BUSINESS INTELLIGENCE AND DATA ANALYTICS IN SERVICES - 2026/7

Module Overview

Business intelligence and data analytics are central to decision-making in service industries, including hospitality, tourism, transport, and events. Organisations rely on data to monitor performance, identify patterns, and support strategic and operational decision-making in competitive and transparent environments.

This module develops students¿ understanding of how business intelligence and analytics are applied within service contexts. It combines foundational statistical concepts with practical application using industry-relevant tools to prepare, analyse, and visualise data, including the use of AI-assisted analytical tools. Students learn how to design and interpret key performance indicators (KPIs), apply descriptive and introductory predictive analytics, and communicate insights effectively through dashboards and data storytelling.

The module emphasises data-driven decision-making, critical interpretation of analytical outputs, and professional communication of insights to managerial audiences.

Module provider

Surrey Hospitality & Tourism Management

Module Leader

PEREIRA DOEL Pablo (Hosp & Tour)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 5

Module cap (Maximum number of students): N/A

Module Availability

Semester 2

Prerequisites / Co-requisites

None

Module content

Indicative content includes:
¿ The role of business intelligence and data analytics in hospitality, tourism, transport, and events
¿ The use of AI tools to support data analysis and business intelligence
¿ Principles of data-driven decision-making and managerial problem framing
¿ Data preparation and management using business intelligence tools
¿ Fundamentals of descriptive analytics and application of managerial statistical techniques
¿ Fundamentals of predictive analytics, including forecasting and trend analysis
¿ Introduction to prescriptive thinking and scenario analysis for decision support
¿ Key performance indicators (KPIs) and performance measurement in service organisations
¿ Principles of data visualisation, dashboard design, and storytelling for managerial communication
¿ Ethical and responsible use of data in service industries

Assessment pattern

Alternative Assessment

Assessment 1 (Resit): Data Preparation for Business Intelligence: Problem Definition and Data Structuring (Individual)
Students will complete an individual problem-driven data preparation task using a new dataset, including the definition of research question(s), selection of relevant variables, and the cleaning and structuring of data in Tableau Prep.

Assessment 2 (Resit): Digital Business Intelligence Dashboard and Storytelling (Individual)
Students will complete an individual business intelligence project involving the development of a Tableau dashboard and story, and the presentation of data-driven insights through a recorded video aimed at a managerial audience.

Assessment Strategy

The assessment strategy is designed to enable students to demonstrate their understanding of business intelligence and data analytics and their ability to apply analytical techniques to support organisational decision-making in service industries. The module is structured to allow students to evidence their achievement of the learning outcomes through a combination of individual and group-based coursework, reflecting key stages of the business intelligence process.

The assessment comprises:

1. An individual assignment in which students define relevant business or research question(s), identify appropriate variables, and clean, transform, and structure raw data using Tableau Prep to produce a dataset suitable for analysis.
2. A group project in which students develop a business intelligence dashboard and structured Tableau Story to communicate data-driven insights, supported by a recorded video presentation aimed at a managerial audience.

Formative assessment and feedback will be provided as follows:

• An explanation of assessment requirements and marking criteria during the first lab session.
• Ongoing verbal feedback during lectures, seminars, and guided practical activities.
• Peer and lecturer feedback during group-based lab exercises.
• A formative feedback opportunity on students¿ analytical approach and dashboard/story development prior to final submission.
• Written summative feedback, including a breakdown of marks aligned with assessment criteria.

Module aims

• ¿ Develop a critical understanding of the role and importance of business intelligence and data analytics in service industries, including hospitality, tourism, transport, and events
• ¿ Develop the ability to transform data into business intelligence outputs to support organisational decision-making and problem-solving in service contexts
• ¿ Develop foundational statistical and analytical competence to interpret patterns, trends, and relationships in business data
• ¿ Critically evaluate how data-driven decision-making influences managerial practice and organisational performance in service industries

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategy is designed to develop students¿ understanding of business intelligence and data analytics within service industries. The module combines foundational statistical concepts with applied analytical practice, focusing on data-driven decision-making, business intelligence reporting, and effective visual communication.

Conceptual foundations in business analytics are reinforced through practical workshops using an analytical software package to prepare, analyse, and visualise data. Students apply analytical techniques to real-world service datasets in order to generate, interpret, and communicate managerial insights. The module emphasises critical thinking, structured problem-solving, and evidence-based decision-making within service contexts.

The learning and teaching methods include:

¿ Lectures introducing key concepts in business intelligence, analytics, statistical reasoning, and data visualisation
¿ Practical laboratory workshops applying analytical techniques to real-world datasets
¿ A group-based project requiring the development of a Business Intelligence dashboard and analytical narrative
¿ Guided exercises and structured problem-solving activities
¿ Formative feedback opportunities embedded throughout the module
¿ Independent learning through reading, software practice, collaborative work, and assessment preparation

Students are expected to take responsibility for their learning within a supportive environment, developing analytical confidence, collaborative skills, and resilience when working with unstructured data and business problems.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: MAN2231

Other information

Digital Capabilities This module develops students¿ digital capabilities through the practical application of business intelligence and data analytics tools. Students gain experience in data preparation, descriptive and introductory predictive analytics, dashboard design, and data visualisation using Tableau. They engage with SurreyLearn and other digital platforms to access learning materials, collaborate with peers, and communicate analytical insights. The group assessment requires students to analyse datasets and develop a professional business intelligence dashboard and Story. Employability The module develops industry-relevant analytical and decision-making skills widely used across service industries. Students learn how to transform data into actionable business insights, design performance dashboards, and communicate findings to managerial audiences. These capabilities enhance employability in data-informed service-sector roles. Sustainability Students consider how data analytics and performance measurement can support sustainable and responsible decision-making in service industries. The module also introduces sustainability challenges associated with the use of big data, including issues related to data storage, energy consumption, ethical data use, and responsible governance. Students reflect on how business intelligence practices can contribute to more sustainable and accountable organisational decision-making. Resourcefulness and Resilience Students work with real-world datasets and unstructured business problems, requiring independent analysis, critical thinking, and collaborative problem-solving. This process develops analytical confidence, adaptability, and resilience. Global and Cultural Capabilities Students analyse service-sector datasets that reflect diverse organisational and geographical contexts. They interpret performance patterns and managerial implications within a global service environment, developing awareness of cross-sector and cross-regional variation.

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2026/7 academic year.