Abstract
Data quality is an important factor that determines the value of information in organisations. Information creates financial value, but depends largely on the quality of the underlying data. Today, data is more and more processed using machine-learning techniques applied to data in order to convert raw source data into valuable information. Furthermore, data and information are not directly accessed by their users, but are provided in the form of ’as-a-service’ offerings. We introduce here a framework based on a number of quality factors for machine-learning generated information models. Our aim is to link back the quality of these machine-learned information models to the quality of the underlying source data. This would enable to (i) determine the cause of information quality deficiencies arising from machine-learned information models in the data space and (ii) allowing to rectify problems by proposing remedial actions at data level and increase the overall value. We will investigate this for data in the Internet-of-Things context.