Data Quality is both a philosophy and the practice of collecting and synthesising data. It includes aspects of data relevance, completeness, consistency and timeliness, but also relates to the credibility and discretion of research and evaluation practitioners.
Evidence shows that poor data quality costs time and resources. Businesses and IT companies have known this for years and been working to achieve high quality data to minimise errors and improve client satisfaction. As researchers and evaluators, we should also be in constant pursuit of data quality; low quality data is time-consuming and can also impact on the credibility of research and evaluation findings and conclusions.
Data Quality is achieved through engaging with both the philosophy and the practice of collecting and synthesising data. Being aware of our data quality demonstrates intellectual integrity and accountability, while ensuring the efficiency and credibility of our research and evaluation.