An integrated big data analytics-enabled transformation model: Application to health care

A big data analytics-enabled transformation model based on practice-based view is developed, which reveals the causal relationships among big data analytics capabilities, IT-enabled transformation practices, benefit dimensions, and business values. This model was then tested in healthcare setting. By analyzing big data implementation cases, we sought to understand how big data analytics capabilities transform organizational practices, thereby generating potential benefits. In addition to conceptually defining four big data analytics capabilities, the model offers a strategic view of big data analytics. Three significant path-to-value chains were identified for healthcare organizations by applying the model, which provides practical insights for managers.     

Does big data mean big knowledge? Integration of big data analysis and conceptual model for social commerce research

The Big Data era has descended on many communities, from governments and e-commerce to health organizations. Information systems designers face great opportunities and challenges in developing a holistic big data research approach for the new analytics savvy generation. In addition business intelligence is largely utilized in the business community and thus can leverage the opportunities from the abundant data and domain-specific analytics in many critical areas. The aim of this paper is to assess the relevance of these trends in the current business context through evidence-based documentation of current and emerging applications as well as their wider business implications. In this paper, we use BigML to examine how the two social information channels (i.e., friends-based opinion leaders-based social information) influence consumer purchase decisions on social commerce sites. We undertake an empirical study in which we integrate a framework and a theoretical model for big data analysis. We conduct an empirical study to demonstrate that big data analytics can be successfully combined with a theoretical model to produce more robust and effective consumer purchase decisions. The results offer important and interesting insights into IS research and practice.     

Orchestrating the digital transformation of a business ecosystem

The nature, scope, and impact of digital transformation reaches well beyond the boundaries of a single firm. This suggests information systems research should consider how digital transformation unfolds within business ecosystems that consist of multiple interdependent firms, and how this process can ideally be managed. We pursue this research opportunity by introducing orchestration as a concept through which to view digital transformation in business ecosystems, and by presenting empirical insights from a longitudinal in-depth case study that highlights how a focal firm became the orchestrator of digital transformation in its business ecosystem. We explain that becoming an orchestrator of digital transformation occurs through three distinct phases: initiating, opening-up, and integrating. We also identify the interplay of activities by which a focal firm strategizes, mobilizes, and aligns other actors and their resources, as it orchestrates the digital transformation of its business ecosystem. We conclude by outlining how our work serves as an important foundation for future information systems research and offer managerial guidelines outlining how to orchestrate digital transformation processes within business ecosystems.     

Business Analytics-Based Enterprise Information Systems

Big data analytics and business analytics are a disruptive technology and innovative solution for enterprise development. However, what is the relationship between business analytics, big data analytics, and enterprise information systems (EIS)? How can business analytics enhance the development of EIS? How can analytics be incorporated into EIS? These are still big issues. This article addresses these three issues by proposing ontology of business analytics, presenting an analytics service-oriented architecture (ASOA) and applying ASOA to EIS, where our surveyed data analysis showed that the proposed ASOA is viable for developing EIS. This article then examines incorporation of business analytics into EIS through proposing a model for business analytics service-based EIS, or ASEIS for short. The proposed approach in this article might facilitate the research and development of EIS, business analytics, big data analytics, and business intelligence.     

Business value of big data analytics: A systems-theoretic approach and empirical test

Although big data analytics have been widely considered a key driver of marketing and innovation processes, whether and how big data analytics create business value has not been fully understood and empirically validated at a large scale. Taking social media analytics as an example, this paper is among the first attempts to theoretically explain and empirically test the market performance impact of big data analytics. Drawing on the systems theory, we explain how and why social media analytics create super-additive value through the synergies in functional complementarity between social media diversity for gathering big data from diverse social media channels and big data analytics for analyzing the gathered big data. Furthermore, we deepen our theorizing by considering the difference between small and medium enterprises (SMEs) and large firms in the required integration effort that enables the synergies of social media diversity and big data analytics. In line with this theorizing, we empirically test the synergistic effect of social media diversity and big data analytics by using a recent large-scale survey data set from 18,816 firms in Italy. We find that social media diversity and big data analytics have a positive interaction effect on market performance, which is more salient for SMEs than for large firms.     

Digital transformation of business ecosystems: Evidence from the Korean pop industry

The notion of business ecosystems is gaining prominence among academics and practitioners. Scholars advise that business ecosystems are inherently fluid, unbounded and amorphous and thus that their boundaries can shift. Practitioners further suggest that business ecosystems are characterised by inter‐network – as opposed to inter‐firm – competition, and, moreover, that they are mainly driven by technological advancements. And yet few studies examine the role of information technology (IT) in driving boundary practices that enable the formation and transformation of business ecosystems. Through an in‐depth case study of technology‐enabled transformations within the Korean pop music (K‐pop) industry, our study examines how the digital transformation of business ecosystems unfolded. Our study contributes to the emergent body of knowledge on business ecosystems in a number of ways. Our investigations uncover the conditions under which the constituent firms operate and analyse the role of IT and its implications on new organisational forms. From our analysis, we present a framework that reveals insights on critical boundary practices and formative strategies for digital business ecosystems. We illustrate how these boundary practices drive industry change, from a largely linear content delivery system resembling a value chain to a new value network of co‐specialisation and self‐organisation among firms in a new digital business ecosystem.     

Vergleich von Kompetenzanforderungen an Business-Intelligence- und Big-Data-Spezialisten

While many studies on big data analytics describe the data deluge and potential applications for such analytics, the required skill set for dealing with big data has not yet been studied empirically. The difference between big data (BD) and traditional business intelligence (BI) is also heavily discussed among practitioners and scholars. We conduct a latent semantic analysis (LSA) on job advertisements harvested from the online employment platform monster.com to extract information about the knowledge and skill requirements for BD and BI professionals. By analyzing and interpreting the statistical results of the LSA, we develop a competency taxonomy for big data and business intelligence. Our major findings are that (1) business knowledge is as important as technical skills for working successfully on BI and BD initiatives; (2) BI competency is characterized by skills related to commercial products of large software vendors, whereas BD jobs ask for strong software development and statistical skills; (3) the demand for BI competencies is still far bigger than the demand for BD competencies; and (4) BD initiatives are currently much more human-capital-intensive than BI projects are. Our findings can guide individual professionals, organizations, and academic institutions in assessing and advancing their BD and BI competencies.     

Creating business value with process mining

Information systems research has a long-standing interest in how organizations gain value through information technology. In this article, we investigate a business process intelligence (BPI) technology that is receiving increasing interest in research and practice: process mining. Process mining uses digital trace data to visualize and measure the performance of business processes in order to inform managerial actions. While process mining has received tremendous uptake in practice, it is unknown how organizations use it to generate business value. We present the results of a multiple case study with key stakeholders from eight internationally operating companies. We identify key features of process mining – data & connectivity, process visualization, and process analytics – and show how they translate into a set of affordances that enable value creation. Specifically, process mining affords (1) perceiving end-to-end process visualizations and performance indicators, (2) sense-making of process-related information, (3) data-driven decision making, and (4) implementing interventions. Value is realized, in turn, in the form of process efficiency, monetary gains, and non-monetary gains, such as customer satisfaction. Our findings have implications for the discourse on IT value creation as we show how process mining constitutes a new class of business intelligence & analytics (BI&A) technology, that enables behavioral visibility and allows organizations to make evidence-based decisions about their business processes.     

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.     

From free to fee: Monetizing digital content through expected utility-based recommender systems

This study proposes a novel framework for designing business rule analytics to assist businesses offering digital content in effectively converting free-only users (FOUs) into paying customers. Based on the theory of expected utility, we expand upon traditional frequency-driven rule analytics by integrating three business-relevant factors (target size, conversion profit, and conversion likelihood) into the process of generating recommendations for FOUs in digital content markets. The framework was tested using two different types of empirical analysis. We conducted a field experiment collaborating with a nationwide e-book store to determine how FOUs responded to the recommendations generated under the proposed framework. Furthermore, we analyzed over 5 million transactions collected from the e-book seller and a mobile application provider to examine the impact of customer segmentation on the effectiveness of our approach. Our findings suggest that business analytics derived from the utility-based mechanisms can significantly enhance digital content providers' business performance.     

Disciplined autonomy: How business analytics complements customer involvement for digital innovation

The rise of big data and the fluid boundaries of digital products are driving companies to use business analytics (BA) to power their customer involvement. The complementarity view offers unique competence to generate value from BA because capability complementarity is less likely to be replicated or imitated. Unlike prior studies on BA-enabled value realization, our research investigates the interactions of BA and customer involvement capabilities using the complementarity view. We tested our model using data collected from 317 IT companies in China. Our results suggest that BA value realization requires both a top-down mechanism in which BA skills provide global guidance for alignment with a company’s goals and a bottom-up mechanism in which BA culture empowers local autonomy for adaptation to ever-changing needs. Our BA-complemented mechanisms provide research and practice with a way to concurrently use BA and customer involvement capabilities to address the duality of digital innovation. We further suggest that BA skills are necessary but insufficient for digital innovation because BA culture demonstrates a stronger effect in complementing organizations’ existing capabilities than BA skills do.     

Preemptive Security Through Information Analytics

ABSTRACT

Business security and threat actors continue to play a dangerous cat-and-mouse game with businesses intellectual property, customer data, and business reputations at stake. Businesses need to delve into a new way of doing business security to break out of this game. Businesses are sitting on repositories full of security-relevant data that is not being capitalized upon with the current information security and physical security organizations within businesses. This article poses the introduction of a data scientist role and a new supporting central data correlation technology platform based on big data predictive analytics into business security functions. The goal is to intelligently and autonomously identify, correlate and pinpoint normally innocuous or unnoticed security event attributes to allow security personnel to preemptively remediate physical and information risks before exploitation or loss of intellectual property occurs.     

A survey on big data-driven digital phenotyping of mental health

The landscape of mental health has undergone tremendous changes within the last two decades, but the research on mental health is still at the initial stage with substantial knowledge gaps and the lack of precise diagnosis. Nowadays, big data and artificial intelligence offer new opportunities for the screening and prediction of mental problems. In this review paper, we outline the vision of digital phenotyping of mental health (DPMH) by fusing the enriched data from ubiquitous sensors, social media and healthcare systems, and present a broad overview of DPMH from sensing and computing perspectives. We first conduct a systematical literature review and propose the research framework, which highlights the key aspects related with mental health, and discuss the challenges elicited by the enriched data for digital phenotyping. Next, five key research strands including affect recognition, cognitive analytics, behavioral anomaly detection, social analytics, and biomarker analytics are unfolded in the psychiatric context. Finally, we discuss various open issues and the corresponding solutions to underpin the digital phenotyping of mental health.     

Enhancing multimodal learning through personalized gesture recognition

Gestural recognition systems are important tools for leveraging movement‐based interactions in multimodal learning environments but personalizing these interactions has proven difficult. We offer an adaptable model that uses multimodal analytics, enabling students to define their physical interactions with computer‐assisted learning environments. We argue that these interactions are foundational to developing stronger connections between students' physical actions and digital representations within a multimodal space. Our model uses real time learning analytics for gesture recognition, training a hierarchical hidden‐Markov model with a “one‐shot” construct, learning from user‐defined gestures, and accessing 3 different modes of data: skeleton positions, kinematics features, and internal model parameters. Through an empirical comparison with a “pretrained” model, we show that our model can achieve a higher recognition accuracy in repeatability and recall tasks. This suggests that our approach is a promising way to create productive experiences with gesture‐based educational simulations, promoting personalized interfaces, and analytics of multimodal learning scenarios.     

Sustainable Product Design — Is there more to it than Science,Systems and Computers?1

The conditions are examined through which a company can take an active part in the evolutionary process towards ecologically sustainable societies. Productivity measures, quality perceptions, ecological performance and critical contradictions between business targets have to be identified, based on systematic retrieval and structuring of information of product, processes and practices. It is important that the company selects ecological performance parameters and sets performance targets which show the way towards ecologically sound products, processes and practices and at the same time secure its competitiveness in today's market. It is suggested that an extended quality function deployment process (QFD) can be used for this purpose. By combining this information structure with the modelling possibilities of products, processes and practices that are available in modern computer assisted engineering software (CAE), consequences and feasibilities of new ideas and creative solutions can be checked out continuously. New ideas and creative solutions that really will lead the way towards ecologically sustainable societies, seem to require development- and design-teams that are getting inspiration, visions and wisdom from other areas than the conventional business and university environment. Close cooperation should be encouraged between industry and the universities for building up such inspired environments.     

Business analytics and business value: A comparative case study

With growing adoption of business analytics, it is important for investing firms to understand how business value is created from investments. Studies in IT domain have highlighted how higher investment in technology may not bring more returns, rather how IT as an organizational capability acts as a key mediator in value creation. This research extends the model to business analytics, to identify elements of analytics technology assets and business analytics capability and to understand the mechanism of business value creation using multiple case studies. We capture how analytics resources contribute to business performance by developing operational and organizational performance measures.     

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.     

Scalable graph-based OLAP analytics over process execution data

In today’s knowledge-, service-, and cloud-based economy, businesses accumulate massive amounts of data from a variety of sources. In order to understand businesses one may need to perform considerable analytics over large hybrid collections of heterogeneous and partially unstructured data that is captured related to the process execution. This data, usually modeled as graphs, increasingly come to show all the typical properties of big data: wide physical distribution, diversity of formats, non-standard data models, independently-managed and heterogeneous semantics. We use the term big process graph to refer to such large hybrid collections of heterogeneous and partially unstructured process related execution data. Online analytical processing (OLAP) of big process graph is challenging as the extension of existing OLAP techniques to analysis of graphs is not straightforward. Moreover, process data analysis methods should be capable of processing and querying large amount of data effectively and efficiently, and therefore have to be able to scale well with the infrastructure’s scale. While traditional analytics solutions (relational DBs, data warehouses and OLAP), do a great job in collecting data and providing answers on known questions, key business insights remain hidden in the interactions among objects: it will be hard to discover concept hierarchies for entities based on both data objects and their interactions in process graphs. In this paper, we introduce a framework and a set of methods to support scalable graph-based OLAP analytics over process execution data. The goal is to facilitate the analytics over big process graph through summarizing the process graph and providing multiple views at different granularity. To achieve this goal, we present a model for process OLAP (P-OLAP) and define OLAP specific abstractions in process context such as process cubes, dimensions, and cells. We present a MapReduce-based graph processing engine, to support big data analytics over process graphs. We have implemented the P-OLAP framework and integrated it into our existing process data analytics platform, ProcessAtlas, which introduces a scalable architecture for querying, exploration and analysis of large process data. We report on experiments performed on both synthetic and real-world datasets that show the viability and efficiency of the approach.