Addressing barriers to big data

Increasingly, big data is viewed as the most strategic resource of the 21st century, similar in importance to gold and oil. While sitting on these vast pools of data, many organizations are simply not ready to take advantage of this new strategic resource. Embracing big data requires addressing a number of barriers that fall into the domains of technology, people, and organization. A holistic, socio-technical approach is required to overcome these barriers. This article introduces the specific tactics we recommend for addressing big data barriers, which involve changes to technology infrastructure, a focus on privacy, promotion of big data and analytic skills development, and the creation of a clear organizational vision related to big data.     

The ‘big data’ myth and the pitfalls of ‘thick data’ opportunism: on the need for a different ontology of markets and consumption

ABSTRACT

The twin pillars of big data and data analytics are rapidly transforming the institutional conditions that situate marketing research. In response, many proponents of culturalist paradigms have adopted the vernacular of ‘thick data’ to defend their vulnerable position in the marketing research field. However, thick data proselytising fails to challenge several outmoded ontological assumptions that are manifest in the big data myth and it situates socio-cultural modes of marketing thought in a counterproductive technocratic discourse. In building this argument, I first discuss the relevant historical continuities and discontinuities that have shaped the big data myth and the thick data opportunism. Next, I argue that culturally oriented marketing researchers should promote a different ontological frame— the analytics of marketplace assemblages—to address how big data, or more accurately its socio-technical infrastructure, produces new kinds of emergent and hybrid market structures, modes of social aggregation, consumption practices, and prosumptive capacities.     

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.     

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。     

供应链中大数据分析应用研究综述

近年来,供应链管理实践中产生的数据量呈指数增长,大数据分析在供应链中存在巨大的发展空间,然而,当前对于供应链中大数据分析应用还缺乏深入研究。通过相关文献梳理,对国外供应链中大数据应用进行深入探析,结合国内外研究成果回顾了不同行业供应链中的大数据应用及其商业价值,鉴于已有研究,对未来该领域研究进行大胆展望。文章将丰富国内供应链中的大数据分析应用理论,为学术界和实务界在供应链管理各个方面实施大数据分析应用提供指导。     

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.     

Connecting big data management capabilities with employee ambidexterity in Chinese multinational enterprises through the mediation of big data value creation at the employee level

Drawing from the knowledge-based dynamic capabilities (KBDCs) view, this study examines the association of big data management capabilities with employee exploratory and exploitative activities at the individual level. Furthermore, it also investigates the mediating role of big data value creation in the association of big data management capabilities with exploratory and exploitative activities. The partial least square method was employed to analyse the hypotheses using data collected from 308 employees of 20 Chinese multinational enterprises. The existing literature gives scant attention to the role of big data management capabilities at the individual level. The main contribution of this study is that it conceptualises big data management as the ability to utilise external knowledge (generated from global users) under the resource constrained environment of an emerging economy. Furthermore, this study builds upon the existing literature on KBDC to explain big data management capabilities as antecedents to ambidexterity at the individual employee level.     

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.     

Thirsty in an Ocean of Data? Pitfalls and Practical Strategies When Partnering With Industry on Big Data Supply Chain Research

Increased volume, velocity, and variety of data provides new opportunities for businesses to take advantage of data science techniques, predictive analytics, and big data. However, firms are struggling to make use of their disjointed and unintegrated data streams. Despite this, academics with the analytic tools and training to pursue such research often face difficulty gaining access to corporate data. We explore the divergent goals of practitioners and academics and how the gap that exists between the communities can be overcome to derive mutual value from big data. We describe a practical roadmap for collaboration between academics and practitioners pursuing big data research. Then we detail a case example of how, by following this roadmap, researchers can provide insight to a firm on a specific supply chain problem while developing a replicable template for effective analysis of big data. In our case study, we demonstrate the value of effectively pairing management theory with big data exploration, describe unique challenges involved in big data research, and develop a novel and replicable hierarchical regression‐based process for analyzing big data.     

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.     

Big data dreams: A framework for corporate strategy

The phenomenon of big data—large, diverse, complex, and/or longitudinal data sets—is having a stark influence on organizational strategy making. An increase in levels of data and technological capabilities is redefining innovation, competition, and productivity. This article contributes to both practical strategic application and academic research in the strategic management domain by presenting a framework that identifies how big data improves functional capabilities within organizations, shapes entirely new industries, and is a key component of innovative and disruptive strategies used by learning organizations to diversify and break down barriers of traditionally defined industries. This framework provides an appropriate basis for internal corporate strategy discussions that surround big data investments by explaining how firms create value through various approaches. In addition, we offer guidance for how firms might derive their own big data approach through the merits of aligning data strategy aspirations with data strategy authenticity.     

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.     

浅议电子商务对国际贸易的影响

电子商务作为因特网技术发展日益成熟的直接结果,是未来商业发展的新方向。它体现的开放性、全球性、地域性、低成本和高效率等内在特征,在符合商业经济内在要求的同时,还使其超越了作为一种新的贸易形式所具有的价值。它不仅改变了企业本身的生产、经营、管理,而且对传统的贸易方式带来冲击。其最明显的标志就是增加了贸易机会、降低贸易成本以及提高贸易效益。在带动经济结构变革的同时,对整个现代经济生活产生了巨大而且深远的影响。对此,中国作为经济正在发展的贸易大国,要大力发展电子商务,加强信息基础设施建设,在国际贸易竞争中占据主动。     

Leveraging Big Data to Develop Supply Chain Management Theory: The Case of Panel Data

Increased data availability is poised to shape both business practice and supply chain management (SCM) research. This article addresses an issue that can arise when trying to use big data to answer academic research questions. This issue is that distilled data often have a panel structure whereby repeated measurements are available on one or more variables for a substantial number of subjects. Thus, to fully leverage the richness of big data for academic research, SCM scholars need an understanding regarding the different types of research questions answerable with panel data. In this article, we devise a framework detailing different types of research questions SCM scholars can answer with panel data. This framework provides a basis to categorize how SCM scholars have examined the services supply chain setting of health care with public data regarding hospital‐level patient satisfaction. We extend prior research by testing a series of three questions not yet examined in this area by fitting a series of structured latent curve models to seven years of hospital‐level patient satisfaction for nearly 4,000 hospitals. The discussion highlights theoretical and methodological challenges SCM scholars are likely to encounter as they use the panel data in their research.     

大数据时代食品监管模式创新性研究

以大数据为时代背景,通过研究食品监管制度,创造性地提出企业定制监管模式。大数据是一种对海量数据处理分析的技术手段,将大数据的手段运用到食品监管中,建立企业数据库、数据共享平台和数据公开平台,对食品企业数据进行收集与挖掘,为企业定制个性化的监管模式,有针对性地进行监管,优化监管资源的配置,提升监管效能。     

大数据、人工智能与厂商竞争路径

经济学对市场竞争路径的学理性分析，主要集中在价格确定、产量确定、规模经济、产业组织等方面，而对科技进步引发市场竞争路径的变化并没有足够的关注。其实，市场竞争路径变化的底蕴是科技进步，只是经济学家在分析市场竞争路径时偏好于将科技因素作为外生变量处理。大数据和人工智能等的发展可谓是一场史无前例的科技革命，它对人类经济活动产生广泛而深刻的影响主要表现为：大数据及其运用怎样影响厂商投资经营，大数据与机器学习等人工智能手段相融合会在哪些方面改变厂商竞争路径，厂商如何提高数据智能化和实现网络协同化，在什么样的条件下会出现行业垄断，等等。文章的基本分析观点是：厂商竞争路径变化是贯穿于大数据、互联网和人工智能等相互融合过程的一种现象，这种现象对应于新科技进步和运用的不同层级；微观经济分析需要将新科技因素作为内生变量，通过分析大数据、机器学习与厂商竞争路径之间的关联，揭示厂商竞争路径变化机理以及由此引致的产业组织等问题。     

Big data: Dimensions,evolution, impacts,and challenges

Big data represents a new technology paradigm for data that are generated at high velocity and high volume, and with high variety. Big data is envisioned as a game changer capable of revolutionizing the way businesses operate in many industries. This article introduces an integrated view of big data, traces the evolution of big data over the past 20 years, and discusses data analytics essential for processing various structured and unstructured data. This article illustrates the application of data analytics using merchant review data. The impacts of big data on key business performances are then evaluated. Finally, six technical and managerial challenges are discussed.     

The myth of big data: Chinese advertising practitioners’ perspective

A qualitative study was conducted to explore Chinese advertising practitioners’ perceptions and interpretations of big data in the Chinese market. Twenty-two in-depth interviews were conducted to collect the data. Four overarching themes emerged regarding the interviewees’ perception of the Chinese advertising market, the definition of big data, the application of big data and the future development of big data. Based on the themes, a theoretical framework was developed to demonstrate big data's application and development in the Chinese market. Theoretical and practical implications were offered.     

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.