extended-abstract

Understanding the Needs of Enterprise Users in Collaborative Python Notebooks: This paper examines enterprise user needs in collaborative Python notebooks through a dyadic interview study

Authors:

Talie Massachi,

Jordan Eschler,

Jeff HuangAuthors Info & Claims

CHI EA '23: Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems

Article No.: 402, Pages 1 - 7

https://doi.org/10.1145/3544549.3573843

Published: 19 April 2023 Publication History

Abstract

Python notebooks are an important productivity tool for technical employees in software companies. The Python notebook format originates from open-source coding projects and scientific research; notebooks were intended to spread knowledge about solving problems and modeling analytic approaches through code. In this case study writeup, we describe a qualitative study of Python notebooks as sites of user collaboration among varied roles (engineers, data scientists, and technical investigators) in a Fortune 500 software enterprise. Findings of the case study build on previous research on collaboration via notebooks, and articulate specific collaborative tasks undertaken by participants, the benefits of these collaborative tasks to the user and the broader enterprise, and design implications of findings around user needs for collaborative workflows. Finally, we reflect on the findings of this study in terms of applying a method specific to the use context of interest, as well as the study's impact on enterprise software strategy.

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Cited By

Qian CReif EKahng M(2024)Understanding the Dataset Practitioners Behind Large Language ModelsExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3651007(1-7)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3651007

Index Terms

Understanding the Needs of Enterprise Users in Collaborative Python Notebooks: This paper examines enterprise user needs in collaborative Python notebooks through a dyadic interview study
1. Human-centered computing
  1. Collaborative and social computing
    1. Collaborative and social computing design and evaluation methods
  2. Human computer interaction (HCI)
    1. Interaction paradigms
      1. Collaborative interaction

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Published In

cover image ACM Conferences

CHI EA '23: Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems

April 2023

3914 pages

ISBN:9781450394222

DOI:10.1145/3544549

Editors:
Albrecht Schmidt
LMU Munich, Germany
,
Kaisa Väänänen
Tampere University, Finland
,
Tesh Goyal
Google Research, USA
,
Per Ola Kristensson
University of Cambridge, UK
,
Anicia Peters
University of Namibia, Namibia

Copyright © 2023 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 19 April 2023

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Cited By

Qian CReif EKahng M(2024)Understanding the Dataset Practitioners Behind Large Language ModelsExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3651007(1-7)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3651007

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