Process Guidance for the Successful Deployment of a Big Data Project: Lessons Learned from Industrial Cases

Ponsard, Christophe; Majchrowski, Annick; Mouton, Stéphane; Touzani, Mounir

doi:10.5220/0006357403500355

Cited by 10 publications

(6 citation statements)

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“…The seven papers that covered both the workflow and agility themes presented a more comprehensive methodology for project execution. Several proposed new frameworks ( Grady, Payne & Parker, 2017 ; Ponsard, Touzani & Majchrowski, 2017 ; Ponsard et al, 2017 ; Ahmed, Dannhauser & Philip, 2019 ). All of the newly proposed frameworks defined a new workflow (typically based on CRISP-DM), and also suggested that the project do iterations and focus on creating a minimal viable product (MVP).…”

Section: Resultsmentioning

confidence: 99%

“…Furthermore, there no consensus on how to integrate the data science life cycle into each iteration. In fact, two papers didn’t explicitly address this question ( Ponsard, Touzani & Majchrowski, 2017 ; Ponsard et al, 2017 ) and another article implied that something should be done for each phase in each sprint ( Grady, Payne & Parker, 2017 ). Yet another article suggested that maybe some iterations focus on a specific phase and other iterations might focus on more than one phase ( Ahmed, Dannhauser & Philip, 2019 ).…”

Section: Resultsmentioning

confidence: 99%

“…It has been frequently noted that project management (PM) is a key challenge for successfully executing data science projects. In other words, a key reason many data science projects fail is not technical in nature, but rather, the process aspect of the project ( Ponsard et al, 2017 ). Furthermore, Espinosa & Armour (2016) argue that task coordination is a major challenge for data projects.…”

Section: Background and Related Workmentioning

confidence: 99%

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Current approaches for executing big data science projects—a systematic literature review

Saltz

Krasteva

2022

PeerJ Computer Science

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There is an increasing number of big data science projects aiming to create value for organizations by improving decision making, streamlining costs or enhancing business processes. However, many of these projects fail to deliver the expected value. It has been observed that a key reason many data science projects don’t succeed is not technical in nature, but rather, the process aspect of the project. The lack of established and mature methodologies for executing data science projects has been frequently noted as a reason for these project failures. To help move the field forward, this study presents a systematic review of research focused on the adoption of big data science process frameworks. The goal of the review was to identify (1) the key themes, with respect to current research on how teams execute data science projects, (2) the most common approaches regarding how data science projects are organized, managed and coordinated, (3) the activities involved in a data science projects life cycle, and (4) the implications for future research in this field. In short, the review identified 68 primary studies thematically classified in six categories. Two of the themes (workflow and agility) accounted for approximately 80% of the identified studies. The findings regarding workflow approaches consist mainly of adaptations to CRISP-DM (vs entirely new proposed methodologies). With respect to agile approaches, most of the studies only explored the conceptual benefits of using an agile approach in a data science project (vs actually evaluating an agile framework being used in a data science context). Hence, one finding from this research is that future research should explore how to best achieve the theorized benefits of agility. Another finding is the need to explore how to efficiently combine workflow and agile frameworks within a data science context to achieve a more comprehensive approach for project execution.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Background and Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Current approaches for executing big data science projects—a systematic literature review

Saltz

Krasteva

2022

PeerJ Computer Science

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“…The lack of a well-accepted data science process was demonstrated in a survey which found that 82% of the data scientists did not follow an explicit process; yet, 85% of the data scientists thought that their results would improve with a more systematic process methodology [6]. Hence, not surprisingly, it has been reported that project management is a key challenge for successfully executing data science projects and that a key reason many data science projects fail is not technical in nature, but rather, process oriented [7].…”

Section: Introductionmentioning

confidence: 99%

Achieving Lean Data Science Agility Via Data Driven Scrum

Saltz¹,

Sutherland²,

Hotz³

2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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This paper first reviews the concept of a lean data science project and defines four principles that a team should follow to achieve lean data science. It then describes a new team process framework called Data Driven Scrum (DDS) which enables lean data science project agility and addresses the key challenges that have been identified when using Scrum in a data science context. As compared to Scrum, DDS increases the focus in observing and analyzing the output of each iteration (i.e., each experiment). DDS also defines capability-based iterations (as compared to Scrum's time-based sprints). The paper then reports on how to integrate DDS with data science workflow frameworks such as CRISP-DM as well as on a pilot study of an organization that adopted DDS.

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“…Hence, not surprisingly, it has been reported that project management is a key challenge for successfully executing data science projects and that a key reason many data science projects fail is not technical in nature, but rather, the process aspect of the project [12]. For example, Espinosa and Armour [13] argue that task coordination is a major challenge for data projects.…”

Section: Introductionmentioning

confidence: 99%

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

Saltz¹,

Hotz²

2021

Proceedings of the Annual Hawaii International Conference on System Sciences

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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 apply to data science. Teams can use the developed process adoption model to make a more informed decision when selecting their data science project management process methodology.

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Process Guidance for the Successful Deployment of a Big Data Project: Lessons Learned from Industrial Cases

Cited by 10 publications

References 0 publications

Current approaches for executing big data science projects—a systematic literature review

Current approaches for executing big data science projects—a systematic literature review

Achieving Lean Data Science Agility Via Data Driven Scrum

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

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