MAN 5100 Analytics for Strategic Management

MAN 5100 Analytics for Strategic Management

Course code: 
MAN 5100
Strategy and Entrepreneurship
Course coordinator: 
Espen Andersen
John Chandler Johnson
Alessandra Luzzi
Product category: 
Master of Management
2020 Autumn
Active status: 
Teaching language: 
Course type: 
Associate course
Course codes for multi- or associated courses.
MAN 5101 - 1. semester
MAN 5102 - 2. semester

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 sciences 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 sciences 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 projects 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.

General Competence

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 some modules. 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.

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 and exercises - 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. Between this module and the next, students will develop an initial formulation of their group project.

Module 2: Analytics Concepts and Processes for Strategic Management
Module Outcome: Understand the Analytics process, with an emphasis on data collection and preparation
Focusing on data acquisition and management, this module introduces analytics as a scientific process involving problem specification, modeling, data collection, analysis, and summary. This prepares students for the subsequent, modeling-focused modules. 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.

Modules 3-4: Implementing Analytics for Strategic Management
Module Outcome: Using algorithms for analytics projects in Strategic Management
Module 3 is the first of two modules emphasizing technology for algorithm-based modeling. These modules use the Python programming language to illustrate the modeling process. 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. Thats OK! The course uses collaborative exercises and teamwork to ensure student success.

These modules also introduce automated machine learning in both Python and the cloud-based DataRobot package.

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 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:
Webinars may be arranged between modules, mostly to present specific, well-contained topics or to help students prepare for assignments.

Teaching and learning activities

The programme is conducted through five course modules over two semesters, a total of approx. 150 lecturing hours. Some of these hours will be spent as tutorials working with faculty on student projects.

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 home exam with own computer counting 40%. The term paper is written in groups of 2-3 students. 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.

Participating in the course will require access to a computer, and installation of free software including Anaconda and MySQL. Students will also need to purchase a 9-month DataRobot student license (~$80), which is cloud-based.

Software tools
Software defined under the section "Teaching and learning activities".

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 categoryWeightInvigilationDurationGroupingComment exam
Exam category:
Form of assessment:
Written submission
Exam code:
MAN 51001
Grading scale:
Grading rules:
Internal and external examiner
Examination when next scheduled course
60No2 Semester(s)Group (2 - 3)Term paper, counting 60% of the total grade.
Exam category:
Form of assessment:
Written submission
Exam code:
MAN 51002
Grading scale:
Grading rules:
Internal and external examiner
Examination when next scheduled course
40No3 Hour(s)Individual Individual 3 hours home exam with own computer, counting 40% of the total grade.
Exam category:Submission
Form of assessment:Written submission
Grouping (size):Group (2-3)
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
Grouping (size):Individual
Duration:3 Hour(s)
Comment:Individual 3 hours home exam with own computer, counting 40% of the total grade.
Exam code: MAN 51002
Grading scale:ECTS
Resit:Examination when next scheduled course
Type of Assessment: 
Ordinary examination
Total weight: 
Student workload
Teaching on Campus
150 Hour(s)
Prepare for teaching
150 Hour(s)
Student's own work with learning resources
500 Hour(s)
Self study, term paper and exam
Sum workload: 

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.