Similar to a product supply chain, an information supply chain is a dynamic environment where networks of information-sharing agents gather data from many sources and utilize the same data for different tasks. Unfortunately, raw data arriving from a variety of sources are often plagued by errors (Ballou et al. 1998), which can lead to poor decision making. Supporting decision making in this challenging environment demands a proactive approach to data quality management, since the decision maker has no control over these data sources (Shankaranarayan et al. 2003). This is true in health care, and in particular in health planning, where health care resource allocation is often based on summarized data from a myriad of sources such as hospital admissions, vital statistic records, and specific disease registries.

This work investigates issues of data quality in the information supply chain. It proposes three result-driven data quality metrics that inform and aid decision makers with incomplete and inconsistent data and help mitigate insensitivity to sample size, a well known decision bias. To design and evaluate the result-driven data quality metrics this thesis utilizes the design science paradigm (Simon 1996; Hevner, March et al. 2004). The metrics are implemented within a simple OLAP interface, utilizing data aggregated from several healthcare data sources, and presented to decision makers in four focus groups. This research is one of the first to propose and outline the use of focus groups as a technique to demonstrate utility and efficacy of design science artifacts.

Results from the focus groups demonstrate that the proposed metrics are useful, and that the metrics are efficient in altering a decision maker's data analytic strategies. Additionally, results indicate that comparative techniques, such as benchmarking or scenario based approaches, are promising approaches in data quality.

Finally, results from this research reveal that decision making literature needs to be considered in the design of BI tools. Participants of the focus groups confirmed that people are insensitive to sample size, but when attention was drawn to small sample sizes, this bias was mitigated.