Database Use and Design (Spring 2019)

Abstract

The course gives an overview of the technical aspects of data management, explores the value of data and discusses the risks associated to big data.

Description

A data ‘revolution’ is underway, one that is already reshaping how knowledge is produced, business conducted, and governance enacted. Data has traditionally been time-consuming and costly to generate, analyse and interpret, and generally provided a relatively static and coarse snapshot of phenomena.

This state of affairs is changing now. Rather than being scarce and limited in scope, the production of data is increasingly becoming a ‘deluge’ i.e. a wide flow of real-time, varied, resolute and relational data that are relatively low in cost. 

Outside of business, data is increasingly becoming open as well. This data abundance (as opposed to data scarcity) is reshaping how we work with, circulate, trade, analyse and exploit data. This development is founded on the latest wave of information and communication technologies such as the plethora of digital devices encountered in homes, workplaces and public spaces as well as mobile, distributed and cloud computing; social media, and inter-worked sensors and devices. 

These technical infrastructures are leading to evermore aspects of everyday life – work, consumption, travel, communication, and leisure – being captured as data. Moreover, they are re-configuring the production, circulation and interpretation of data, producing what has been termed ‘big data’. 

The students gain an understanding of the technical aspects of data management and the opportunities and risks they create for organisations.

During the course the students will relate to the (changing) nature of data generation and use, including:

• Databases and data infrastructures
  
• Big data and analytics
  
• The value of data
  
• The risks associated to personal data
  

Formal prerequisites

Information about the course of study This course is part of the second semester in the bachelor's degree in Global Business Informatics.

Intended learning outcomes

After the course, the student should be able to:

• Explain the difference between the relational and non-relational data models
• Describe the concept of big data
• Describe the challenges and opportunities of open data
• Account for enablers and sources of big data
• Explain techniques and methods for data analytics and visualization
• Analyse the governmental and business rationales for big data: Governing people, managing organisations, leveraging value, creating better places
• Discuss organisation challenges and opportunities related to data: Scope of data sets, access to data, quality of data, data integration and interoperability, the application of data analytics
• Discuss ethical issues related to data generation and use in government, business, and society at large