MAN 5100 Analytics for Strategic Management

MAN 5100 Analytics for Strategic Management

Course code: 
MAN 5100
Department: 
Strategy and Entrepreneurship
Credits: 
30
Course coordinator: 
Espen Andersen
John Chandler Johnson
Alessandra Luzzi
Course name in Norwegian: 
Analytics for Strategic Management
Product category: 
Executive
Portfolio: 
Master of Management
Semester: 
2017 Autumn
Active status: 
Active
Level of study: 
Master
Teaching language: 
English
Course type: 
Associate course
Course codes for multi- or associated courses.
MAN 5101 - 1. semester
MAN 5102 - 2. semester
Introduction

Business digitalization changes the content and process of strategic analysis. While classical “big picture” analysis skills remain relevant, more and more executives now need to understand data availability, analyzability and advanced forms of data analytics to articulate and evaluate strategies. While data science and analytics professionals typically come from either computer science or statistics, and have years of technical training, strategic decision-makers are typically trained and experienced in business management. As strategy-making grows increasingly dependent on analytics, organizational capacity to bridge the distance between decision-makers and data scientists becomes a distinct source of value creation.

Analytics for Strategic Management aims to create professionals who can bridge this gap as sophisticated data consumers who understand the requirements and process of data analysis. We particularly encourage employers that are expanding data-driven capabilities to use this course as a universally accessible introduction to data-driven strategy for existing employees.

Through lectures, in-class hands-on exercises, workshops, and real-world cases, the course introduces data science and analytics for public, private, and non-profit strategic management applications.

We will introduce the concepts, intuition, and technical execution underlying data science, all of which we contextualize in strategy applications. As we proceed through the technical material, we will introduce examples of strategic problems through which data science offered otherwise elusive insight. During the course, in preparation for the term paper, students will use their expanding skillsets to specify real strategic management projects that could be passed to an internal IT team for execution.

Data science’s origins in statistics and computer science mean that the lingua franca can be technically complicated and often “another world” for many professionals. We recognize this, and intend the program to be a self-contained introduction to the field; there are no math or programming prerequisites. Students will grapple with technical issues, and we know this. Students can expect us to be responsive when they need help.

Learning outcomes - Knowledge

After completing the program, students will understand strategic management applications of data science’s concepts, algorithms, and techniques. Students will leave the course better able to communicate with technical partners (e.g., data science and business intelligence teams) and better able to contribute to data-driven strategy development.
 

Learning outcomes - Skills

Students will learn analytics’ foundational concepts and how to apply those to strategic management. The students will draft a specification detailing a project’s analytics requirements. We will train the students to conduct small analytics projects and to specify fully formulated project requirements for complex or large-scale analytics projects. These skills will facilitate the “handshake” between professionals’ business expertise and dedicated data science teams’ technical execution.

Learning Outcome - Reflection

A critical aspect of this course is that once finished, students will speak enough data science to specify their analytics needs and critically assess analytics findings that will enrich strategic decision-making.

Course content

Analytics for Strategic Management consists of five modules of 3 or 4 days, with webinars between each module. For each module, there will be core readings, interactive workshops and cases for discussion, aiming to help the students develop their term paper projects. The modules build on each other, where the final deliverable in each module is the starting point for the subsequent module.

In groups, the students will pick a potential analytics project addressing an existing strategic question. Following each module, the students will expand this proposed analytics project, further specifying the data, concepts, and techniques they expect may yield actionable information. For the final project, each group will submit a short analytics specification that a dedicated data science team could implement. Each group will present their project to the class.

Inter-module webinars will help the students working on the critical steps of their term project before the next module starts.

Module 1: Competitive Advantage through Data
Module Outcome: Awareness of strategic questions potentially answerable with data
To contextualize the strategic possibilities enabled through analytics, this module begins with an overview of contemporary strategy perspectives. Organizations’ information systems increasingly allow application of data science to business problems. This module explores - through real examples - how data collection has changed in the past few years and how this has the potential to affect business strategy and decision-making. We explore cases where data science has upset traditional understandings, introduced new insights, and changed landscapes.

The end of the module includes a ‘getting started’ workshop in which the students pick a term analysis project, present the strategic challenge to their fellow students, and form groups based on the presentations. The group participants then present initial analysis and aims of their chosen project. The module concludes with a high-level project specification for each group.

Module 2: Analytics Concepts and Processes for Strategic Management
Module Outcome: Understand the Analytics process
This module introduces analytics as a scientific process involving problem specification, modeling, data collection, analysis, and summary. This provides a high-level, qualitative overview of the analytics process and anticipates the more granular approach in Modules 3, 4, and 5. We will use the examples in strategic management from module 1 and students’ term project proposals to illustrate the stages of this process.

At the end of the module, each group should develop an outline for each of the stages of the analytics project they envision will answer their overarching strategic question. The last half day will consist of a workshop in which each group presents their data collection method and the expected outcome.

Module 3: Implementing Analytics for Strategic Management
Module Outcome: Using classification algorithms for analytics projects in Strategic Management
Module 3 is the first of two more technical modules. These modules formalize Module 2’s concepts, and implement and visualize those concepts using the Python programming language. For many students, this may be the first time they have done any programming, and perhaps the first time in years they have looked at equations. That’s OK! The course uses collaborative exercises and teamwork to ensure student success.

By the end of Modules 3 and 4, students will know enough about foundational analytics algorithms and implementations to conceptualize their strategic decisions as data science problems and clearly communicate those problems to data science professionals. Each group will specify the analyses they expect to conduct and the data required to inform their strategic question. The last day in each module will include a mini workshop in which each group will present the current status of their project.

The machine learning algorithms introduced in this module explore strategic questions such as: industry analysis, response to architectural innovation, and evaluating M&As. We will illustrate applications using examples such as IBM’s M&A Pro tool and Viacom’s response to content streaming.

Module 4: Summarizing Data driven recommendations
Module Outcome: Using Data Visualization to communicate analytics projects’ strategy implications
Module 4 continues Module 3’s introduction to algorithms for analytics in strategic management, and adds visualization techniques.

This module uses machine learning algorithms to explore core strategy core questions such as market entry, market expansion, sustaining cost leadership, and evaluating M&As. To illustrate strategic opportunities, this module will examine corporate strategy implications of techniques such as: Airbus’ increasing use of sensors and American Express’ evaluation of new markets.

Module 5: Workshop on Implementing Analytics for Strategic Management
Module Outcome: Use Modules 2-4 to implement term projects
This module integrates Modules 2, 3, and 4 as a workshop in which students apply analytics’ process and procedures to their term projects. Students will iteratively present in class and work in teams, together with faculty, to refine their strategic management projects.

Webinars, between modules:
There will be a lecture between each module, delivered via AdobeConnect. Each lecture will be a tutorial on how to do the next pre-module assignment. In addition, two weeks before the final exam, there will be a final webinar on exam structure and preparation.

Learning process and requirements to students

The programme is conducted through five course modules over two semesters, a total of approx. 150 lecturing hours.

Project tutorials differ in each Executive Master of Management programme. It will consist of personal tutorials and tutorials given in class. Generally the students may expect consulting tutorials, not evaluating tutorials. The total hours of tutorials offered is estimated to 4 hours per term paper.

Please note that while attendance is not compulsory in all programmes, it is the student's own responsibility to obtain any information provided in class that is not included on the course homepage/ itslearning or other course materials.

The students are evaluated through a term paper, counting 60% of the total grade and a 3 hours individual written exam with own computer counting 40%. The term paper may be written individually or in groups of maximum three persons. All evaluations must be passed to obtain a certificate for the programme. 

The term paper is included in the degree’s independent work of degree, cf national regulation on requirements for master’s degree, equivalent to 18 ECTS credits per. programme. For the Executive Master of Management degree, the independent work of degree represents the sum of term papers from three programmes.

Software tools
No specified computer-based tools are required.
Qualifications

Bachelor degree, corresponding to 180 credits from an accredited university, university college or similar educational institution
The applicant must be at least 25 years of age
At least four years of work experience. For applicants who have already completed a master’s degree, three years of work experience are required.

Exam categoryWeightInvigilationDurationSupport materialsGroupingComment exam
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
MAN 51001
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
60No2 Semester(s)Group/Individual (1 - 3)Term paper, counting 60% of the total grade.
Exam category:
Submission
Form of assessment:
Written submission
Exam code:
MAN 51002
Grading scale:
ECTS
Grading rules:
Internal and external examiner
Resit:
Examination when next scheduled course
40Yes3 Hour(s)
  • All printed and handwritten support materials
  • BI-approved exam calculator
  • Simple calculator
Individual Individual 3 hours written exam with own computer, counting 40% of the total grade.
Exams:
Exam category:Submission
Form of assessment:Written submission
Weight:60
Invigilation:No
Grouping (size):Group/Individual (1-3)
Support materials:
Duration:2 Semester(s)
Comment:Term paper, counting 60% of the total grade.
Exam code:MAN 51001
Grading scale:ECTS
Resit:Examination when next scheduled course
Exam category:Submission
Form of assessment:Written submission
Weight:40
Invigilation:Yes
Grouping (size):Individual
Support materials:
  • All printed and handwritten support materials
  • BI-approved exam calculator
  • Simple calculator
Duration:3 Hour(s)
Comment:Individual 3 hours written exam with own computer, counting 40% of the total grade.
Exam code:MAN 51002
Grading scale:ECTS
Resit:Examination when next scheduled course
Exam organisation: 
Ordinary examination
Total weight: 
100
Sum workload: 
0

A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 30 ECTS credit corresponds to a workload of at least 800 hours.