions