Research

Research

We conduct research to understand how data science teams and broader organizations currently execute their projects. More importantly, we assess how they can improve their processes to deliver better results. Specific topics include team collaboration, data science and big data process methodologies, ethics, risk management, agile, lean, pair programming, team roles, and more. Contact us if you’re interested in conducting research with us.

Title & ConferenceYear
Current approaches for executing big data science projects—a systematic literature review PeerJ Computer Science Journal 2022
Achieving Lean Data Science Agility Via Data Driven Scrum Proceedings of the 55th Hawaii International Conference on System Sciences 2022
The Risk Management Process for Data Science: Gaps in Current Practices Proceedings of the 55th Hawaii International Conference on System Sciences 2022
Identifying and Addressing 6 Key Questions when Using Data Driven Scrum IEEE International Conference on Big Data (Big Data) 2021
CRISP-DM for Data Science: Strengths, Weaknesses and Potential Next Steps IEEE International Conference on Big Data (Big Data) 2021
Evaluating MIDST, A System to Support Stigmergic Team Coordination Proceedings of the ACM on Human-Computer Interaction 2021
Factors that Influence the Selection of a Data Science Process Management Methodology: An Exploratory Study Proceedings of the 54th Hawaii International Conference on System Sciences 2021
Identifying the most Common Frameworks Data Science Teams Use to Structure and Coordinate their Projects 2020 IEEE International Conference on Big Data (Big Data) 2020
Exploring which agile principles students internalize when using a kanban process methodology Journal of Information Systems Education 2020
The Need for an Enterprise Risk Management Framework for Big Data Science Projects. DATA 2020
MIDST: an enhanced development environment that improves the maintainability of a data science analysis International Journal of Information Systems and Project Management 2020
Achieving Agile Big Data Science: The Evolution of a Team’s Agile Process Methodology IEEE International Conference on Big Data (Big Data) 2019
SKI: An Agile Framework for Data Science IEEE International Conference on Big Data (Big Data) 2019
Data science ethical considerations: a systematic literature review and proposed project framework Ethics and Information Technology 21 (3), 197-2082019
Integrating ethics within machine learning courses ACM Transactions on Computing Education (TOCE) 19 (4), 1-262019
Towards an integrated process model for new product development with data-driven features (NPD3) Research in Engineering Design 30 (2), 271-2892019
A predictive model to identify Kanban teams at risk Model Assisted Statistics and Applications 14 (4), 321-3352019
Exploring pair programming beyond computer science: a case study in its use in data science/data engineering International Journal of Higher Education and Sustainability 2 (4), 265-2782019
Ethics In Data Science Projects: Current Practices and Perceptions Proceedings of the 27th European Conference on Information Systems (ECIS)2019
Visualizing Kanban Work: Towards an Individual Contributor View Proceedings of the 25th Americas Conference on Information Systems (AMCIS)2019
Using a coach to improve team performance when the team uses a Kanban process methodology International Journal of Information Systems and Project Management 7 (2), 61-772019
Socio-technical Affordances for Stigmergic Coordination Implemented in MIDST, a Tool for Data-Science Teams Proc. ACM Hum.-Comput. Interactions2019
Helping Data Science Students Develop Task Modularity. Proceedings of the 52nd Hawaii International Conference on System Sciences, 1-102019
Will Deep Learning Change How Teams Execute Big Data Projects? 2018 IEEE International Conference on Big Data (Big Data), 2813-28172018
Improving Data Science Projects by Enriching Analytical Models with Domain Knowledge 2018 IEEE International Conference on Big Data (Big Data), 2828-28372018
A Framework to Explore Ethical Issues When Using Big Data Analytics on the Future Networked Internet of Things International Conference on Future Network Systems and Security, 49-602018
Key concepts for a data science ethics curriculum Proceedings of the 49th ACM technical symposium on computer science …2018
Thoughts on current and future research on agile and lean: ensuring relevance and rigor Proceedings of the 51st Hawaii International Conference on System Sciences2018
Data Science Roles and the Types of Data Science Programs Communications of the Association for Information Systems 43 (1), 332018
Identifying the Key Drivers for Teams to Use a Data Science Process Methodology Proceedings of the 26th European Conference on Information Systems (ECIS), 582018
Exploring Project Management Methodologies Used Within Data Science Teams Proceedings of the 24th Americas Conference on Information Systems (AMCIS)2018
Does pair programming work in a data science context? An initial case study 2017 IEEE International Conference on Big Data (Big Data), 2348-23542017
The ambiguity of data science team roles and the need for a data science workforce framework 2017 IEEE International Conference on Big Data (Big Data), 2355-23612017
Predicting data science sociotechnical execution challenges by categorizing data science projects Journal of the Association for Information Science and Technology 68 (12 …2017
Modular design of data-driven analytics models in smart-product development ASME 2017 International Mechanical Engineering Congress and Exposition2017
Exploring How Different Project Management Methodologies Impact Data Science Students Proceedings of the 25th European Conference on Information Systems (ECIS), 29392017
Acceptance Factors for Using a Big Data Capability and Maturity Model In Proceedings of the 25th European Conference on Information Systems (ECIS …2017
Comparing data science project management methodologies via a controlled experiment Proceedings of the 50th Hawaii International Conference on System Sciences2017
Big data team process methodologies: A literature review and the identification of key factors for a project’s success 2016 IEEE International Conference on Big Data (Big Data), 2872-28792016
Not all software engineers can become good data engineers 2016 IEEE International Conference on Big Data (Big Data), 2896-29012016
A framework for describing big data projects International Conference on Business Information Systems, 183-1952016
Exploring the process of doing data science via an ethnographic study of a media advertising company 2015 IEEE International Conference on Big Data (Big Data), 2098-21052015
The need for new processes, methodologies and tools to support big data teams and improve big data project effectiveness 2015 IEEE International Conference on Big Data (Big Data), 2066-20712015