A Reference Model for Collaborative Business Intelligence Virtual Assistants

Collaborative Business Analysis (CBA) is a methodology that involves bringing together different stakeholders, including business users, analysts, and technical specialists, to collaboratively analyze data and gain insights into business operations. The primary objective of CBA is to encourage knowledge sharing and collaboration between the different groups involved in business analysis, as this can lead to a more comprehensive understanding of the data and better decision-making. CBA typically involves a range of activities, including data gathering and analysis, brainstorming, problem-solving, decision-making and knowledge sharing. These activities may take place through various channels, such as in-person meetings, virtual collaboration tools or online forums. This paper deals with virtual collaboration tools as an important part of Business Intelligence (BI) platform. Collaborative Business Intelligence (CBI) tools are becoming more user-friendly, accessible, and flexible, allowing users to customize their experience and adapt to their specific needs. The goal of a virtual assistant is to make data exploration more accessible to a wider range of users and to reduce the time and effort required for data analysis. It describes the unified business intelligence semantic model, coupled with a data warehouse and collaborative unit to employ data mining technology. Moreover, we propose a virtual assistant for CBI and a reference model of virtual tools for CBI, which consists of three components: conversational, data exploration and recommendation agents. We believe that the allocation of these three functional tasks allows you to structure the CBI issue and apply relevant and productive models for human-like dialogue, text-to-command transferring, and recommendations simultaneously. The complex approach based on these three points gives the basis for virtual tool for collaboration. CBI encourages people, processes, and technology to enable everyone sharing and leveraging collective expertise, knowledge and data to gain valuable insights for making better decisions. This allows to respond more quickly and effectively to changes in the market or internal operations and improve the progress

Analytics as a Service: Cloud Computing and the Trans-formation of Business Analytics Business Models and Ecosystems

Due to the growth of data volumes, volatility and variety, business analytics (BA) become an essential driver of today’s business strategies. However, BA is mainly adopted by large enterprises because it may require a complex and costly infrastructure. As many companies strive to make better use of their data and to adopt data-driven management paradigms, cloud computing has been discussed as a costeffective approach to BA implementation challenges. To date, there has been little attention on the emerging class of analytical cloud services, “Analytics as a service” (AaaS). This article aims at demarcating AaaS as a cloud offering through an explorative research approach based on multiple case studies. Based on the analysis of 28 AaaS offerings, we derive a classification scheme for AaaS business model configurations and derive five business model archetypes. We discuss cloud computing’s implications on the business analytics ecosystem where partner networks play an important role at all levels. By clarifying the definition and characteristics of AaaS business models, our study contributes to the ‘Theory for Analyzing’ that lays the groundwork for future research

Cloud-Based Business Intelligence and Analytics Applications – Business Value and Feasibility

In several application domains, Cloud Computing has been established as an accepted IT sourcing alternative. The usage in more sophisticated areas like Business Intelligence and Analytics (BIA) is, however, still in its infancy. The presented research aims at carving out viable application scenarios for Cloud BIA and at analyzing them regarding their potential business value and feasibility. The scenarios are derived from a case study and are further explored quantitatively with a survey of BIA experts. The results indicate that while there is an interest in Cloud-based BIA solutions, it is mostly directed towards self-contained and simple front-end driven solutions. Furthermore, the study highlights the need for a broader perspective on the subject of Cloud BIA that also considers issues of organizational and technical compatibility. The findings contribute to BIA research by gathering insights into the adaption of Cloud BIA. For business practice, the results support a more differentiated approach towards integrating Cloud technologies into the BIA landscape

Business Intelligence and Analytics in Small and Medium-Sized Enterprises

This thesis presents a study of Business Intelligence and Analytics (BI&A) adoption in small and medium-sized enterprises (SMEs). Although the importance of BI&A is widely accepted, empirical research shows SMEs still lag in BI&A proliferation. Thus, it is crucial to understand the phenomenon of BI&A adoption in SMEs. This thesis will investigate and explore BI&A adoption in SMEs, addressing the main research question: How can we understand the phenomenon of BI&A adoption in SMEs? The adoption term in this thesis refers to all the IS adoption stages, including investment, implementation, utilization, and value creation. This research uses a combination of a literature review, a qualitive exploratory approach, and a ranking-type Delphi study with a grounded Delphi approach. The empirical part includes interviews with 38 experts and Delphi surveys with 39 experts from various Norwegian industries. The research strategy investigates the factors influencing BI&A adoption in SMEs. The study examined the investment, implementation, utilization, and value creation of BI&A technologies in SMEs. A thematic analysis was adopted to collate the qualitative expert interview data and search for potential themes. The Delphi survey findings were further examined using the grounded Delphi method. To better understand the study’s findings, three theoretical perspectives were applied: resource-based view theory, dynamic capabilities, and IS value process models. The thesis’ research findings are presented in five articles published in international conference proceedings and journals. This thesis summary will coherently integrate and discuss these results.publishedVersio