How does business analytics contribute to business value?

This paper presents a model, synthesized from the literature, of factors that explain how business analytics contributes to business value. It also reports results from a preliminary assessment of that model. The model consists of two parts: a process and a variance model. The process model depicts the analyze‐insight‐decision‐action process through which use of an organization's business analytic capabilities is intended to create business value. The variance model proposes that the five factors in Davenport et al.'s DELTA model of business analytics success factors, six from Watson & Wixom and three from Seddon et al.'s model of organizational benefits from enterprise systems, assist a firm to gain business value from business analytics. A preliminary assessment of the model was conducted using data from 100 customer success stories from vendors such as IBM, SAP and Teradata. Our conclusion is that the business analytics success model is likely to be a useful basis for future research.     

Big Data Analytics Services for Enhancing Business Intelligence

This article examines how to use big data analytics services to enhance business intelligence (BI). More specifically, this article proposes an ontology of big data analytics and presents a big data analytics service-oriented architecture (BASOA), and then applies BASOA to BI, where our surveyed data analysis shows that the proposed BASOA is viable for enhancing BI and enterprise information systems. This article also explores temporality, expectability, and relativity as the characteristics of intelligence in BI. These characteristics are what customers and decision makers expect from BI in terms of systems, products, and services of organizations. The proposed approach in this article might facilitate the research and development of business analytics, big data analytics, and BI as well as big data science and big data computing.     

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.     

Exploration–Exploitation: How business analytics powers organisational ambidexterity for environmental sustainability

Simultaneous exploration and exploitation (i.e., exploration–exploitation) can help a firm address short-term environmental requirements and ensure long-term environmental viability. Exploration–exploitation, however, challenges organisational practices because they compete for resources and time. While business analytics (BA) offers the potential to overcome these challenges, research to date offers very limited insights into how BA capabilities interact with ambidextrous capabilities to realise environmental value. We address this issue by conducting a comparative case study at a bank and at a real-estate trust through the theoretical lens of dynamic capabilities. We develop a process model to explain how BA powers ambidextrous practices to achieve sustainability outcomes over time. We uncover two mechanisms: a BA-powered context shaping mechanism by which BA powers contextual ambidexterity at the employee level using data availability, timeliness, and analytics culture; and a BA-powered resource linking mechanism by which BA powers structural ambidexterity at intra- and inter-organisational levels using holistic insights and analytics leadership. Our model highlights the contextual factors that condition the extent to which a firm moves along the continuum of exploration–exploitation. We also define a new dimension of sustainability outcomes which we label eco-awareness to explain how BA shapes employees' environmental alertness and enables the paradigm shift in an organisation's sustainability mindset.     

Exploring the relationship between big data analytics capability and competitive performance: The mediating roles of dynamic and operational capabilities

A central question for information systems (IS) researchers and practitioners is if, and how, big data can help attain a competitive advantage. To address this question, this study draws on the resource-based view, dynamic capabilities view, and on recent literature on big data analytics, and examines the indirect relationship between a firm’s big data analytics capability (BDAC) and competitive performance. The study extends existing research by proposing that BDACs enable firms to generate insight that can help strengthen their dynamic capabilities, which, in turn, positively impact marketing and technological capabilities. To test our proposed research model, we used survey data from 202 chief information officers and IT managers working in Norwegian firms. By means of partial least squares structural equation modeling, results show that a strong BDAC can help firms build a competitive advantage. This effect is not direct but fully mediated by dynamic capabilities, which exerts a positive and significant effect on two types of operational capabilities: marketing and technological capabilities. The findings suggest that IS researchers should look beyond direct effects of big data investments and shift their attention on how a BDAC can be leveraged to enable and support organizational capabilities.     

Viewing Visual Analytics as Model Building

To complement the currently existing definitions and conceptual frameworks of visual analytics, which focus mainly on activities performed by analysts and types of techniques they use, we attempt to define the expected results of these activities. We argue that the main goal of doing visual analytics is to build a mental and/or formal model of a certain piece of reality reflected in data. The purpose of the model may be to understand, to forecast or to control this piece of reality. Based on this model‐building perspective, we propose a detailed conceptual framework in which the visual analytics process is considered as a goal‐oriented workflow producing a model as a result. We demonstrate how this framework can be used for performing an analytical survey of the visual analytics research field and identifying the directions and areas where further research is needed.     

Big Data Initiatives: Minimizing Risk and Maximizing Value with Governance

Abstract

Big Data Analytics can be a fantastic business opportunity for many organizations. Already organizations are using advanced analytics to streamline production processes, optimize back office activities, market more effectively, and better satisfy customer demand. That said, it goes without saying (as recent headlines can attest) that sometimes enhanced analytics capabilities can introduce risks such as erosion of privacy, overly-intrusive knowledge about customers, etc.

Given this dichotomy, making the decision about when, whether, how much, and how to invest in big data analytics initiatives can be a challenge. Invest too soon and you may obviate existing investments or disrupt business activities; invest too late and you may find that competitors gain advantages that make the market landscape asymmetric.

This article outlines how and why applying “tried and true” governance principles can help make this decision easier. For those that have formalized governance structures in place, how they might inform the decision an organization makes in this regard – and for those that don’t have a formalized governance program – how they might co-opt some of those principles to help make this decision more approachable.     

Applying social learning analytics to message boards in online distance learning: A case study

Social learning analytics introduces tools and methods that help improving the learning process by providing useful information about the actors and their activity in the learning system. This study examines the relation between SNA parameters and student outcomes, between network parameters and global course performance, and it shows how visualizations of social learning analytics can help observing the visible and invisible interactions occurring in online distance education.The findings from our empirical study show that future research should further investigate whether there are conditions under which social network parameters are reliable predictors of academic performance, but also advises against relying exclusively in social network parameters for predictive purposes. The findings also show that data visualization is a useful tool for social learning analytics, and how it may provide additional information about actors and their behaviors for decision making in online distance learning.     

Data analytics competency for improving firm decision making performance

This study develops and validates the concept of Data Analytics Competency as a five multidimensional formative index (i.e., data quality, bigness of data, analytical skills, domain knowledge, and tools sophistication) and empirically examines its impact on firm decision making performance (i.e., decision quality and decision efficiency). The findings based on an empirical analysis of survey data from 151 Information Technology managers and data analysts demonstrate a large, significant, positive relationship between data analytics competency and firm decision making performance. The results reveal that all dimensions of data analytics competency significantly improve decision quality. Furthermore, interestingly, all dimensions, except bigness of data, significantly increase decision efficiency. This is the first known empirical study to conceptualize, operationalize and validate the concept of data analytics competency and to study its impact on decision making performance. The validity of the data analytics competency construct as conceived and operationalized, suggests the potential for future research evaluating its relationships with possible antecedents and consequences. For practitioners, the results provide important guidelines for increasing firm decision making performance through the use of data analytics.     

基于MOOC的大数据分析技术

MOOC近几年发展迅猛,在使用过程中,大规模的学习者和海量的教学资源积累了庞大的学习行为数据。因此,基于MOOC的大数据分析成为了一个新兴的研究热点,其分析框架中涉及的四大核心是：大数据从哪里获取（Where）、MOOC大数据的类型（What）、如何进行大数据分析（How）和大数据分析应用（Do）。本文通过对MOOC现状的分析、特征及分类的梳理,提出一种Where-What-How-Do大数据分析框架,并对上述的四大核心进行阐述和回答。最后,结合Canvas Network数据集进行聚类分析和多元回归分析,得出关于MOOC数据的一些启示和应用。      

Improving Cyber Security Awareness and Training Programs with Data Analytics

ABSTRACT

This article it is a modest attempt to explore the issues facing cyber security awareness and training programs and potential benefits of using learning analytics, an emerging field in data analytics, for combining existing data sources to provide additional value to these programs. This article was written under the assumption that awareness and training are valid preventive controls and therefore the pros and cons of implementing such programs are not being discussed.     

Business analytics maturity of firms: an examination of the relationships between managerial perception of IT,business analytics maturity and success

This study develops a measure for business analytics (BA) maturity and empirically examines the relationships between managerial perception of IT, BA maturity and BA success. The findings suggest that (1) BA maturity can be measured via BA integration & management support, process-level benefits of BA and technology & data analytics capabilities, (2) BA maturity positively affects organizations’ overall BA success, and (3) managerial perception of IT positively influence organizations’ achievement of BA maturity.     

Design Factors for Summary Visualization in Visual Analytics

Data summarization allows analysts to explore datasets that may be too complex or too large to visualize in detail. Designers face a number of design and implementation choices when using summarization in visual analytics systems. While these choices influence the utility of the resulting system, there are no clear guidelines for the use of these summarization techniques. In this paper, we codify summarization use in existing systems to identify key factors in the design of summary visualizations. We use quantitative content analysis to systematically survey examples of visual analytics systems and enumerate the use of these design factors in data summarization. Through this analysis, we expose the relationship between design considerations, strategies for data summarization in visualization systems, and how different summarization methods influence the analyses supported by systems. We use these results to synthesize common patterns in real‐world use of summary visualizations and highlight open challenges and opportunities that these patterns offer for designing effective systems. This work provides a more principled understanding of design practices for summary visualization and offers insight into underutilized approaches.     

基于用户视角下学习分析技术的应用与挑战

In the big data environment, with the rapid development of education information technology, learning analytics has been a hot research topic in recent years. In order to provide references for the follow-up study, based on the relevant literatures, this paper expounds the concept and characteristics of the learning analytics, from the user point of view of learners, teachers and teaching managers, and discusses the application of learning analytics in network learning and the problems and challenges faced by them.     

A Survey of Visual Analytics for Public Health

We describe visual analytics solutions aiming to support public health professionals, and thus, preventive measures. Prevention aims at advocating behaviour and policy changes likely to improve human health. Public health strives to limit the outbreak of acute diseases as well as the reduction of chronic diseases and injuries. For this purpose, data are collected to identify trends in human health, to derive hypotheses, e.g. related to risk factors, and to get insights in the data and the underlying phenomena. Most public health data have a temporal character. Moreover, the spatial character, e.g. spatial clustering of diseases, needs to be considered for decision-making. Visual analytics techniques involve (subspace) clustering, interaction techniques to identify relevant subpopulations, e.g. being particularly vulnerable to diseases, imputation of missing values, visual queries as well as visualization and interaction techniques for spatio-temporal data. We describe requirements, tasks and visual analytics techniques that are widely used in public health before going into detail with respect to applications. These include outbreak surveillance and epidemiology research, e.g. cancer epidemiology. We classify the solutions based on the visual analytics techniques employed. We also discuss gaps in the current state of the art and resulting research opportunities in a research agenda to advance visual analytics support in public health.     

工业数据可视分析综述

自 2013 年工业 4.0 的概念被提出以来,全世界的工业进程都飞速奔向智能制造时代。而数据感知技术的发展进一步帮助收集海量工业数据,工业信息化革新正是机遇。但是,工业数据具有规模大、维度高、结构多变和内容复杂的特性,对其进行分析是一项严峻的挑战。多变的应用场景又导致分析的灵活度要求提高,往往需要专家参与分析循环,因此可视化在工业数据分析中有了更广泛的应用。该综述首先按生产阶段及属性分别总结了工业场景下常用的数据类型;其次,根据数据属性,按时间、空间、时空结合分类,介绍了对应的可视化方法;再次,总结了可视分析在工业场景下的应用,并讨论如何合理地集成自动化分析方法以提高分析能力;最后,展望了工业数据可视分析的发展前景,并提出未来的研究方向。     

Exploring Students Engagement Towards the Learning Management System (LMS) Using Learning Analytics

Learning analytics is a rapidly evolving research discipline that uses the insights generated from data analysis to support learners as well as optimize both the learning process and environment. This paper studied students’ engagement level of the Learning Management System (LMS) via a learning analytics tool, student’s approach in managing their studies and possible learning analytic methods to analyze student data. Moreover, extensive systematic literature review (SLR) was employed for the selection, sorting and exclusion of articles from diverse renowned sources. The findings show that most of the engagement in LMS are driven by educators. Additionally, we have discussed the factors in LMS, causes of low engagement and ways of increasing engagement factors via the Learning Analytics approach. Nevertheless, apart from recognizing the Learning Analytics approach as being a successful method and technique for analyzing the LMS data, this research further highlighted the possibility of merging the learning analytics technique with the LMS engagement in every institution as being a direction for future research.     

面向出租车出行规律的预测式可视分析方法

针对传统交通数据可视分析方法缺乏预测分析能力的问题,提出了基于出租车出行数据的预测式可视分析方法,支持用户更有效地探索未来的交通状况.在可视分析模型中,提出了结合天气、星期几等多种非交通因素的预测模型,提高了预测的准确度;提出了基于预测数据和广义地点类型约束的路径规划方法,获得了更优的路径规划结果;以多种可视化手段分析和预测了出租车司机的运营状况,帮助司机进行运营决策.以温州市出租车数据进行的实验结果表明,与传统方法相比,文中方法能更准确地预测交通状况和运营状况,并获得更合理的路径规划结果.