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Business Analytics

Objectives

This course offers a strategic view of business analytics and machine learning as the core of contemporary organizations, with a strong applied learning component. It offers concise and comprehensible explanations of the key technological, organizational, and leadership components required for a fully data-driven organization, where business analytics serves to support but also inform autonomous decision-making and service delivery to the customers. Building from real-life cases and hands-on examples, the students will develop an understanding of the role of business analytics, data, infrastructure, algorithm, but also ethical and legal considerations in organizations.?

General characterization

Code

14225

Credits

2

Responsible teacher

Leid Zejnilovic

Hours

Weekly - Available soon

Total - Available soon

Teaching language

English

Prerequisites

Available soon

Bibliography

• Tom Davenport (2006) Competing on Analytics, Harv. Bus. Rev 84(1):98-107, 134
• Ianisti, M., Lakhani, K.R., 2020. Competing in the Age of AI. Harv. Bus. Rev. Jan-Feb.
• Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.F., Breazeal, C., Crandall, J.W., Christakis, N.A., Couzin, I.D., Jackson, M.O., Jennings, N.R., Kamar, E., Kloumann, I.M., Larochelle, H., Lazer, D., McElreath, R., Mislove, A., Parkes, D.C., Pentland, A. ‘Sandy,’ Roberts, M.E., Shariff, A., Tenenbaum, J.B., Wellman, M., 2019. Machine behaviour. Nature 568, 477–486.
• Faraj, S., Pachidi, S., Sayegh, K., 2018. Information and Organization Working and organizing in the age of the learning algorithm. Inf. Organ. 0–1.

Teaching method

The course follows a seminar format, with highly interactive sessions, focusing on the applications of the introduced concepts. Short practical exercises will be introduced at the end of each part of the lecture.

Evaluation method

The assessment of this curricular unit is done together with the block of curricular units of the same area of knowledge. This assessment has 3 moments, which together define the final grade of the curricular unit:
• Individual exam with a weighting of 50% of the total mark
• Group work with a weighting of 35% of the total grade value
• Individual reflection-action exercise carried out at the end of the curricular unit, with a weighting of 15% of the total grade value. The set of individual action-reflection exercises is a journaling activity, which will constitute, at the end, a learning portfolio capable of synthesising the contributions of the Executive Master for that student

Subject matter

Session I – Competition in the AI era
Introduce the new paradigm of competition in the era of AI, where organizations build highly automated systems to collect and analyze data, inform actions, and deliver scalable service at a low-cost to masses.
Session II – Data, Infrastructure, Algorithms, and Analytics
Identify the basic building blocks of competitive organizations: data, computational and operational infrastructure, algorithms, and problem-specific analytics. The participants will learn how to identify which type of analytics solve which problem and how to effectively create and appropriate value from such projects.
Session III – Leading data-driven organizations
Discuss what it means to lead data-driven organizations, to build and maintain competitive advantage, and the meaning of responsible organizations, that can identify and act upon ethical and legal issues and deliberately think and act to ensure fairness and equity in service provision.