Key performance indicators for successful metadata governance - Nair, Dyuthi (My University, 2012)
Metadata in increasing levels of sophistication has been the most powerful concept used in management of unstructured information ever since the first librarian used the Dewey decimal system for library classifications. It remains to be seen, however, what the best approach is to implementing metadata to manage huge volumes of unstructured information in a large organization. Also, once implemented, how is it possible to track whether it is adding value to the company, and whether the implementation has been successful? Existing literature on metadata seems to either focus too much on technical and quality aspects or describe issues with respect to adoption for general information management initiatives. This research therefore, strives to contribute to these gaps: to give a consolidated framework for striving to understand the value added by implementing metadata. The basic methodology used is that of case study, which incorporates aspects of design science, surveys, and interviews in order to provide a holistic approach to quantitative and qualitative analysis of the case. The research identifies the various approaches to implementing metadata, particularly studying the one followed by the unit of analysis of case study, a large company in the Oil and Gas Sector. Of the three approaches identified, the selected company already follows an approach that appears to be superior. The researcher further explores its shortcomings, and proposes a slightly modified approach that can handle them. The research categorically and thoroughly (in context) identifies the top effectiveness criteria, and corresponding key performance indicators(KPIs) that can be measured to understand the level of advancement of the metadata management initiative in the company. In an effort to contrast and have a basis of comparison for the findings, the research also includes views from information managers dealing with core structured data stored in ERPs and other databases. In addition, the results include the basic criteria that can be used to evaluate metrics, in order to classify a metric as a KPI.