Title: Development of an innovative framework for missing data in retail data science

Authors: Ashok Mahapatra; Srikanta Patnaik; Manoranjan Dash; Ananya Mahapatra

Addresses: Institute of Business and Computer Studies, SOA University, India ' Institute of Technical Education and Research, SOA University, India ' Institute of Business and Computer Studies, SOA University, India ' Faculty of Science, University of British Columbia, Canada

Abstract: Although handling missing data and missing value imputation are widely researched subjects, missing data identification and treatment has not been pursued as a principal apparatus in retail data science applications. Critical data science derived strategies for assortment optimisation, customer purchasing behaviour and supply chain draw conclusions mostly based on the assumptions of non-missing, complete datasets. Therefore, we not only explore missing data scenarios in retail holistically: 1) from a data science perspective; 2) from an operational perspective; 3) from an implementation perspective, such that we can develop a robust framework, but also, we fill the gaps in: 1) identification; 2) treatment of missing data. To make our recommendations robust and comprehensive, we have proposed an implementable framework that harnesses the missing data scenarios in retail holistically and bridges the gaps in identification and treatment of it. At the core of the framework is a decision tree conjoining systematically derived two options trees, one from the retail industry operations and the other from the spectrum of missing data methods in the realm of data science.

Keywords: innovative framework; retail strategy; retail data science; missing value; big data.

DOI: 10.1504/IJADS.2022.123876

International Journal of Applied Decision Sciences, 2022 Vol.15 No.4, pp.426 - 464

Received: 03 Mar 2021
Accepted: 01 May 2021
Published online: 04 Jul 2022 *

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