How good is a company's data quality? Answering this question requires usable data quality metrics. Currently, most data quality measures are developed on an ad hoc basis to solve specific problems [6, 8], and fundamental principles necessary for developing usable metrics in practice are lacking. In this article, we describe principles that can help organizations develop usable data quality metrics.

The majority of the information presented in this paper has been presented in another paper I have reviewed, namely the Data quality assessment from the user’s perspective paper. However, it should be noted that this paper was written earlier and has been used as a reference for the paper mentioned above. Table 1 presents 16 dimensions of data quality that can be evaluated and could be adapted to measure the quality of a user contributions. However, some of these dimensions would be applicable in measuring the data quality of the overall social software service but not of individual user contributions. Additionally, the objective assessment of these dimensions can be dependent or independent of how the data is used (i.e. the task). The authors also mention 3 functional forms for measuring data quality dimensions. I did not make the connection before but the indexed UCM calculation approach developed in my Honours thesis could be classified under the weighted average functional form. The authors propose that the use of subjective and objective measure to improve organisational data quality requires a three step process as depicted in Figure 2.

• Codd's column integrity
• Sensitivity factor
• Data quality assessment process