Building a Connection Between Decision Maker and Data-Driven Decision Process

It is quite common that most of companies’ decisions are made based on feelings, intuitions or personal experiences. The reasons for such patterns have organizational, technical and process oriented backgrounds. For instance, there is no structured way to deal with the analytical results on both sides simultaneously – organizational and technical. Usually, in case of analytics the ones doing analysis (e.g. data scientists) and the ones using results of analytics (e.g. decision makers) are different persons. As a result, such a structure leads to ambiguity and misunderstanding between the involved parties. In order to bridge the existing gap between data scientists and decision makers, we introduced the Data Product Profile which links both data science and data-driven decision processes

Business Intelligence Implementation Success Framework: A Literature Review

In contemporary competitive business context, managers are increasingly using Business Intelligence (BI) as the technique and solution to improve their understanding of a business environment.  This paper introduces studies performed on the implementation of BI in SMEs between 2000 and 2015, and it is multi-purpose. First, it examines types of research questions addressed by studies of BI carried out in developing countries. Second, its purpose is to identify the gaps in the BI studies in these countries. Third, it aims to be a base for developing a framework for BI implementation success through the classification of the critical success factors (CSFs) found in the relevant literature. Due to this model, BI stakeholders can identify and understand the crucial factors behind the successful implementation of BI systems in SMEs. Keywords: business intelligence, small and medium-sized enterprises, literature review, critical success factors. DOI: 10.7176/EJBM/11-6-0

Big data educational portal for Small and Medium Sized Enterprises (SMEs)

Big Data refers to the massive amount of data generated from IT systems, sensors, and mobile devices. The values of big data are achieved by descriptive, predictive and prescriptive analytics. Small and Medium Sized Enterprises (SMEs) play a significant role in contributing to economic development. Big data is seen as a strategic and innovative tool for SMEs to stay competitive in the marketplace. However, there is lack of research in studying the value of big data to SMEs. Moreover, due to the shortage of quality learning platforms, SMEs have limited understanding of the potential benefits big data offers their businesses. This research aims to propose an educational portal of big data for SMEs by incorporating the pedagogy aspects. The research is underpinned by design science research. The portal contributes theoretically and methodologically by deriving the design knowledge of such portal and practically by increasing big data knowledge among SMEs

THE EFFECT OF KNOWLEDGE MANAGEMENT ON THE STRATEGIC MANAGEMENT PROCESS MEDIATED BY COMPETITIVE INTELLIGENCE IN THE SMALL BUSINESS COMPANY

The purpose of this study is to discuss the synergic and separate use of knowledge management and competitive intelligence in stage of strategic management process. This study presents two independent variables (knowledge management and competitive intelligence) and one dependent variable (strategic management process). 145 samples from Indonesia small businesses considered valid, thereby indicating 88.41% validity rate. The results show that the proposed variable, Knowledge management and competitive intelligence significantly influences strategic management process from the perspective of the small business in West Indonesia. This result implies a lack of mediation effect presented by the competitive intelligence. This finding clarifies the research question that knowledge management and competitive intelligence are associated with strategic management proces

TOWARDS A CONSOLIDATED RESEARCH MODEL FOR UNDERSTANDING BUSINESS INTELLIGENCE SUCCESS

Research about the factors that determine the success of information systems (IS) suggests that IS success is an elusive phenomenon that can only be explained in terms of a multi-dimensional construct. Despite the usefulness and unique qualities of Business Intelligence (BI) solutions, the factors responsible for the success of BI solutions remain poorly understood. Our article attempts to illuminate a path towards a clearer understanding of how BI solutions succeed by drawing on the existing body of literature and critically reflecting on the updated model of information systems success presented by DeLone and McLean (2003) and Wixom and Watson’s (2001) model of data warehousing success. The principal research contribution consists of expanding, adapting, and synthesising these two models into a consolidated model for BI success. We derive a second order model, delineate its constructs, and conceptualise their relationships based on prior research related to IS success. The operationalization of these factors has the potential of leading to a more precise instrument for understanding, evaluating and analysing the success of BI solutions

A Process-Oriented Model to Business Value – the Case of Real-Time IT Infrastructures

Which investments in real-time capabilities and decision-support IT-infrastructures are appropriate? In view of the recent in-memory systems this poses an urgent question to companies in many industries. Despite ample research on the causal relationship between IS investments and business value, especially the value quantification remains a difficult challenge. This paper contributes a business value measurement model that structures and assesses the internal organizational benefits of real-time IT infrastructures. A case study from the automotive industry aims to validate the model

Tendencias del comportamiento de consumidores mediante herramientas de Data Mining, en supermercados del Perú: una revisión de la literatura científica

A pesar de que el Data Mining es una herramienta clave para comprender el comportamiento de clientes de tiendas minoristas como supermercados, hace falta una revisión exhaustiva de la literatura que ayude a comprender mucho mejor su aplicación y los beneficios que esta trae. Esta revisión proporciona una base de datos dentro de un periodo de diez años, entre el 2009 y 2019, de donde en un primer momento se extrajeron 48 artículos por tener relación con la aplicación de Data Mining para encontrar tendencias de comportamiento de consumidores, se extrajeron de nueve bases de datos especializadas: Emerald Insight, ResearchGate, IEEE Xplore, ScienceDirect, Semantic Scholar, Google Scholar, Cuaderno Activa, Taylor and Francis y Mary Ann Liebert, INC. Los resultados demuestran que para entender el comportamiento del consumidor se debe minar: i) Su ruta de compras, temporadas, cantidades y preferencias con el fin de organizar los productos en la tienda y realizar promociones acorde a su perfil; ii) El CLV con el fin de separarlos en segmentos y enfocarse en ellos con mayor claridad; iii) Sus necesidades y quejas para mejorar su CRM; iv) Sus percepciones y requisitos para aplicar estrategias de marketing más precisas; v) Qué productos buscan y comparan en tiendas de retail online, tema del cual se tiene poca información, por lo que se recomienda ahondar a detalle para otras investigaciones. Todo ello mediante diferentes técnicas de DM y se demostró que las redes neuronales, seguidas por los árboles de decisión y las técnicas de reglas de asociación son recomendadas para los que no son expertos en DM. Nuestra investigación sirve como un derrotero para la futura creación de conocimiento sobre los temas abordados

Improving Data-Driven Decision Making through Human-Centered Knowledge Sharing

This research focuses on human-centered knowledge sharing within data-driven decision-making processes enabled by advanced analytics. The paper describes an exploratory study of an innovative approach to ongoing improvement of complex data-driven decision making processes found in a large retail distribution company by considering a complex interplay of business intelligence (BI) /business analytics, business processes and human-centered knowledge management. Using the relevant IS frameworks as analytical lens the paper investigates the evolving relationship between decision-making and decision-support technology, as well as the relationship among information, decisions and the corresponding business processes in this context. The most important finding of this research is in identification of human-centered knowledge sharing as the key success factor for ongoing improvement of BI-enabled decision making in the case organisation, rather than complex technology. This in turn indicates the significance of various organisational factors, including carefully designed and implemented human-resource (HR) strategies to encourage knowledge sharing among decision makers using advanced analytics systems. Finally, this paper also confirms the latest industry reports that more mature analytical organisations are looking beyond technology and focusing on business-related issues as the next source of competitive advantage, as it was the case with our chosen organisation