How I Met Your Data Science Team: A Tale of Effective Communication

Roy, Aayushi; Raghunandan, Deepthi; Elmqvist, Niklas; Battle, Leilani

doi:10.1109/vl-hcc57772.2023.00032

Cited by 3 publications

(1 citation statement)

References 42 publications

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“…Jupyter Notebook and Git version control systems) or process-based communication (e.g. emails and Slack) [26,42]. To ensure effective collaboration, data scientists and domain experts must continue to establish content common ground (shared understanding of work subject) and process common ground (shared understanding of procedures) throughout the lifecycle of ML tasks [26].…”

Section: Related Workmentioning

confidence: 99%

Leveraging Large Language Models to Enhance Domain Expert Inclusion in Data Science Workflows

Shih,

Mohanty,

Katsis

et al. 2024

Extended Abstracts of the CHI Conference on Human Factors in Computing Systems

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Domain experts can play a crucial role in guiding data scientists to optimize machine learning models while ensuring contextual relevance for downstream use. However, in current workflows, such collaboration is challenging due to differing expertise, abstract documentation practices, and lack of access and visibility into low-level implementation artifacts. To address these challenges and enable domain expert participation, we introduce CellSync, a collaboration framework comprising (1) a Jupyter Notebook extension that continuously tracks changes to dataframes and model metrics and (2) a Large Language Model powered visualization dashboard that makes those changes interpretable to domain experts. Through CellSync's cell-level dataset visualization with code summaries, domain experts can interactively examine how individual data and modeling operations impact different data segments. The chat features enable data-centric conversations and targeted feedback to data scientists. Our preliminary evaluation shows that CellSync provides transparency and promotes critical discussions about the intents and implications of data operations. CCS CONCEPTS• Human-centered computing → User interface programming; Collaborative and social computing systems and tools; Information visualization.

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Section: Related Workmentioning

confidence: 99%

Leveraging Large Language Models to Enhance Domain Expert Inclusion in Data Science Workflows

Shih,

Mohanty,

Katsis

et al. 2024

Extended Abstracts of the CHI Conference on Human Factors in Computing Systems

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Increasing the collaboration of data science stakeholders with a knowledge management system

Civelek,

Eren,

Gökalp

2024

BPMJ

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PurposeThis paper presents the design and implementation of collaborative data science framework (CoDS), a knowledge management system for consolidating data science activities in an enterprise.Design/methodology/approachThe development of the CoDS framework is grounded on the design science research methodology for information systems research. In our case study, we first designed the initial framework for CoDS based on a systematic literature review. Then, we collected the expert opinions of eight data scientists to validate the need for generic content for such a knowledge management system. In the second iteration, a portfolio prototype is developed by the same data scientists as a part of our technical action research. Finally, a survey is conducted with 57 data analyst candidates in the last iteration.FindingsUsing the CoDS portfolio strengthened the communication among data scientists and stakeholders to improve development and scaling activities. It eased the reuse or modification of existing analytical solutions in other company processes.Practical implicationsThe CoDS presents a platform on which business details, data-related knowledge, modeling procedures and deployment steps are shared for (1) mediating and scaling ongoing projects, (2) enriching knowledge transfer among stakeholders, (3) facilitating ideation of new products and (4) supporting the onboarding of new employees and developers.Originality/valueThis study proposes a novel structure and a roadmap for creating a data science knowledge management system for the collaboration of all stakeholders in an enterprise.

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Interdisciplinary Perspectives in Data-Driven Marketing

Kulkarni,

Deshmukh,

Behare

et al. 2024

Advances in Marketing, Customer Relationship Management, and E-Services

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The chapter explores the crucial role of interdisciplinary collaboration in modern data-driven marketing. It highlights the significance of bridging the gap between data scientists and marketing professionals, emphasizing effective communication and mutual understanding of each other's expertise. Exploring cross-disciplinary research opportunities, the chapter discusses how insights from psychology, sociology, anthropology, computer science, and information technology can inform targeted marketing strategies. Integration with business intelligence and analytics is also explored, showcasing how organizations can leverage data-driven insights to align marketing efforts with broader business objectives. By maximizing the value of interdisciplinary collaboration, organizations can unlock new insights, drive innovation, and stay ahead in a rapidly evolving market landscape.

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How I Met Your Data Science Team: A Tale of Effective Communication

Cited by 3 publications

References 42 publications

Leveraging Large Language Models to Enhance Domain Expert Inclusion in Data Science Workflows

Leveraging Large Language Models to Enhance Domain Expert Inclusion in Data Science Workflows

Increasing the collaboration of data science stakeholders with a knowledge management system

Interdisciplinary Perspectives in Data-Driven Marketing

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