Authors:Shelernaz Azimi andClaus Pahl
Affiliation:Free University of Bozen - Bolzano, Bolzano, Italy
Keyword(s):Data Quality, Information Value, Machine Learning, Big Data, Data Quality Improvement, Data Analysis.
Abstract:Data quality is an important factor that determines the value of information in organisations. Data, when given meaning, results in information. This then creates financial value that can be monetised or provides value by supporting strategic and operational decision processes in organisations. In recent times, data is not directly accessed by the consumers, but is provided ’as-a-service’. Moreover, machine-learning techniques are now widely applied to data, helping to convert raw, monitored source data into valuable information. In this context, we introduce a framework that presents a range of quality factors for data and resulting machine-learning generated information models. Our specific aim is to link the quality of these machine-learned information models to the quality of the underlying source data. This takes into account the different types of machine learning information models as well as the value types that these model provide. We will look at this specifically in the context of numeric data, where we use an IoT application that exhibits a range of typical machine learning functions to validate our framework.(More)
Data quality is an important factor that determines the value of information in organisations. Data, when given meaning, results in information. This then creates financial value that can be monetised or provides value by supporting strategic and operational decision processes in organisations. In recent times, data is not directly accessed by the consumers, but is provided ’as-a-service’. Moreover, machine-learning techniques are now widely applied to data, helping to convert raw, monitored source data into valuable information. In this context, we introduce a framework that presents a range of quality factors for data and resulting machine-learning generated information models. Our specific aim is to link the quality of these machine-learned information models to the quality of the underlying source data. This takes into account the different types of machine learning information models as well as the value types that these model provide. We will look at this specifically in the context of numeric data, where we use an IoT application that exhibits a range of typical machine learning functions to validate our framework.