Exploring data value assessment: a survey method and investigation of the Perceived relative importance of data value dimensions

This paper describes the development and execution of a data value assessment survey of data professionals and academics. Its purpose was to explore more effective data value assessment techniques and to better understand the perceived relative importance of data value dimensions for data practitioners. This is important because despite the current deep interest in data value, there is a lack of data value assessment techniques and no clear understanding of how individual data value dimensions contribute to a holistic model of data value. A total of 34 datasets were assessed in a field study of 20 organisations in a range of sectors from finance to aviation. It was found that in 17 out of 20 of the organisations contacted that no data value assessment had previously taken place. All the datasets evaluated were considered valuable organisational assets and the operational impact of data was identified as the most important data value dimension. These results can inform the community’s search for data value models and assessment techniques. It also assists further development of capability maturity models for data value assessment and monitoring. This is to our knowledge the first publication of the underlying data for a multi-organization data value assessment and as such it represents a new stage in the evolution of evidence-based data valuation

A Semantic Data Value Vocabulary Supporting Data Value Assessment and Measurement Integration

In this paper we define the Data Value Vocabulary (DaVe) that allows for the comprehensive representation of data value. This vocabulary enables users to extend it using data value dimensions as required in the context at hand. DaVe caters for the lack of consensus on what characterises data value, and also how to model it. This vocabulary will allow users to monitor and asses data value throughout any value creating or data exploitation efforts, therefore laying the basis for effective management of value and efficient value exploitation. It also allows for the integration of diverse metrics that span many data value dimensions and which most likely pertain to a range of different tools in different formats. This data value vocabulary is based on requirements extracted from a number of value assessment use cases extracted from literature, and is evaluated using Gruber?s ontology design criteria, and by instantiating it in a deployment case study