Open Data Use Cases

Over the recent years, more and more data has become openly available on the internet. However, currently this valuable base of information remains largely unused by companies. In order to fill this gap, we develop the Data App Store, an online platform that supports businesses in the discovery, integration and use of open data.

  • Conduct a case study on the usage of open data in one of partner companies of the Data App Store project, namely Nestlé, Swisscom, and SBB.

Contact: Andreas Lang or Christine Legner

Requirements and Notation for Information Supply Chains

The concept of an information supply chain consists of all activities and work associated with the transformation of raw data to the delivery of information products to the end consumer and involves the participation of several actors. It functions as an analogy to product supply chain. The goal is to understand how data circulates throughout various corporate systems and functions.

  • Review existing literature on the topic
  • Identify requirements for Information Supply Chains
  • Propose a notation scheme
  • Otto, B., & Ofner, M. (2010). Towards a Process Reference Model for Information Supply Chain Management. ECIS.
  • Sun, S., & Yen, J. (2005). Information Supply Chain: A Unified Framework for Information-Sharing. ISI.

A Data Management Perspective on Information Security Frameworks

Information Security is covered by a variety of general purpose frameworks (relating to governance and auditing, among others). Data management is a subset of these topics that falls under the umbrella of these frameworks, and may be either explicitely or implicitely addressed.

  • Identify security-related data management design areas (e.g. access rights, privacy compliance)
  • Select and review information security frameworks
  • Provide a mapping of data management design areas and information security requirements
  • National Institute of Standards and Technology (NIST), & United States of America. (2014). Framework for Improving Critical Infrastructure Cybersecurity.
  • De Haes, S., Van Grembergen, W., & Debreceny, R. S. (2013). COBIT 5 and enterprise governance of information technology: Building blocks and research opportunities. Journal of Information Systems, 27(1), 307-324.

Contact: Clément Labadie or Christine Legner

Approaches for Big Data Management

Big Data is a relatively new technological trend – as such, the way it should be used and manage in corporate environments still needs further definition. Big data management is the organization, administration and governance of large volumes of both structured and unstructured data.

  • Review Big Data-related litterature
  • Identify design areas and requirements for Big Data Management
  • Suggest an approach for Big Data Management in corporate environments
  • Chen, J., Chen, Y., Du, X., Li, C., Lu, J., Zhao, S., & Zhou, X. (2013). Big data challenge: a data management perspective. Frontiers of Computer Science, 7, 157-164.
  • Cohen, E., Hirama, K., & Rossi, R. (2015). Characterizing Big Data Management.

Contact: Clément Labadie or Christine Legner

Analytics as a Service: Self-Service Analytics

Analytical solutions are mainly adopted by large enterprises, however cloud services provide a cost-effective approach to support its adoption by a wider range of organizations. In fact, the global analytics as a service (AaaS) market is expected to grow from $5.9 billion in 2015 to $22.24 billion in 2020 (ResearchandMarkets, 2016). Besides the reduced costs for implementation, several other factors favor cloud services for business analytics, particularly increased agility owing to the scalability of cloud.

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