Data-Driven Transformation

Group	Teacher	Department	Language
Year 1	David Lottenbach	Operaciones, Innovación y Data Sciences	ENG

Course Learning Objectives

As digital technology furiously progresses, we are witnessing a fundamental disruption from product business to data-driven solution business. We call it the "product to solution? paradigm shift. This universal groundswell spares hardly any industry and drives a massive transformation of erstwhile firmly established business models. It is built on the capability of data-driven processes to deliver smart individualized solutions at scale - without the productivity and scalability hurdles that previously plagued them.

This is indeed a universal wave: We see product- and hardware-centric car manufacturers morphing into data-driven, solution-centric mobility providers - shifting the century-old "owned vehicle? business model to a "vehicle as a service? and even "mobility as a service? model. What used to be manufacturers of door locks are becoming data driven managers of smart, efficient, and safe people flow through physical and digital infrastructures. With data driven artificial intelligence (AI) surpassing human intelligence (HI), local insurance brokers get quickly substituted by superscalable and supersmart chatbots of global insurers. The list of transforming industries is endless. For a majority of incumbent companies, the transformation is not only timely and urgent, but ultimately inevitable. For the startup challengers, it is a unique chance to carve out the biggest piece in the value chain.

This transformation towards mass-customized smart solutions is fundamentally data-driven. Sensors, actuators, data, cloud computing and data analytics are the indispensable key enablers. Recent advances in data analytics like large language models (LLM) are boosting change at breakneck speed. Together, they allow mass-individualized, smart solution business at scale, with disruptive business models that re-distribute the value captured among the market players.

This course deals with intelligently transforming businesses from a product-centric archetype to a data-driven solution paradigm. It is about blending the best of two worlds - the "henry ford world? of efficiently mass-producing and mass-selling versus the "boutique world? of handcrafting individualized value - through data and analytics. It is about entrepreneurially identifying the opportunities and challenges of data driven transformation and executing them through all the layers - from business model and leadership to technology, business processes, culture and organizational structure.

The overall objective of this course is to empower you to catalyze and lead the transformation of product-centric businesses to data-driven solution businesses. The course will put your data analytics skills into a strategic perspective and appeal to your entrepreneurial instincts.

At the end of the course, you should be able to:

- Understand the global "groundswell? that takes product businesses to data-driven solutions
- Apply the business model framework for analyzing, (re-)designing and executing value creating, data-driven solution businesses
- Understand the success factors of the solution business model, and the particularly transforming role of data, data analytics and IoT
- Design a new, data-driven solution business model for an incumbent or new enterprise
- Apply an integrated change management approach towards data-driven solution business
- Execute a practical roadmap for transformation to the new solution business model

CONTENT

1. Session 1: The ¿Product to Solution¿ Transformation Wave (Introduction)
2. Session 2: A practical and modular Business Model Framework (toolbox)
3. Session 3: Solution vs. Product: essential differences and the role of Data
4. Session 4: Winning data-driven Solution Models (examples and exploration)
5. Session 5: Data Driven Solution Models I (solution architecture and data drivers)
6. Session 6: Data Driven Solution Models II (execution)
7. Session 7: Solution Lab (student cases pitched to jury)
8. Session 8: Change Management: Change Architecture, Change Leadership
9. Session 9: Practical pitfalls and remedies on the way to the data driven solution Nirvana
10. Session 10: Final Exam

1. Session 1: The ¿Product to Solution¿ Transformation Wave (Introduction)

2. Session 2: A practical and modular Business Model Framework (toolbox)

3. Session 3: Solution vs. Product: essential differences and the role of Data

4. Session 4: Winning data-driven Solution Models (examples and exploration)

5. Session 5: Data Driven Solution Models I (solution architecture and data drivers)

6. Session 6: Data Driven Solution Models II (execution)

7. Session 7: Solution Lab (student cases pitched to jury)

8. Session 8: Change Management: Change Architecture, Change Leadership

9. Session 9: Practical pitfalls and remedies on the way to the data driven solution Nirvana

10. Session 10: Final Exam

Methodology

The course uses a case-based approach blending theory and practice. It integrates case studies, faculty presentations, guest workshops, team projects and a variety of readings.

CASE STUDIES: The chosen case studies will provide insights into the complexities of data-driven business models, the business context and the challenges involved in change management. You are required to solve the cases individually by submitting answers (in essay format) to the case questions before class. This way, you will be well prepared for group and plenum discussions during the subsequent class. All case studies including the questions will be available in the course Moodle page ahead of time.

REQUIRED READINGS: For some sessions, the case studies will be supplemented by required readings and videos. They will allow you to dig deeper in the subject and thus facilitate your class participation and help you preparing your exams. All required readings as well as the youtube links will be available on Moodle.

OPTIONAL READINGS: These readings provide further individual insight into some of the topics discussed during the session. They are marked as optional and are not required for the exams, but they might help you to actively participate in class. Optional readings will be available on Moodle.

LECTURES: Lectures introduce course topics for discussion, present managerially relevant theoretical and analytical frameworks, and ensure that we jointly draw conclusions from the cases. The lecture approach is interactive and inclusive.

GUEST SPEAKERS: In two of the sessions, international senior executives (C-level) will present and discuss their own company's case, in-person or by video connection. These events offer students a unique opportunity to get first-hand knowledge of the success factors and challenges related to transforming "product? to a "data-driven solution?, and to network with seasoned executives.

"SOLUTION LAB?: Each student will be assigned to a fixed Team at the beginning of the course. "Solution Lab? is an important and intensive team assignment in which your team will have the opportunity to design a data-driven business model and professionally pitch the newly created business to a jury consisting of real-life investment managers and startup advisors and investors, as well as ESADE academic staff.

TEAM PRESENTATIONS: In at least two (2) of the classes, your Team will be tasked to condense the individual case study results into a joint set of compelling insights and present them vividly to the class. These team presentations play a significant role in both yours and, importantly, your colleagues' in-class learning experience.

Assessment criteria

Grade component Weight
Participationn 25%
Two MCQ tests 15%
"Solution Lab" pitched to Jury 15%
Team presentations 10%
Final exam 35%

Participation (25%)
Your active and substantial participation in class discussions is integral to the design of this course. Towards this goal, it is crucial that you diligently prepare the assigned material (case studies, readings, course slides) for every class.
Therefore, your participation grade will be a balanced combination of the quality of your oral contributions to class discussion as well as the quality of your individual case solutions (specifid answers and/or write-ups in essay style) submitted before class.
I reward oral contributions that enrich the quality of discussion in class, i.e. contributions that are relevant and move the class discussion forward with arguments grounded in factual reasoning or experience. I promote inclusive participation, and therefore I will "cold-call? students (ask questions to students that don't raise their arms)
The quality of each of your individual case solution will be assessed by the TA. The case results must be submitted at the latest at midnight before class. Failing to submit cases will have a significantly negative impact on your participation grade. Submitting team results (as opposed to results worked out individually), or submitting results generated by conversational AI (e.g. Chat GPT) will be considered as non-compliance with the values and regulations of ESADE and will have grave consequences.
Please note that unjustified class absence will negatively impact the participation grade.

Two MCQ Tests (15%)
The two tests will take place after the third and sixth course session. The tests are multiple-choice questions based on lecture content and/or cases previously discussed in class. The tests will be completed by both sections simultaneously at a given time in a lecture theater and will be fully digital so that results will be available immediately after the test.
Please note that you will not be allowed to use any materials during the MCQ. The TA and other helpers will make sure that you are not opening any other web tab or application than the Moodle MCQ tool. Opening other pages than the Moodle MCQ or exchanging information with fellow students or third parties will disqualify you (grade 0) without the chance of a re-take of the MCQ.

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Team Presentations (10%)
You will be assigned to a fixed DDT team before the first session of the course. There will be two (2) team presentations to the rest of the class. Your team will be tasked to condense the individual case study results into a joint set of compelling insights and present them vividly to the class. Specifically, your team will prepare a 120 second pitch presentation. Lecturer and TA will grade the teams after class.

Solution Lab (15%)
Your assigned fixed DDT team team will build a data-driven business model, based on objectives and guidelines and a data-driven solution business idea of your own choosing. Your virtual startup will prepare a 600-second pitch presentation plus Q&A to an online jury composed of tech investors, startup advisors, and ESADE academic staff. You will receive a minimum of pitching training beforehand. The basic grading will be done real-time by the jury using a "will certainly invest - will maybe invest - will not invest? scale. This grading will supplemented after the session by ESADE with additional criteria (case adequacy, structure, presentation etc.).

Final Exam (35%)
In the final exam, I expect students to apply the tools and concepts covered in the course and in the case analyses. The final exam will be an individual, open book case write?up in "paper and pen? format (no digital submissions in order to avoid illicit use of conversational AI). Please write your answers clearly, so that your exam answers can be scanned and converted to digital text. You can take paper books, your paper notes or any other paper printout to the exam. Digital devices are strictly not allowed during the exam. The exam will be taken in a physical exam room and you will be supervised physically by exam supervisors. The questions will be offered on paper immediately before the exam.
The exam results will be assessed individually by the prof and the TA.
More details on the final exam will be offered in class in session 9.

Type:	OBL	Curs:	1	Period:	S semester
ECTS Credits:	4 ECTS

Data-Driven Transformation (2235.YR.014109.1)

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