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Orienting, Framing, Bridging, Magic, and Counseling: How Data Scientists Navigate the Outer Loop of Client Collaborations in Industry and Academia

Authors:

Philip GuoAuthors Info & Claims

Proceedings of the ACM on Human-Computer Interaction, Volume 5, Issue CSCW2

Article No.: 311, Pages 1 - 28

https://doi.org/10.1145/3476052

Published: 18 October 2021 Publication History

Abstract

Data scientists often collaborate with clients to analyze data to meet a client's needs. What does the end-to-end workflow of a data scientist's collaboration with clients look like throughout the lifetime of a project? To investigate this question, we interviewed ten data scientists (5 female, 4 male, 1 non-binary) in diverse roles across industry and academia. We discovered that they work with clients in a six-stage outer-loop workflow, which involves 1) laying groundwork by building trust before a project begins, 2) orienting to the constraints of the client's environment, 3) collaboratively framing the problem, 4) bridging the gap between data science and domain expertise, 5) the inner loop of technical data analysis work, 6) counseling to help clients emotionally cope with analysis results. This novel outer-loop workflow contributes to CSCW by expanding the notion of what collaboration means in data science beyond the widely-known inner-loop technical workflow stages of acquiring, cleaning, analyzing, modeling, and visualizing data. We conclude by discussing the implications of our findings for data science education, parallels to design work, and unmet needs for tool development.

References

[1]

Sara Alspaugh, Nava Zokaei, Andrea Liu, Cindy Jin, and Marti A. Hearst. 2019. Futzing and Moseying: Interviews with Professional Data Analysts on Exploration Practices. IEEE Transactions on Visualization and Computer Graphics 25, 1 (Jan. 2019), 22--31. https://doi.org/10.1109/TVCG.2018.2865040

Digital Library

[2]

Alex Ball. [n.d.]. Review of Data Management Lifecycle Models.

[3]

Johan Kaj Blomkvist, Johan Persson, and Johan Åberg. 2015. Communication through Boundary Objects in Distributed Agile Teams. Association for Computing Machinery, New York, NY, USA, 1875--1884. https://doi.org/10.1145/2702123.2702366

Digital Library

[4]

Irwin D. J. Bross. 1974. The Role of the Statistician: Scientist or Shoe Clerk. The American Statistician 28, 4 (1974), 126--127. https://doi.org/10.1080/00031305.1974.10479092

[5]

Joohee Choi and Yla Tausczik. 2017. Characteristics of Collaboration in the Emerging Practice of Open Data Analysis. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (Portland, Oregon, USA) (CSCW '17). Association for Computing Machinery, New York, NY, USA, 835--846. https://doi.org/10.1145/2998181.2998265

Digital Library

[6]

Herbert H. Clark and Susan E. Brennan. 1991. Grounding in Communication. In Perspectives on Socially Shared Cognition, L.B. Resnick, J.M. Levine, and S.D. Teasley (Eds.). American Psychological Association, 127--149.

[7]

Juliet M. Corbin and Anselm L. Strauss. 2008. Basics of qualitative research: techniques and procedures for developing grounded theory. SAGE Publications, Inc.

[8]

Nigel Cross. 2011. Design Thinking: Understanding How Designers Think and Work. Bloomsbury.

[9]

Nigel Cross. 2018. Expertise in Professional Design (2 ed.). Cambridge University Press, 372--388. https://doi.org/10.1017/9781316480748.021

[10]

James Densmore. 2017. There are two types of data scientists -- and two types of problems to solve. https://medium.com/@jamesdensmore/there-are-two-types-of-data-scientists-and-two-types-of-problems-to-solve-a149a0148e64. Accessed: 2020--10--10.

[11]

Conor Dewey. 2018. An Ode to the Type A Data Scientist. Towards Data Science -- https://towardsdatascience.com/ode-to-the-type-a-data-scientist-78d11456019. Accessed: 2020--10--10.

[12]

David Donoho. 2017. 50 Years of Data Science. Journal of Computational and Graphical Statistics 26, 4 (2017), 745--766. https://doi.org/10.1080/10618600.2017.1384734 arXiv:https://doi.org/10.1080/10618600.2017.1384734

[13]

Paul Dourish. 2001. Process Descriptions as Organisational Accounting Devices: The Dual Use of Workflow Technologies. In Proceedings of the 2001 International ACM SIGGROUP Conference on Supporting Group Work (Boulder, Colorado, USA) (GROUP '01). Association for Computing Machinery, New York, NY, USA, 52--60. https://doi.org/10.1145/500286.500297

Digital Library

[14]

Ian Drosos, Titus Barik, Philip J. Guo, Robert DeLine, and Sumit Gulwani. 2020. Wrex: A Unified Programming-by-Example Interaction for Synthesizing Readable Code for Data Scientists. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Honolulu, HI, USA) (CHI '20). Association for Computing Machinery, New York, NY, USA, 1--12. https://doi.org/10.1145/3313831.3376442

Digital Library

[15]

D. J. Finney. 1982. The questioning statistician. Statistics in Medicine 1, 1 (1982), 5--13. https://doi.org/10.1002/sim.4780010103 arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1002/sim.4780010103

[16]

Cristina Gallego Gómez and Consuelo Puchades Ruiz. 2016. The Inclusion of Methodologies User Experience in the Consulting Industry: An Approach to the Experience of Capgemini. In Proceedings of the XVII International Conference on Human Computer Interaction (Salamanca, Spain) (Interacción '16). Association for Computing Machinery, New York, NY, USA, Article 24, 2 pages. https://doi.org/10.1145/2998626.2998635

Digital Library

[17]

Alicia A Grandey. 2000. Emotion regulation in the workplace: a new way to conceptualize emotional labour. Journal of Occupational Health Psychology 5 (2000), 95--100.

[18]

Philip J. Guo. 2012. Software Tools to Facilitate Research Programming. Ph.D. Dissertation. Stanford University.

[19]

Philip J. Guo, Sean Kandel, Joseph M. Hellerstein, and Jeffrey Heer. 2011. Proactive Wrangling: Mixed-Initiative End-User Programming of Data Transformation Scripts. In Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology (Santa Barbara, California, USA) (UIST '11). Association for Computing Machinery, New York, NY, USA, 65--74. https://doi.org/10.1145/2047196.2047205

Digital Library

[20]

Bob Hayes. 2020. Who Does the Machine Learning and Data Science Work? customer think -- https://customerthink.com/who-does-the-machine-learning-and-data-science-work/. Accessed: 2021-01--10.

[21]

Daniel Hellmann, Carleen Maitland, and Andrea Tapia. 2016. Collaborative Analytics and Brokering in Digital Humanitarian Response. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work and Social Computing (San Francisco, California, USA) (CSCW '16). Association for Computing Machinery, New York, NY, USA, 1284--1294. https://doi.org/10.1145/2818048.2820067

Digital Library

[22]

Stephanie C. Hicks and Roger D. Peng. 2019. Elements and Principles for Characterizing Variation between Data Analyses. arXiv:1903.07639 [stat.AP]

[23]

C. Hill, R. Bellamy, T. Erickson, and M. Burnett. 2016. Trials and tribulations of developers of intelligent systems: A field study. In 2016 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC). 162--170. https://doi.org/10.1109/VLHCC.2016.7739680

[24]

Arlie Russell Hochschild. 2012. The Managed Heart: Commercialization of Human Feeling (1 ed.). University of California Press.

[25]

Youyang Hou and Dakuo Wang. 2017. Hacking with NPOs: Collaborative Analytics and Broker Roles in Civic Data Hackathons. Proc. ACM Hum.-Comput. Interact. 1, CSCW, Article 53 (Dec. 2017), 16 pages. https://doi.org/10.1145/3134688

Digital Library

[26]

Marina Jirotka, Charlotte P. Lee, and Gary M. Olson. 2013. Supporting Scientific Collaboration: Methods, Tools and Concepts. Comput. Supported Coop. Work 22, 4--6 (Aug. 2013), 667--715. https://doi.org/10.1007/s10606-012--9184-0

Digital Library

[27]

Sean Kandel, Andreas Paepcke, Joseph Hellerstein, and Jeffrey Heer. 2011. Wrangler: Interactive Visual Specification of Data Transformation Scripts. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (Vancouver, BC, Canada) (CHI '11). Association for Computing Machinery, New York, NY, USA, 3363--3372. https://doi.org/10.1145/1978942.1979444

Digital Library

[28]

Sean Kandel, Andreas Paepcke, Joseph M. Hellerstein, and Jeffrey Heer. 2012. Enterprise Data Analysis and Visualization: An Interview Study. IEEE Transactions on Visualization and Computer Graphics 18, 12 (Dec. 2012), 2917--2926. https://doi.org/10.1109/TVCG.2012.219

Digital Library

[29]

Miryung Kim, Thomas Zimmermann, Robert DeLine, and Andrew Begel. 2016. The Emerging Role of Data Scientists on Software Development Teams. In Proceedings of the 38th International Conference on Software Engineering (Austin, Texas) (ICSE '16). Association for Computing Machinery, New York, NY, USA, 96--107. https://doi.org/10.1145/2884781.2884783

Digital Library

[30]

Sean Kross and Philip J. Guo. 2019. Practitioners Teaching Data Science in Industry and Academia: Expectations, Workflows, and Challenges. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI '19). Association for Computing Machinery, New York, NY, USA, 1--14. https://doi.org/10.1145/3290605.3300493

Digital Library

[31]

Sean Kross, Roger D. Peng, Brian S. Caffo, Ira Gooding, and Jeffrey T. Leek. 2020. The Democratization of Data Science Education. The American Statistician 74, 1 (2020), 1--7. https://doi.org/10.1080/00031305.2019.1668849 arXiv:https://doi.org/10.1080/00031305.2019.1668849

[32]

Sam Lau, Ian Drosos, Julia M. Markel, and Philip J. Guo. 2020. The Design Space of Computational Notebooks: An Analysis of 60 Systems in Academia and Industry. In Proceedings of the IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC) (VL/HCC '20).

[33]

Katherine A. Lawrence. 2006. Walking the Tightrope: The Balancing Acts of a Large e-Research Project. Comput. Supported Coop. Work 15, 4 (Aug. 2006), 385--411. https://doi.org/10.1007/s10606-006--9025-0

[34]

Diane Lending and Thomas W. Dillon. 2013. Identifying Skills for Entry-Level IT Consultants. In Proceedings of the 2013 Annual Conference on Computers and People Research (Cincinnati, Ohio, USA) (SIGMIS-CPR '13). Association for Computing Machinery, New York, NY, USA, 87--92. https://doi.org/10.1145/2487294.2487311

Digital Library

[35]

Bertram Ludäscher, Ilkay Altintas, Chad Berkley, Dan Higgins, Efrat Jaeger, Matthew Jones, Edward A. Lee, Jing Tao, and Yang Zhao. 2006. Scientific workflow management and the Kepler system: Research Articles. Concurr. Comput.: Pract. Exper. 18, 10 (2006), 1039--1065. https://doi.org/10.1002/cpe.v18:10

Digital Library

[36]

Willam Lurie. 1958. The Impertinent Questioner: The Scientist's Guide to the Statistician's Mind. American Scientist 46, 1 (1958), 57--61.

[37]

Yaoli Mao, Dakuo Wang, Michael Muller, Kush R. Varshney, Ioana Baldini, Casey Dugan, and Aleksandra Mojsilovic. 2019. How Data Scientists Work Together With Domain Experts in Scientific Collaborations: To Find The Right Answer Or To Ask The Right Question? Proc. ACM Hum.-Comput. Interact. 3, GROUP, Article 237 (Dec. 2019), 23 pages. https://doi.org/10.1145/3361118

Digital Library

[38]

Pietro Mazzoleni, Sweefen Goh, Richard Goodwin, Manisha Bhandar, Shyh-Kwei Chen, Juhnyoung Lee, Vibha Singhal Sinha, Senthil Mani, Debdoot Mukherjee, Biplav Srivastava, Pankaj Dhoolia, Elad Fein, and Natalia Razinkov. 2009. Consultant Assistant: A Tool for Collaborative Requirements Gathering and Business Process Documentation. In Proceedings of the 24th ACM SIGPLAN Conference Companion on Object Oriented Programming Systems Languages and Applications (Orlando, Florida, USA) (OOPSLA '09). Association for Computing Machinery, New York, NY, USA, 807--808. https://doi.org/10.1145/1639950.1640025

Digital Library

[39]

Hui Miao, Ang Li, Larry S. Davis, and Amol Deshpande. 2017. Towards Unified Data and Lifecycle Management for Deep Learning. In 2017 IEEE 33rd International Conference on Data Engineering (ICDE). 571--582. https://doi.org/10.1109/ICDE.2017.112

[40]

Wendy Moncur. 2013. The Emotional Wellbeing of Researchers: Considerations for Practice. Association for Computing Machinery, New York, NY, USA, 1883--1890. https://doi.org/10.1145/2470654.2466248

Digital Library

[41]

Michael Muller, Ingrid Lange, Dakuo Wang, David Piorkowski, Jason Tsay, Q. Vera Liao, Casey Dugan, and Thomas Erickson. 2019. How Data Science Workers Work with Data: Discovery, Capture, Curation, Design, Creation. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (Glasgow, Scotland Uk) (CHI '19). Association for Computing Machinery, New York, NY, USA, Article 126, 15 pages. https://doi.org/10.1145/3290605.3300356

Digital Library

[42]

Tom Oinn, Mark Greenwood, Matthew Addis, M. Nedim Alpdemir, Justin Ferris, Kevin Glover, Carole Goble, Antoon Goderis, Duncan Hull, Darren Marvin, Peter Li, Phillip Lord, Matthew R. Pocock, Martin Senger, Robert Stevens, Anil Wipat, and Chris Wroe. 2006. Taverna: lessons in creating a workflow environment for the life sciences: Research Articles. Concurr. Comput. : Pract. Exper. 18, 10 (2006), 1067--1100. https://doi.org/10.1002/cpe.v18:10

Digital Library

[43]

Gary M. Olson and Judith S. Olson. 2000. Distance Matters. Hum.-Comput. Interact. 15, 2 (Sept. 2000), 139--178.

Digital Library

[44]

Gary M. Olson, Ann Zimmerman, and Nathan Bos. 2008. Scientific Collaboration on the Internet. The MIT Press.

[45]

Roger Peng. 2019. How Data Scientists Think - A Mini Case Study. Simply Stats blog -- https://simplystatistics.org/2019/01/09/how-data-scientists-think-a-mini-case-study/. Accessed: 2020--10--10.

[46]

Roger Peng. 2019. The Tentpoles of Data Science. Simply Stats blog -- https://simplystatistics.org/2019/01/18/the-tentpoles-of-data-science/. Accessed: 2020--10--10.

[47]

Roger Peng and Hilary Parker. 2018. Not So Standard Deviations podcast, episodes on Design Thinking (Episodes 63--69). https://nssdeviations.com/63-book-club-part-1. Accessed: 2020--10--10.

[48]

Roger D. Peng. 2011. Reproducible Research in Computational Science. Science 334, 6060 (2011), 1226--1227. https://doi.org/10.1126/science.1213847 arXiv: https://science.sciencemag.org/content/334/6060/1226.full.pdf

[49]

Kathleen H. Pine and Max Liboiron. 2015. The Politics of Measurement and Action. Association for Computing Machinery, New York, NY, USA, 3147--3156. https://doi.org/10.1145/2702123.2702298

Digital Library

[50]

ProjectPro. 2020. Type A Data Scientist vs. Type B Data Scientist. https://www.dezyre.com/article/type-a-data-scientist-vs-type-b-data-scientist/194. Accessed: 2020--10--10.

[51]

Noopur Raval and Paul Dourish. 2016. Standing Out from the Crowd: Emotional Labor, Body Labor, and Temporal Labor in Ridesharing. In Proceedings of the 19th ACM Conference on Computer-Supported Cooperative Work & Social Computing (San Francisco, California, USA) (CSCW '16). Association for Computing Machinery, New York, NY, USA, 97--107. https://doi.org/10.1145/2818048.2820026

Digital Library

[52]

Arvind Satyanarayan and Jeffrey Heer. 2014. Lyra: An Interactive Visualization Design Environment. In Proceedings of the 16th Eurographics Conference on Visualization (Swansea, Wales, United Kingdom) (EuroVis '14). Eurographics Association, Goslar, DEU, 351--360.

[53]

Benjamin Saunders, Julius Sim, Tom Kingstone, Shula Baker, Jackie Waterfield, Bernadette Bartlam, Heather Burroughs, and Clare Jinks. 2018. Saturation in qualitative research: exploring its conceptualization and operationalization. Quality & quantity 52, 4 (2018).

[54]

Carlos E. Scheidegger, Huy T. Vo, David Koop, Juliana Freire, and Claudio T. Silva. 2008. Querying and re-using workflows with VisTrails. In SIGMOD '08 (Vancouver, Canada). ACM. https://doi.org/10.1145/1376616.1376747

Digital Library

[55]

Petr Slovák and Geraldine Fitzpatrick. 2015. Teaching and Developing Social and Emotional Skills with Technology. ACM Trans. Comput.-Hum. Interact. 22, 4, Article 19 (June 2015), 34 pages. https://doi.org/10.1145/2744195

Digital Library

[56]

Susan Stager. 1986. The Consultant as Collaborator: The Process Facilatator Model. SIGUCCS Newsl. 16, 2 (June 1986), 22--26. https://doi.org/10.1145/382151.382978

Digital Library

[57]

Sara Stoudt, Váleri N. Vásquez, and Ciera C. Martinez. 2021. Principles for data analysis workflows. PLOS Computational Biology 17, 3 (03 2021), 1--26. https://doi.org/10.1371/journal.pcbi.1008770

[58]

Lucy Suchman. 1993. Do Categories Have Politics? The Language/Action Perspective Reconsidered. In Proceedings of the Third Conference on European Conference on Computer-Supported Cooperative Work (Milan, Italy) (ECSCW'93). Kluwer Academic Publishers, USA, 1--14.

Digital Library

[59]

Hanxin Tang. 2019. The Building of Trust in Client-Consultant Relationships and Its Influence on Data Protection in Consulting. In Proceedings of the 2019 2nd International Conference on Information Management and Management Sciences (Chengdu, China) (IMMS 2019). Association for Computing Machinery, New York, NY, USA, 75--79. https://doi.org/10.1145/3357292.3357295

Digital Library

[60]

Dakuo Wang, Justin D. Weisz, Michael Muller, Parikshit Ram, Werner Geyer, Casey Dugan, Yla Tausczik, Horst Samulowitz, and Alexander Gray. 2019. Human-AI Collaboration in Data Science: Exploring Data Scientists' Perceptions of Automated AI. Proc. ACM Hum.-Comput. Interact. 3, CSCW, Article 211 (Nov. 2019), 24 pages. https://doi.org/10.1145/3359313

Digital Library

[61]

Hadley Wickham and Garrett Grolemund. 2017. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data (1st ed.). O'Reilly Media, Inc.

[62]

Karlijn Willems. 2017. Data Scientist vs. Data Engineer. https://www.datacamp.com/community/blog/data-scientist-vs-data-engineer. Accessed: 2020--10--10.

[63]

Kanit Wongsuphasawat, Yang Liu, and Jeffrey Heer. 2019. Goals, Process, and Challenges of Exploratory Data Analysis: An Interview Study. arXiv:1911.00568 [cs.HC]

[64]

Kanit Wongsuphasawat, Dominik Moritz, Anushka Anand, Jock Mackinlay, Bill Howe, and Jeffrey Heer. 2016. Voyager: Exploratory Analysis via Faceted Browsing of Visualization Recommendations. IEEE Trans. Visualization & Comp. Graphics (Proc. InfoVis) (2016). http://idl.cs.washington.edu/papers/voyager

[65]

Amy X. Zhang, Michael Muller, and Dakuo Wang. 2020. How Do Data Science Workers Collaborate? Roles, Workflows, and Tools. Proc. ACM Hum.-Comput. Interact. 4, CSCW1, Article 022 (May 2020), 23 pages. https://doi.org/10.1145/3392826

Digital Library

Cited By

Shankar SGarcia RHellerstein JParameswaran A(2024)"We Have No Idea How Models will Behave in Production until Production": How Engineers Operationalize Machine LearningProceedings of the ACM on Human-Computer Interaction10.1145/36536978:CSCW1(1-34)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3653697
Tan MLee HWang DSubramonyam H(2024)Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in EducationProceedings of the ACM on Human-Computer Interaction10.1145/36373588:CSCW1(1-32)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3637358
Reinmund TKunze LJirotka M(2024)Transitioning Towards a Proactive Practice: A Longitudinal Field Study on the Implementation of a ML System in Adult Social CareProceedings of the CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642247(1-19)Online publication date: 11-May-2024
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Index Terms

Orienting, Framing, Bridging, Magic, and Counseling: How Data Scientists Navigate the Outer Loop of Client Collaborations in Industry and Academia
1. Human-centered computing
  1. Human computer interaction (HCI)

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cover image Proceedings of the ACM on Human-Computer Interaction

Proceedings of the ACM on Human-Computer Interaction Volume 5, Issue CSCW2

CSCW2

October 2021

5376 pages

EISSN:2573-0142

DOI:10.1145/3493286

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Jeff Nichols
Apple Inc., United States

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Published: 18 October 2021

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https://dl.acm.org/doi/10.1145/3653697
Tan MLee HWang DSubramonyam H(2024)Is a Seat at the Table Enough? Engaging Teachers and Students in Dataset Specification for ML in EducationProceedings of the ACM on Human-Computer Interaction10.1145/36373588:CSCW1(1-32)Online publication date: 26-Apr-2024
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