Towards an Automatic Data Value Analysis Method for Relational Databases

Data is becoming one of the world’s most valuable resources and it is suggested that those who own the data
\nwill own the future. However, despite data being an important asset, data owners struggle to assess its value.
\nSome recent pioneer works have led to an increased awareness of the necessity for measuring data value.
\nThey have also put forward some simple but engaging survey-based methods to help with the first-level data
\nassessment in an organisation. However, these methods are manual and they depend on the costly input of
\ndomain experts. In this paper, we propose to extend the manual survey-based approaches with additional
\nmetrics and dimensions derived from the evolving literature on data value dimensions and tailored specifically
\nfor our use case study. We also developed an automatic, metric-based data value assessment approach that (i)
\nautomatically quantifies the business value of data in Relational Databases (RDB), and (ii) provides a scoring
\nmethod that facilitates the ranking and extraction of the most valuable RDB tables. We evaluate our proposed
\napproach on a real-world RDB database from a small online retailer (MyVolts) and show in our experimental
\nstudy that the data value assessments made by our automated system match those expressed by the domain
\nexpert approach.

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