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Track05: Data Governance and Data Quality Management

Track Chairs

Boris Otto, University of St. Gallen, Switzerland, Boris.Otto@unisg.ch
Andy Koronios, University of South Australia, Australia, andy.koronios@unisa.edu.au
Bernd Heinrich, University of Regensburg, Germany, Bernd.Heinrich@wiwi.uni-regensburg.de
Mathias Klier, University of Regensburg, Germany, Mathias.Klier@wiwi.uni-regensburg.de

Description

At present a number of economic, technological, and societal developments can be observed which, among other things, lead to a “renaissance of data”. One example is the rise of business data service providers such as Factual  and InfoChimps  who build novel business models upon community and crowd sourcing approaches to make data available to enterprises. A second example is the increasing volumes of structured and unstructured data enterprises have to make sense of. The BBC has reported on the current global data storage capacity, which exceeded 295 Exabyte in 2007, and a 58 percent combined annual growth rate of computing capacity over the last two decades. In early 2010, Apple sold the ten-billionth song over its iTunes platform. Third, the capability to manage data and data quality efficiently and effectively becomes business-critical as the number of legal and regulatory provisions keeps increasing. The European directive REACH requires manufacturers of chemical products to be able to report accurate, complete, consistent, and up-to-date data on ingredients and implications of their products and refuses access to the market if these requirements are not met (“no data, no market”). Enterprises need guidance and support to take the opportunities and mitigate the risks related to this contemporary phenomenon which analyst company Gartner refers to as the “data economy”.

Data Governance and Data Quality Management are considered key presuppositions for enterprises in this endeavor. Data Quality Management is an enterprise function aiming at optimizing data quality by using methods and approaches for defining, measuring, analyzing, improving, and controlling data quality. Data Governance is a framework of decision-making rights and responsibilities regarding the management and use of data, aiming at optimizing the value of data.

Recently, scholars identified a number of important research directions for Data Governance and Data Quality Management. Some researchers stipulate researching data quality aspects in new ways to deliver and provide data to the end-user (e.g. linked data or distributed architectures) and to take a broader perspective at societal or group level (e.g. elderly people), for example. And others suggest Data Governance research to take a business networking view.

Topics of Interest

  • Business models built on the concept of data quality
  • Case studies on Data Governance and Data Quality Management
  • Comprehensive societal, technological, and economic perspectives on data quality
  • Data Governance and Data Quality Management as organizational capabilities
  • Data quality in “big data” environments
  • Data quality in specific data domains (e.g. social media, multimedia, geographical information)
  • Data quality in supply chains, in healthcare, e-government etc.
  • Data quality in the Cloud
  • Data quality metrics and their role for Data Governance
  • Data ownership and data stewardship
  • Design and evolution of Data Governance arrangements
  • Design-oriented approaches to Data Governance and Data Quality Management
  • Economic value of data quality
  • Enterprise and master data management
  • Maturity of Data Governance arrangements and Data Quality Management capabilities in enterprises
  • Metadata management and its role for Data Governance
  • Operational and analytical viewpoints on Data Governance and Data Quality Management
  • Theoretical foundations for establishing Data Governance and Data Quality Management in enterprises

Associate Editors

Roger Blake, University of Massachusetts Boston, USA
Ismael Caballero, Universidad de Castilla-La Mancha, Spain
Cinzia Cappiello, Politecnico di Milano, Italy
Barbara Dinter, University of St. Gallen, Switzerland
Adir Even, Ben-Gurion University of the Negev, Israel
Martin Gaedcke, Chemnitz University of Technology, Germany
Barbara Klein, University of Michigan at Dearborn, USA
Yang W. Lee, Northeastern University, USA
Susanne Leist, University of Regensburg, Germany
Zoltan Miklos, University of Rennes 1, France
Leo L. Pipino, University of Massachusetts, USA
Shazia Sadiq, The University of Queensland, Australia
Guido Schryen, University of Regensburg, Germany
Ganesan Shankaranarayanan, Babson College, USA
Rolf T. Wigand, University of Arkansas at Little Rock, USA
Harry Zhu, Old Dominion University, USA

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