module specification

This module focusses on the methods and techniques of using big data in business with a specific focus on organisations operating within the Built Environment. Given the increase in available big data, organisations are aware of the need to effectively utilise this to gain a competitive advantage. In this module, you will explore the means by which organisations can benefit from big data.

You will appraise the technologies available to organisations and means to deploy them to aid the decision-making process.  Using data analytics and data visualisation tools, you will prepare and present solutions to scenario-based problems.

By the end of the module, you should be able to demonstrate a deep knowledge and understanding of:

• the business opportunity and value creation possible through the utilisation of big data and business analytics
• how to appraise and select appropriate approaches to big data technologies and business analytics
• how to achieve business advantage through the analysis of big data means to present big data to a variety of audiences using visualisation tools

The syllabus is informed by contemporary research and practice in the area of Big Data Analytics. Topics will include:

• Introduction to the age of big data (LO1)
• Big data and competitive business advantages (LO1,2)
• Big data strategies (LO2)
• Big data - Methods, techniques, and tools (LO2,3,4)
• Big data and the project lifecycle (LO2,5)
• Visualisation tools (LO4)
• Ethics and big data capture, analysis, and use (LO2,5)
• Future developments in big data capture, analysis, and use (LO1,2)

This module will adopt a technology-enhanced learning approach to support student engagement and study. The delivery of this module will be facilitated through online lectures, tutorials, seminars, and guided asynchronous activities. Where applicable, activities will be recorded and made available for students to access asynchronously.
Workshops and seminars will offer opportunities for students to engage in discussions about current trends and challenges within big data applications in the Built Environment and related sectors. Through seminar activities, students will work with global case studies to stimulate discussion, enhancing their understanding of how big data is applied in construction project management. These discussions will help students connect theoretical knowledge with real-world applications within the framework of existing project management bodies of knowledge and industry standards.

Problem-based learning will be used to encourage independent learning through proposing a problem that is complex and with more than one right answer, challenging the students to work individually and in small groups to develop solutions, thereby developing their problem-solving abilities. Staff will act as facilitators throughout the activities. Tasks will be performed through group and independent study to develop critical thinking skills of analysis, evaluation, and synthesis. Throughout the module, student outputs will be reviewed, and formative feedback will be given to ensure clarity and comprehension.

The learning and teaching in classes will be supported by the University’s VLE (Virtual Learning Environment) and a blended learning approach, sharing class materials, recommended reading, and case studies. Group tutorials will also be offered to support students in the preparation of their assessments, with opportunities for students to receive forward feedback.

Students will reflect on their learning, which will contribute towards their online Professional Development Journals (PDJ).

On completion of the module, the learner, operating independently and applying their knowledge and skills, should be able to:

1. propose how the utilisation of big data can create opportunity for businesses.
2. appraise big data technologies and data analytics in the context of the built environment.
3. analyse big data and make decisions to achieve positive outcomes.
4. generate and present solutions using data-visualisation tools.
5. communicate solutions at different levels of details to a client and project team audience.

There is no single text currently in publication that provides a comprehensive coverage of all aspects of this module. There are several books which cover various parts of the module in some detail, and these are listed in the online reading list below:

SU6051 Big Data and the Built Environment | London Metropolitan University