HOW TO CULTIVATE ANALYTICS CAPABILITIES WITHIN AN ORGANIZATION? – DESIGN AND TYPES OF ANALYTICS COMPETENCY CENTERS

Today, the ability to exploit big data using advanced analytics bears considerable potential to create competitive advantages. Therefore, business leaders need to make crucial design decisions on how to cultivate these capabilities within their organization. Analytics Competency Centers (ACCs) are an important organizational solution to spread analytics capabilities by providing leadership, expertise and infrastructure. In this paper, we analyze nine analytics competency centers of major global players across several industries - based on a series of interviews with executives, consultants and data scientists. We identify strategic and structural design options, common processes, best-practices, and potential future development paths. In particular, we distinguish between two generic types of centers that differ in their strategic orientation and their choice of design options. Our work contributes to organizational design theory addressing the question on how analytics capabilities can be nurtured for competitive advantage. It should provide concrete guidance to business leaders on how to design and apply ACCs as an organizational option

Linking Big Data and Business: Design Parameters of Data-Driven Organizations

Big data analytics is accepted to be an important driver of business value. However, this value does not come without a cost. Becoming a data-driven organization (DDO) necessitates a substantial transformation along the components structure, actors, task, and technology. Moreover, as successfully generating value from big data requires the utilization of data insights in business, attention needs to be assigned to the different actors from the data and business side, and their interrelation and collaboration. By taking a socio-technical systems perspective and utilizing a multi-case research approach, we developed a taxonomy to structure insights about different design parameters of a DDO. Thus, we contribute to the information systems literature by proposing a holistic design framework for DDOs paying tribute to its high collaboration requirements, and offer a compendium for managers with pathways how to design a DDO

DATA-DRIVEN SERVICE INNOVATION: A SYSTEMATIC LITERATURE REVIEW AND DEVELOPMENT OF A RESEARCH AGENDA

The potential created by ongoing developments in data and analytics permeates a multitude of research areas, such as the field of Service Innovation. In this paper, we conduct a Systematic Literature Review (SLR) to investigate the integration of data and analytics as an analytical unit into the field of Service Innovation – referred to as Data-Driven Service Innovation (DDSI). Overall, the SLR reveals three main research perspectives that span the research field of Data-Driven Service Innovation: Explorative DDSI, validative DDSI, and generative DDSI. This integrated theoretical framework describes the distinct operant roles of data analytics for Service Innovation, and thus contributes to the body of knowledge in the field of DDSI by providing three unified lenses, which researchers can use to describe and locate their existing and future research endeavors in this ample field. Building up on the insights from the SLR, a research agenda is proposed in order to trigger and guide further discussions and future research surrounding DDSI. Ultimately, this paper aims at contributing to the body of knowledge of Service Innovation in general and Data-Driven Service Innovation in particular by presenting a three-dimensional research space model structuring DDSI towards its advancement

NMC Horizon Report: 2017 Higher Education Edition

The NMC Horizon Report > 2017 Higher Education Edition is a collaborative effort between the NMC and the EDUCAUSE Learning Initiative (ELI). This 14th edition describes annual findings from the NMC Horizon Project, an ongoing research project designed to identify and describe emerging technologies likely to have an impact on learning, teaching, and creative inquiry in education. Six key trends, six significant challenges, and six important developments in educational technology are placed directly in the context of their likely impact on the core missions of universities and colleges. The three key sections of this report constitute a reference and straightforward technology-planning guide for educators, higher education leaders, administrators, policymakers, and technologists. It is our hope that this research will help to inform the choices that institutions are making about technology to improve, support, or extend teaching, learning, and creative inquiry in higher education across the globe. All of the topics were selected by an expert panel that represented a range of backgrounds and perspectives

Toward big data and analytics governance: redefining structural governance mechanisms

Big Data and Analytics (BDA) enable innovative business models and, simultaneously, increase existing business processes’ efficiency and effectiveness. Although BDA’s potential is widely recognized, companies face a variety of challenges when adopting BDA and endeavoring to generate business value. Researchers and practitioners emphasize the need for effective governance to delineate data and analytics’ roles and responsibilities. Existing studies focus either on data or on analytics governance, even though both approaches are closely interlinked and depend on each other. Our study aims to integrate these two distinct research perspectives into a unified view on structural mechanisms for BDA. Using design science research, we iteratively develop data and analytics roles, clarify their responsibilities and provide guidelines for their organizational assignment. Our study contributes to advancing research on data and analytics governance and supports practitioners managing BDA

Structural Equation Modeling for the Business Performance of Private Hospitals in Thailand: Management Perspective

This research aimed to study the causal factors affecting the business performance of private hospitals in Thailand from a management perspective. The sample consisted of 411 executives from private hospitals in Thailand, selected through purposive sampling. Data were collected via questionnaire, with SEM being used for analysis. The results indicated that the development of an enterprise resource system, including the competency and capability of entrepreneurs, positively influenced the focus on competitive differentiation. In turn, this focus had a positive effect on customer relationship management. Customer relationship manage­ment positively impacted brand loyalty, which subsequently enhanced business performance. In contrast, the competency and capability of entrepreneurs did not have an effect on business performance. The findings suggest that the growth and sustainability of business performance in private hospitals depends on various supportive factors. These range from policy formulation and the development of technological systems in services to strategies for building customer relationships, all contributing to competitive advantages, service loyalty, and success in achieving set goals

Delineating the Business Value of Data-driven Initiatives in Organizations – Findings from a Systematic Review of the Information Systems Literature

A key objective of data-driven transformations is to utilize big data analytics (BDA) to create data-driven business value (DDBV). While prior research shows the potential of BDA to achieve DDBV, the concept remains blurry and an overview of realizable DDBVs is still lacking. To better understand the multidimensionality of the DDBV concept and to obtain insights into the bandwidth of achievable DDBVs, we conducted a systematic review of the information systems literature. Based on our results, we present a comprehensive overview of 34 DDBVs, which are classified according to their tangibility and locus of value realization. Furthermore, we describe three research deficiencies: (1) the missing operationalization of the DDBV concept, (2) the lack of explanatory mechanisms for DDBV realization, and (3) missing qualitative, in-depth insights into DDBV realization processes. Future research may build upon our systematization and help closing these research gaps, thereby increasing the success likelihood of data-driven initiatives

Building Business Intelligence & Analytics Capabilities - A Work System Perspective

Although enterprises believe that they can achieve a competitive advantage with big data and AI, their analytics initiatives’ success rate still lags behind expectations. Existing research reveals that value creation with business intelligence and analytics (BI&A) is a complex process with multiple stages between the initial investments in BI&A resources and ultimately obtaining value. While prior research mostly focused on value generation mechanisms, we still lack a thorough understanding of how enterprises actually build BI&A capabilities. We explain the process in our research using work system theory (WST). Based on case studies and focus groups, we identify four prevalent BI&A capabilities: reporting, data exploration, analytics experimentation, and analytics production. For each identified BI&A capability, we derive patterns for BI&A resource orchestration, using the WST lens. Our findings complement the BI&A value creation research stream by providing insights into capability building

How Do Different Types of BA Users Contribute to Business Value?

To explain how different types of business analytics (BA) users contribute to business value, we propose a new variance model called organizational benefits from business analytics use (OBBAU). The model captures three key mechanisms through which two distinct types of BA users drive organizational benefits: 1) data scientists providing advisory services, 2) end users using BA tools, and 3) both data scientists and end users creating and enhancing BA tools. To build the OBBAU, we thoroughly reviewed the BA and IS literatures and interviewed 15 BA experts

Becoming a Data-Driven Organization: A Comparative Case Study on Digital Transformation Strategies

In today’s data-centric era, organizations increasingly aim to operate more data-driven and therefore engage in digital transformations toward becoming a data-driven organization (DDO). To govern such transformations, top managers develop digital transformation strategies (DTS) characterized by different organizational ambidexterity approaches. This study analyzes how such DTS influence the process and (intermediate) outcomes of organizations’ digital transformations toward becoming a DDO by studying two organizations undertaking such DDO transformations using the concept of organizational ambidexterity as a theoretical lens. On this empirical basis, we find that DTS characterized by different organizational ambidexterity approaches lead to different transformation processes and (intermediate) outcomes. Thereby, this study contributes to existing academic literature in the field of DDOs and DTS, as such transformation journeys toward becoming a DDO have not been studied in its entirety yet. Furthermore, our paper offers practical guidance for top managers to develop and implement a DTS suitable for their organization