Business Analytics Revisited: A gap analysis of research and practice

With the growing use of business analytics (BA), organisations have benefited from new ways to extract value from data and drive strategic, evidence-based decision making. However, much less thought about how Business Analytics contributes to business value in practice has been given. We have conducted an in-depth qualitative paper of fourteen semi-structured interviews of positions integral to BA within organisations using five value drivers and inhibiting factors that surround value generation. This paper takes a retrospective look at what has been done, and how well it compares to the practice of business analytics. This paper seeks to bridge the current knowledge gap through providing a holistic view of all five value factors and how they affect value generation. In order to answer the research question of “How does Business Analytics contribute to business value in organisations?”. The results of this research can be utilised by managers of firms creating value through data-driven decisions, as well as by others in the ecosystem for analysing business analytic solutions. As well as identifying in what ways business analytics contributes to value by bridging the gap between research and practice

DATA MONETIZATION CHALLENGES IN ESTABLISHED ORGANIZATIONS: A SYSTEMATIC LITERATURE REVIEW

Over the last decades, researchers and practitioners have looked at data as a valuable asset for improving business processes in organizations. However, nowadays, they see data more as a tradable asset that can be monetized. Data monetization here refers to generating revenue from selling data and data-based products and services. Despite providing opportunities for generating new revenue streams, data monetization is not without challenges, especially in established organizations. Previous research shows that an organization’s data monetization capability is constrained by its existing business model, infrastructure, and organizational culture. Although Information Systems (IS) research and practice have shown an increasing interest in data monetization, we lack a thorough understanding of its challenges. As a first step in addressing this gap, we set out to identify challenges that established organizations face in monetizing their data. To that end, we conducted a systematic literature review and identified 21 challenges reported in the extant literature. Based on their nature, we divided these challenges into five categories, including business model, legal & regulatory, security & privacy, organizational, and data management challenges. Our study has several implications for IS research and practice

Understanding Effective Use of Big Data: Challenges and Capabilities (A Management Perspective)

While prior research has provided insights into challenges and capabilities related to effective Big Data use, much of this contribution has been conceptual in nature. The aim of this study is to explore such challenges and capabilities through an empirical approach. Accordingly, this paper reports on a multiple case study approach, involving eight organizations from the private and public sectors. The study provides empirical support for capabilities and challenges identified through prior research and identifies additional insights viz. problem-driven approach, time to value, data readiness, data literacy, data misuse, operational agility, and organizational maturity assessment

Digital transformation in business and management research: An overview of the current status quo

It is no surprise that research on digital transformation (DT) has raised vast interest among academics in recent decades. Countries, cities, industries, companies, and people all face the same challenge of adapting to a digital world. The aim of the paper is twofold. First, map the thematic evolution of the DT research in the areas of business and management, because existing research in these areas to date has been limited to certain domains. To achieve this, articles were identified and reviewed that were published in the Chartered Association of Business Schools’ (ABS) ≥ 2-star journals. Based on these findings, the second objective of this paper will be to propose a synergistic framework that relates existing research on DT to the areas of business and management, which will help form the evolutionary perspective taken in this paper. Considering the emerging development of the topic under investigation, the framework is understood as a sound basis for continued discussion and forthcoming research.info:eu-repo/semantics/publishedVersio

The Impact of Artificial Intelligence on Strategic and Operational Decision Making

openEffective decision making lies at the core of organizational success. In the era of digital transformation, businesses are increasingly adopting data-driven approaches to gain a competitive advantage. According to existing literature, Artificial Intelligence (AI) represents a significant advancement in this area, with the ability to analyze large volumes of data, identify patterns, make accurate predictions, and provide decision support to organizations. This study aims to explore the impact of AI technologies on different levels of organizational decision making. By separating these decisions into strategic and operational according to their properties, the study provides a more comprehensive understanding of the feasibility, current adoption rates, and barriers hindering AI implementation in organizational decision making

WELCOME TO DIGITAL TRANSFORMATION ERA: FROM PROOF-OF-CONCEPT TO BIG DATA INSIGHTS CREATION

Digital transformation (DT) is no longer an optional strategic priority, but the direction for managers of traditional firms that their success is built in the pre-digital era. With all hype around DT opportunities, it is rather a highly complex challenge that affects many or all segments of a firm and more so at the early stages of DT. Firms at the early stage of DT face the challenge of choosing among a big variety of existing and emerging technologies on the market, neglecting technological uncertainty, navigating through the technological solutions ocean, and avoiding hype-driven decisions while being technology competence-less. With this respect, the phase preceding any adoption or rejection of a new DT initiative and aiming at the first meeting and proving feasibility and commercial opportunities becomes increasingly important. The thesis investigates three particular phenomena of the earliest Digital Transformation (DT) stage, that are seemingly well-known and intuitively clear but suffer from the lack of empirical and conceptual evidence base as well as theoretical ground on closer inspection, namely, proof-of-concept, data-driven decision-making, and Big Data insights creation. Focusing on the three aspects of the early stage of DT allows building a research agenda that consists of complementing each other parts. Three-essays research was run with three related objectives. Each objective is addressed by conducting independent research using comparative methods. The thesis applies the qualitative approach as the overarching, with the relative to the three essays methodologies, namely, qualitative case study, ethnography, and participatory observation. The thesis uses qualitative methods to derive main findings and quantitative methods based on novel computational techniques to add more nuances to the results. This allows a new empirical and conceptual perspective on the earliest stages of DT. The findings suggest that a) cognitive biases drive what I labeled as perceived technology potentiality, moreover, technology awareness develops step-wise as PoC is run moving from borrowed technology awareness to minimum acquired technology awareness and enhanced technology awareness. These findings were used to explain how PoC dynamic changes with time. Further, findings show how b) different types of traps (cognitive and data) drive managerial trust in data when data-driven decision-making is first used. The findings were taken as the ground to build the three traps zones notion, where the decisions and trust in data are driven by different combinations of traps. Finally, findings reveal that c) Big Data dimensions have their related sub-dimensions, differences and similarities of which led to the discovery of the two effects of Big Data dimensions, namely, Proliferation and Additive. These findings helped to explain how exactly Big Data dimensions participate in the Big Data insights creation and to build the conceptual matrix of Big Data insights creation. In this vein, the research contributes to the technology innovation literature by shedding light on the phenomena of the earliest stage of DT and by initiating the first comprehensive conversation on PoC, data-driven decision-making, and Big Data insights creation. Further, the research contributes to the existing literature on managerial cognition, decision-making, and Big Data usefulness. Finally, contributions to methods in the technology innovation field are drawn

Analítica de datos para el rendimiento en los cultivos de aguacate Hass en Colombia

132 páginasEn el sector agrícola y específicamente los productores agrícolas tienen que tomar decisiones complejas todos los días para sortear los problemas y situaciones durante la administración de la planificación de los cultivos. La estimación del rendimiento constituye una medida pertinente para determinar la eficiencia de los resultados con respecto a los factores que impactan en la producción en los cultivos de aguacate Hass. Aunque existen muchos más factores involucrados en el rendimiento de la producción agrícola para este proyecto se hizo énfasis en las variables meteorológicas, con el propósito de analizar la influencia de las principales variables y obtener un modelo predictivo de analítica de datos que determine el comportamiento del rendimiento con respecto a las variables meteorológicas. Con el objetivo de demostrar los beneficios del uso de la información en la toma de decisiones a través de herramientas de análisis de datos y la facilidad de acceso a estas herramientas, se utilizó la plataforma de Microsoft Machine Learning Studio para procesar y analizar la información obtenida de fuentes abiertas de datos en de portales públicos Colombianos como AGRONET y el IDEAM.In the agricultural sector and specifically agricultural producers have to make complex decisions every day to overcome problems and situations during the administration of crop planning. The yield estímate is a relevant measure to determine the efficiency of the results with respect to the factors that impact production in Hass avocado crops. Although there are many more factors involved in the performance of agricultural production, this project, has emphasis was placed on meteorological variables, with the purpose of analyzing the influence of the main variables and obtaining a predictive model of data analytics that determines performance behavior with respect these variables. In arder to demonstrate the benefits of using information in decision making through data analysis too Is and the ease of access to these tools, the Microsoft Machine Learning Studio platform was used to process and analyze the information downloaded from Colombian's open sources data like AGRONET and IDEAM portals.Magíster en Gerencia Estratégica de Tecnologías de InformaciónMaestrí

Creating a Culture of Data-Driven Decision-Making

Researchers have consistently shown that a supportive culture is one of the most crucial success factors in the implementation of any big data solution. Creating a culture that supports data-driven decision-making is a difficult but ultimately required step in transforming an organization into one that can readily and successfully adopt business intelligence technologies. The purpose of this qualitative case study was to understand the ways in which organizations can foster a culture of smarter decision-making and accountability so that businesses can improve operational metrics and ultimately profitability. Participants identified three major themes that drive the adoption of a data-driven culture. These themes included building trust between decision-makers and their data, developing a team-driven culture, and instituting data governance and standard work processes to maintain quality of systems

How Healthcare Big Data Analytics Information Asymmetry Influences Organizational Design Absorptive Choices

Although the relationship between big data analytics (BDA) organizational, firm, and financial performance is well supported, little attention has been paid in extant research to exploring the organizational design issues resulting from information asymmetry caused by BDA; in particular, organizational absorptive choices that include acquisitions, mergers, reorganization, executive changes, or board of director adjustments. The purpose of this qualitative single case study within a U.S. hospital was to explore the conditions and circumstances that influence absorptive organizational design choices of hospital administration. The theoretical base of this study is Pfeffer and Salancik’s resource dependence theory (RDT). Logic model data analysis approach was conducted on primary data attained from semistructured interviews of 12 volunteers of hospital administration and secondary data from grey literature. The findings of this study suggest resource dependence theory activities of executive changes and intra-organizational structural changes moderate information asymmetry. Communication was the major theme, while properly formed BDA questions, prospective reimbursement models, evolving BDA demands, intellectual capacity gap, and operational complexity were minor themes that influenced organizational design decisions. The practical implications emphasize communication among multidisciplinary groups and boundary-spanning organizational design strategies to moderate information asymmetry. Lastly, the positive social change implication may be the increased BDA adoption in hospital administration from the improved communication among individual actors of multidisciplinary BDA groups