Data science in organizations: Conceptualizing its breakthroughs and blind spots

2021 ◽  
pp. 026839622098853
Author(s):  
Jacob L Cybulski ◽  
Rens Scheepers
Keyword(s):  
Data Science ◽  
Theoretical Principle ◽  
Science Literature ◽  
Computational Statistics ◽  
Data Intensive ◽  
Requisite Variety ◽  
Cross Domain ◽  
Law Of Requisite Variety ◽  
Complex Phenomena ◽  
Blind Spots

The field of data science emerged in recent years, building on advances in computational statistics, machine learning, artificial intelligence, and big data. Modern organizations are immersed in data and are turning toward data science to address a variety of business problems. While numerous complex problems in science have become solvable through data science, not all scientific solutions are equally applicable to business. Many data-intensive business problems are situated in complex socio-political and behavioral contexts that still elude commonly used scientific methods. To what extent can such problems be addressed through data science? Does data science have any inherent blind spots in this regard? What types of business problems are likely to be addressed by data science in the near future, which will not, and why? We develop a conceptual framework to inform the application of data science in business. The framework draws on an extensive review of data science literature across four domains: data, method, interfaces, and cognition. We draw on Ashby’s Law of Requisite Variety as theoretical principle. We conclude that data-scientific advances across the four domains, in aggregate, could constitute requisite variety for particular types of business problems. This explains why such problems can be fully or only partially addressed, solved, or automated through data science. We distinguish between situations that can be improved due to cross-domain compensatory effects, and problems where data science, at best, only contributes merely to better understanding of complex phenomena.

scholarly journals Service Assessment Planning for the Hunt Library Dataspace

2019 ◽  
Author(s):  
Mia Partlow ◽  
Karen Ciccone ◽  
Margaret Peak
Keyword(s):  
North Carolina ◽  
Statistical Analysis ◽  
Data Science ◽  
Planning Process ◽  
Service Development ◽  
State University ◽  
University Libraries ◽  
New Service Development ◽  
Data Intensive ◽  
Critical Data

Presentation given at TRLN Annual Meeting, Durham, North Carolina, July 1, 2019. The Hunt Library Dataspace was launched in August 2018 to provide students with access to the tools and support they need to develop critical data skills and perform data intensive tasks. It is outfitted with specialized computing hardware and software and staffed by graduate student Data Science Consultants who provide drop-in support for programming, data analysis, statistical analysis, visualization, and other data-related topics.Prior to launching the Dataspace the Libraries’ Director of Planning and Research worked with the Data & Visualization Services department to develop a plan for assessing the new Dataspace services. The process began with identifying relevant goals based on NC State University and the NC State University Libraries’ strategic priorities. Next we identified measures that would assess our success in relation to those goals. This talk describes the assessment planning process, the measures and methods employed, outcomes, and how this information will be used to improve our services and inform new service development.

Current Trends in Cloud Computing for Data Science Experiments

2021 ◽  
Vol 11 (4) ◽  
pp. 80-99
Author(s):  
Syed Imran Jami ◽  
Siraj Munir
Keyword(s):  
Cloud Computing ◽  
Resource Sharing ◽  
Data Science ◽  
Job Scheduling ◽  
Resource Scheduling ◽  
Data Intensive ◽  
Organizational Issues ◽  
Current Trends ◽  
Recent Trends ◽  
And Storage

Recent trends in data-intensive experiments require extensive computing and storage resources that are now handled using cloud resources. Industry experts and researchers use cloud-based services and resources to get analytics of their data to avoid inter-organizational issues including power overhead on local machines, cost associated with maintaining and running infrastructure, etc. This article provides detailed review of selected metrics for cloud computing according to the requirements of data science and big data that includes (1) load balancing, (2) resource scheduling, (3) resource allocation, (4) resource sharing, and (5) job scheduling. The major contribution of this review is the inclusion of these metrics collectively which is the first attempt towards evaluating the latest systems in the context of data science. The detailed analysis shows that cloud computing needs research in its association with data-intensive experiments with emphasis on the resource scheduling area.

scholarly journals Life Cycle Engineering 4.0: A Proposal to Conceive Manufacturing Systems for Industry 4.0 Centred on the Human Factor (DfHFinI4.0)

2020 ◽  
Vol 10 (13) ◽  
pp. 4442 ◽  
Author(s):  
Susana Suarez-Fernandez de Miranda ◽  
Francisco Aguayo-González ◽  
Jorge Salguero-Gómez ◽  
María Jesús Ávila-Gutiérrez
Keyword(s):  
Life Cycle ◽  
Industry 4.0 ◽  
Manufacturing Systems ◽  
Human Factor ◽  
Management Training ◽  
Exploratory Research ◽  
Reference Architecture ◽  
Life Cycle Engineering ◽  
Requisite Variety ◽  
Law Of Requisite Variety

Engineering 4.0 environments are characterised by the digitisation, virtualisation, and connectivity of products, processes, and facilities composed of reconfigurable and adaptive socio-technical cyber-physical manufacturing systems (SCMS), in which Operator 4.0 works in real time in VUCA (volatile, uncertain, complex and ambiguous) contexts and markets. This situation gives rise to the interest in developing a framework for the conception of SCMS that allows the integration of the human factor, management, training, and development of the competencies of Operator 4.0 as fundamental aspects of the aforementioned system. The present paper is focused on answering how to conceive the adaptive manufacturing systems of Industry 4.0 through the operation, growth, and development of human talent in VUCA contexts. With this objective, exploratory research is carried, out whose contribution is specified in a framework called Design for the Human Factor in Industry 4.0 (DfHFinI4.0). From among the conceptual frameworks employed therein, the connectivist paradigm, Ashby’s law of requisite variety and Vigotsky’s activity theory are taken into consideration, in order to enable the affective-cognitive and timeless integration of the human factor within the SCMS. DfHFinI4.0 can be integrated into the life cycle engineering of the enterprise reference architectures, thereby obtaining manufacturing systems for Industry 4.0 focused on the human factor. The suggested framework is illustrated as a case study for the Purdue Enterprise Reference Architecture (PERA) methodology, which transforms it into PERA 4.0.

scholarly journals Agentic Misfit: An Empirical Demonstration of Non-Matching Human Agency amid Complexity

2020 ◽  
pp. 017084062094455 ◽  
Author(s):  
Konstantinos Poulis ◽  
Efthimios Poulis ◽  
Paul Jackson
Keyword(s):  
Corporate Governance ◽  
Human Agency ◽  
Organizational Decline ◽  
Requisite Variety ◽  
Wide Range ◽  
Law Of Requisite Variety ◽  
The Law ◽  
Novel Concept ◽  
Empirical Demonstration ◽  
Do So

Alignment of organizations with external imperatives is seen as a sine qua non of proper organizing and strategizing by many fit and complexity scholars. Any deviation from this management mantra engenders organizational decline and, ultimately, mortality. We put this axiomatic principle under empirical scrutiny and use the law of requisite variety as our organizing principle to do so. The law is an iconic cornerstone of this matching contingency logic and it has served to legitimize a wide range of fit decisions in, e.g., leadership, organizational learning and corporate governance. Inspired by organizational vignettes inhabiting antithetical complexity regimes, we introduce a novel concept, which we label ‘agentic misfit’. In this way, we deconstruct deterministic assumptions related to environmental fittingness, we challenge teleological orientations in the fit literature, and we flesh out the viability of non-matching human agency amid complexity.

scholarly journals How Computational Statistics Became the Backbone of Modern Data Science

2011 ◽  
pp. 3-16
Author(s):  
James E. Gentle ◽  
Wolfgang Karl Härdle ◽  
Yuichi Mori
Keyword(s):  
Data Science ◽  
Computational Statistics

Multiple objectivity: an anti‐relativist approach to situated knowledge

Kybernetes ◽  
2011 ◽  
Vol 40 (7/8) ◽  
pp. 995-1003 ◽  
Author(s):  
Ron Eglash
Keyword(s):  
Science And Technology Studies ◽  
Alternative Form ◽  
Situated Knowledge ◽  
Content Type ◽  
Requisite Variety ◽  
Law Of Requisite Variety ◽  
Relativist Approach ◽  
Epistemological Relativism ◽  
Cybernetic Concept

PurposeThe paper aims to describe the inadequate nature of both “mono‐objectivist” approaches, which deny any role of social influence in science, and relativist social constructions, which fail to distinguish between science and pseudoscience. It outlines an alternative conceptual framework that allows for the possibility of social construction of science, while preventing epistemological relativism.Design/methodology/approachThe study utilizes the cybernetic concept of recursion to show how science can bend back on itself, investigating its own foundations, without undermining its ability to improve our empirical understanding of the world. The paper makes use of several case studies to define specific mechanisms that show how the process of knowledge production in science can steer a course between reduction to a single “right answer,” and fragmentation into subjective interpretations.FindingsThe paper concludes by showing how the cybernetic recursion of multiple objectivity can also be applied to cybernetics itself. In particular, it suggests that such recursive investigations allow us to reconsider the Law of Requisite Variety, and envision an alternative form that can better account for the complexity that arises in self‐generating systems.Research limitations/implicationsThe research is unlikely to be of use to scientists looking for epistemological proof of singular right answers, or social constructivists looking for proof of epistemological relativism.Practical implicationsThe paper suggests that researchers in constructivism need not limit their work for fear that it will lead to relativist conclusions.Originality/valueThis paper fulfils an identified need to offer an alternative to current developments in the field of science and technology studies.

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