Research on the Impact of Data Driven on the Cultivation of Innovative Talents in Economics Majors in China

With the application of Internet big data in the economic field, the requirements for the cultivation of innovative and entrepreneurial talents have been put forward for the current economic universities. Based on the current situation of my country's economic professional talents innovation training under the data-driven background, this article analyzes the impact of data-driven on the training of economic professional innovative talents in my country, and points out the problems in the training of economic professional innovative talents driven by data. And put forward targeted suggestions and opinions.

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Big Data

International Journal of Service Science Management Engineering and Technology ◽

10.4018/ijssmet.2015040101 ◽

2015 ◽

Vol 6 (2) ◽

pp. 1-17 ◽

Cited By ~ 23

Author(s):

Ashok Kumar Wahi ◽

Yajulu Medury ◽

Rajnish Kumar Misra

Keyword(s):

Big Data ◽

Unstructured Data ◽

Data Driven ◽

Enterprise 2.0 ◽

Huge Amount ◽

The World ◽

Data Driven Decisions ◽

The Impact

Big data has taken the world by storm. Everyone from every industry is not only talking about the impact of big data but is looking for ways to effectively leverage the power of big data. This challenge has heightened with the huge amount of unstructured data flowing from every direction, bringing along with it the increasing pressure to make data driven decisions rather than the gut-driven decisions. This article sheds light on how big data can be an enabler for smart enterprises if the organization is able to address the challenges posed by big data. Enterprises need to equip themselves with relevant technology, desired skills and a supporting managerial attitude to swim through the challenges of big data. It also highlights the need for all enterprises making the journey from 1.0 stage to Enterprise 2.0 to master the art of Big Data if they have to make the transition successful.

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Understanding big data-driven supply chain and performance measures for customer satisfaction

Benchmarking An International Journal ◽

10.1108/bij-01-2021-0034 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Ramadas Thekkoote

Keyword(s):

Big Data ◽

Supply Chain ◽

Customer Satisfaction ◽

Performance Measures ◽

Data Driven ◽

Equation Modeling ◽

Supply Chain Performance ◽

Content Type ◽

Different Dimensions ◽

The Impact

PurposeSupply chain analytics with big data capability are now growing to the next frontier in transforming the supply chain. However, very few studies have identified its different dimensions and overall effects on supply chain performance measures and customer satisfaction. The aim of this paper to design the data-driven supply chain model to evaluate the impact on supply chain performance and customer satisfaction.Design/methodology/approachThis research uses the resource-based view, emerging literature on big data, supply chain performance measures and customer satisfaction theory to develop the big data-driven supply chain (BDDSC) model. The model tested using questionnaire data collected from supply chain managers and supply chain analysts. To prove the research model, the study uses the structural equation modeling technique.FindingsThe results of the study identify the supply chain performance measures (integration, innovation, flexibility, efficiency, quality and market performance) and customer satisfaction (cost, flexibility, quality and delivery) positively associated with the BDDSC model.Originality/valueThis paper fills the significant gap in the BDDSC on the different dimensions of supply chain performance measures and their impacts on customer satisfaction.

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Learning Analytics and Education Data Mining in Higher Education

Advancing the Power of Learning Analytics and Big Data in Education - Advances in Educational Technologies and Instructional Design ◽

10.4018/978-1-7998-7103-3.ch005 ◽

2021 ◽

pp. 108-126

Author(s):

Samira ElAtia ◽

Donald Ipperciel

Keyword(s):

Higher Education ◽

Data Mining ◽

Big Data ◽

Learning Analytics ◽

Large Scale ◽

Educational Data Mining ◽

Current Situation ◽

Research Perspective ◽

Education Data ◽

The Impact

In this chapter, the authors propose an overview on the use of learning analytics (LA) and educational data mining (EDM) in addressing issues related to its uses and applications in higher education. They aim to provide meaningful and substantial answers to how both LA and EDM can advance higher education from a large scale, big data educational research perspective. They present various tasks and applications that already exist in the field of EDM and LA in higher education. They categorize them based on their purposes, their uses, and their impact on various stakeholders. They conclude the chapter by critically analyzing various forecasts regarding the impact that EDM will have on future educational setting, especially in light of the current situation that shifted education worldwide into some form of eLearning models. They also discuss and raise issues regarding fundamentals consideration on ethics and privacy in using EDM and LA in higher education.

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Corporate Attitudes towards Big Data and Its Impact on Performance Management: A Qualitative Study

Sustainability ◽

10.3390/su11030684 ◽

2019 ◽

Vol 11 (3) ◽

pp. 684 ◽

Cited By ~ 6

Author(s):

Irina Pugna ◽

Adriana Duțescu ◽

Oana Stănilă

Keyword(s):

Big Data ◽

Performance Management ◽

Business Environment ◽

Data Driven ◽

Specific Reference ◽

Senior Executives ◽

Organizational Challenges ◽

Critical Areas ◽

Research Questions ◽

The Impact

This paper investigates the organizational challenges raised by Big Data and its impact on the business environment with a focus on performance management. We investigate managers’ perceptions, understanding, and attitudes relating to Big Data and its analytics, in terms of opportunities, extent, limitations, challenges, and implications, with specific reference to performance management. The research methodology we adopt is grounded theory: we develop a reflection guide based on research questions covering the impact and challenges of a data-driven culture on business, and the impact on performance management and the decision-making process. The results obtained from senior executives from 21 Romanian companies leads to a conceptual model that distils the major areas arising from the responses and the interrelationships between them. These reveal several key areas of managerial relevance and suggest fruitful action. In particular, we find that the most critical areas requiring intervention lie in the area of awareness and understanding, goal setting, assessing benefits and limitations, learning to trust data, and commitment to an embedded data-driven culture. In addition to changes within organizations themselves, there are also implications for other stakeholders, such as education providers.

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Using mobile big data to support emergency preparedness and address economically vulnerable communities during the COVID-19 pandemic in Nigeria

Data & Policy ◽

10.1017/dap.2021.12 ◽

2021 ◽

Vol 3 ◽

Author(s):

Joanne Gilbert ◽

Olubayo Adekanmbi ◽

Charlie Harrison

Keyword(s):

Big Data ◽

Resource Planning ◽

Data Sources ◽

Data Driven ◽

Second Phase ◽

Case Scenario ◽

Worst Case ◽

Mobile Big Data ◽

The Government ◽

The Impact

Abstract With the declaration of the coronavirus disease 2019 (COVID-19) pandemic in Nigeria in 2020, the Nigeria Governors’ Forum (NGF) instigated a collaboration with MTN Nigeria to develop data-driven insights, using mobile big data (MBD) and other data sources, to shape the planning and response to the pandemic. First, a model was developed to predict the worst-case scenario for infections in each state. This was used to support state-level health committees to make local resource planning decisions. Next, as containment interventions resulted in subsistence/daily paid workers losing their income and ability to buy essential food supplies, NGF and MTN agreed a second phase of activity, to develop insights to understand the population clusters at greatest socioeconomic risk from the impact of the pandemic. This insight was used to promote available financial relief to the economically vulnerable population clusters in Lagos state via the HelpNow crowdfunding initiative. This article discusses how anonymized and aggregated mobile network data (MBD), combined with other data sources, were used to create valuable insights and inform the government, and private business, response to the pandemic in Nigeria. Finally, we discuss lessons learnt. Firstly, how a collaboration with, and support from, the regulator enabled MTN to deliver critical insights at a national scale. Secondly, how the Nigeria Data Protection Regulation and the GSMA COVID-19 Privacy Guidelines provided an initial framework to open the discussion and define the approach. Thirdly, why stakeholder management is critical to the understanding, and application, of insights. Fourthly, how existing relationships ease new project collaborations. Finally, how MTN is developing future preparedness by creating a team that is focused on developing data-driven insights for social good.

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The Impact of Big Data on Banking Operations

10.21203/rs.3.rs-573323/v1 ◽

2021 ◽

Author(s):

Morshadul Hasan ◽

Thi Le ◽

Ariful Hoque

Keyword(s):

Big Data ◽

Banking Industry ◽

Mobile Internet ◽

Data Driven ◽

Significant Implication ◽

Technological Advances ◽

Qualitative Research Methodology ◽

Research Concept ◽

The Impact ◽

Banking Operations

Abstract In the current technological advances with the rise of the information revolution through mobile internet, cloud computing, big data, and the Internet of Things (IoT), the banking industry is receiving new opportunities and facing critical challenges. It motivates us to develop the proposed research concept to examine how data innovation influences banking operations. We employ the systematic qualitative research methodology based on the existing literature from Web of Science and SCOPUS database to accomplish our research objectives. The findings of this study include the positive implications of big data, challenges, and banking security as essential data-driven banking issues. This research will have a significant implication in the banking industry that big data operation is critical for data-driven banking decisions.

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From computer-assisted to data-driven: Journalism and Big Data

Journalism ◽

10.1177/1464884915620205 ◽

2016 ◽

Vol 18 (4) ◽

pp. 408-424 ◽

Cited By ~ 21

Author(s):

Philip Hammond

Keyword(s):

Big Data ◽

Network Theory ◽

Actor Network Theory ◽

Data Access ◽

Data Driven ◽

Computer Assisted ◽

Human Subjectivity ◽

Data Journalism ◽

The Impact ◽

Dominant Paradigm

Despite claims of continuity, contemporary data journalism is quite different from the earlier tradition of computer-assisted reporting. Although it echoes earlier claims about being scientific and democratic, these qualities are understood as resulting from better data access rather than as being something achieved by the journalist. In the context of Big Data in particular, human subjectivity tends to be downgraded in importance, even understood as getting in the way if it means hubristically theorising about causation rather than working with correlation and allowing the data to speak. Increasing ‘datafication’ is not what is driving changes in the profession, however. Rather, the impact of Big Data tends to be understood in ways that are consonant with pre-existing expectations, which are shaped by the broader contemporary post-humanist political context. The same is true in academic analysis, where actor–network theory seems to be emerging as the dominant paradigm for understanding data journalism, but in largely uncritical ways.

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Are Numbers Not Trusted in a “Post-Truth” Era? An Experiment on the Impact of Data on News Credibility

Electronic News ◽

10.1177/1931243119883839 ◽

2019 ◽

Vol 13 (4) ◽

pp. 179-195

Author(s):

Y. Roselyn Du ◽

Lingzi Zhu ◽

Benjamin K. L. Cheng

Keyword(s):

Big Data ◽

Data Driven ◽

News Production ◽

Perceived Credibility ◽

Use Of Data ◽

News Credibility ◽

The Impact ◽

Data Visualizations ◽

Positive Effect

The term “post-truth” was declared by Oxford Dictionaries to be its 2016 “International Word of the Year,” signifying the advent of a so-called post-truth era with rising misinformation and declining trust in media. Meanwhile, the “age of data” has seen a proliferation of big data alongside an increase in data-driven journalism, which is one critical way to make professional journalists distinctive with the production of fact-based, authoritative news. Using devised variations of one news report as stimuli, this experiment involves five test groups to determine whether data and data visualizations impact the perceived credibility of news. Results show that only when accompanied by visualizations does the use of data have a positive effect. Findings suggest the necessity and significant role of data visualizations in news production. The study also reveals that increased use of data components in the news does not always contribute to its audience’s perception of news credibility.

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IAMM: A Maturity Model for Measuring Industrial Analytics Capabilities in Large-scale Manufacturing Facilities

International Journal of Prognostics and Health Management ◽

10.36001/ijphm.2016.v7i4.2466 ◽

2020 ◽

Vol 7 (4) ◽

Author(s):

Peter O’Donovan ◽

Ken Bruton ◽

Dominic T.J. O’Sullivan

Keyword(s):

Big Data ◽

Data Analytics ◽

Large Scale ◽

Big Data Analytics ◽

Data Driven ◽

Maturity Model ◽

Competitive Advantages ◽

Engineering Statistics ◽

Industrial Big Data ◽

The Impact

Industrial big data analytics is an emerging multidisciplinary field, which incorporates aspects of engineering, statistics and computing, to produce data-driven insights that can enhance operational efficiencies, and produce knowledgebased competitive advantages. Developing industrial big data analytics capabilities is an ongoing process, whereby facilities continuously refine collaborations, workflows and processes to improve operational insights. Such activities should be guided by formal measurement methods, to strategically identify areas for improvement, demonstrate the impact of analytics initiatives, as well as deriving benchmarks across facilities and departments. This research presents a formal multi-dimensional maturity model for approximating industrial analytics capabilities, and demonstrates the model’s ability to assess the impact of an initiative undertaken in a real-world facility.

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Big(ger) data as better data in open distance learning

The International Review of Research in Open and Distributed Learning ◽

10.19173/irrodl.v16i1.1948 ◽

2015 ◽

Vol 16 (1) ◽

Cited By ~ 21

Author(s):

Paul Prinsloo ◽

Elizabeth Archer ◽

Glen Barnes ◽

Yuraisha Chetty ◽

Dion Van Zyl

Keyword(s):

Big Data ◽

Data Sources ◽

Data Driven ◽

Descriptive Case Study ◽

Student Data ◽

Current State ◽

The University ◽

The Impact ◽

Dominant Paradigm

In the context of the hype, promise and perils of Big Data and the currently dominant paradigm of data-driven decision-making, it is important to critically engage with the potential of Big Data for higher education. We do not question the potential of Big Data, but we do raise a number of issues, and present a number of theses to be seriously considered in realising this potential.The University of South Africa (Unisa) is one of the mega ODL institutions in the world with more than 360,000 students and a range of courses and programmes. Unisa already has access to a staggering amount of student data, hosted in disparate sources, and governed by different processes. As the university moves to mainstreaming online learning, the amount of and need for analyses of data are increasing, raising important questions regarding our assumptions, understanding, data sources, systems and processes.This article presents a descriptive case study of the current state of student data at Unisa, as well as explores the impact of existing data sources and analytic approaches. From the analysis it is clear that in order for big(ger) data to be better data, a number of issues need to be addressed. The article concludes by presenting a number of theses that should form the basis for the imperative to optimise the harvesting, analysis and use of student data.

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