Data has become an essential part of products and services throughout all sectors of society and is popularly considered to be the new oil since it has become a valuable commodity. All data has social and commercial value, based on the impact of its use in different dimensions, including commercial, technical, societal, financial, and political. This has resulted in many entities and businesses that hoard data with the aim of exploiting it. Yet, the "simple" exploitation of data results in entities who are not obtaining the highest benefits from the data, which as yet is not considered to be a fully-fledged enterprise asset. Such data can exist in a duplicated, fragmented, and isolated form, and the sheer volume of available data further complicates the situation. Issues such as the latter highlight the need for value-based data governance, where the management of data assets is based on the quantification of the data value.
Despite the growing literature on data as an asset and data exploitation, there is little work on how to directly assess or quantify the value of specific datasets held or used by an organisation within an information system. Without assessment, effective management of value and hence efficient exploitation is highly unlikely. It is becoming more and more evident that failing to value data will result in a number of consequences such as retaining information that has little to no value, reduction in data usage, and leaving data investments vulnerable to budget cuts. Data value assessment involves the monitoring of the dimensions that characterise data value within a data value chain, such as data quality, usage of data, and cost. In real-world information systems this involves integration of metrics and measures from many sources, for example; log analysis, data quality management systems, and business functions such as accounting. This value assessment and integration task is further exacerbated by the lack of consensus on the definition of data value itself.
The context of this research effort is therefore threefold:
Read more about OptimusChain, the EDGE Marie-Curie funded project on Data Value here.
Read our publications on Data Value here.
Discover events on Data Value here.
Find information about our Data Value Workshop here.