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.
"Capturing Enterprise Data Integration Challenges Using a Semiotic Data Quality Framework,"
Business & Information Systems Engineering:
Vol. 57: Iss. 1, 27-36.
Available at: https://aisel.aisnet.org/bise/vol57/iss1/4