Data-driven manufacturing: An assessment model for data science maturity

Today, data science presents immense opportunities by turning raw data into manufacturing intelligence in data-driven manufacturing that aims to improve operational efficiency and product quality together with reducing costs and risks. However, manufacturing firms face difficulties in managing their data science endeavors for reaping these potential benefits. Maturity models are developed to guide organizations by providing an extensive roadmap for improvement in certain areas. Therefore, this paper seeks to address this problem by proposing a theoretically grounded Data Science Maturity Model (DSMM) for manufacturing organizations to assess their existing strengths and weaknesses, perform a gap analysis, and draw a roadmap for continuous improvements in their progress towards data-driven manufacturing. DSMM comprises six maturity levels from “Not Performed” to” Innovating” and twenty-eight data science processes categorized under six headings: Organization, Strategy Management, Data Analytics, Data Governance, Technology Management, and Supporting. The applicability and usefulness of DSMM are validated through multiple case studies conducted in manufacturing organizations of various sizes, industries, and countries. The case study results indicate that DSMM is applicable in different settings and is able to reflect the organizations’ current data science maturity levels and provide significant insights to improve their data science capabilities.     

Prediction methods and applications in the science of science: A survey

Science of science has become a popular topic that attracts great attentions from the research community. The development of data analytics technologies and the readily available scholarly data enable the exploration of data-driven prediction, which plays a pivotal role in finding the trend of scientific impact. In this paper, we analyse methods and applications in data-driven prediction in the science of science, and discuss their significance. First, we introduce the background and review the current state of the science of science. Second, we review data-driven prediction based on paper citation count, and investigate research issues in this area. Then, we discuss methods to predict scholar impact, and we analyse different approaches to promote the scholarly collaboration in the collaboration network. This paper also discusses open issues and existing challenges, and suggests potential research directions.     

Analytics Capabilities and the Decision to Invest in Analytics

The increasing use of data-driven decision making and big data is leading organizations to invest in analytics software and services. However, little is known about the type of analytics capabilities within IT that are required and whether there is a common progression or development model of analytics capabilities. Also unknown is how the level of analytics capabilities and other factors influence a firm’s decision to invest in analytics. The purpose of this research is to explore the relationships between levels of distinct analytics capabilities and to understand how they and other factors influence the analytics investment decision. The findings suggest that there is a distinct progression in the development of analytics capabilities, and that firm size is associated with increased capability. The results suggest that firms more likely to invest in analytics have higher current levels of specific analytics capabilities, are larger, and are located in less-competitive industries.     

A Model to Create Organizational Value with Big Data Analytics

Value creation is a major factor not only in the sustainability of organizations but also in the maximization of profit, customer retention, business goals fulfillment, and revenue. When the value is intended to be created from Big Data scenarios, value creation entails being understood over a broader range of complexity. A question that arises here is how organizations can use this massive quantity of data and create business value? The present study seeks to provide a model for creating organizational value using Big Data Analytics (BDA). To this end, after reviewing the related literature and interviewing experts, the BDA-based organizational value creation model is developed. Accordingly, five hypotheses are formulated, and a questionnaire is prepared. Then, the respective questionnaire is given to the research statistical population (i.e., IT managers and experts, particularly those specializing in data analysis) to test the research hypotheses. In next phase, connections between model variables are scrutinized using the structural equation modeling (measurement and structural models). The results of the study indicate that investigating the infrastructures of the Big Data Analytics, as well as the capabilities of the organization and those of Big Data Analytics is the initial requirement to create organizational value using BDA. Thereby, the Big Data Analytics strategy is formulated, and ultimately, the organizational value is created as well.     

Resourcing with data: Unpacking the process of creating data-driven value propositions

This paper examines how organizations create data-driven value propositions. Data-driven value propositions define what customer value is created based on data. We study the dynamics underlying this process in a European postal-service organization. We develop a model that shows that the process of creating data-driven value propositions is emergent, consisting of iterative resourcing cycles. We find that creating data-driven value propositions involves the performance of two types of resourcing actions: data reconstructing and data repurposing. The process is shaped by two types of data qualities: apparent qualities, i.e., qualities perceived ex-ante as potentially significant for creating value propositions; and latent qualities, which raise unforeseen consequences en route. We discuss the implications of these findings for the literature on creating data-driven value propositions, for our understanding of data as a strategic resource, and for the literature on resourcing.     

Regimes of Knowledge,Stories of Power: A Treatise on Knowledge Management

Discussions on Knowledge Management have to date been largely uncritical, ignoring the broader political implications of attempting to manage ‘knowledge’. More recently there have been numerous calls to engage with the issue of knowledge in organizations in a more critical manner. The objective of this paper is to contribute to a critical discussion through unpacking the issue of knowledge in organizations through reference to the empirical data gained from a study of CoastElectric, a regional electricity company. In the paper we argue that organizations can be thought of regimes of knowledge, whereby the capabilities of an organization are interwoven with the relations of power.     

Information technology,the organizational capability of proactive corporate environmental strategy and firm performance: a resource-based analysis

The study of the relationships between information technology (IT), environmental organizational issues and firm performance is a cutting-edge research topic for the information systems (IS) community. However, at present we know very little about these relationships. Drawing on the perspective of IT-enabled organizational capabilities and the literature on organizations and the natural environment, our study introduces conceptually the construct organizational capability of proactive corporate environmental strategy to the IS field. We propose that IT capability may enable the implementation of a proactive environmental strategy and that this strategy could play a significant role in determining the business value of IT. Using structural equations modeling with data collected from 63 firms, we find that IT capability is an enabler of proactive environmental strategy and that this strategy plays a significant role in mediating the effects of IT on firm performance. Our study provides initial evidence on the role of IT in the implementation of proactive environmental practices. Our results suggest to IT executives that their decisions matter in shaping environmental sustainability, which in turn will generate business value from IT.     

Architecturing large integrated complex information systems: an application to healthcare

The global enterprise-wide approaches help organizations to model and understand the enterprise key components and their relationships and manage the organizations’ transformations and change. However, many of these approaches lack of insights into how to manage complexities related to the multitude of applications developed in silos such as the various systems in health organizations that were designed independently from each other. This paper contributes to the solutions addressing this issue by proposing a methodology and tools to create foundations based on key components to help develop the information architecture at the heart of the enterprise architecture that can guarantee the evolution of the organization. These core components are a set of reusable Field Actions representing the non-contextual persistent information, a common canonical Corporate Conceptual Data Model capturing all the vital data in the organization, and Views or sub-schema of this global data model that represent information for different stakeholders in the organization. To show the effectiveness of the proposed approach and to gain more insights into its practical value, the architecturing approach is applied in the healthcare domain to create the information architecture and the enterprise architecture for the Quebec healthcare network.     

EntityManager: Managing Dirty Data Based on Entity Resolution

Data quality is important in many data-driven applications, such as decision making, data analysis, and data mining. Recent studies focus on data cleaning techniques by deleting or repairing the dirty data, which may cause information loss and bring new inconsistencies. To avoid these problems, we propose EntityManager, a general system to manage dirty data without data cleaning. This system takes real-world entity as the basic storage unit and retrieves query results according to the quality requirement of users. The system is able to handle all kinds of inconsistencies recognized by entity resolution. We elaborate the EntityManager system, covering its architecture, data model, and query processing techniques. To process queries efficiently, our system adopts novel indices, similarity operator and query optimization techniques. Finally, we verify the efficiency and effectiveness of this system and present future research challenges.     

Dynamic information technology capability: Concept definition and framework development

In a digital world, information technology (IT) units routinely update their capabilities to cope with changing business requirements and frequent technology releases. Extending the dynamic capabilities literature, this article presents the concept of dynamic IT capability, a multidimensional first-order dynamic capability that enables IT units to assist firms in appropriating business value from IT resources by influencing a set of IT-related ordinary capabilities. Scholars currently lack a dynamic capabilities framework that explains, from an IT unit’s perspective, how IT resources can be acquired, deployed, integrated, and reconfigured to fulfill business objectives. To bridge this research gap, we develop a high-level framework that highlights three constituent components of dynamic IT capability: dynamic digital platform capability, dynamic IT management capability, and dynamic IT knowledge management capability. Through an extensive literature review, we identify and summarize the set of ordinary capabilities that each dynamic IT capability component creates and reconfigures. We then offer guidance on future instrument development. To encourage further exploration of this critical construct, we close by highlighting future avenues for dynamic IT capability research.     

The impact of big data analytics on firms’ high value business performance

Big Data Analytics (BDA) is an emerging phenomenon with the reported potential to transform how firms manage and enhance high value businesses performance. The purpose of our study is to investigate the impact of BDA on operations management in the manufacturing sector, which is an acknowledged infrequently researched context. Using an interpretive qualitative approach, this empirical study leverages a comparative case study of three manufacturing companies with varying levels of BDA usage (experimental, moderate and heavy). The information technology (IT) business value literature and a resource based view informed the development of our research propositions and the conceptual framework that illuminated the relationships between BDA capability and organizational readiness and design. Our findings indicate that BDA capability (in terms of data sourcing, access, integration, and delivery, analytical capabilities, and people’s expertise) along with organizational readiness and design factors (such as BDA strategy, top management support, financial resources, and employee engagement) facilitated better utilization of BDA in manufacturing decision making, and thus enhanced high value business performance. Our results also highlight important managerial implications related to the impact of BDA on empowerment of employees, and how BDA can be integrated into organizations to augment rather than replace management capabilities. Our research will be of benefit to academics and practitioners in further aiding our understanding of BDA utilization in transforming operations and production management. It adds to the body of limited empirically based knowledge by highlighting the real business value resulting from applying BDA in manufacturing firms and thus encouraging beneficial economic societal changes.     

面向开放大数据环境的动态数据保护系统

大数据成为国家基础性战略资源,数据的开放共享是我国大数据战略的核心.云原生技术和湖仓一体架构正在重构大数据基础设施,并推动数据共享和价值传播.大数据产业和技术的发展都需要更强的数据安全和数据共享能力.然而,开放环境下数据的安全问题已成为制约大数据技术发展与利用的瓶颈.无论开源大数据生态还是商业大数据系统,所引发的数据安全及隐私保护问题都日益凸显.开放大数据环境下的动态数据保护系统面临着数据可用性、处理高效性和系统可扩展性等方面的挑战.提出了面向开放大数据环境的动态数据保护系统BDMasker,通过一种基于查询依赖模型(querydependencymodel)的精准查询分析及查询改写技术,能够精准感知但不改变原始业务请求,实现动态脱敏全过程对业务零影响;通过面向多引擎的统一安全策略框架,实现了动态数据保护能力的纵向扩展和在多种计算引擎中的横向扩展;利用大数据执行引擎的分布式计算能力,提升系统的数据保护处理性能.实验结果表明, BDMasker提出的精准SQL分析及改写技术是有效的,系统具有良好的扩展能力和性能表现,在TPC-DS和YCSB基准测试中,整体性能波动在3%之内.     

公安领域数据仓库体系结构研究

公安领域各个应用系统的存储数据是分布式和异构的,而传统的知识发现系统和数据仓库建立在同质数据源基础上,并不能满足公安领域知识发现的需要.因此,构建公安领域的数据仓库体系结构是在公安领域成功应用知识发现技术的关键.在综合考虑数据仓库传统构建策略的基础上,将公安部门分布式、异构数据库同社会其它相关部门数据仓库融合在起来,提出了一种联合数据仓库体系结构,该体系结构支持跨数据集市访问,更加符合公安领域知识发现对数据信息的实际需求.     

BDMasker: Dynamic Data Protection System for Open Big Data Environment

Big data has become a national basic strategic resource, and the opening and sharing of data is the core of China''s big data strategy. Cloud native technology and lake-house architecture are reconstructing the big data infrastructure and promoting data sharing and value dissemination. The development of the big data industry and technology requires stronger data security and data sharing capabilities. However, data security in an open environment has become a bottleneck, which restricts the development and utilization of big data technology. The issues of data security and privacy protection have become increasingly prominent both in the open source big data ecosystem and the commercial big data system. Dynamic data protection system under the open big data environment is now facing challenges in regards such as data availability, processing efficiency, and system scalability. This paper proposes the dynamic data protection system BDMasker for the open big data environment. Through a precise query analysis and query rewriting technology based on the query dependency model, it can accurately perceive but does not change the original business request, which indicates that the whole process of dynamic masking has zero impact on the business. Furthermore, its multi-engine-oriented unified security strategy framework realizes the vertical expansion of dynamic data protection capabilities and the horizontal expansion among multiple computing engines. The distributed computing capability of the big data execution engine can be used to improve the data protection processing performance of the system. The experimental results show that the precise SQL analysis and rewriting technology proposed by BDMasker is effective. The system has good scalability and performance, and the overall performance fluctuates within 3% in the TPC-DS and YCSB benchmark tests.     

Entrepreneurship, knowledge integration capability, and firm performance: An empirical study

From the knowledge-based view, an organization is considered an entity that integrates and distributes knowledge to produce products and services. Knowledge is acknowledged as a sustainable basis of competitive advantage that many organizations possess. Entrepreneurial activity also has been viewed as an essential feature for organizations to survive and prosper in today??s turbulent environment. In this study, we explore the effect of entrepreneurship on organizational performance through knowledge integration capability. Our research model depicts the firm as a knowledge integration institution that produces its offerings through specialized knowledge integration capability that consists of learning culture, knowledge management process, and information technology capability. The results show a strong support for the relationship between entrepreneurship and knowledge integration capability. We also found that the effect of entrepreneurial activities on firm performance was mediated by knowledge integration capability.     

How social-media-enabled co-creation between customers and the firm drives business value? The perspective of organizational learning and social Capital

Contemporary business organizations are increasingly turning their attention to value co-creation using social media between individual customers and business organizations in the process of new product development (NPD). However, little is known about the mechanisms underlying social-media-based customer-firm co-creation and their implications for business value in NPD. To address this knowledge gap, this study develops a model from the perspective of organizational learning and social capital to examine how the social-media-based customer-firm co-creation mechanism conceptualized as the structural, cognitive, and relational dimension of social capital influences the first-order knowledge outcome (knowledge transfer effectiveness) and second-order dynamic capability outcome (absorptive capacity), and how these co-creation outcomes ultimately influence organizational performance. The model is tested using survey data from 149 Chinese mobile application developers. The results indicate that social-media-based structural, cognitive, and relational linkage, in particular the structural linkage, is an important co-creation mechanism to improve organizational performance. Knowledge transfer effectiveness and absorptive capacity have significant mediating effects in this co-creation mechanism-outcomes-performance framework. Further, the moderating effects of social media use level on the relationships between co-creation mechanism and outcomes are largely supported. The study contributes to theory and practice by shedding light on the social-media-based customer-firm co-creation in NPD at a process level.     

A hybrid data fusion approach with twin CNN architecture for enhancing image source identification in IoT environment

With the proliferation of digital devices in internet of things (IoT) environment featuring advanced visual capabilities, the task of Image Source Identification (ISI) has become increasingly vital for legal purposes, ensuring the verification of image authenticity and integrity, as well as identifying the device responsible for capturing the original scene. Over the past few decades, researchers have employed both traditional and machine-learning methods to classify image sources. In the current landscape, data-driven approaches leveraging deep learning models have emerged as powerful tools for achieving higher accuracy and precision in source prediction. The primary focus of this research is to address the complexities arising from diverse image sources and variable quality in IoT-generated multimedia data. To achieve this, a Hybrid Data Fusion Approach is introduced, leveraging multiple sources of information to bolster the accuracy and robustness of ISI. This fusion methodology integrates diverse data streams from IoT devices, including metadata, sensor information, and contextual data, amalgamating them into a comprehensive data set for analysis. This study introduces an innovative approach to ISI through the implementation of a Twin Convolutional Neural Network Architecture (TCA) aimed at enhancing the efficacy of source classification. In TCA, the first CNN architecture, referred to as DnCNN, is employed to eliminate noise from the original data set, generating 256 × 256 patches for both training and testing. Subsequently, the second CNN architecture is employed to classify images based on features extracted from various convolutional layers using a 3 × 3 filter, thereby enhancing prediction efficiency. The proposed model demonstrates exceptional accuracy in effectively classifying image sources, showcasing its potential as a robust solution in the realm of ISI.     

The role of information governance in big data analytics driven innovation

The age of big data analytics is now here, with companies increasingly investing in big data initiatives to foster innovation and outperform competition. Nevertheless, while researchers and practitioners started to examine the shifts that these technologies entail and their overall business value, it is still unclear whether and under what conditions they drive innovation. To address this gap, this study draws on the resource-based view (RBV) of the firm and information governance theory to explore the interplay between a firm’s big data analytics capabilities (BDACs) and their information governance practices in shaping innovation capabilities. We argue that a firm’s BDAC helps enhance two distinct types of innovative capabilities, incremental and radical capabilities, and that information governance positively moderates this relationship. To examine our research model, we analyzed survey data collected from 175 IT and business managers. Results from partial least squares structural equation modelling analysis reveal that BDACs have a positive and significant effect on both incremental and radical innovative capabilities. Our analysis also highlights the important role of information governance, as it positively moderates the relationship between BDAC’s and a firm’s radical innovative capability, while there is a nonsignificant moderating effect for incremental innovation capabilities. Finally, we examine the effect of environmental uncertainty conditions in our model and find that information governance and BDACs have amplified effects under conditions of high environmental dynamism.     

Smart urban planning using Big Data analytics to contend with the interoperability in Internet of Things

The recent growth and expansion in the field of Internet of Things (IoT) is providing a great business prospective in the direction of the new era of smart urban. The insight of the smart urban is extensively preferred, as it improves the excellence of life of citizens, connecting several regulations, that is, smart transportation, smart parking, smart environment, smart healthcare, and so forth. Continuous intensification of the multifaceted urban set-up is extensively challenged by real-time processing of data and smart decision capabilities. Consequently, in this paper, we propose a smart city architecture which is based on Big Data analytics. The proposed scheme is comprised of three modules: (1) data acquisition and aggregation module collects varied and diverse data interrelated to city services, (2) data computation and processing module performs normalization, filtration, processing and data analysis, and (3) application and decision module formulates decisions and initiates events. The proposed architecture is a generic solution for the smart urban planning and variety of datasets is analyzed to validate this architecture. In addition, we tested reliable datasets on Hadoop server to verify the threshold limit value (TLV) and the investigation demonstrates that the proposed scheme offer valuable imminent into the community development systems to get better the existing smart urban architecture. Moreover, the efficiency of proposed architecture in terms of throughput is also shown.     

Towards user-driven earth observation-based slum mapping

Earth observation (EO) capabilities to produce up-to-date geographical information on slums over large areas supporting urban planning and evidence-based policymaking are largely acknowledged. Most EO studies typically use a data-driven approach without an understanding of end-user requirements. This study addresses this gap by aligning EO methods with societal needs and concerns using a user-driven approach in Accra, Ghana. By carrying out in-situ observations and slum experts interviews, we produced a user-driven slum map that meets potential users' expectations. To do so, we used a random forest classifier, SPOT 6 imagery, and ancillary geospatial data such as OpenStreetMap information. The overall classification accuracy for the user-driven approach reached 84%. The results show that the addition of local context-knowledge, end-user requirements, and geo-ethics, help to better contextualise and conceptualise slums. Our research demonstrates an approach of slum mapping that is reflective and open to societal needs and concerns.