Data Science: Nature and Pitfalls

Cao, Longbing

doi:10.1109/mis.2016.86

Cited by 24 publications

(36 citation statements)

References 44 publications

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“…He adds that-if this situation continues, the old saying of the 1980s "garbage in, garbage out" will remain true in the big data age. On the other hand, Cao [34] stated that data science-being creative, intelligent, exploratory, nonstandard, and personalized-inevitably involves various quality issues. Beyond these three groups of professionals, data stewards (already mentioned above and called data curators as well), are professionals involved in curation, cleansing, annotation, selection, and appraisal of data.…”

Section: The Stakeholders Of Research Data Qualitymentioning

confidence: 99%

Quality of Open Research Data: Values, Convergences and Governance

Koltay

2020

Information

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This paper focuses on the characteristics of research data quality, and aims to cover the most important issues related to it, giving particular attention to its attributes and to data governance. The corporate word’s considerable interest in the quality of data is obvious in several thoughts and issues reported in business-related publications, even if there are apparent differences between values and approaches to data in corporate and in academic (research) environments. The paper also takes into consideration that addressing data quality would be unimaginable without considering big data.

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Section: The Stakeholders Of Research Data Qualitymentioning

confidence: 99%

Quality of Open Research Data: Values, Convergences and Governance

Koltay

2020

Information

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“…Familiarity with the disciplinary norms and standards of the given field is a must for both groups of professionals [6]. Giving attention to data quality is a similarly shared issue [42]. Moreover, the basic missions of librarianship, LIS, and data science overlap to a considerable extent as all of them invariably focus on the information (communication) chain, including the creation, dissemination, organisation, storage, and use [43].…”

Section: Data-related Professional Rolesmentioning

confidence: 99%

Identifying new roles for academic libraries in supporting data-intensive research

Koltai

2020

Библиосфера

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Reacting to the appearance of data-intensive research prompts academic libraries to become service providers for scholars, who work with research data. Although this is an imperative for libraries worldwide, due to the differences between countries and institutions, the level of readiness to engage in related activities differs from country to country. While some of the related tasks are fairly novel, others heavily build on librarians’ traditional, well-known skills. To identify these tasks, as well as making an inventory of the required skills and abilities, this paper, based on a non-exhaustive review of the recent literature, presents both theoretical and practical issues. It is demonstrated that the most obvious directions of the service development in academic libraries to support data-intensive science are research data management, data curation, data literacy education for users, and data literacy education for librarians.

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“…Migrating from the original push in the statistics communities, various disciplines have been involved in promoting the disciplinary development of data science. This involves the disciplinary structure, intrinsic challenges and directions, course structure and curriculum design, and qualifications for next-generation data scientists [Cao 2016b[Cao , 2016c[Cao , 2016d.…”

Section: Data Science Disciplinary Developmentmentioning

confidence: 99%

“…These are weak areas, and there are significant gaps in the current body of knowledge, organizational maturity [Paulk et al 1993;Crowston and Qin 2011], education, and training. The requirement is to "think with data," "manage data," "compute with data," "mine on data," "communicate with data," "deliver with data," and "take action on data" [Cao 2016c]. This section discusses these important matters.…”

Section: Data Education: Capabilities and Competencymentioning

confidence: 99%

Data Science

Cao

2017

ACM Comput. Surv.

Self Cite

295

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The 21st century has ushered in the age of big data and data economy, in which data DNA, which carries important knowledge, insights, and potential, has become an intrinsic constituent of all data-based organisms. An appropriate understanding of data DNA and its organisms relies on the new field of data science and its keystone, analytics. Although it is widely debated whether big data is only hype and buzz, and data science is still in a very early phase, significant challenges and opportunities are emerging or have been inspired by the research, innovation, business, profession, and education of data science. This article provides a comprehensive survey and tutorial of the fundamental aspects of data science: the evolution from data analysis to data science, the data science concepts, a big picture of the era of data science, the major challenges and directions in data innovation, the nature of data analytics, new industrialization and service opportunities in the data economy, the profession and competency of data education, and the future of data science. This article is the first in the field to draw a comprehensive big picture, in addition to offering rich observations, lessons, and thinking about data science and analytics.

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Data Science: Nature and Pitfalls

Abstract: Data science is creating very exciting trends as well as significant controversy.

Cited by 24 publications

References 44 publications

Quality of Open Research Data: Values, Convergences and Governance

Quality of Open Research Data: Values, Convergences and Governance

Identifying new roles for academic libraries in supporting data-intensive research

Data Science

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