Prioritize the Dimensions of Knowledge Quality in Data Driven Organizations (Case Study: Iranian Research Institute for Information Science and Technology)

Knowledge is one of the most important competitive and value creating advantages of organizations, and for effective use of knowledge, attention should be paid to its quality. Data driven organizations rely on their data assets. Quality and reliable data in organizations leads to better decision making, and the knowledge quality in data driven organizations leads to growth and increase in profitability and innovation. The present research was conducted in order to investigate and prioritize the dimensions of knowledge quality in data driven organizations.

The statistical population of this research is made up of 10 experts in this field in the Research Iranian Research Institute for Information Science and Technology (IranDoc). In order to collect data, while reviewing the literature on the subject, interviews with experts of Iranian Research Institute for Information Science and Technology and questionnaires were used. For data analysis, the fuzzy DEMATEL method was used to determine the relationships between indicators and the ANP method was used for weighting and prioritizing criteria.

The results of data analysis through questionnaires and solutions using EXCEL software show that among the main criteria, the representational knowledge quality is the highest quality priorities. Also, among the sub-criteria, the degree of Expandability with a weight of (0.15089) was the first priority, Understandability with a weight of (0.14039) was the second priority, and Interpretability was the third priority with a weight of (0.13687).

It is observed that the sub-criterion of expandability and use of knowledge content in the future through knowledge sharing and integration has a much greater impact on the perceived quality of knowledge in data-oriented organizations than other sub-criteria.