The ambiguity of data science team roles and the need for a data science workforce framework

Jeffrey S. Saltz; Nancy W. Grady

doi:10.1109/BigData.2017.8258190

The ambiguity of data science team roles and the need for a data science workforce framework

Jeffrey S. Saltz, Nancy W. Grady

School of Information Studies

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

21 Scopus citations

Abstract

This paper first reviews the benefits of well-defined roles and then discusses the current lack of standardized roles within the data science community, perhaps due to the newness of the field. Specifically, the paper reports on five case studies exploring five different attempts to define a standard set of roles. These case studies explore the usage of roles from an industry perspective as well as from national standard big data committee efforts. The paper then leverages the results of these case studies to explore the use of data science roles within online job postings. While some roles appeared frequently, such as data scientist and data engineer, no role was consistently used across all five case studies. Hence, the paper concludes by noting the need to create a data science workforce framework that could be used by students, employers, and academic institutions. This framework would enable organizations to staff their data science teams more accurately with the desired skillsets.

Original language	English (US)
Title of host publication	Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Editors	Jian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2355-2361
Number of pages	7
ISBN (Electronic)	9781538627143
DOIs	https://doi.org/10.1109/BigData.2017.8258190
State	Published - Jul 1 2017
Event	5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States Duration: Dec 11 2017 → Dec 14 2017

Publication series

Name	Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Volume	2018-January

Other

Other	5th IEEE International Conference on Big Data, Big Data 2017
Country/Territory	United States
City	Boston
Period	12/11/17 → 12/14/17

Keywords

big data
data science
data science roles
project management

ASJC Scopus subject areas

Computer Networks and Communications
Hardware and Architecture
Information Systems
Information Systems and Management
Control and Optimization

Access to Document

10.1109/BigData.2017.8258190

Cite this

Saltz, J. S., & Grady, N. W. (2017). The ambiguity of data science team roles and the need for a data science workforce framework. In J-Y. Nie, Z. Obradovic, T. Suzumura, R. Ghosh, R. Nambiar, C. Wang, H. Zang, R. Baeza-Yates, R. Baeza-Yates, X. Hu, J. Kepner, A. Cuzzocrea, J. Tang, & M. Toyoda (Eds.), Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017 (pp. 2355-2361). (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017; Vol. 2018-January). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData.2017.8258190

The ambiguity of data science team roles and the need for a data science workforce framework. / Saltz, Jeffrey S.; Grady, Nancy W.

Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. ed. / Jian-Yun Nie; Zoran Obradovic; Toyotaro Suzumura; Rumi Ghosh; Raghunath Nambiar; Chonggang Wang; Hui Zang; Ricardo Baeza-Yates; Ricardo Baeza-Yates; Xiaohua Hu; Jeremy Kepner; Alfredo Cuzzocrea; Jian Tang; Masashi Toyoda. Institute of Electrical and Electronics Engineers Inc., 2017. p. 2355-2361 (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017; Vol. 2018-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Saltz, JS & Grady, NW 2017, The ambiguity of data science team roles and the need for a data science workforce framework. in J-Y Nie, Z Obradovic, T Suzumura, R Ghosh, R Nambiar, C Wang, H Zang, R Baeza-Yates, R Baeza-Yates, X Hu, J Kepner, A Cuzzocrea, J Tang & M Toyoda (eds), Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017, vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 2355-2361, 5th IEEE International Conference on Big Data, Big Data 2017, Boston, United States, 12/11/17. https://doi.org/10.1109/BigData.2017.8258190

Saltz JS, Grady NW. The ambiguity of data science team roles and the need for a data science workforce framework. In Nie J-Y, Obradovic Z, Suzumura T, Ghosh R, Nambiar R, Wang C, Zang H, Baeza-Yates R, Baeza-Yates R, Hu X, Kepner J, Cuzzocrea A, Tang J, Toyoda M, editors, Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 2355-2361. (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017). https://doi.org/10.1109/BigData.2017.8258190

Saltz, Jeffrey S. ; Grady, Nancy W. / The ambiguity of data science team roles and the need for a data science workforce framework. Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017. editor / Jian-Yun Nie ; Zoran Obradovic ; Toyotaro Suzumura ; Rumi Ghosh ; Raghunath Nambiar ; Chonggang Wang ; Hui Zang ; Ricardo Baeza-Yates ; Ricardo Baeza-Yates ; Xiaohua Hu ; Jeremy Kepner ; Alfredo Cuzzocrea ; Jian Tang ; Masashi Toyoda. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 2355-2361 (Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017).

@inproceedings{06ee6253aae34eb8b4a5f271e42a27f4,

title = "The ambiguity of data science team roles and the need for a data science workforce framework",

abstract = "This paper first reviews the benefits of well-defined roles and then discusses the current lack of standardized roles within the data science community, perhaps due to the newness of the field. Specifically, the paper reports on five case studies exploring five different attempts to define a standard set of roles. These case studies explore the usage of roles from an industry perspective as well as from national standard big data committee efforts. The paper then leverages the results of these case studies to explore the use of data science roles within online job postings. While some roles appeared frequently, such as data scientist and data engineer, no role was consistently used across all five case studies. Hence, the paper concludes by noting the need to create a data science workforce framework that could be used by students, employers, and academic institutions. This framework would enable organizations to staff their data science teams more accurately with the desired skillsets.",

keywords = "big data, data science, data science roles, project management",

author = "Saltz, {Jeffrey S.} and Grady, {Nancy W.}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 5th IEEE International Conference on Big Data, Big Data 2017 ; Conference date: 11-12-2017 Through 14-12-2017",

year = "2017",

month = jul,

day = "1",

doi = "10.1109/BigData.2017.8258190",

language = "English (US)",

series = "Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2355--2361",

editor = "Jian-Yun Nie and Zoran Obradovic and Toyotaro Suzumura and Rumi Ghosh and Raghunath Nambiar and Chonggang Wang and Hui Zang and Ricardo Baeza-Yates and Ricardo Baeza-Yates and Xiaohua Hu and Jeremy Kepner and Alfredo Cuzzocrea and Jian Tang and Masashi Toyoda",

booktitle = "Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017",

}

TY - GEN

T1 - The ambiguity of data science team roles and the need for a data science workforce framework

AU - Saltz, Jeffrey S.

AU - Grady, Nancy W.

PY - 2017/7/1

Y1 - 2017/7/1

N2 - This paper first reviews the benefits of well-defined roles and then discusses the current lack of standardized roles within the data science community, perhaps due to the newness of the field. Specifically, the paper reports on five case studies exploring five different attempts to define a standard set of roles. These case studies explore the usage of roles from an industry perspective as well as from national standard big data committee efforts. The paper then leverages the results of these case studies to explore the use of data science roles within online job postings. While some roles appeared frequently, such as data scientist and data engineer, no role was consistently used across all five case studies. Hence, the paper concludes by noting the need to create a data science workforce framework that could be used by students, employers, and academic institutions. This framework would enable organizations to staff their data science teams more accurately with the desired skillsets.

AB - This paper first reviews the benefits of well-defined roles and then discusses the current lack of standardized roles within the data science community, perhaps due to the newness of the field. Specifically, the paper reports on five case studies exploring five different attempts to define a standard set of roles. These case studies explore the usage of roles from an industry perspective as well as from national standard big data committee efforts. The paper then leverages the results of these case studies to explore the use of data science roles within online job postings. While some roles appeared frequently, such as data scientist and data engineer, no role was consistently used across all five case studies. Hence, the paper concludes by noting the need to create a data science workforce framework that could be used by students, employers, and academic institutions. This framework would enable organizations to staff their data science teams more accurately with the desired skillsets.

KW - big data

KW - data science

KW - data science roles

KW - project management

UR - http://www.scopus.com/inward/record.url?scp=85047788905&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85047788905&partnerID=8YFLogxK

U2 - 10.1109/BigData.2017.8258190

DO - 10.1109/BigData.2017.8258190

M3 - Conference contribution

AN - SCOPUS:85047788905

T3 - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017

SP - 2355

EP - 2361

BT - Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017

A2 - Nie, Jian-Yun

A2 - Obradovic, Zoran

A2 - Suzumura, Toyotaro

A2 - Ghosh, Rumi

A2 - Nambiar, Raghunath

A2 - Wang, Chonggang

A2 - Zang, Hui

A2 - Baeza-Yates, Ricardo

A2 - Hu, Xiaohua

A2 - Kepner, Jeremy

A2 - Cuzzocrea, Alfredo

A2 - Tang, Jian

A2 - Toyoda, Masashi

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th IEEE International Conference on Big Data, Big Data 2017

Y2 - 11 December 2017 through 14 December 2017

ER -

The ambiguity of data science team roles and the need for a data science workforce framework

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this