Towards an Automatic Data Value Analysis Method for Relational Databases

Malika Bendechache, Nihar Limaye, Rob Brennan

Abstract

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.

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Paper Citation


in Harvard Style

Bendechache M., Limaye N. and Brennan R. (2020). Towards an Automatic Data Value Analysis Method for Relational Databases.In Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-423-7, pages 833-840. DOI: 10.5220/0009575508330840


in Bibtex Style

@conference{iceis20,
author={Malika Bendechache and Nihar Limaye and Rob Brennan},
title={Towards an Automatic Data Value Analysis Method for Relational Databases},
booktitle={Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2020},
pages={833-840},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009575508330840},
isbn={978-989-758-423-7},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 22nd International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Towards an Automatic Data Value Analysis Method for Relational Databases
SN - 978-989-758-423-7
AU - Bendechache M.
AU - Limaye N.
AU - Brennan R.
PY - 2020
SP - 833
EP - 840
DO - 10.5220/0009575508330840