Data science in the business environment: Insight management for an Executive MBA

2022 ◽  
Vol 20 (1) ◽  
pp. 100588
Author(s):  
Jing Lu
Keyword(s):  
Data Science ◽  
Business Environment ◽  
Executive Mba

scholarly journals Remote work and human capital: new challenges and threats (international experience)

2021 ◽  
pp. 107-119
Author(s):  
Oleksandr Burov
Keyword(s):  
Artificial Intelligence ◽  
Human Capital ◽  
Data Science ◽  
Well Being ◽  
Business Environment ◽  
Remote Work ◽  
Science Data ◽  
Creative Abilities ◽  
The Impact

Keywords: human capital, remote work, cybersecurity, workforce, digital economics The article considers the role of human capital in the transitionto the remote work. The analysis of world changes in the field of safe and effectiveuse of digital business environment and qualification of workforce in the conditions ofgrowth of remote work is carried out. The analysis was conducted in the following areas:general features of the digitalizing in crisis and innovation, a new paradigm of business«Data is the new gold», the organization of the workforce in the transition to teleworking,the priorities of today's professions, the problems of cybersecurity in teleworking. It has been articulated that the main requirements for the today’s workforce are intellectualand creative abilities, competence in the field of creation and use of ICT, bigdata (data science, data mining, data analytics) and artificial intelligence, the role ofwhich has grown even more due to the COVID-19 pandemic. The human component ofintellectual capital (in the form of knowledge, skills and competencies, as well as intellectualand creative abilities) is gaining new importance in the digital economy.The analysis of relationship of the crisis and innovation made on the basis of the ClarivateDerwent report has demonstrated the impact of the pandemic on the global lifecycle of research and innovation projects in the first half of 2020, namely that COVID-19violated innovation strategy of the innovative leaders worldwide. The analysis hasdemonstrated: in the new conditions of accelerated digitalization, ingenuity and speed ofdecision-making and innovation are needed more than ever. These priorities will affectthe world economy in the coming year.Special attention in analysis has been paid to the new business paradigm related touse and role of data. It was highlighted that digitization generates vast amounts of datathat offer many opportunities for business, human well-being, and the environment. As aresult, new capabilities and opportunities arise for business with the ecosystem of cooperationand partnership, as well as collaboration of stakeholders.The core of changes in digitalization is reskilling and upskilling of the workforce accountingnew workplaces and new requirements for them. It is recognized that talentmanagement and creative people selection can be the main engine in future transformationof economics, and workforce becomes an effective pole for investments. At the sametime, it is argued that remote worker is outside the scope of corporate protection, and virtuallyany production information, like human capital, becomes much more vulnerablein such conditions and requires appropriate cybersecurity methods.As a conclusion, it is articulated that the ability of companies to use big data is beginningto play a significant role in the economy, which in turn requires the involvementand training of data processing and analysis specialists. The direction of professions thatis being actively formed recently — data science — is one of the most priority in the labormarket. At the same time, the labor market needs skills and abilities in the field of interpersonalcommunication (soft skills), which are able to ensure the effective operation ofpeople and systems of hybrid intelligence «human-artificial intelligence».For the further research it has been recommended a comprehensive study of protectionof objects and subjects of intellectual property in open networks.

scholarly journals Challenges in Designing Curriculum for Trans-Disciplinary Education: On Cases of Designing Concentration on Informing Science and Master Program on Data Science

10.28945/4300 ◽  
2019 ◽  
Vol 22 ◽  
pp. 019-030
Author(s):  
Dimitar Grozdanov Christozov ◽  
Katia Rasheva-Yordanova ◽  
Stefka Toleva-Stoimenova
Keyword(s):  
Professional Education ◽  
Data Science ◽  
Business Processes ◽  
Business Environment ◽  
Working Environment ◽  
Future Research ◽  
Master Program ◽  
Analytical Skills ◽  
Training Professionals ◽  
Science Methodology

Aim/Purpose: The growing complexity of the business environment and business processes as well as the Big Data phenomenon has an impact on every area of human activity nowadays. This new reality challenges the effectiveness of traditional narrowly oriented professional education. New areas of competences emerged as a synergy of multiple knowledge areas – transdisciplines. Informing Science and Data Science are just the first two such new areas we may identify as transdisciplines. Universities are facing the challenge to educate students for those new realities. Background: The purpose of the paper is to share the authors’ experience in designing curriculum for training bachelor students in Informing Science as a concentration within an Information Brokerage major, and a master program on Data Science. Methodology: Designing curriculum for transdisciplines requires diverse expertise obtained by both academia and industries and passed through several stages - identifying objectives, conceptualizing curriculum models, identifying content, and development pedagogical priorities. Contribution: Sharing our experience acquired in designing transdiscipline programs will contribute to a transition from a narrow professional education towards addressing 21st-century challenges. Findings: Analytical skills, combined with training in all categories of so-called “soft skills”, are essential in preparing students for a successful career in a transdiciplinary area of activities. Recommendations for Practitioners: Establishing a working environment encouraging not only sharing but close cooperation is essential nowadays. Recommendations for Researchers: There are two aspects of training professionals capable of succeeding in a transdisciplinary environment: encouraging mutual respect and developing out-of-box thinking. Impact on Society: The transition of higher education in a way to meet current challenges. Future Research The next steps in this research are to collect feedback regarding the professional careers of students graduating in these two programs and to adjust the curriculum accordingly.

scholarly journals Challenges in Designing Curriculum for Trans-Disciplinary Education: On Cases of Designing Concentration on Informing Science and Master Program on Data Science

10.28945/4278 ◽  
2019 ◽  
Keyword(s):  
Professional Education ◽  
Data Science ◽  
Business Processes ◽  
Business Environment ◽  
Working Environment ◽  
Future Research ◽  
Master Program ◽  
Analytical Skills ◽  
Training Professionals ◽  
Science Methodology

[This Proceedings paper was revised and published in the 2019 issue of Informing Science: The International Journal of an Emerging Transdiscipline, Volume 22] Aim/Purpose: The growing complexity of the business environment and business processes as well as the Big Data phenomenon has an impact on every area of human activity nowadays. This new reality challenges the effectiveness of traditional narrowly oriented professional education. New areas of competences emerged as a synergy of multiple knowledge areas – transdisciplines. Informing Science and Data Science are just the first two such new areas we may identify as transdisciplines. Universities are facing the challenge to educate students for those new realities. Background: The purpose of the paper is to share the authors’ experience in designing curriculum for training bachelor students in Informing Science as a concentration within an Information Brokerage major, and a master program on Data Science. Methodology: Designing curriculum for transdisciplines requires diverse expertise obtained by both academia and industries and passed through several stages - identifying objectives, conceptualizing curriculum models, identifying content, and development pedagogical priorities. Contribution: Sharing our experience acquired in designing transdiscipline programs will contribute to a transition from a narrow professional education towards addressing 21st-century challenges. Findings: Analytical skills, combined with training in all categories of so-called “soft skills”, are essential in preparing students for a successful career in a transdiciplinary area of activities. Recommendations for Practitioners: Establishing a working environment encouraging not only sharing but close cooperation is essential nowadays. Recommendations for Researchers: There are two aspects of training professionals capable of succeeding in a transdisciplinary environment: encouraging mutual respect and developing out-of-box thinking. Impact on Society: The transition of higher education in a way to meet current challenges. Future Research The next steps in this research are to collect feedback regarding the professional careers of students graduating in these two programs and to adjust the curriculum accordingly.

Data science for business: benefits, challenges and opportunities

2020 ◽  
Vol 33 (2) ◽  
pp. 149-163 ◽  
Author(s):  
Mauricius Munhoz de Medeiros ◽  
Norberto Hoppen ◽  
Antonio Carlos Gastaud Maçada
Keyword(s):  
Organizational Performance ◽  
Performance Management ◽  
Data Science ◽  
Business Environment ◽  
Data Driven ◽  
Business Organizations ◽  
Data Governance ◽  
Economic Sectors ◽  
Content Type ◽  
Challenges And Opportunities

Purpose This paper aims to identify the benefits of data science (DS) for organizations, highlighting the challenges and opportunities related to developing this capability. Design/methodology/approach Initially, a literature review was performed. Later, empirical data were collected through a structured electronic interview answered by 211 informants, who are most experienced managers of medium and large organizations from different economic sectors, and data were submitted to content analysis. Findings The most frequently observed benefits are as follows: support for data analysis and insight generation with agility; creation of a data-driven culture; improvement of data quality; facilitating the understanding of the business environment, opportunity sensing; and organizational performance management. The most observed challenges are as follows: data-driven culture; DS training; allocation of investments in analytical technologies; and data governance and strategy. Research limitations/implications In addition, to mapping the state of the art on the subject, it contributes to the expansion of scientific knowledge through the identification and disclosure of 11 benefit indicators and 16 challenge indicators associated with analytical capabilities. Practical implications To transform data into information and add value to the business, organizations need to make efforts to enable executive mindset change, the formulation of strategies and governance mechanisms gave the renewal of workforce competencies and the allocation of investments in information technology. Originality/value A vast body of empirical evidence is gathered that consolidates different views on the benefits and challenges associated with DS for business.

The Business, Societal, and Enterprise Architecture Framework

2021 ◽  
pp. 447-490
Author(s):  
Antoine Trad
Keyword(s):  
Decision Making ◽  
Data Science ◽  
Enterprise Architecture ◽  
Business Environment ◽  
Transformation Process ◽  
Business Transformation ◽  
Architecture Framework ◽  
Decision Making System ◽  
Enterprise Architecture Framework ◽  
Using Data

In this chapter, the author presents an artificial intelligence (AI)-based generic concept for decision making using data science. The applied holistic mathematical model for AI (AHMM4AI) focuses on data and access management. A decisive business decision in a business transformation process of a traditional business environment into an automated AI-based business environment is the capacity of the decision-making system and the profile of the business transformation manager (BTM, or simply the manager). The manager and his team are supported by a holistic framework. The role of data science and the needed data modelling techniques are essential for managing data models in a transformation project. For that reason, the development of the big data for AI (BGD4AI) is an essential start.

Data science in the business environment: customer analytics case studies in SMEs

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jing Lu ◽  
Lisa Cairns ◽  
Lucy Smith
Keyword(s):  
Decision Making ◽  
Case Studies ◽  
Process Model ◽  
Data Science ◽  
Business Environment ◽  
Data Driven ◽  
Complex Data ◽  
Data Driven Decision Making ◽  
Content Type ◽  
Customer Analytics

Purpose A vast amount of complex data is being generated in the business environment, which enables support for decision-making through information processing and insight generation. The purpose of this study is to propose a process model for data-driven decision-making which provides an overarching methodology covering key stages of the business analytics life cycle. The model is then applied in two small enterprises using real customer/donor data to assist the strategic management of sales and fundraising. Design/methodology/approach Data science is a multi-disciplinary subject that aims to discover knowledge and insight from data while providing a bridge to data-driven decision-making across businesses. This paper starts with a review of established frameworks for data science and analytics before linking with process modelling and data-driven decision-making. A consolidated methodology is then described covering the key stages of exploring data, discovering insights and making decisions. Findings Representative case studies from a small manufacturing organisation and an independent hospice charity have been used to illustrate the application of the process model. Visual analytics have informed customer sales strategy and donor fundraising strategy through recommendations to the respective senior management teams. Research limitations/implications The scope of this research has focused on customer analytics in small to medium-sized enterprise through two case studies. While the aims of these organisations are rather specific, they share a commonality of purpose for their strategic development, which is addressed by this paper. Originality/value Data science is shown to be applicable in the business environment through the proposed process model, synthesising micro- and macro-solution methodologies and allowing organisations to follow a structured procedure. Two real-world case studies have been used to highlight the value of the data-driven model in management decision-making.

Model-Based Clustering and Classification for Data Science

2019 ◽  
Author(s):  
Charles Bouveyron ◽  
Gilles Celeux ◽  
T. Brendan Murphy ◽  
Adrian E. Raftery
Keyword(s):  
Data Science ◽  
Model Based Clustering ◽  
Model Based ◽  
Clustering And Classification

From Craft to Institution: Accounting in Italy from the Middle ages to the Early Modern Period

2012 ◽  
pp. 135-152 ◽  
Author(s):  
O. Volkova
Keyword(s):  
Early Modern ◽  
Middle Ages ◽  
Social Economic ◽  
Early Modern Period ◽  
Social Institution ◽  
Business Environment ◽  
Technical Standards ◽  
Medieval Italy ◽  
Modern Period ◽  
Professional Field

The article describes the evolution of accounting from the simple registration technique to economic and social institution in medieval Italy. We used methods of institutional analysis and historical research. It is shown that the institutionalization of accounting had been completed by the XIV century, when it became a system of codified technical standards, scholar discipline and a professional field. We examine the interrelations of this process with business environment, political, social, economic and cultural factors of Italy by the XII—XVI centuries. Stages of institutionalization are outlined.

Corruption as “one‑stop‑shop”: theory and empirical analysis

2018 ◽  
pp. 32-51
Author(s):  
R. Yu. Kochnev ◽  
L. I. Polishchuk ◽  
A. Yu. Rubin
Keyword(s):  
Comparative Analysis ◽  
Business Environment ◽  
Enterprise Performance ◽  
Double Jeopardy ◽  
One Stop ◽  
The Commons ◽  
Public Production ◽  
Performance Project ◽  
The Impact ◽  
Sector Theory

We present the comparative analysis of the impact of centralized and decentralized corruption for private sector. Theory and empirical evidence point out to a “double jeopardy” of decentralized corruption which increases the burden of corruption upon private firms and weakens the incentives of bureaucracy to provide public production inputs, such as infrastructure. These outcomes are produced by simultaneous free-riding and the tragedy of the commons effects. The empirical part of the paper utilizes data of the Business Environment and Enterprise Performance project.

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