1) DISCUSS CRITICALLY THE MAJOR DEVELOPMENTS IN DATA STORAGE WITH RESPECT TO DATA WAREHOUSING AND CLOUD SOLUTIONS
Enquiry

2) DISCUSS CRITICALLY THE USE OF DATA WITHIN ORGANISATIONS, WITH RESPECT TO DATA WAREHOUSING, AND QUALITY OF DATA ETC.
Knowledge and Understanding

3) ANALYSE, DESIGN AND BUILD AN APPROPRIATE SYSTEM UTILISING CURRENT DATA WAREHOUSING TECHNOLOGIES.
Analysis
Problem Solving

4) IDENTIFY AND DESIGN KEY COMPONENTS OF A CUSTOMER ECOSYSTEM ARCHITECTURE
Application

The coursework will comprise two elements:

An Individual Research Paper (70%) (2,000 words) (Learning Outcomes 1 and 2).
An Individual practical assignment (30%) 20 minutes demo (Learning Outcome 3 and 4).

This module will address:

- An overview of Data Warehousing principles and techniques
- Architectures, schemas design, tuning, loading (ETL), staging areas, data marts, and operational data stores etc.
- Components of a Customer Ecosystem
- Customer interaction with hub architectures
- Success and failure issues in Customer Ecosystems
- Digital business models
- Digital design patterns
- Kinball v Inman, OLAP and OLTP
- Logical v physical design
- Dimensional modelling
- Cloud storage, architectures and legislation
- Issues with respect to mobile, complex data, and biometrics data
- Structures to support CRM
- Quality and Integrity of Data

As a distance learning module, there will not be a traditional lecture every week. The main learning environment that will be used is our Virtual Learning Environment (VLE). Students will work through the module material provided on the VLE at a pace suggested by the instructors. The Instructors will monitor student progress, providing their expertise where necessary for assisting in solving problems. Students will be encouraged to discuss relevant aspects within discussion forums that are part of the VLE. The forums will allow discussion with a student's peer group as well as the module tutor.
As a distance learning module, there will not be a traditional lecture every week. The main learning environment that will be used is our Virtual Learning Environment (VLE). Students will work through the module material provided on the VLE at a pace suggested by the instructors. The Instructors will monitor student progress, providing their expertise where necessary for assisting in solving problems. Students will be encouraged to discuss relevant aspects within discussion forums that are part of the VLE. The forums will allow discussion with a student's peer group as well as the module tutor.
Online materials, data sets and useful articles made available through BB including discussion forum
Videos/recorded material will be made available for DL students.
Continuous engagement with weekly tasks and activities (e.g., consolidating video lectures, reading and engaging with resources/links, inquiry-based learning, discussion boards, formative assessments).

Kang, U. and Ee-Peng, L. (2017) Trends and Applications in Knowledge Discovery and Data Mining: PAKDD 2017 Workshops, MLSDA, BDM, DM-BPM Jeju, South Korea, May 23, 2017, Revised Selected Papers (Lecture Notes in Computer Science), ISBN-10: 3319672738.
Krishnan, K. (2013) Data warehousing in the age of big data, Morgan Kaufmann, ISBN 970124058910.
Kinmball, R. et al, (2013) The data warehouse toolkit, Wiley, ISBN 9781118530801.
Bauer, S. (2013) Getting started with Amazon Redshirt, Packt, ISBN 978172178085.
Skilton, S. (2015) Building Digital Ecosystem Architectures: A Guide to Enterprise Architecting Digital Technologies in the Digital Enterprise (Business in the Digital Economy), Palgrave Macmillan, ISBN-10: 113755410X

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