A Framework for Describing Big Data Projects

Saltz, Jeffrey S.; Shamshurin, Ivan; Connors, Colin

doi:10.1007/978-3-319-52464-1_17

Cited by 9 publications

(11 citation statements)

References 12 publications

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“…Considering strategies for processing and structuring data is a necessary aspect of data investigations. Data scientists spend much of their time processing big data (e.g., Agarwal, 2018;EDC, 2016;Donoho, 2017;Saltz et al, 2017). Attention should be paid to considering strategies and techniques that are most useful for accomplishing the investigator's goals, and should take into account: efficiency, ease, expertise, and available resources.…”

Section: Process Datamentioning

confidence: 99%

Investigating Data Like a Data Scientist: Key Practices and Processes

Lee

Mojica

THRASHER

et al. 2022

SERJ

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With a call for schools to infuse data across the curriculum, many are creating curricula and examining students’ thinking in data-intensive problems. As the discipline of statistics education broadens to data science education, there is a need to examine how practices in data science can inform work in K-12. We synthesize literature about statistics investigation processes, data science as a field and practices of data scientists. Further, we provide results from an ethnographic and interview study of the work of data scientists. Together, these inform a new framework to support data investigation processes. We explicate the practices and dispositions needed and offer a glimpse of how the framework can be used to move the discipline of data science education forward.

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Section: Process Datamentioning

confidence: 99%

Investigating Data Like a Data Scientist: Key Practices and Processes

Lee

Mojica

THRASHER

et al. 2022

SERJ

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show abstract

“…In other terms, [50] brings out problems to engage the proper team for the project and [45,46,43,48,51] highlight the lack of people with analytics skills. These shortages of specialized analytical labor have caused every major university to launch new big data, analytics or data science programs [42].…”

Section: Building Data Analytics Teamsmentioning

confidence: 99%

“…The exploratory nature of such projects makes challenging to set adequate expectations [17], establish realistic project timelines and estimate how long projects would take to complete [8]. In this regard, [50,53] point out that the scope of the project can be difficult to know ex ante, and understanding the business goals is also a troubling task.…”

Section: Building Data Analytics Teamsmentioning

confidence: 99%

“…A reiterated complain in real data science projects by data scientists is the quality of the data: whether data is difficult to access [46], or whether is "dirty" and has issues, data scientists usually conclude that data has not enough potential to be suitable for machine learning algorithms. Understanding what data might be available [50], its representativeness for the problem at hand [46] and its limitations [53] is critical for the success of the project. In fact, [59] states that the lack of coordinated data cleaning or quality assurance checks can lead to erroneous results.…”

Section: Driving With Datamentioning

confidence: 99%

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Data Science Methodologies: Current Challenges and Future Approaches

2021

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Data science has employed great research efforts in developing advanced analytics, improving data models and cultivating new algorithms. However, not many authors have come across the organizational and socio-technical challenges that arise when executing a data science project: lack of vision and clear objectives, a biased emphasis on technical issues, a low level of maturity for ad-hoc projects and the ambiguity of roles in data science are among these challenges. Few methodologies have been proposed on the literature that tackle these type of challenges, some of them date back to the mid-1990, and consequently they are not updated to the current paradigm and the latest developments in big data and machine learning technologies. In addition, fewer methodologies offer a complete guideline across team, project and data & information management. In this article we would like to explore the necessity of developing a more holistic approach for carrying out data science projects. We first review methodologies that have been presented on the literature to work on data science projects and classify them according to the their focus: project, team, data and information management. Finally, we propose a conceptual framework containing general characteristics that a methodology for managing data science projects with a holistic point of view should have. This framework can be used by other researchers as a roadmap for the design of new data science methodologies or the updating of existing ones.

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“…The rationale is that managers will be required to be knowledgeable about increasingly more sophisticated technologies and how to apply them to meet business needs. This issue has been raised by several recent papers in which competencies such as how to define project goal in data science projects [16], being able to prioritize initiatives and link them to strategic goals [17], and having domain knowledge and foreseeing ways in which data science can help resolve business issues, are widely argued as being core aspects of contemporary managers [18]. Furthermore, there is an expanding debate around the skills that project managers should have in big data projects on coordinating and managing a big data team [18].…”

Section: Introductionmentioning

confidence: 99%

Investigating the Data Science Skill Gap: An Empirical Analysis

Mikalef

Krogstie

2019

2019 IEEE Global Engineering Education Conference (EDUCON)

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With big data analytics constantly growing in importance for contemporary organizations so does the need for skilled professionals. Perhaps the most critical item noted in the age of data is the lack of people with the required skill-set to turn raw data into actionable insight. Building on this pressuring issue, the objective of this paper is to survey the status quo of technical and businessrelated data analytics skills in a range of different industries and identify the most important skills that will be needed in the next few years. To do so, this study builds on a sample of 202 survey responses from key executives from Norwegian firms. Our analysis reveals the level of skill-fulfilment in for technically and business-oriented employees in a number of key industries. In addition, we use survey data from an additional sample of 27 executives and interviews with 6 managers and provide a ranking of the perceived importance of data analytics-related skills according to respondents in three categories, technical skills, business and project management skills, and soft skills. Our study concludes with findings regarding the skill-gap that exists in the domain of data science as well as suggestions on how to fulfil these needs, indicating specific subject-areas that are of heightened importance.

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A Framework for Describing Big Data Projects

Cited by 9 publications

References 12 publications

Investigating Data Like a Data Scientist: Key Practices and Processes

Investigating Data Like a Data Scientist: Key Practices and Processes

Data Science Methodologies: Current Challenges and Future Approaches

Investigating the Data Science Skill Gap: An Empirical Analysis

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