The questions we ask: Opportunities and challenges for using big data analytics to strategically manage human capital resources

Big data analytics have transformed research in many fields, including the business areas of marketing, accounting and finance, and supply chain management. Yet, the discussion surrounding big data analytics in human resource management has primarily focused on job candidate screenings. In this article, we consider how significant strategic human capital questions can be addressed with big data analytics, enabling HR to enhance overall firm performance. We also examine how new data sources that help assess workforce performance in real time can assist in the identification and development of the knowledge stars that contribute to firm performance disproportionately as well as help reinforce firm capabilities. But in order for big data analytics to be successful in the HR field, regulatory and ethical challenges must also be addressed; these include privacy concerns and, in Europe, the General Data Protection Regulation (GDPR). We conclude by discussing how big data analytics can facilitate strategic change within HR and the organization as a whole.     

Incorporating big data within retail organizations: A case study approach

The rapid growth in consumer-generated Big Data that are mostly sourced from various types of mobile devices and sensor technologies has placed increasing competitive pressure on retailers to leverage such data within their location decision-making practices. This paper examines the incorporation of Big Data within retail organizations. Through the analysis of three in-depth case-studies of major retail-related organizations operating in Canada, this research addresses the following two inter-related objectives: (i) to identify the awareness, availability, and development of Big Data environments; and, (ii) to explore the opportunities and challenges associated with new Big Data-based decision support systems within retail organizations. The characteristics of an ideal data environment for Big Data adoption and development to take place are identified. The key findings reveal that while there was general awareness of the importance of Big Data, it was evident that the adoption and development of Big Data decision support was heavily reliant on a data environment that promotes transparency and a clear corporate data strategy.     

Big data? Big deal: Searching for big data’s performance effects in HR

Big data continues to gather increasing interest in the business press as well as within the management literature. While this interest has spilled over into the realm of human resources (HR) management, solid evidence of its positive performance impacts is lacking. I explore three possibilities for this lack of evidence: (1) HR possesses big data but largely lacks the ability to use it; (2) HR does not actually possess big data; and (3) big data is generating value for HR and positively affects organizational performance, but the winners in the race to utilize big data in HR are not publicizing their successes. Following this, I discuss current forms of big data implementation, highlighting an evolutionary progression of implementations in various settings and emphasizing the importance of balancing deductive with inductive analytical approaches. Finally, I discuss conditions under which big data may hold greater value for the HR function, and I suggest ways managers and organizations can make the most of big data.     

Social media analytics for enterprises: Typology,methods, and processes

This article provides an overview of social media analytics for managers that seek to utilize the practice for social media intelligence. Currently, managers are challenged to analyze an abundance of social media data but lack a framework within which to do so. Toward this end, this article presents a simple typology of social media analytics for enterprises. It also discusses various analytics methods for social media data. Then, this article discusses management processes of social media analytics for enterprises. An illustration of social media analytics is provided with real-world consumer review data. Finally, four challenges are discussed.     

The perils of working with big data,and a SMALL checklist you can use to recognize them

The use of big data to help explain fluctuations in the broader economy and key business performance indicators is now so commonplace that in some instances it has even begun to rival more traditional measures. Big data sources can very often provide advantages when compared with these more traditional data sources, but with these advantages also come potential pitfalls. We lay out a checklist called SMALL that we have developed in order to help interested parties as they navigate the big data minefield. Based on a set of five questions, the SMALL checklist should help users of big data draw justifiable conclusions and avoid making mistakes in matters of interpretation. To demonstrate, we provide several case studies that demonstrate the subtle nuances of several of these new big data sets and show how the problems they face often closely relate to age-old concerns that more traditional data sources are also forced to tackle.     

Implementing big data strategies: A managerial perspective

Despite considerable recent advances in big data analytics, there is substantial evidence that many organizations have failed to incorporate them effectively in their own decision-making processes. Advancing the existing understandings, this article lays out the steps necessary to implement big data strategies successfully. To this end, we first explain how the big data analytics cycle can provide useful insights into the characteristics of the environments in which many organizations operate. Next, we review some common challenges faced by many organizations in their uses of big data analytics and offer specific recommendations for mitigating them. Among these recommendations, which are rooted in the findings of strategy implementation research, we emphasize managerial responsibilities in providing continued commitment and support, the effective communication and coordination of efforts, and the development of big data knowledge and expertise. Finally, in order to help managers obtain a fundamental knowledge of big data analytics, we provide an easy-to-understand explanation of important big data algorithms and illustrate their successful applications through a number of real-life examples.     

On realising the utopian potential of big data analytics for maximising return on marketing investments

ABSTRACT

This commentary explores the Big Data transition of the ?eld of Marketing. The potential value of Big Data Analytics for both ?rms and customers is investigated and impediments for Marketing are identi?ed. It is concluded that despite the threats and obstacles, exciting challenges and opportunities for creating value are to be explored and exploited by marketing scholars and practitioners.     

The intermediating role of big data analytics capability between responsive and proactive market orientations and firm performance in the retail industry

Big data analytics capability (BDAC) is the key resource for competitive advantage in the drastically changing market. Although some studies have investigated the impacts on firm performance, there is limited understanding of how firms enhance their BDAC. This study draws on organisational culture and investigates the effects of responsive and proactive market orientations on BDAC and firm performance. The results show that both responsive and proactive market orientations increase BDAC. Further, BDAC fully mediates the relationship between these two market orientations and firm performance. Our findings suggest that BDAC researchers should focus on market orientations that enhance BDAC.     

The Role of Big Data and Predictive Analytics in Retailing

The paper examines the opportunities in and possibilities arising from big data in retailing, particularly along five major data dimensions—data pertaining to customers, products, time, (geo-spatial) location and channel. Much of the increase in data quality and application possibilities comes from a mix of new data sources, a smart application of statistical tools and domain knowledge combined with theoretical insights. The importance of theory in guiding any systematic search for answers to retailing questions, as well as for streamlining analysis remains undiminished, even as the role of big data and predictive analytics in retailing is set to rise in importance, aided by newer sources of data and large-scale correlational techniques. The Statistical issues discussed include a particular focus on the relevance and uses of Bayesian analysis techniques (data borrowing, updating, augmentation and hierarchical modeling), predictive analytics using big data and a field experiment, all in a retailing context. Finally, the ethical and privacy issues that may arise from the use of big data in retailing are also highlighted.     

Can Pleasant Goat and Big Big Wolf Save China's Animation Industry?

"My dreamed husband is big big wolf," claimed Miss Fang, a young lady who works in KPMG Beijing Office. This big big wolf is a lovely cartoon wolf appeared in a Pleasant Goat and Big Big Wolf produced independently by Chinese.     

确权、交易、资产化：对大数据转为生产要素基础理论问题的再思考

大数据是数字经济的基础性、战略性资源，是后疫情时代经济发展的重要生产要素，但社会各界对大数据的理论认知仍落后于其应用实践，而且大数据确权、交易定价、资产化问题也存在诸多争议，这对大数据产业和数字经济的持续健康发展形成潜在隐患，也成为制约大数据向生产要素正常转化并参与社会大生产的关键。文章在对大数据概念、属性重新认识的基础上，综合财经、法律等理论观点和实操技巧，阐述了大数据如何从商品流通要素演变为社会生产要素的市场逻辑以及使用价值如何注入大数据的资产化过程，并从实践视角提出大数据确权的法理基础和大数据交易所生态下交易定价模型。同时提出以数据税来弥补大数据采集环节个人和企业放弃的小微权利，将私权转化为社会公共产品，为大数据产业和要素市场发展提供新的借鉴。     

Implementing Big Data Analytics in Small Firms: A Situated Human Practice Approach

This study identifies and addresses an important gap in the nascent literature on big data analytics, using a longitudinal case study to investigate the implementation and application of big data analytics into a small firm specialized in transport logistics. Our research is rooted in Practice Theory, considering the implementation of new technologies in organizations as a result of multiple social negotiations, interpretations, and interactions. Our findings indicate the importance and centrality of human factors in decision-making and operational implementation, technology representing only a means to a clearly specified and collectively assumed objective. Big data analytics adoption and use in the case-study firm represents a gradual process, with each stage justified by the need to solve the problems caused by heavy and unpredictable road traffic. This approach validates the entrepreneurial effectuation model, which defines a firm's strategy as a fragmented but continuous effort to find and implement effective solutions to the market challenges encountered.     

Business curriculum redesign: Integrating data analytics

Data analytics is an integral part of planning and decision making in business. Priorities have shifted to hiring skilled employees to support a company’s analytics requirements. The authors discuss the background of big data and data analytics, demand for trained professionals, and information on the development of a data analytics curriculum. Curriculum design models are explored and emphasis placed on university curriculum redesign. This is a perspective piece that also addresses interdisciplinary collaboration, accreditation, and related challenges.     

大数据在海基测控联合信息环境中的应用构想

针对海基测控领域存在的信息化烟囱、数据孤岛、信息安全等问题,开展了大数据在海基测控领域的应用研究,提出了基于大数据构建海基测控联合信息环境的系统架构和运行模式,对构建联合信息环境的相关技术进行了探索分析,应用大数据技术实现广域分布式环境下数据、信息和信息技术服务共享,将数据优势转换为信息优势,进而发挥决策优势。     

Big data

Big data is defined and distinguished from a mere moment in the “ancient quest to measure.” Specific discontinuities in the practice of information science are identified which, the paper argues, have large consequences for the social order. The infrastructure that runs on big data is described as diffusing with unprecedented speed but as being difficult to analyze and critique, and therefore the designers of society’s big data infrastructure, whether human or machines, play an unacknowledged legislative function of great consequence.     

Big web data: Challenges related to data,technology, legality,and ethics

The digital data available online is currently measured in zettabytes. These vast repositories of big web data are increasingly viewed as a strategic resource comparable in value to land, gold, and oil. This big web data can be extracted and analyzed by organizations to gain a better understanding of their internal and external environment and improve organizational performance. Because of these opportunities, automated retrieval and organization of web data (i.e., web scraping) for research projects is becoming a common practice. This article outlines the data-related, technical, legal, and ethical issues related to web scraping. Awareness of these issues can help researchers save time and resources and, most importantly, mitigate the potential risk of ethical controversies or lawsuits related to the retrieval and use of big web data.     

大数据下“互联网+冰雪旅游”产业融合发展对策

十九大报告指出:"要推动互联网、大数据、人工智能和实体经济深度融合"。首先,分析大数据对冰雪旅游产业发展的作用;调查"互联网+冰雪旅游"产业融合现状,分析出现的问题;提出大数据背景下"互联网+冰雪旅游"产业融合发展对策是构建冰雪旅游大数据分析中心,实现精准营销;强化政府主导和支持力度,均衡市场发展;培养复合型旅游专业人才,提升创新能力;创新市场运营模式,增强融合动力。     

Data Science,Predictive Analytics,and Big Data in Supply Chain Management: Current State and Future Potential

While data science, predictive analytics, and big data have been frequently used buzzwords, rigorous academic investigations into these areas are just emerging. In this forward thinking article, we discuss the results of a recent large‐scale survey on these topics among supply chain management (SCM) professionals, complemented with our experiences in developing, implementing, and administering one of the first master's degree programs in predictive analytics. As such, we effectively provide an assessment of the current state of the field via a large‐scale survey, and offer insight into its future potential via the discussion of how a research university is training next‐generation data scientists. Specifically, we report on the current use of predictive analytics in SCM and the underlying motivations, as well as perceived benefits and barriers. In addition, we highlight skills desired for successful data scientists, and provide illustrations of how predictive analytics can be implemented in the curriculum. Relying on one of the largest data sets of predictive analytics users in SCM collected to date and our experiences with one of the first master's degree programs in predictive analytics, it is our intent to provide a timely assessment of the field, illustrate its future potential, and motivate additional research and pedagogical advancements in this domain.     

大数据时代下电子商务平台的探索和研究——以淘宝网为例

随着网络用户的日益增长,互联网记录着大量的用户个人信息和由智能终端产生的图片和信息,这些信息爆炸性的增长,并不断涌入网络海洋,产生海量的数据信息。在电子商务环境中大数据处理将会发展出更多强大和多元的功能。本文以淘宝网为例,分析研究如何合理利用数据,为企业的经营模式做出相应调整,并且在变化中不断进行企业新技术、新方法和新思路的探索。同时指出数据泄露的隐患,并给出相应建议。     

Big Data

“Big data” describes technologies that promise to fulfill a fundamental tenet of research in information systems, which is to provide the right information to the right receiver in the right volume and quality at the right time. For information systems research as an application-oriented research discipline, opportunities, and risks arise from using big data. Risks arise primarily from the considerable number of resources used for the explanation and design of fads. Opportunities arise because these resources lead to substantial knowledge gains, which support scientific progress within the discipline and are of relevance to practice as well. From the authors’ perspective, information systems research is ideally positioned to support big data critically and use the knowledge gained to explain and design innovative information systems in business and administration – regardless of whether big data is in reality a disruptive technology or a cursory fad. The continuing development and adoption of big data will ultimately provide clarity on whether big data is a fad or if it represents substantial progress in information systems research. Three theses also show how future technological developments can be used to advance the discipline of information systems. Technological progress should be used for a cumulative supplement of existing models, tools, and methods. By contrast, scientific revolutions are independent of technological progress.