Data valuation considering knowledge transformation, process models and data models

Abstract

Interest for data valuation is on the rise. Data is often compared to highly valued commodities and is considered the currency of digital economy. However, there is no widely used method to estimate the value, therefore similar type of data could be valued differently within the same processes, lacking consistency. Additionally, data volume has an exponential growth and data are shared among multiple vendors especially when Industrial-Internet-of-Things platforms and digital ecosystems are engaged. Therefore, knowing the worth of data and its derivatives or phases such as information, knowledge and wisdom are important for stakeholders. The goal of this research is to derive a practical approach for valuation, by considering past, present and future benefits of the collected data, considering already known Key Performance Indicators and Key Prediction Indicators that will be developed based on predictive analytics capabilities. Categorization of data, and its subsequent phases within a system will be modelled and profound value in each phase, and overall value in a network of systems will be developed.