Master’s Degree in Business Intelligence

This course serves as an introduction to the concept of data-based governance. Students will learn to contextualize Business Intelligence strategies within the framework of decision-making support systems. They will study the role of the growing availability of data, and how the comprehensive use of data allows for the progressive advance of ERP and contributes to the improvement and acceleration of the processes surrounding strategic business decision-making, which has recently been established as an essential part of competitive business strategy.

Once they understand the importance of data, students will analyze data scope and content, and will develop an initial approach to the content, terminology, and techniques specific to this area. Students will also develop a theoretical basis for understanding the main elements of an information system. This will serve as an introduction to two topics related to the key elements of data-driven ERP system design and implementation, a business intelligence strategy, which range from architecture and software to the techniques and alternatives available for their implementation and use.

The course closes with an analysis of certain risks associated with responsibility that must be considered when developing a data-based corporate governance strategy. This refers to the fact that personal information may be accidentally leaked during the process of collecting, storing, and using data, which is why security and protection are a necessary part of designing and implementing any data-based corporate governance strategy.

Additionally, students will analyze ethical aspects related to the design and implementation of data-driven management. The objective is for students to learn the specific terminology surrounding data protection and privacy, as well as the main rules and regulations that determine the legal protection and privacy of personal data. There will be a focus on all the European laws regarding the protection and transfer of data to other countries. Students will explore the ethical limitations of using information and big data analysis with respect to the application, monitoring, and tracking of individuals, their habits, and the personalized offering of goods and services, without restricting their knowledge of other possibilities that exist on the market.

This course explores the important role data plays in company decision-making, specifically in marketing. Relationship marketing has positioned the customer at the center of a business. This makes getting to know the customer essential for all types of corporate decision-making, including new product launches, rebranding, and even the structure of an organization. Therefore, students will first analyze the value of a customer-centric business strategy, its foundations, and what it has to offer companies in the era of digital transformation and big data.

The second part of the course focuses on exploring how data can help decision-making processes related to products, prices, distribution, and communication.

The third part revolves around data collection and marketing data systems, as well as using data in market research and the different analyses available according to the type of variable.

Finally, using information technology to gain a deeper understanding of markets, students will explore key areas of online customer management, an important aspect of the current landscape.

The Advanced Data Visualization course covers all the principles and techniques needed for data visualization. Students will learn to understand data through visualization as they study the problems and solutions data can create for organizations.

Using basic principles and a strategy focused on effective visualization, students will be equipped to select the best strategy for finding information using data and will be able to communicate their findings effectively using the different mediums available. Additionally, they will understand the problems that result from poor data representation and will get to know the wide range of possibilities for design as well as their sustainability, according to the types of data information and the objective. They will also become familiar with the latest data visualization tools, along with their usability, functionality, cost, and availability.

Students will also discover how to create an effective dashboard for presenting data, utilizing one of the top data visualization tools. This will enable them to present data using storytelling techniques to communicate the information effectively and achieve their objectives.

By the end of the course, after several practical case studies, students will have the knowledge they need to design a data visualization project, choose the type of presentation most appropriate for the data and communication objectives, and generate dashboards to communicate results in a visual, efficient, and understandable way.

This course gives students a basic understanding of data-driven science. It is divided into two parts. The first is an introduction to data science; it presents the current methodologies as well as the phases or lifecycles of data-oriented projects. The second part is more technical; it focuses on data analysis and modeling.

The first part of the course presents the concept of a project’s lifecycle based on data science as well as on the techniques related to collecting, preparing, and storing data. Students will explore techniques for storing unstructured data and big data.

The second part begins with a number of techniques for analyzing statistics. This includes statistical analysis techniques, both descriptive and specific; techniques for analyzing different types of data, which helps students understand the relationship between the different variables that make up an information system; and multivariate statistical analysis techniques. All of these are highly applicable to corporate decision-making processes. In this part of the course, students will explore predictive techniques for the quantitative estimation of variables through both theoretical and practical explanations of time series models and their business applications for performance forecasting (operations, sales, marketing, etc.) This will be followed by an exploration of the concept of machine learning, and students will learn the most important related techniques. Finally, students will analyze NLP techniques for analyzing text data from unstructured sources.

This course focuses on information technology fundamentals and the databases needed to understand business intelligence solutions. It familiarizes students with basic technological language and principles, empowering them to communicate effectively with business intelligence project teams and technical profiles.

The first part of the course is dedicated to information technology infrastructure, while the second part focuses on business intelligence fundamentals. The first part covers two topics related to the main components of information technology infrastructure (hardware platforms, operating systems, business applications), computer networks (local, global, and wireless), computer network security, protection tools for computer resources, and cloud computing, focusing on the services available and the necessary infrastructure. The objective is to give students a basic understanding of these technologies.

The second part is dedicated to databases: their objectives, functionality, architecture, and models. This part of the course focuses on the study of relational databases, analyzing related models and query languages. It also addresses database design based on entity-relationship modeling. Finally, it teaches students about the technological foundations of business intelligence, data integration and storage, and online analytical processing. Students will also explore the differences between business intelligence and business analytics.

The objective of this course is to consolidate the knowledge students acquired in previous courses to foster a deep understanding of how to effectively manage a business intelligence project today. The course demonstrates the importance of data, their sources, and their treatment as key tools for projects. By exploring the history of the methodologies for business transformation, students will master the latest strategies for launching and implementing business transformation based on information from business intelligence systems.

Students will study the necessary elements and supporting technologies to apply to a project, as well as the main difference between a business intelligence project and a business software project.

This difference will be highlighted, along with the idiosyncracies of projects based on data and information. Students will analyze the processes of a comprehensive business intelligence project from beginning to end, focusing on all the elements and key people.

Students will learn how to design the architecture of a project, exploring technological resources, access to data, and the definition of indicators and reports generated by the system.

Finally, they will study determining factors for success in managing a business intelligence project, as well as ways to ensure success. They will use practical case studies of project implementation to identify the elements that promote and impede success.

To sum up, the objective of the course is to provide students with all the skills they need to successfully implement a data-based business intelligence project at an organization.

This course educates students on all the different aspects related to business intelligence, helping them gain a clear perspective on the application of all the techniques analyzed.

By studying the evolution of the corporate environment and where it stands today, students will understand the threats and opportunities it faces. They will also learn the decision-making process currently in practice all over the world, which requires continuously updated information.

In order to achieve organizational change at a company and adapt it to the current environment by pursuing opportunities and eliminating threats, students will explore the requirements of corporate culture based on analysis and human resources needs that arise.

To conclude and obtain a global perspective on the application of business intelligence, students will explore information-based business models and the data they generate to create corporate profit, as well as the application of marketing, production, and logistics: two key aspects of business.

Equipped with these skills, along with those acquired in previous courses, students will be able to implement a business intelligence strategy at any type of corporation. They will also explore case studies to see which decisions lead to a company’s success or failure.
This course educates students on all the different aspects related to business intelligence, helping them gain a clear perspective on the application of all the techniques analyzed.

By studying the evolution of the corporate environment and where it stands today, students will understand the threats and opportunities it faces. They will also learn the decision-making process currently in practice all over the world, which requires continuously updated information.

In order to achieve organizational change at a company and adapt it to the current environment by pursuing opportunities and eliminating threats, students will explore the requirements of corporate culture based on analysis and human resources needs that arise.

To conclude and obtain a global perspective on the application of business intelligence, students will explore information-based business models and the data they generate to create corporate profit, as well as the application of marketing, production, and logistics: two key aspects of business.

Equipped with these skills, along with those acquired in previous courses, students will be able to implement a business intelligence strategy at any type of corporation. They will also explore case studies to see which decisions lead to a company’s success or failure.

The objective of this course is to provide students with the tools they need for business information management—covering everything from data gathering to the technologies required for processing and modeling—to ensure effective, efficient, and viable management for customers. 

In this way, the course helps students understand not just the importance of effective and efficient information management in the current marketing landscape, but also the evolution of the technological platforms that support this type of management. 

The course content revolves around the following areas:

• Focus for business strategies: from a product-centric approach to a customer-centric approach.
• Processes for collecting, enriching, and qualifying data: students will explore the processes necessary to collect customer information, as well as the current methodologies for implementation and external sources of information (OpenData) that allow for the inclusion of different types of information beyond purely transactional data (a very important factor for generating relevant messages depending on customers’ likes and preferences).
• Geomarketing and why this type of analysis is essential for micromarketing decision-making.
• Customer relationship management system: its components and objectives, as well as its benefits for both companies and customers.
• Omnichannel commerce: this current trend, now a reality in many companies, consists of changing the focus of customer contact strategies from multichannel to omnichannel.
  • Steps needed to implement an omnichannel contact strategy.
  • Customer management process: audiences/objectives
  • The customer journey: definition, according to the type of customer and/or the objectives, and its benefits.
  • The concept of triggers: definition according to the operational and/or transactional process.

In this course, students will acquire the necessary skills to carry out multivariate statistical analyses, with the main objective of highlighting the role of statistics in marketing decision-making.

Students will explore multivariate analysis techniques (dependency and interdependency methods), which are directly applicable to marketing processes. They will learn factor analysis techniques, among others. These have a wide range of applications (new product launches, etc.), due to their ability to reduce the dimensions of a business problem by aggregating the variables that define it.

Additionally, students will learn about essential statistical techniques for evaluating the effectiveness of marketing campaigns (very useful for optimizing marketing budgets), while simultaneously combining econometric theory with its practical application in sales forecasting, measuring the impact of sales strategies on the market, and calculating profitability.

The course focuses on:

• Descriptive, inferential, and predictive multivariate statistical analysis processes (dependency and interdependency methods).
• Predictive techniques for estimating quantitative variables through the theoretical and practical study of time series models (ARIMA and VAR), as well as their marketing applications for sales forecasting.
• Market response models with two main marketing decisions: promotion and price.
• Techniques for estimating demand elasticity at different price levels: processes to determine optimum prices (pricing).

This course focuses on the following areas:

• Exploring the steps in the current marketing intelligence process, starting with customer intelligence, and putting this knowledge into practice.
• Strategies for effective customer segmentation, the advantages of a marketing focus (treatment prioritization and investment distribution), as well as advanced segmentation methods that should be used, metrics, and learning processes for objective optimization.
• Customer lifecycle: phases and touchpoints, the value of the customer lifecycle, and how this information should be used when designing marketing plans.
• Defining and creating RFM (recency, frequency, monetary) models, which establish a segmented and strategic perspective on customers.
• Methodologies for identifying optimal sales actions, minimizing dilution costs and supporting sustainability.
• The correct process for measuring results from marketing strategies, both at a tactical and global level.
• The development of various predictive models using different methodologies, and the advantages this type of modeling brings to marketing strategies.