Big Data Governance in Agile and Data-Driven Software Development: A Market Entry Case in the Educational Game Industry

Big Data Governance in Agile and Data-Driven Software Development: A Market Entry Case in the Educational Game Industry

Lili Aunimo (Haaga-Helia University of Applied Sciences, Finland), Ari V. Alamäki (Haaga-Helia University of Applied Sciences, Finland) and Harri Ketamo (Headai Ltd., Finland)
Copyright: © 2019 |Pages: 21
DOI: 10.4018/978-1-5225-7077-6.ch008

Abstract

Constructing a big data governance framework is important when a company performs data-driven software development. The most important aspects of big data governance are data privacy, security, availability, usability, and integrity. In this chapter, the authors present a business case where a framework for big data governance has been built. The business case is about the development and continuous improvement of a new mobile application that is targeted for consumers. In this context, big data is used in product development, in building predictive modes related to the users and for personalization of the product. The main finding of the study is a novel big data governance framework and that a proper framework for big data governance is useful when building and maintaining trustworthy and value adding big data-driven predictive models in an authentic business environment.
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Background

The field of big data governance emerged with the advent of big data. Big data are data that cannot be processed using traditional data processing software and infrastructure on a personal computer or on a dedicated server (e.g., Liebowitz, 2013). In addition, big data differ from traditional data in volume, variety, and/or velocity (Liebowitz, 2013). Typical examples of big data include web and social media data, machine-to-machine data, big transaction data, biometric data and human-generated data (Soares, 2012). The authors include Internet of Things (IoT) data and data generated in industrial processes in the broad set of transactional data.

Key Terms in this Chapter

Data Governance: The processes and technical infrastructure that an organization has in place to ensure data privacy, security, availability, usability, and integrity.

Data Usability: The data in an organization can be used to meet the goals defined in the corporate strategy, including data monetization.

Data Privacy: Data containing information about a person should be treated with special attention according to the organization’s data privacy policy and legislation.

Data Security: The processes and technologies that ensure that sensitive and confidential data about an organization are kept secure according to the organization’s policies.

Data Integrity: The trustworthiness of the data, including data integration, data lifecycle management, and data quality monitoring.

IT Governance: The processes that ensure the effective and efficient use of IT in enabling an organization to achieve its goals.

GDPR: The general data protection regulation. It is a regulation in European Union (EU) law on data protection and privacy for all individuals within the EU and the European Economic Area.

DAMA: The global data management community. It is a non-profit and vendor-independent association that provides a community and support for information professionals.

DMBOK: The DAMA International Guide to Data Management Body of Knowledge . A publication that is dedicated to advancing the concepts and practices of information and data management.

Big Data: Data that cannot be processed using traditional data analytics software and infrastructure on a personal computer or on a data analytics server. Compared with traditional data, big data have greater volume, variety, and/or velocity than traditional data.

Predictive Model: A data-driven model, which is used to predict a future event, in contrast to a descriptive model, which is used to explain a past event.

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