Towards an automatic data value analysis method for relational databases
Bendechache, MalikaORCID: 0000-0003-0069-1860, Limaye, Nihar and Brennan, RobORCID: 0000-0001-8236-362X
(2020)
Towards an automatic data value analysis method for relational databases.
In: 22nd International Conference on Enterprise Information Systems (ICEIS), 5-May-7Jun 2020, Czech Republic, online.
ISBN 978-989-758-423-7
Data is becoming one of the world’s most valuable resources and it is suggested that those who own the data
will own the future. However, despite data being an important asset, data owners struggle to assess its value.
Some recent pioneer works have led to an increased awareness of the necessity for measuring data value.
They have also put forward some simple but engaging survey-based methods to help with the first-level data
assessment in an organisation. However, these methods are manual and they depend on the costly input of
domain experts. In this paper, we propose to extend the manual survey-based approaches with additional
metrics and dimensions derived from the evolving literature on data value dimensions and tailored specifically
for our use case study. We also developed an automatic, metric-based data value assessment approach that (i)
automatically quantifies the business value of data in Relational Databases (RDB), and (ii) provides a scoring
method that facilitates the ranking and extraction of the most valuable RDB tables. We evaluate our proposed
approach on a real-world RDB database from a small online retailer (MyVolts) and show in our experimental
study that the data value assessments made by our automated system match those expressed by the domain
expert approach.
Metadata
Item Type:
Conference or Workshop Item (Paper)
Event Type:
Conference
Refereed:
Yes
Uncontrolled Keywords:
Data Value, RDB, Information Systems, CMM, Metrics