The importance of data in today’s society is unquestionable. A large proportion of companies – those known as digital companies – base their business model on the collection, storage and analysis of any data relevant to their business. This philosophy implies a radical change in the management of organizations’ operations, and requires the digitalization of all their business processes (e.g. creating computer systems to interact with customers and suppliers by websites, mobile applications or GPS systems, adding sensors to mechanical processes to monitor them, etc).

While the digitalization of an organization is an arduous task, the data generated and collected can be analyzed in order to generate important information for making business decisions. This has now been identified as a determinant and differentiating success factor that increases organizations’ competitiveness.

Today, the term Big Data is used to refer to a new type of systems that gather and analyse all kinds of data, and the challenges they entail. The most popular definition of the term Big Data is based on the three Vs, which represent its three main challenges: volume (digitalization of some processes can generate large volumes of data) variety (from heterogeneous data sources) and velocity (in terms of potential arrival time and data processing in real time). Today, to address these three major challenges, Big Data is based on two cornerstones: new architectures (mainly based on Cloud Computing and distributed and memory data management) and new data models (such as documents, graphs, key-value and streams).

However, the barrier to entry for incorporating Big Data solutions remains very high for most organizations, as they are managed and maintained in a very different way from any other system. Furthermore, the tools currently used are not yet mature and require a high degree of expertise if they are to be used properly. For this reason, specialization in this field involves specific recycling based on the main concepts behind these technologies. Today, it is necessary to make a distinction between data management in Big Data systems (Big Data Management) and using these data to extract knowledge relevant to the organization with Data Mining and Machine Learning algorithms (Big Data Analytics). In addition, there is no universal solution for either management or exploitation that can be easily replicated in any field, since by definition, the solution in these environments depends on the specific case of use (exploitation).

This master’s degree in Big Data, Data Science & Engineering therefore provides an overview of the Big Data ecosystem and an in-depth examination of both aspects: management (Big Data Management) and exploitation of data (Big Data Analytics), while providing applicability and a business vision within this world.

According to the Digital Talent Overview 2025 report, profiles related to big data are among the most in-demand and with the highest growth potential, with an expected increase of up to 110% between 2025 and 2030. Moreover, they are becoming strategic roles within organizations, with strong demand both locally and internationally. These figures highlight the importance of advanced training to lead the data-driven digital transformation.

More information: Master’s Degree in Big Data, Data Science & Engineering