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Profiling Essential Professional Skills of Chief Data Officers Through Topical Modeling Algorithms

2017 | Americas Conference on Information Systems | Citations: 1
Authors: Dai, Wei; Wu, Ningning

Abstract: Today enterprises are increasingly dependent on data to keep their business comp ... Expand

Abstract: Today enterprises are increasingly dependent on data to keep their business competitive and successful. To better harness values of data, more and more organizations are establishing Chief Data Officer (CDO) position. The professional skills of CDOs are rather diverse because CDOs are expected to undertake a variety of roles in their companies including enterprise data architect, data quality and governance manager, business strategy leader, business regulation compliance officer, etc. CDO is an emerging research field, few studies have been done on CDO. This paper tries to profile what are the key professional skills and education background that current CDOs have by studying their resumes on LinkedIn using topic modeling technique. This work is a step forward towards understanding the roles of CDOs in organizations and what are the professional skills and experience they may need have in order to undertake their responsibilities of managing data and realizing its true values for their organizations. Collapse

Semantic filters: non negative matrix factorization machine learning case study

Topics: LinkedIn social network analytical information system open source Python

Methods: topic model non negative matrix factorization data transformation machine learning latent dirichlet allocation
Fused latent models for assessing product return propensity in online commerce
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2016 | Decision Support Systems | Citations: 1
Authors: Fu, Yanjie; Liu, Guannan; Papadimitriou, Spiros; Xiong, Hui; Li, Xiaolin; Chen, Guoqing

Abstract: In online shopping, product returns are very common. Liberal return policies hav ... Expand

Abstract: In online shopping, product returns are very common. Liberal return policies have been widely used to attract shoppers. However, returns often increase expense and inconvenience for all parties involved: customers, retailers, and manufacturers. Despite the large fraction of purchases that are returned, there are few systematic studies to explain the underlying forces that drive return requests, and to assess the return propensity at the level of individual purchases (i.e., a particular customer purchasing a particular product), rather than in aggregate. To this end, in this paper, we provide a systematic framework for personalized predictions of return propensity. These predictions can help retailers enhance inventory management, improve customer relationships, and reduce return fraud and abuse. Specifically, we treat product returns as a result of inconsistency arising during a commercial transaction. We decompose this inconsistency into two components, one for the buying phase (e.g., product does not match description) and another for the shipping phase (e.g., product damaged during shipping). Along these lines, we introduce a generalized return propensity latent model (RPLM). We further propose a complete framework, called fused return propensity latent model (FRPLM), to jointly model the correlation among user profiles, product features, and return propensity. We present comprehensive experimental results with real-world data to demonstrate the effectiveness of the proposed method for assessing return propensity. Collapse

Semantic filters: non negative matrix factorization machine learning case study

Topics: electronic commerce recommender system website operating system Microsoft Windows

Methods: machine learning cluster analysis computational algorithm experiment Bayesian analysis

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Profiling Essential Professional Skills of Chief Data Officers Through Topical Modeling Algorithms

Fused latent models for assessing product return propensity in online commerce