Currently, Big Data and Business Intelligence are two main economical drivers of our society. Most companies are shifting to a data-driven paradigm where (digital) data gathered is properly stored, managed and exploited in order to extract objective evidences to support their day-by-day decision making processes.
However, such projects are a challenge for any company. On the one hand, these projects may fall into any of these technological challenges:
- The size of data gathered and available in external sources may be huge, which difficults data management (both modeling the database and the data flows nurturing the system) and demands new solutions (e.g., NoSQL and the so-called Big Data paradigm),
- The need to adapt traditional data analysis techniques (e.g, Data Mining techniques) on top of these new data management solutions,
- The difficulty to deal with relatively new technologies with a low degree of maturity, which results in a lack of available off-the-shelf frameworks.
All in all, these projects require to build in-house solutions tailored to the problem to be tackled. As consequence, the IT participants need to show a considerable expertise in data management and data analysis since they cannot benefit from well-established frameworks hidden the communication and coupling complexities between both worlds. For this reason, this course will put into practice all the skills earned by the students during their master studies.
On the other hand, this project acknowledges the relevance of the business side in such project and elaborates on the need to consider business users as first-class citizens during the design and implementation in order to meet their needs. In this sense, Agile Software Development techniques are introduced for the project management. However, the specificities of BI systems do not allow us to directly apply traditional Agile techniques, which need to be adapted for BI.
This course simulates an environment whose conditions are similar to those of a BI industrial project. So that, the students are required to work in a team and undertake a project by planning the project, modeling the BI processes, gathering requirements, analyze and specify a system meeting the project requirements, designing the system and testing, while documenting all the process. Interaction with the business side will be key during the process. Eventually, a prototype is required.
In order to simulate a real BI project, a joint real-world project potentially ready to be launched as a start-up will be coordinated with other sibling subjects: BIP, SEAIT, WS and VBP. The project management solution is the main outcome of BIP, the analysis of performance of the technologies will be developed in SOBI, the interfaces in WS, the business idea and business plan in VBP, and the ethical part of the business model will be checked at SEAIT.
For this reason, the enrolment of BIP is strongly advised to be done at the same time with, at least, SOBI, to facilitate the adequate progress of the students throughout these subjects.
Oscar Romero Moral (
Sergi Nadal Francesch (
Lead and manage software projects for Business Intelligence
Enabling flexible and dynamic environments with Agile Software Development principles and practices
Model and Deploy Business Intelligence Processes
Design and deploy ETL processes
Design and deploy data warehouses
Assessing the need of non-traditional tools / systems / methods for Big Data and Business Intelligence projects: NOSQL databases, integration techniques and data quality issues
Data visualization for Business Intelligence systems
Oral presentations and defense in simulated business environments with clients
Project management techniques for Business Intelligence projects
Specificities of BI projects. Dynamicity (need for changes at any development stage), volatility (of the requirements), heterogeneity (of data sources) and the end-user as a main actor during the whole process.
Tools and systems to meet such requirements.
Agile methodologies for Business Intelligence Projects
On the need of flexible development ecosystems: SCRUM, TDD (test-driven development), BDD (behaviour-driven development)
Enabling effective Business Intelligence projects by means of data visualization techniques
The relevance of data visualization for Business Intelligence. Integrating external tools / APIs to enable powerful data visualization for end-users
The student attends the lectures, listens, takes notes and participates in the discussion Objetivos:13 Contenidos:
The student designs and implements a system according to the requirements introduced by the lecturer. He / she manages and coordinates with his / her teammates, applying the management techniques introduced in the lectures, in order to produce a prototype meeting the project requirements. Objetivos:12345 Contenidos:
Each group presents the prototype developed during the course to the other classmates and the lecturer.
They must behave as if presenting the system developed to the end-user. The other classmates and the lecturer will behave as end-users Objetivos:6 Semana:
examen de laboratorio
During the three first weeks the students are introduced to the specifities of business intelligence projects. Some seminars may be held to introduce the students in some specific concepts.
Then, the students start a project (in groups of three) to develop an information system modeling business processes handed in in the form of requirements by the lecturer. Each student must play a role to be chosen at the beginning of the project. During the project, every two weeks, there will be a meeting with the lecturer, simulating a meeting with end-users where new requirements may be introduced and the students show the progress made during the last two weeks.
In the last week of course, each group must present their system to their peers and the lecturers, as if it was the final meeting with the end-users. Furthermore, a project documentation must be handed in, where all the main decisions made in the project must be defended.
Método de evaluación
The final mark is computed as follows:
30% SM + 20% PD + 30% MM + 20% PM
Where SM is the sprint mark, PD is the mark for the project defense, MM is the mark for the project management tasks during the course and PM is the mark obtained by the student teammates.
SM: Sprint mark. The lecturer will evaluate the progress of each group with the sprint rubric.
PD: Mark for the project defense. The lecturers will evaluate the progress of each group with the defense rubric.
PM: Peer-marking. This mark is finally given by the lecturer based on equally weighting the marks given by the student teammates (see the peer-marking activities) for evaluating the student's work.
MM: Management mark. The lecturer will evaluate the progress of each group with the management rubric.
BI projects require to build in-house solutions tailored to the problem to be tackled. As consequence, the IT participants need to show a considerable expertise in data management and data analysis since they cannot benefit from well-established frameworks hidden the communication and coupling complexities between both worlds. For this reason, this course will put into practice all the skills earned by the students during their master studies.
More precisely, the course demands:
- Basics on Business Intelligence: Data Warehousing and ETL.
- Basics on databases and software engineering.
- Basics on data analysis (e.g., data mining / machine learning).
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