I greatly enjoy teaching content that is related to my research. In that regard, I teach both about core IT topics as well as about the tools and methods I frequently use. My course portfolio starts with courses con digital transformation, going through data management, and concluding with quantitative methods.

Digital Transformation

The appropriate use of IT in firms is not only a requirement, but also a potential factor that drives competitive advantages. However, in order to reach high levels of innovation and efficiency in the use of IT, it is necessary to understand how some of its technical attributes intertwine with firm’s overall strategy. This course prepares students to become the vital link between the firm’s strategy and the underlying IT architecture. We talk about IT strategy, ERP implementation, IT infrastructure, application architecture and development, security and business continuity, cloud computing, business intelligence, cognitive computing, and other trending topics.

The Digital Transformation courses are complemented with videos I’ve created and that are available in the INCAE Digital Transformation channel on YouTube. Also, in one of the courses students develop practical labs such as deploying an e-commerce website from their laptops, deploy applications on cloud infrastructure services, and even train IBM Watson for some business use case. I was invited to talk about these labs to peer faculty from top schools at the 2017 IT Teaching Workshop. Here’s the video of that talk:

Data Management

Data management is the foundation of data-driven enterprises and a cornerstone for business analytics. Through this course, students first gain competence in practical database querying and data modeling using SQL. Then, having the R programming language as tool, they learn and practice data ingestion, storage, cleansing, integration, and exploration. All these are key elements of the data pipeline that enables firms to extract value from their data. In other words, the course provides students the skills and tools to take raw data to the point at which they can be fed into statistical or machine learning models.

Quantitative Methods

This course aims to provide students with the quantitative tools to satisfy the needs and demands of an MBA program as well as to perform data analyses at their future jobs. After reviewing the fundamentals of probability and statistics, we gradually start employing software tools to perform increasingly sophisticated statistical analyses of data (e.g., linear regressions with multiple explanatory variables). The examples we discuss to illustrate statistical techniques will come from other areas of study in the program: finance, economics, marketing, operations and IT. During the course we also address business problems with the Monte Carlo simulation technique, using for this a simulation software package (@Risk).

If you’d like to watch some videos and tutorials about this course, feel free to browse the Quantitative Methods playlist on INCAE’s DataMining YouTube Channel.

Case Studies

  1. Fernández, Carlos and German Retana (2016) “Mossack Fonseca: Panama Papers,” accepted for publication in CLADEA/BALAS and soon available at HBSP.
  2. Retana, German and Carlos Fernández, “Grupo ReRe.” Teaching case has two parts: “(A) Replacing a Legacy System at a SMB” and “(B) Digitized Platform in a SMB.” The case has been accepted for publication in CLADEA/BALAS and will soon be available at HBSP.
  3. Rothaermel, Frank, Grigoriou, Konstantinos, German Retana and David King (2015) IBM at the Crossroads.
  4. Grigoriou, Konstantinos, German Retana and Frank Rothaermel, “IBM and the Emerging Cloud Computing Industry,” teaching case study in Rothaermel (2012) Strategic Management: Concepts and Cases. McGraw-Hill/Irwin.