Capturing Enterprise Data Integration Challenges Using a Semiotic Data Quality Framework

John Krogstie ()

Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, 2015, vol. 57, issue 1, 27-36

Abstract: Enterprises have a large amount of data available, represented in different formats normally accessible for different specialists through different tools. Integrating existing data, also those from more informal sources, can have great business value when used together as discussed for instance in connection to big data. On the other hand, the level of integration and exploitation will depend both on the data quality of the sources to be integrated, and on how data quality of the different sources matches. Whereas data quality frameworks often consist of unstructured list of characteristics, here a framework is used which has been traditionally applied for enterprise and business model quality, with the data quality characteristics structured relative to semiotic levels, which makes it easier to compare aspects in order to find opportunities and challenges for data integration. A case study presenting the practical application of the framework illustrates the usefulness of the approach for this purpose. This approach reveals opportunities, but also challenges when trying to integrate data from different data sources typically used by people in different roles in an organization. Copyright Springer Fachmedien Wiesbaden 2015

Keywords: Enterprise data integration; Data integration; Data quality; SEQUAL (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://hdl.handle.net/10.1007/s12599-014-0365-x (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:binfse:v:57:y:2015:i:1:p:27-36

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/12599

DOI: 10.1007/s12599-014-0365-x

Access Statistics for this article

Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK is currently edited by Martin Bichler

More articles in Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK from Springer, Gesellschaft für Informatik e.V. (GI)
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().