ACCEPTANCE FACTORS FOR USING A BIG DATA CAPABILITY AND MATURITY MODEL

Big data is an emerging field that combines expertise across a range of domains, including software development, data management and statistics. However, it has been shown that big data projects suffer because they often operate at a low level of process maturity. To help address this gap, the Diffusion of Innovation Theory is used as a theoretical lens to identify factors that might drive an organization to try and improve their process maturity. Specifically, thirteen acceptance factors for teams to use (or not use) a Big Data CMM are identified. These results suggest that a positive perception exists with respect to relative advantage, compatibility and observability factors, and a negative perception exists with respect to perceived complexity. While more work is required to refine the list of factors, this insight can help guide the improvement of big data team processes

Execution time supports for adaptive scientific algorithms on distributed memory machines

Optimizations are considered that are required for efficient execution of code segments that consists of loops over distributed data structures. The PARTI (Parallel Automated Runtime Toolkit at ICASE) execution time primitives are designed to carry out these optimizations and can be used to implement a wide range of scientific algorithms on distributed memory machines. These primitives allow the user to control array mappings in a way that gives an appearance of shared memory. Computations can be based on a global index set. Primitives are used to carry out gather and scatter operations on distributed arrays. Communications patterns are derived at runtime, and the appropriate send and receive messages are automatically generated

Dynamic measuring tools for online discourse

When evaluating participation within an Asynchronous Learning Network (ALN), current best practices include counting messages and reviewing participant surveys. To understand the impact of more advanced dynamic measurement tools for use within an ALN, a web-based tool, known as iPET (the integrated Participation Evaluation Tool), was created. iPET, which leverages Social Network Analysis and Information Visualization techniques, was then evaluated via an empirical study. This research demonstrates that using a tool such as iPET increases participation within an ALN without increasing facilitator workload. Due to the fact that active online discussion is a key factor in the success of an ALN, this research demonstrates that dynamic measuring tools for online participation can help ensure a positive outcome within an online learning environment

The Risk Management Process for Data Science: Gaps in Current Practices

Data science projects have unique risks, such as potential bias in predictive models, that can negatively impact the organization deploying the models as well as the people using the deployed models. With the increasing use of data science across a range of domains, the need to understand and manage data science project risk is increasing. Hence, this research leverages qualitative research to help understand the current practices with respect to the risk management processes organizations currently use to identify and mitigate data science project risk. Specifically, this research reports on 16 semi-structured interviews, which were conducted across a diverse set of public and private organizations. The interviews identified a gap in current risk management processes, in that most organizations do not fully understand, nor manage, data science project risk. Furthermore, this research notes the need to a risk management framework that specifically addresses data science project risks

Evaluating Data Science Project Agility by Exploring Process Frameworks Used by Data Science Teams

The lack of effective team process is often noted as one of the key drivers of data science project inefficiencies and failures. To help address this challenge, this research reports on semi-structured interviews, across 16 organizations, which explored data science agile framework usage. While 62% of the organizations reported using an agile framework, none actually followed the Scrum Guide (or any other published framework), but rather, each organization had defined their own process that incorporated one or more aspects of Scrum. The other organizations used a proprietary / ad-hoc approach, often based on a proprietary data science life cycle. In short, while many data science teams are trying to be agile, they are adapting existing frameworks to work within a data science context. Future research could explore how data science teams can best achieve agility, perhaps via new agile frameworks that address the unique data science project management challenges

Exploring Which Agile Principles Students Internalize When Using a Kanban Process Methodology

This paper reports on a case study of the Agile Kanban project methodology, which while growing in popularity, has had far less analysis on its usefulness in the classroom as compared to other frameworks such as Agile Scrum. Our study provides insight into why the Kanban methodology is useful by mapping student comments about the methodology to the twelve principles laid down in the Agile Manifesto. Our analysis identified two key agile principles that help to explain the value of Kanban. Specifically, we found that the students focused on self-organizing teams and reflection at regular intervals, and that these two principles led to improved team communication and coordination. Our findings are useful for those looking to use or define a process management methodology for student teams as well as others exploring the more general challenge of incorporating agile into the classroom

SKI: A New Agile Framework that supports DevOps, Continuous Delivery, and Lean Hypothesis Testing

This paper explores the need for a new process framework that can effectively support DevOps and Continuous Delivery teams. It then defines a new framework, which adheres to the lean Kanban philosophy but augments Kanban by providing a structured iteration process. This new Structured Kanban Iteration (SKI) framework defines capability-based iterations (as opposed to Kanban-like no iterations or Scrum-like time-based sprints) as well as roles, meetings and artifacts. This structure enables a team to adopt a well-defined process that can be consistently used across groups and organizations. While many of SKI’s concepts are similar to those in found in Scrum, SKI’s capability-based iterations can support the demands of product development as well as operational support efforts, and hence, is well suited for DevOps and Continuous Delivery. SKI also supports lean hypothesis testing as well as more traditional software development teams where capability-based iterations are deemed more appropriate than time-based sprints

Factors that Influence the Selection of a Data Science Process Management Methodology: An Exploratory Study

This paper explores the factors that impact the adoption of a process methodology for managing and coordinating data science projects. Specifically, by conducting semi-structured interviews from data scientists and managers across 14 organizations, eight factors were identified that influence the adoption of a data science project management methodology. Two were technical factors (Exploratory Data Analysis, Data Collection and Cleaning). Three were organizational factors (Receptiveness to Methodology, Team Size, Knowledge and Experience), and three were environmental factors (Business Requirements Clarity, Documentation Requirements, Release Cadence Expectations). The research presented in this paper extends recognized factors for IT process adoption by bringing together influential factors that are applicable within a data science context. Teams can use the developed process adoption model to make a more informed decision when selecting their data science project management process methodology

Using a coach to improve team performance when the team uses a Kanban process methodology

Teams are increasing their use of the Kanban process methodology across a range of information system projects, including software development and data science projects. While the use of Kanban is growing, little has been done to explore how to improve team performance for teams that use Kanban. One possibility is to introduce a Kanban Coach (KC). This work reports on exploring the use of a Kanban Coach, with respect to both how the coach could interact with the team as well as how the use of a coach impacts team results. Specifically, this paper reports on an experiment where teams either had, or did not have, a Kanban Coach. A quantitative and qualitative analysis of the data collected during the experiment found that introducing KC led to significant improvement of team performance. Coordination Theory and Shared Mental Models were then employed to provide an explanation as to why a KC leads to better project results. While this experiment was done within a data science project context, the results are likely applicable across a range of information system projects