APPLIES TO ACADEMIC YEAR 2016/2017

MAN 3172/3173/3174/3175 Analytics for Strategic Management

Responsible for the course
Espen Andersen, John Chandler Johnson, Alessandra Luzzi

Department
Department of Strategy

Term
According to study plan

ECTS Credits
30

Language of instruction
English

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 outcome

Acquired 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.

Acquired 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.

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.

Prerequisites
Bachelor degree, corresponding to 180 credits, and 4 years of work experience. Please consult our student regulations.

Compulsory reading
Books:
Danner, George.. 2015. Profit From Science: Solving Business Problems Using Data, Math, and the Scientific Process.
Davenport, Thomas H. and Jinho Kim. 2013. Keeping up with the Quants: Your Guide to Understanding + Using Analytics
Eric Siegel. 2015. Predictive Analytics, Revised and Updated
Grant, Robert M.. 2016. Contemporary Strategy Analysis (ninth edition)
Grus, Joel. 2015. Data Science from Scratch
Levitt, Steven D and Stephen D. Dubner. 2006. Freakonomics
Marsland, Stephen. 2014. Machine Learning: An Algorithmic Perspective
Provost, Foster and Tom Fawcett. 2013. Data Science for Business: What You Need to Know About Data Mining and Data Analytic Thinking

Collection of articles:
2015. Core Curriculum: Strategy. Harvard Business School

Other:
Assorted Articles


Recommended reading

Other:
Assorted Articles


Course outline

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.

Computer-based tools
The course will use the Python programming language to introduce analytics techniques. We intend the program to be a self-contained introduction to the field; there are no programming prerequisites.

Learning process and workload
The program includes five course modules totaling 150 lecture hours.

Project tutorials differ in each Executive Master of Management program. 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 two hours pr. students following an ordinary Master of Management program. For students taking the program as their final Master of Management program the tutorials offered are estimated to a total of six hours per term paper.

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

Examination
The students are evaluated through a term paper, counting for 18 credit hours and an 3 hours individual written exam, counting for 12 credit hours. Both evaluations must be passed to obtain a certificate for the program. The term paper may be written individually or in groups of maximum three persons.

For students taking this program as the final Master of Management Program the following applies:
The students are evaluated through a term paper, counting for 24 credit hours and an 3 hours individual written exam, counting for 6 credit hours. The term paper may be written individually or in groups of maximum two persons. Both evaluations must be passed to obtain a certificate for the program

During the first module, students will form groups to work on 3 assignments (done in groups of up to 3 students for regular students and up to 2 students taking the course as the final one). These assignments are ungraded, but each assignment is a building block for the final term paper. The instructors will use the assignments to provide feedback during the course and to ensure that students are making good progress on the term paper.

Assignment 1 (deadline to get feedback: Before module 2.):
The student groups will choose an organization and suggest a strategic management question for which analytics can add insight. The groups are encouraged to choose an organization they know well, preferably one for which they work or have worked and for whom the question is of real interest.
Assignment 2 (deadline to get feedback: Before module 3.):
The student groups will deliver an outline specifying their project’s motivation, data requirements, data collection technique, analytics tasks, and target outcomes.
Assignment 3 (deadline to get feedback: Before module 5.):
The students will deliver a written document specifying the analytics routines they expect to conduct to inform their strategic question.
Term Paper (deadline: After module 5.):

Based on the assignments, cumulative faculty feedback, and the workshop session, students will write a term paper specifying an analytics project that would inform their strategic question. The term paper will clearly specify the strategic problem, demonstrate the problem’s significance, and identify data and analytics methods capable of informing a response to the problem.
Examination code(s)
MAN 31721 - Term paper; accounts for 100 % of the grade to pass the program MAN 3172, 18 credits
MAN 31731 - Individual written examination; accounts for 100 % of the grade to pass the program MAN 3173, 12 credits
Both evaluations must be passed to obtain a certificate for the program.

For students taking this program as the final Master of Management Program the following applies:
MAN 31741 - Term paper; accounts for 100 % of the grade to pass the program MAN 3174, 24 credits
MAN 31751 - Individual written examination; accounts for 100 % of the grade to pass the program MAN 3175, 6 credits;.
Both evaluations must be passed to obtain a certificate for the program..

Examination support materials
Written exam - None support materials allowed

Examination support materials at written examinations are specifies under "Examination" in the student portal @BI . Please, note the use of calculator and dictionary. https://at.bi.no/EN/Pages/Exa_Hjelpemidler-til-eksamen.aspx

Re-sit examination
It is only possible to re-sit an examination at the next ordinary examination. When a programme is discontinued, the re-sit examination will take place in a replacement programme.

The assessment is mainly based on more than one examination code. Where this is the case, you may re-sit only the assessed components of one of these examination codes. Where this is not the case, all of the assessed components of the course must be retaken. All re-sit examinations will incur an additional fee.

Additional information