The body of knowledge on data and information quality is highly diversified, primarily due to the cross-disciplinary nature of data quality problems, coupled with a strong focus on fitness for use principle in developing data quality solutions. As a result, research and practice in data and information quality is characterized by methodological as well as topical diversity. Although research pluralism is highly warranted, there is evidence that substantial developments in the past have been isolationist. As a first step towards bridging gaps between various communities involved in data quality research and practice, we undertook a literature review of data quality research published in a range of Information System (IS) and Computer Science (CS) publication outlets and identified the key themes of research from last 20 years. In this paper, we utilize the above results to explore the impact of these themes within the data quality professional community. To that end, we developed an initial model of data quality factors (based on the identified key research themes), and conducted a survey of data quality practitioners to test the model. Our study found that the effective implementation of data quality assessment practices, data quality frameworks, and data constraints and rules, has a significant impact on overall data quality levels in organizations, whereas focus on other factors do not appear to significantly affect data quality. Results from this study can assist organizations in prioritising their data quality initiatives to focus on the factors that have the potential to contribute most significantly to overall data and information quality.