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.     

Bias in consumer panel and store audit data

Sample survey data such as consumer panel data and store audit data may deviate remarkably from each other and from internal secondary data. These deviations are called data biases. In this study we use information regarding some brands of a fast moving nondurable consumer good to show that the data bias may lead to a large bias in parameter estimation, implying wrong indications for the use of marketing decision variables.A formal analysis explaining the coverage factor - measuring data bias - by a number of exogeneous variables can lead to a reduction in the case of consumer panel data.A similar reduction of the data bias in store audit data can be reached through the use of a reweighing procedure.     

Improving survey research using diagrams of data structure

Using diagrams of data structure, or conceptual models, is important in businesses. Survey research often has if-then data structure, but discussion of diagramming survey data structure is rare. This study uses the U.S. Fishing, Hunting and Wildlife-Associated Recreation (FHWAR) survey data and a Taiwan survey in the analysis of benefits of using data structure diagrams in survey research. Examples of data structure diagram use show how diagramming can support consistent and logical data collection, as well as improved data storage and analysis. Analysis also shows how storing if-then (conditional) data in entities/tables allows simple and intuitively meaningful unconditional variable names and can facilitate consideration of conditions that should/can affect analysis. A general conclusion is that the time has come for tourism and business survey researchers to benefit from using diagrams of data structure in planning data accumulation and to benefit from using modern systems in data collection, storage and analysis.     

机载软件数据质量评价参数及验证方法

通过分析影响机载数据质量的因素,建立了机载软件数据传输模型。提出了机载软件数据质量的定性评价方法,按照机载软件对于数据的需求及相关工业标准要求,总结了层次化的机载软件数据质量评价参数。根据不同安全关键等级的数据需求,提出了针对性的数据质量确认、验证方法及其实现。本研究对于机载软件数据库的审定和机载软件数据质量的验证有一定的理论价值。     

卫星入轨段多源测轨数据融合定轨的加权方法

针对卫星发射入轨段遥外测多种不等精度、不同类型的测轨数据放在一起定轨时的合理加权问题,采用基于微分轨道改进基本原理,推导得到适用于不同类测轨数据联合定轨的动力学条件方程,提出了依据不同测量数据均方差确定该测量元素对应的动力学条件方程权重的方法。仿真验算和实战数据检验表明,该方法充分发挥了分布于各种测轨数据中的高精度测元的作用,融合后的定轨精度得到显著提高。     

海上测控任务数据中心一体化管理平台设计

根据海上测控任务数据和信息分布的特点,设计了海上测控任务数据中心的一体化管理平台。首先,平台设计采用基于无共享存储的高性能集群模式数据库架构,具备统一的计算与存储系统能力以及良好的动态可扩展性;其次,平台采用层次化设计,包括数据采集与处理、数据存储、数据管理和数据应用4层,实现统一的数据中心管理和应用。根据应用需求,重点设计了航天器遥测故障预警与诊断、测控设备远程监控与故障诊断、外测数据精度分析与数据挖掘等典型数据应用模块,实现数据核心价值。     

ACTS数据有效性检验算法仿真

为解决空战训练系统飞行数据在实时传输过程中的数据延时、丢失和错误等问题,搭建了一 种 空战训练系统(ACTS)数据有效性检验算法仿真实验环境,采用两台PC和两部电台来进行收发 飞行数据的模拟 ,并利用某型飞机的试飞数据对设计的数据有效性检验算法进行了验证,实现了地面监控系 统对空中态势的实时显示和评估。     

互联网独角兽企业生态圈与数字经济环境的双向影响机制——基于滴滴和美团的案例分析

构建商业生态系统逐渐成为现代企业的重要战略选择,然而,在数字经济情境下,近几年崛起的平台型互联网独角兽企业呈现出有别于传统商业生态系统的互联网生态圈模式。结合滴滴和美团生态圈构建案例,从数据生成、数据获取、数据分析、数据应用和数据辅助五个阶段总结了在数字经济环境下平台型互联网独角兽企业生态圈的构建动机和逻辑,并从动态视角探讨了独角兽企业的互联网生态圈与数字经济环境的交互和结果,在此基础上补充了商业生态系统的评价体系。研究发现,第一,互联网独角兽企业的生态圈建设与过去的国际化生态、上下游供应链生态等模式相比,更倾向于选择通过成立新企业而非传统的依托于其他企业的方式来构建商业生态系统,且更强调商业生态系统的独立性,而非依托于传统的供应链;第二,众多互联网独角兽企业选择具有很大共性的“反常”跨界战略作为生态圈构建的战略方向,是因为它们面对着诸多数据环境压力,包括市场信心带来的数据覆盖压力、下游市场屏障带来的基础数据压力、行业壁垒低带来的数据接入压力和机会、用户黏性低带来的数据黏性压力、人口红利和专利法律政策带来的数据规模和数据模仿特点、垄断型市场格局带来的数据竞争压力、业务扩张带来的数据差异化压力、重大突发公共事件带来的数据网络化压力、盈利缺失带来的数据变现压力等;第三,独角兽企业的互联网生态圈具有广泛的环境影响力,应从用户黏性与生态圈数据沉淀、用户数据权与数据价值密度、用户消费习惯与数据价值深度、平台数据创新与数据应用多样化四方面评价其与数字化环境的双向影响。新时代的企业生态越来越成为依托数据的数据网络生态,数字化企业尤其是大规模的独角兽企业在构建生态圈的过程中应充分发挥自身数据优势,构建圈内圈外的数据交换体系,并最大化地利用数据方法和效应提升企业的生态竞争力,找到适合自己的生态位。     

A Primer on Using Behavioral Data for Testing Theories in Advertising Research

Interactions with and between customers in digital, social, and mobile environments are commonly recorded, producing behavioral data that have the potential to advance advertising research. This article provides an accessible guide on how to leverage such data for advertising researchers who may have thus far relied mostly on lab experiment or survey data. Specifically, we suggest potential sources for behavioral data and present a process for analyzing and interpreting behavioral data. Each step of the process is discussed: exploring, understanding and preparing data; specifying and estimating models; and interpreting and presenting the results. Some fundamental issues with using multiple regression to analyze such data are covered, including standardization, outliers, transformations, multicollinearity, and the omitted variable bias. We also discuss issues that are especially problematic with using behavioral data in advertising research, including endogeneity, count data, data with many zeros, and grouped data. More advanced versions of regression that address these issues are surveyed, including instrumental variables, propensity scoring, generalized linear models, and mixed models. General advice for thinking about behavioral data is provided.     

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。     

电路交换域数据采集系统的FPGA设计

随着大数据应用的发展,从电路交换域获取数据也是丰富海量数据库的一个重要途径。为了提高电路交换域数据采集的效率,丰富采集手段,结合电路交换域数据通信的特点以及数据组包的要求,提出了一种基于FPGA实现电路交换域数据采集的设计方案,给出了FPGA的数据采集模型,并对该模型的组成模块进行了详细阐述,实现了数据从电路交换域到分组交换域的自动转换。测试结果表明,该数据采集系统丰富了时分复用线路数据采集的效率,实现了预期的功能。     

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。     

美军战术数据链的发展及作战运用

按照H-O定理以及S-S定理,贸易自由化将使我国劳动密集型产品生产增加,劳动要素收入上升,但是,改革开放以来对外依存度最高的制造业职工工资变动的数据并不支持这一结论:工资总额占GDP比重趋于下降,职工平均工资增长率低于人均GDP以及工业GDP增长率。本文解释了导致这一现象的原因:部分是传统体制下企业吸纳了过量的劳动力、从而使生产效率低下所致;部分纯粹是统计上的原因所致。随着制造业职工就业和工资决定市场化程度的提高,贸易自由化带来的劳动报酬上升的趋势趋于显著。实证检验的结果也支持了S-S定理的结论。最后是关于继续贸易自由化、提高劳动力工资收入的几点建议。     

数据生产者权之证成——以数据资源排他权为视角

非个人数据作为一种资源,其价值在于不断地流通。而在现行法律框架内,合同法+侵权法+技术措施为数据生产者架构了一个事实财产权的框架,在实现数据保护的同时加剧了数据锁定之风险,不能为数据流通提供行为预期。而数据赋权的意义在于,承认数据的事实财产权,并进一步将之塑造为实证法上的权利概念,从而为数据流通提供行为预期。数据生产者(包括单个数据生产者和数据集生产者)作为数据流通的发起者,在数据生产过程中投入了大量的人力、财力,因而需要赋之以权利,通过法律上的激励机制实现数据流通。排他权是权利结构的核心,数据资源的排他权并非没有先例可循,商业秘密即可借鉴。对于数据资源排他权的塑造,其维度有二:一是通过合同界定出价值量相对完整的客体,即单个数据抑或数据集(维度广、种类多、数据量大);二是通过行为规范,即从来源合法和使用不违法两个层面构建数据利用行为规范。只是客体特定化需要通过技术手段(如可追溯日志)甚至法院确权程序(如果有的话)来确定。其结果就是,该项权利消弭了对世性,即有限对世,丧失控制即丧失权利。这样的权利设计既迎合数据资源属性,亦符合财产法原理。     

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.     

数据挖掘在人口学中的应用

随着收集数据能力的加强 ,人口数据和资料日益丰富起来 ,但同时又普遍感到存在着“数据丰富而信息匮乏”的问题。试图将数据挖掘的技术应用到第五次人口普查当中 ,通过具体的例子分析来说明在人口学领域如何运用数据挖掘技术 ,以此探讨如何对五普的数据进行深入分析以及如何提高其应用价值。     

Assessing data quality: A managerial call to action

While awareness of data quality has increased in recent years, there have been very few studies on the actual level of data quality within organizations. This article outlines the analysis of 75 data quality assessments collected over the course of 2 years from a wide range of organizations, data sets, and business processes. The results reveal that data is in far worse shape than most managers realize. On average, 47% of recently created data records have at least one critical error. High-quality data is the exception, with only 3% of the DQ scores rated acceptable (≥97%). Indeed, the results suggest an unhealthy organizational tolerance of bad data and underscore the magnitude of improvement organizations need to make in order to be truly effective in the knowledge economy. By providing a unique insight and benchmark for data quality practitioners, this article serves as a call-to-action for all organizations—regardless of size and type—to determine their level of data quality. Finally, we set out a typology that presents a categorical scheme to promote preemptive actions against the most frequent types of data error.     

Approach to service design based on customer behavior data: a case study on eco-driving service design using bus drivers’ behavior data

Various types and massive amounts of customer behavior data are collected in various industries, such as transportation, healthcare, hospitality, and logistics. The use of customer behavior data can improve the design activities of service firms. Despite the applicability of customer behavior data to service design, only a few studies have examined an approach to utilize customer behavior data in service design. This study proposes an approach for designing services with customer behavior data. The approach is based on a case study on eco-driving service design with the behavior data of bus drivers. This study extends the research on service design by demonstrating how customer behavior data are utilized for service design and assisting service designers in designing services with customer behavior data.     

Organizational Learning and CRM Success: A Model for Linking Organizational Practices,Customer Data Quality,and Performance

A high quality customer database is a cornerstone of successful interactive marketing strategies and tactics. Based on the notion that customer data quality is not only a technical but also an organizational problem, this study develops and tests an organizational learning framework of the relationship between organizational processes, customer data quality and firm performance. The findings show that high quality customer data impact both customer and business performance and that the most important driver of customer data quality comes from the executive suite. A large portion of the impact of organizational culture on performance is mediated by customer data quality and data sharing. The results support the presence of a hierarchy of effects for enhancing data quality that runs from organizational learning (committed to a shared vision for CRM data), to cross-functional learning (marketing/IT cooperation, marketing/IT integration) to functional learning (data sharing).     

大容量高速数据采集系统的设计

提出了一种新颖的高速数据采集系统实现方案,以DSP作为数据采集、传输和存储的核心控制器,利用通用串行总线技术实现与计算机的通信。该采集器采用模块化的思想设计,具有较强的可扩展性,适用于多路高速信号的同步记录。