As data continues to be produced in massive amounts, with increasing volume, velocity and variety, Big Data Science projects are growing in frequency and importance. Furthermore, frameworks such as the Scale Agile Framework (SAFe) and Large Scale Scrum (LeSS) promise faster time-to-market and dramatic improvements in productivity; likewise, software factories suggest improved productivity, quality and capability.
However, the growth in the use of Big Data has outstripped the knowledge of how to support teams that need to do big data science projects. In fact, while much has been written in terms of the use of tools and algorithms that can help generate insightful analysis, much less has been written about methodologies, tools and frameworks that could enable teams to more effectively and efficiently collaborate and “do” big data science projects.
In short, what has not progressed quite so rapidly, is an understanding of how process frameworks, project management, and human system integration can also improve productivity, time-to-market, adoption time, and quality of big data science projects. The situation is much like the early days of software development: software was being developed but organizations had little ability to predict whether a project would be successful, on time or on budget and projects were overly reliant on the heroic efforts of particular individuals.
Following our previous workshops at IEEE Big Data, this workshop will continue to explore methodologies, tools and frameworks that have been or need to be developed to help manage and support Big Data Science projects. The workshop will also focus on how to efficiently, effectively, and affordably integrate human capabilities and limitations into the design, development and deployment of big data solutions.
The workshop will provide a venue to explore new ideas in both emerging research, as well case studies that describe examples of what has, or has not, worked within different Big Data Science teams. Significant work-in-progress papers are also encouraged. To enable a cross pollination of ideas, the workshop welcomes both academic researchers and industry experts.
We invite research results and position statements on topics including, but not limited to:
Paper submissions should be in English and not exceed 10 pages (5 pages for a work-in-progress paper).
Accepted papers will be published as part of the IEEE Big Data conference proceedings.
Paper Submission
Please use this submission link
Important Dates
10 Oct 2021 - Workshop Submission Deadline
1 Nov 2021 - Notification of Acceptance
15 Nov 2021 - Camera-ready of accepted papers due
10 Dec 2021 - IEEE Big Data Starts (likely workshop date)
Program Committee (to be refined)
Contact / Questions
Please email any questions to Jeffrey Saltz (jsaltz[at]syr.edu)
Please join our group
However, the growth in the use of Big Data has outstripped the knowledge of how to support teams that need to do big data science projects. In fact, while much has been written in terms of the use of tools and algorithms that can help generate insightful analysis, much less has been written about methodologies, tools and frameworks that could enable teams to more effectively and efficiently collaborate and “do” big data science projects.
In short, what has not progressed quite so rapidly, is an understanding of how process frameworks, project management, and human system integration can also improve productivity, time-to-market, adoption time, and quality of big data science projects. The situation is much like the early days of software development: software was being developed but organizations had little ability to predict whether a project would be successful, on time or on budget and projects were overly reliant on the heroic efforts of particular individuals.
Following our previous workshops at IEEE Big Data, this workshop will continue to explore methodologies, tools and frameworks that have been or need to be developed to help manage and support Big Data Science projects. The workshop will also focus on how to efficiently, effectively, and affordably integrate human capabilities and limitations into the design, development and deployment of big data solutions.
The workshop will provide a venue to explore new ideas in both emerging research, as well case studies that describe examples of what has, or has not, worked within different Big Data Science teams. Significant work-in-progress papers are also encouraged. To enable a cross pollination of ideas, the workshop welcomes both academic researchers and industry experts.
We invite research results and position statements on topics including, but not limited to:
- Team Process:
- Team Process Frameworks (e.g., Scrum, Kanban): How should teams collaborate
- Scalable Process Frameworks (e.g., SAFe): How big data efforts scale across multiple teams
- Analytics Workflow Tools: How to help improve project modularity
- CMM (Capability Maturity Model): Frameworks to describe team process maturity
- Project Evaluation: How to know the effectiveness of a Big Data team
- Project Management: Challenges specific to Big Data projects
- Team Composition:
- Team Roles: What is needed within a big data team
- Building Teams: Building interdisciplinary teams that work efficiently (data science, engineering, stakeholders, project managers, designers, etc).
- Stakeholder Management:
- Obtaining Strategic Commitment: Getting buy-in of transformational change
- Designing for Trust: Building user trust through model transparency
- Ensuring Actionable Insight: Working with stakeholders to efficiently identify / address key stakeholder/client needs
- Ethics and Accuracy:
- Initial Model Bias: How to identify and mitigate potential algorithmic bias (and ensure fairness)
- Monitoring Bias: Consistently monitoring / updating models (to reduce bias and improve fairness)
- Quality and Reproducibility:
- Quality: How to ensure the results are accurate
- Analytics/Model Management: The importance of an analytical audit trail
- Production Robustness: Operations migration & monitoring
- Case studies on related topics of interest to this workshop
Paper submissions should be in English and not exceed 10 pages (5 pages for a work-in-progress paper).
Accepted papers will be published as part of the IEEE Big Data conference proceedings.
Paper Submission
Please use this submission link
Important Dates
10 Oct 2021 - Workshop Submission Deadline
1 Nov 2021 - Notification of Acceptance
15 Nov 2021 - Camera-ready of accepted papers due
10 Dec 2021 - IEEE Big Data Starts (likely workshop date)
Program Committee (to be refined)
- Jeffrey Saltz, Syracuse University (Co-Chair)
- Mary Magee Quinn, Leidos (Co-Chair)
- David Keever, Leidos
- Kerk Kee, Texas Tech University
- Daniel Asamoah, Wright State University
- Frank Armour, American University
- Bintong Chen, University of Delaware
- Ivan Shamshurin, EY
- James Bliss, Leidos
Contact / Questions
Please email any questions to Jeffrey Saltz (jsaltz[at]syr.edu)
Please join our group