Jeffrey S. Saltz

Exploring the process of doing data science via an ethnographic study of a media advertising company

This paper presents the results of an ethnographic study focused on how data science projects were conducted within a global media advertising company. Observations, via embedding a researcher within the team, as well as more structured interviews and surveys, are documented. Recommendations to improve the current data science methodology within the company are also discussed. Overall, there had been little focus on the teams process methodology and the suggested process improvements would result in the companys data science projects having less risk and shorter timelines. Other big data teams might also benefit from reviewing and refining their work processes, but more work needs to be done to validate this assumption.

Comparing Data Science Project Management Methodologies via a Controlled Experiment

Data Science is an emerging field with a significant research focus on improving the techniques available to analyze data. However, there has been much less focus on how people should work together on a data science project. In this paper, we report on the results of an experiment comparing four different methodologies to manage and coordinate a data science project. We first introduce a model to compare different project management methodologies and then report on the results of our experiment. The results from our experiment demonstrate that there are significant differences based on the methodology used, with an Agile Kanban methodology being the most effective and surprisingly, an Agile Scrum methodology being the least effective.

Predicting data science sociotechnical execution challenges by categorizing data science projects

The challenge in executing a data science project is more than just identifying the best algorithm and tool set to use. Additional sociotechnical challenges include items such as how to define the project goals and how to ensure the project is effectively managed. This paper reports on a set of case studies where researchers were embedded within data science teams and where the researcher observations and analysis was focused on the attributes that can help describe data science projects and the challenges faced by the teams executing these projects, as opposed to the algorithms and technologies that were used to perform the analytics. Based on our case studies, we identified 14 characteristics that can help describe a data science project. We then used these characteristics to create a model that defines two key dimensions of the project. Finally, by clustering the projects within these two dimensions, we identified four types of data science projects, and based on the type of project, we identified some of the sociotechnical challenges that project teams should expect to encounter when executing data science projects.

Big data team process methodologies: A literature review and the identification of key factors for a project's success

This paper reports on our review of published research relating to how teams work together to execute Big Data projects. Our findings suggest that there is no agreed upon standard for executing these projects but that there is a growing research focus in this area and that an improved process methodology would be useful. In addition, our synthesis also provides useful suggestions to help practitioners execute their projects, specifically our identified list of 33 important success factors for executing Big Data efforts, which are grouped by our six identified characteristics of a mature Big Data organization.

Key Concepts for a Data Science Ethics Curriculum

Data science is a new field that integrates aspects of computer science, statistics and information management. As a new field, ethical issues a data scientist may encounter have received little attention to date, and ethics training within a data science curriculum has received even less attention. To address this gap, this article explores the different codes of conduct and ethics frameworks related to data science. We compare this analysis with the results of a systematic literature review focusing on ethics in data science. Our analysis identified twelve key ethics areas that should be included within a data science ethics curriculum. Our research notes that none of the existing codes or frameworks covers all of the identified themes. Data science educators and program coordinators can use our results as a way to identify key ethical concepts that can be introduced within a data science program.

A Framework to Explore Ethical Issues When Using Big Data Analytics on the Future Networked Internet of Things

The networked future will generate a huge amount of data. With this in mind, using big data analytics will be an important capability that will be required to fully leverage the knowledge within the data. However, collecting, storing and analyzing the data can create many ethical situations that data scientists have yet to ponder. Hence, this paper explores some of the possible ethical conundrums that might have to be addressed within a big data network of the future project and proposes a framework that can be used by data scientists working within such a context. These ethical challenges are explored within an example of future networked vehicles. In short, the framework focuses on two high level ethical considerations that need to be considered: data related challenges and model related challenges.

A Scalable Methodology to Guide Student Teams Executing Computing Projects

This article reports on a sequential mixed-methods research study, which compared different approaches on how to guide students through a semester-long data science project. Four different methodologies, ranging from a traditional “just assign some intermediate milestones” to other more Agile methodologies, were first compared via a controlled experiment. The results of this initial experiment showed that the project methodology used made a significant difference in student outcomes. Surprisingly, the Agile Kanban approach was found to be much more effective than the Agile Scrum methodology. Based on these initial results, in the second semester, we focused on use of the Kanban methodology. The findings in the second, more qualitative phase, confirmed the methodologys usefulness and scalability. A key issue when using the scrum methodology was that the students had a very difficult time estimating what could be completed in each of their two-week sprints. The Kanban board, which visually shows and limits work-in-progress, was found to be an effective way for students to communicate with each other as well as with their instructor. In addition, Agile Kanban also streamlined the work required for instructors to efficiently provide guidance to student teams and to understand each teams status. In summary, a scalable Kanban methodology, which can be applied to a wide variety of student computing projects, was found to an effective methodology to guide and manage student projects, improving student outcomes and minimizing instructor workload.

Not all software engineers can become good data engineers

The amount of data that businesses collect and analyze has been rapidly increasing, which has triggered an increase in big data teams. With the growth of both the number and size of big data teams, specialized roles are starting to be defined. One such role is the data engineer, who focuses on ensuring that the data is easily available for advanced analytics. Via a case study, this paper explores the role of the data engineer and the key characteristics that enable someone to be a good data engineer. The paper also explores if good software engineers could become good data engineers. Our findings show that the knowledge and skills required to be a data engineer are significantly different from those required to be a software engineer. Hence, not surprisingly, we found that that not all software engineers could become good data engineers.

A Framework for Describing Big Data Projects

With the ability to collect, store and analyze an ever-growing diversity of data generated with ever-increasing frequency, Big Data is a rapidly growing field. While tremendous strides have been made in the algorithms and technologies that are used to perform the analytics, much less has been done to determine how the team should work together to do a Big Data project. Our research reports on a set of case studies, where researchers were embedded within Big Data teams. Since project methodologies will likely depend on the attributes of a Big Data effort, we focus our analysis on defining a framework to describe a Big Data project. We then use this framework to describe the organizations we studied and some of the socio-technical challenges linked to these newly defined project characteristics.