Call for Papers: Advancing project management through data science: methods, applications and impacts

Although the need for data science methodological training is widely recognized across many disciplines, data science training is often absent from PhD programs. At the same time, Masters-level data science educational programs have seen incredible growth and investment. In 2018, Duke initiated a National Science Foundation (NSF)-funded program to determine whether Masters-level data science programs that universities have already invested in could be leveraged to reduce data science education barriers doctoral students face. Doctoral Fellows from diverse fields worked with teams of master’s students from Duke’s Master in Interdisciplinary Data Science program on applied Capstone projects focused on the doctoral Fellows’ own disciplines and dissertation research. Fellows also gained access to the Master program’s courses and professional development resources. We examined the implementation, experience, and effect of this integration into Master of Data Science program infrastructure using qualitative data collection with doctoral Fellows, master’s students, and Fellows’ doctoral advisors. Master’s students participating in doctoral-led Capstones benefited from their doctoral Fellows’ mentorship, project management, and content knowledge. Participating doctoral students showed increased learning of data science techniques and professional skills development. While some Fellows’ research was advanced through the Capstones, data also showed mismatches between selected master’s program goals and doctoral students’ needs. Overall, this pilot indicated potential promise in harnessing existing Master in Data Science programs to bolster doctoral students’ data science learning and professional readiness while also identifying areas for improving future such efforts.

Firms are seeking to gain greater understanding of and insights into more and more massive quantities of data collected and stored in disparate public and private databases. To effectively and efficiently deploy project resources to the data science search activity and to consequently build and evaluate innovative artifacts, firms are finding that a Design Science Research (DSR) approach can extend into the Data Science (DS) project domain through an iterative, evaluative project management method for the diagnosing, design, implementation, and evolution of data science artifacts. Importantly, DSR also provides a guided, emergent search paradigm that can be integral to finding hidden insights in massive data where the problem and solution domains are both frequently poorly understood at the outset of the DS inquiry. This article examines a case for using the elaborated action design research (eADR) method to inform the DS project management (PM) approach in situ with a Fortune 100 Global Manufacturer. The innovative DS PM approach resulted in multiple innovative DS solution artifacts built and evaluated by a dozen DS PM teams at the firm over the first two years of the DS PM deployment.

'Research through innovation' is the current demand echoing throughout the healthcare industry, healthcare institutions tend to invest heavily in technology. Data Science being the major disruptor across industries is being incepted through establishment of innovation and R&D centers within their respective organizations. Data Science has become a critical component for the healthcare industry, supporting innovative approaches towards advanced clinical practice, clinical research and corporate management, serving to build an intelligent enterprise. Every healthcare institution maintains a good number of technical staffs with IT, Software, data management, BI and analytical capabilities, aiding the institutions to manage report and publish its data in some or the other way, grossly covering most aspects of data science knowingly or unknowingly. Setting up a new entity within the organization by recruitment of staff with Data Science based skill sets would be the first thought to strike the management, which in contrast would end up as disaster when it comes to understanding the organizational culture, processes, infrastructure, platforms, data etc. Hence in order to setup a data science hub, regrouping or realigning some of the existing institutional resources is crucial. With this approach, the Data Science hub would carry out three primary functions. The "Project Management & Data Sourcing", the "Data Management & General Analytics" and "Advanced Analytics". Current resources can be reorganized within the first two functions, further; it would be about establishing an advanced analytics group within the hub which would perform the Machine learning and AI functions.

Successful implementations of machine learning (ML) and data science (DS) applications have enabled innovative business models and brought new opportunities for organizations. On the other hand, research studies report that organizations employing ML and DS solutions are at a high risk of failure and they can easily fall short of their objectives. One major factor is to adopt or tailor a project management method for the specific requirements of ML and DS applications. Therefore, agile project management (APM) may be proposed as a solution. However, there is significantly less study that explores ML and DS project management from an agile perspective. In this chapter, the authors discuss methods and challenges according to the background information and practice areas of ML, DS, and APM. This study can be viewed as an initial attempt to enhance these knowledge and practice domains in view of APM. Therefore, future research efforts will focus on the challenges as well as the experimental implementation of APM methods in real industrial case studies of ML and DS.

Through knowledge extraction from data with various methods, Data Science (DS) allows organizations to achieve improvements in performance. The execution of these projects is mainly supported by DS process models such as CRISP-DM. As a high percentage of DS undertakings are failing, revisions to current DS project management practices become necessary. Amongst others, ensuring traceability, reproducibility, and knowledge retention across the project present important success factors in DS projects. Some of the DS process models feature documentation artifacts for this purpose but not comprehensively for the complete DS lifecycle. Accordingly, in this research, existing documentation deliverables for DS are identified and examined by means of a literature review. Based on the established best practices from the process models, the contents of 18 derived project artifacts for DS documentation are synthesized for the DS lifecycle to improve DS project management.

Data science projects have become commonplace over the last decade. During this time, the practices of running such projects, together with the tools used to run them, have evolved considerably. Furthermore, there are various studies on data science workflows and data science project teams. However, studies looking into both workflows and teams are still scarce and comprehensive works to build a holistic view do not exist. This study bases on a prior case study on roles and processes in data science. The goal here is to create a deeper understanding of data science projects and development processes. We conducted a survey targeted at experts working in the field of data science (n=50) to understand data science projects’ team structure, roles in the teams, utilized project management practices and the challenges in data science work. Results show little difference between big data projects and other data science. The found differences, however, give pointers for future research on how agile data science projects are, and how important is the role of supporting project management personnel. The current study is work in progress and attempts to spark discussion and new research directions.

Many data science endeavors encounter failure, surfacing at any project phase. Even after successful deployments, data science projects grapple with ethical dilemmas, such as bias and discrimination. Current project management methodologies prioritize efficiency and cost savings over risk management. The methodologies largely overlook the diverse risks of sociotechnical systems and risk articulation inherent in data science lifecycles. Conversely, while the established risk management framework (RMF) by NIST and McKinsey aims to manage AI risks, there is a heavy reliance on normative definitions of risk, neglecting the multifaceted subjectivities of data science project failures. This paper reports on a systematic literature review that identifies three main themes: Big Data Execution Issues, Demand for a Risk Management Framework tailored for Large-Scale Data Science Projects, and the need for a General Risk Management Framework for all Data Science Endeavors. Another overarching focus is on how risk is articulated by the institution and the practitioners. The paper discusses a novel and adaptive data science risk management framework – “DS EthiCo RMF” – which merges project management, ethics, and risk management for diverse data science projects into one holistic framework. This agile risk management framework DS EthiCo RMF can bridge the current divide between normative risk standards and the multitude of data science requirements, offering a human-centric method to navigate the intertwined sociotechnical risks of failure in data science projects.

Applying data science to public mental health concerns and devising remedies based on research findings can be challenging and call for sophisticated methods. In contrast to traditional data analysis initiatives. It's critical to possess an extensive project management procedure to guarantee that Project associates are capable and knowledgeable enough to Implement the data science process. As a result, this essay offers a fresh paradigm that mental health practitioners might apply to address issues people encounter when applying data science. Even so, a sizable portion of Many studies on the mental health of the public have been published, not many have talked about data science's application to public mental health. Data science has recently transformed how the healthcare business manages, analyses, and uses data. Because of the scientific methodology employed in data science initiatives, they differ from traditional data analysis. Motivating medical practitioners to use "Data Science" to mental health issues is one of the goals of launching this new framework. It's usually advantageous to have a strong data analysis framework and precise instructions for a thorough examination. Estimating the time and resources required early on in the process can also be helpful in gaining a clear understanding of the problem that needs to be solved.

For the purpose of developing new algorithms, improving data models, and producing sophisticated analytics, data science has made large research investments. However, authors have not frequently addressed the organisational and socio-technical difficulties that arise when carrying out a data science project. These difficulties include the absence of a defined vision and goals, the overemphasis on technical issues, the inadequacy of ad hoc projects, and the uncertainty of responsibilities in data science. There haven't been many methods proposed in the literature to deal with this kind of issue; some of them go as far back as the middle of the 1990s, so they aren't up to speed with the most recent developments in big data and machine learning technology. However, fewer approaches offer a complete framework. We'll discuss the necessity to develop a more thorough technique for working on data science projects in this piece. We first research ways that have been written about in the literature and group them into four categories based on their focuses: project, team, data, and information management. Last but not least, we offer a conceptual framework that describes the essential characteristics that a methodology for managing data science activities from a broad viewpoint should have. This framework could serve as a guide for other academics as they develop new data science methods or update existing ones

As one of the largest data science research incubator initiatives in the country, the University of California, Berkeley's Data Science Discovery Program serves as a case study for a scalable and sustainable model of data science consulting in higher education. This case contributes to the broader literature on data science consulting in higher education by analysing the programme's development, institutional influences; staffing and structural model; and defining features, which may prove instructive to similar programmes at other institutions. The programme is characterised by a unique structure of undergraduate consultations led by graduate student mentorship and governance; a streamlined, multidepartmental model that facilitates scalability and sustainability; and diverse modes for undergraduate consulting—including one‐on‐one ad‐hoc data science consultations, extended data science project development and management, peer mentorship and data science workshop instruction. This case demonstrates that universities may be able to initiate a low‐stakes, small‐scale data science consulting initiative and then progressively scale up the project in collaboration with multiple departments and organisations across campus.

The Challenges of MRI in Clinical Practice (P11-3.008)

Understanding public mental health issues using data science and finding solutions based on the findings from the data science projects can be complex and requires advanced techniques, compared to conventional data analysis projects. It is important to have a comprehensive project management process to ensure that project associates are competent and have enough knowledge to implement the data science process. Therefore, this paper presents a new framework that mental health professionals can use to solve challenges they face using data science. Although a large number of research papers have been published on public mental health, few have addressed the use of data science in public mental health. Recently, has changed the way we manage, analyze and leverage data in healthcare industry. science projects differ from conventional data analysis, primarily because of the scientific approach used during data science projects. One of the motives for introducing a new framework is to motivate healthcare professionals to use Data Science to address the challenges of mental health. Having a good data analysis framework and clear guidelines for a comprehensive analysis is always a plus point. It also helps to predict the time and resources needed in the early in the process to get a clear idea of the problem to be solved.

Objective/scope It has been a challenge to analyze and estimate reliable water cut. The current well test data is not sufficient to satisfy the required information for prediction of the rate and water cut behaviors. Only on wells having stable and good behaviors, water cut levels can be estimated appropriately. The wells have Electrical Submersible Pump (ESP) sensor reading and data acquisition recorded in real-time help to fill this gap. The data are stored and available in KOC data repositories, such as Corporate Database, Well Surveillance Management System (WSMS), and Artificial Lift Management System (ALMS) Engineers spend this effort in spreadsheets and working with multiple data repositories. It is fit for data analysis by combining the data into a simple data set and presentation. Nevertheless, spreadsheets do not address a number of important tasks in a typical analyst's pipeline, and their design frequently complicates the analyses. It may take hours for single well analysis and days for multi-wells analysis and could be too late to plan and take preventive actions. Concerning the above situation, collaboration has been performed between NFD-North Kuwait and Information Management Team. In this first phase, this initiative is to design a conceptual integrated preventive system, which provide easy and quick tool to compute water cut estimation from well tests and downhole sensors data by using data science approach. Method, procedure, process There are 5 steps were applied in this initial work. It was included but not limited to user interview, exercise and performed data dissemination. It included gather full knowledge and defining the goal. Mapping pain points to solution also conducted to identify the technical challenge and find ways to overcome them. In the end of this stage, data and process review was conducted and applied for a given simple example to understand the requirements, demonstrate technical functionality and verify technical feasibility. Then conceptual design was built based on the requirements, features, and solutions gathered. Integrated system solution was recommended to include intermediate layer for integration, data retrieval, running calculation-heavy process in background, model optimization, visual analytics, decision-making, and automation. A roadmap with complete planning of different phases is then provided to achieve the objective. Results, observations, conclusions Process, functionalities, requirements, and finding have been examined and elaborated. The conceptual design has proved and assured the utilization of ESP sensor data in helping to estimate continuous well water cut's behavior. Further, the next implementation phase of data science expects an increase of confidence level of the results into higher degree. The design is promising to achieve the requirement to provide seamless, scalable, and easy to deploy automation capability tools for data analytic workflow with several major business benefits arising. Proposed solution includes combination of technologies, implementation services, and project management. The proposed technology components are distributed into 3 layers, source data, data science layer, and visual analytics layer. Furthermore, a roadmap of the project along with the recommendation for each phase has also been included. Novel/additive information Data Science for Exploration and Production is new area in which research and development will be required. Data science driven approach and application of digital transformation enables an integrated preventive system providing solution to compute water cut estimation from well tests and downhole sensors data. In the next larger scale of implementation, this system is expected to provide automated workflow supporting engineers in their daily tasks leveraging Data to Decision (D2D) approach. Machine learning is a data analytics technique that teaches computers to do what comes naturally to human, which is learn from experience. Machine learning algorithm use computational methods to learn information from the data without relying on predetermined equation as a model. Adding artificial intelligence and machine learning capability into the process requires knowledge on input data, the impact of data on the output, understanding of machine learning algorithm and building the model required to meet the expected output.

In recent years, the data science community has pursued excellence and made significant research efforts to develop advanced analytics, focusing on solving technical problems at the expense of organizational and socio-technical challenges. According to previous surveys on the state of data science project management, there is a significant gap between technical and organizational processes. In this article we present new empirical data from a survey to 237 data science professionals on the use of project management methodologies for data science. We provide additional profiling of the survey respondents' roles and their priorities when executing data science projects. Based on this survey study, the main findings are: (1) Agile data science lifecycle is the most widely used framework, but only 25% of the survey participants state to follow a data science project methodology. (2) The most important success factors are precisely describing stakeholders' needs, communicating the results to end-users, and team collaboration and coordination. (3) Professionals who adhere to a project methodology place greater emphasis on the project's potential risks and pitfalls, version control, the deployment pipeline to production, and data security and privacy.

Data Science Methodologies: Current Challenges and Future Approaches