Showing papers on "Business analytics published in 2019"

Spatial Decision Support Systems: Three decades on

Abstract: This paper uses a bibliometric approach to examine the growth of and changes in the Spatial Decision Support Systems (SDSS) field over the past three decades. Bibliographic databases such as Web of Science (WOS) and Scopus provide valuable information on academic disciplines as they contain both the articles published and the articles cited. The articles published, and the disciplinary categorization of where they are published, are indicative of the changing disciplinary balance in SDSS, while the citation links of these papers illustrate the intellectual structure of the field. The analysis shows that despite conceptual links rooted in DSS, the field of SDSS developed largely independently from DSS, with little interaction between both. This is surprising, given the growing importance of spatial applications in DSS and an overlapping interest in business analytics and big data space-time analytics. The paper argues for greater interest in SDSS developments in the DSS field, including emergency response SDSS and public participation SDSS, as two forms of SDSS which extend DSS.

A bibliometric analysis of research on Big Data analytics for business and management

Abstract: The purpose of this paper is to scrutinize and classify the literature linking Big Data analytics and management phenomena.,An objective bibliometric analysis is conducted, supported by subjective assessments based on the studies focused on the intertwining of Big Data analytics and management fields. Specifically, deeper descriptive statistics and document co-citation analysis are provided.,From the document co-citation analysis and its evaluation, four clusters depicting literature linking Big Data analytics and management phenomena are revealed: theoretical development of Big Data analytics; management transition to Big Data analytics; Big Data analytics and firm resources, capabilities and performance; and Big Data analytics for supply chain management.,To the best of the authors’ knowledge, this is one of the first attempts to comprehend the research streams which, over time, have paved the way to the intersection between Big Data analytics and management fields.

Business Intelligence Effectiveness and Corporate Performance Management: An Empirical Analysis

Abstract: Business intelligence (BI) technologies have received much attention from both academics and practitioners, and the emerging field of business analytics (BA) is beginning to generate academic research. However, the impact of BI and the relative importance of BA on corporate performance management (CPM) have not yet been investigated. To address this gap, we modeled a CPM framework based on the Integrative model of IT business value and on information processing theory. Data were collected from a global survey of senior managers in 337 companies. Findings suggest that the more effective the BI implementation, the more effective the CPM-related planning and analytic practices. BI effectiveness is strongly related to BA, planning and to measurement. In contrast, BA effectiveness is strongly related to planning but less so to measurement. The study suggests that although both BI and BA contribute to corporate management practices, the information needs are different based on the level of uncertainty versus ambiguity characteristic of the management practice.

Big Data and Business Analytics: Trends, Platforms, Success Factors and Applications

Abstract: Big data and business analytics are trends that are positively impacting the business world. Past researches show that data generated in the modern world is huge and growing exponentially. These include structured and unstructured data that flood organizations daily. Unstructured data constitute the majority of the world’s digital data and these include text files, web, and social media posts, emails, images, audio, movies, etc. The unstructured data cannot be managed in the traditional relational database management system (RDBMS). Therefore, data proliferation requires a rethinking of techniques for capturing, storing, and processing the data. This is the role big data has come to play. This paper, therefore, is aimed at increasing the attention of organizations and researchers to various applications and benefits of big data technology. The paper reviews and discusses, the recent trends, opportunities and pitfalls of big data and how it has enabled organizations to create successful business strategies and remain competitive, based on available literature. Furthermore, the review presents the various applications of big data and business analytics, data sources generated in these applications and their key characteristics. Finally, the review not only outlines the challenges for successful implementation of big data projects but also highlights the current open research directions of big data analytics that require further consideration. The reviewed areas of big data suggest that good management and manipulation of the large data sets using the techniques and tools of big data can deliver actionable insights that create business values.

A Fuzzy Logic Based Machine Learning Tool for Supporting Big Data Business Analytics in Complex Artificial Intelligence Environments

Abstract: Business analytics use techniques from data science, data mining, artificial intelligence (especially, machine learning), mathematics and statistics to gain insights and understanding on the performance of business processes. The gained insights and knowledge help driving the business planning. As employees play important roles in the business process, having a tool to classify and predict their wage levels is desirable. Such classification and prediction enables the public or private sector to offer competitive wages for recruiting and retaining employees. In this paper, we present a tool for classifying and predicting wage levels. It incorporates fuzzy logic into a machine-learning tool to support business analytics on big data. Evaluation results show the applicability of our tool for classification and prediction of wages levels in the business world, which in turn supports business analytics in complex artificial intelligence environments.

Emerging procurement technology: data analytics and cognitive analytics

Abstract: The purpose of this paper is to elucidate the emerging landscape of procurement analytics. This paper focuses on the following questions: what are the current and future state of procurement analytics?; what changes in the procurement process will be required to enable integration of analytical solutions?; and what future areas of research arise when considering the future state of procurement analytics?,This paper employs a qualitative approach that relies on three sources of information: executive interviews, a review of current and emerging technology platforms and a small survey of subject matter experts in the field.,The procurement analytics landscape developed in this research suggests that the authors will continue to see major shifts in the sourcing and supply chain technology environment in the next five years. However, there currently exists a low usage of advanced procurement analytics, and data integrity and quality issues are preventing significant advances in analytics. This study identifies the need for organizations to establish a coherent approach to collection and storage of trusted organizational data that build on internal sources of spend analysis and contract databases. In addition, current ad hoc approaches to capturing unstructured data must be replaced by a systematic data governance strategy. An important element for organizations in this evolution is managing change and the need to nourish an analytic culture.,While the majority of forward-looking research and reports merely project broad technological impact of cognitive analytics and big data, much of it does not provide specific insights into functional impacts such as the impact on procurement. The analysis of this study provides us with a clear view of the potential for business analytics and cognitive analytics to be employed in procurement processes, and contributes to development of related research topics for future study. In addition, this study suggests detailed implementation strategies of emerging procurement technologies, contributing to the existing body of the literature and industry reports.

Big Data Analytics and Firm Performance: A Systematic Review

Abstract: The literature on big data analytics and firm performance is still fragmented and lacking in attempts to integrate the current studies’ results. This study aims to provide a systematic review of contributions related to big data analytics and firm performance. The authors assess papers listed in the Web of Science index. This study identifies the factors that may influence the adoption of big data analytics in various parts of an organization and categorizes the diverse types of performance that big data analytics can address. Directions for future research are developed from the results. This systematic review proposes to create avenues for both conceptual and empirical research streams by emphasizing the importance of big data analytics in improving firm performance. In addition, this review offers both scholars and practitioners an increased understanding of the link between big data analytics and firm performance.

How do we start? An approach to learning analytics adoption in higher education

Abstract: Purpose The analysis of data collected from user interactions with educational and information technology has attracted much attention as a promising approach to advancing our understanding of the learning process. This promise motivated the emergence of the field of learning analytics and supported the education sector in moving toward data-informed strategic decision making. Yet, progress to date in embedding such data-informed processes has been limited. The purpose of this paper is to address a commonly posed question asked by educators, managers, administrators and researchers seeking to implement learning analytics – how do we start institutional adoption of learning analytics? Design/methodology/approach A narrative review is performed to synthesize the existing literature on learning analytics adoption in higher education. The synthesis is based on the established models for the adoption of business analytics and finding two projects performed in Australia and Europe to develop and evaluate approaches to adoption of learning analytics in higher education. Findings The paper first defines learning analytics and touches on lessons learned from some well-known case studies. The paper then reviews the current state of institutional adoption of learning analytics by examining evidence produced in several studies conducted worldwide. The paper next outlines an approach to learning analytics adoption that could aid system-wide institutional transformation. The approach also highlights critical challenges that require close attention in order for learning analytics to make a long-term impact on research and practice of learning and teaching. Originality/value The paper proposed approach that can be used by senior leaders, practitioners and researchers interested in adoption of learning analytics in higher education. The proposed approach highlights the importance of the socio-technical nature of learning analytics and complexities pertinent to innovation adoption in higher education institutions.

Big Data and Human Resources Management: The Rise of Talent Analytics

Abstract: The purpose of this paper is to discuss the opportunities talent analytics offers HR practitioners. As the availability of methodologies for the analysis of large volumes of data has substantially improved over the last ten years, talent analytics has started to be used by organizations to manage their workforce. This paper discusses the benefits and costs associated with the use of talent analytics within an organization as well as to highlight the differences between talent analytics and other sub-fields of business analytics. It will discuss a number of case studies on how talent analytics can improve organizational decision-making. From the case studies, we will identify key channels through which the adoption of talent analytics can improve the performance of the HR function and eventually of the whole organization. While discussing the opportunities that talent analytics offer organizations, this paper highlights the costs (in terms of data governance and ethics) that the widespread use of talent analytics can generate. Finally, it highlights the importance of trust in supporting the successful implementation of talent analytics projects.

Embedded analytics: improving decision support for humanitarian logistics operations

Abstract: Analytical techniques continue to advance in efficacy, as well as complexity. However, it is sometimes unrealistic to employ complex analyses during time-constrained humanitarian disaster operations. We propose that simple, embedded analytics tools can provide an effective and practical means toward managing humanitarian operations. In this paper, we demonstrate a real-world application of our technique in a patient evacuation context. This paper contributes to literature and practice by showing how simple analytic methods and open-source imagery tools can offer significant value to the humanitarian operations literature. The application also highlights some challenges to drawing a clear picture from disparate data sources in the humanitarian operations domain.

Prescriptive Analytics: A Survey of Emerging Trends And Technologies

Abstract: This paper provides a survey of the state-of-the-art and future directions of one of the most important emerging technologies within business analytics (BA), namely prescriptive analytics (PSA). BA focuses on data-driven decision-making and consists of three phases: descriptive, predictive, and prescriptive analytics. While descriptive and predictive analytics allow us to analyze past and predict future events, respectively, these activities do not provide any direct support for decision-making. Here, PSA fills the gap between data and decisions. We have observed an increasing interest for in-DBMS PSA systems in both research and industry. Thus, this paper aims to provide a foundation for PSA as a separate field of study. To do this, we first describe the different phases of BA. We then survey classical analytics systems and identify their main limitations for supporting PSA, based on which we introduce the criteria and methodology used in our analysis. We next survey, categorize, and discuss the state-of-the-art within emerging, so-called PSA $$^+$$ , systems, followed by a presentation of the main challenges and opportunities for next-generation PSA systems. Finally, the main findings are discussed and directions for future research are outlined.

Big data business analytics as a strategic asset for health care industry

Abstract: The big data includes the enormous flow of data from variety of applications that does not fit into the traditional data base. They deal with the storing, managing and manipulating of the data acquired from various sources at an alarming rate to gather valuable insights from it. The big data analytics is used provide with the new and better ideas that pave way to the improvising of the business strategies with its broader, deeper insights and frictionless actions that leads to an accurate and reliable systems. The paper proposes the big data analytics for the improving the strategic assets in the health care industry by providing with the better services for the patients, gaining the satisfaction of the patients and enhancing the customer relationship.

The role of business analytics in supporting strategy processes: Opportunities and limitations

Abstract: Many organisations consider business analytics to be a key organisational capability. To date, there is little evidence on how organisations have included analytics at the heart of their strategy p...

The role of business analytics in the controllers and management accountants’ competence profiles: An exploratory study on individual-level data

Abstract: In recent years, the rise of big data has led to an obvious shift in the competence profile expected from the controller and management accountant (MA). Among others, business analytics competences and information technology skills are considered a “must have” capability for the controlling and MA profession. As it still remains unclear if these requirements can be fulfilled by today’s employees, the purpose of this study is to examine the supply of business analytics competences in the current competence profiles of controlling professionals in an attempt to answer the question whether or not a skills gap exists.,Based on a set of 2,331 member profiles of German controlling professionals extracted from the business social network XING, a text analytics approach is conducted to discover patterns out of the semi-structured data. In doing so, the second purpose of this study is to encourage researchers and practitioners to integrate and advance big data analytics as a method of inquiry into their research process.,Apart from the mediating role of gender, company size and other variables, the results indicate that the current competence profiles of the controller do not comply with the recent requirements towards business analytics competences. However, the answer to the question whether a skills gap exist must be made cautiously by taking into account the specific organizational context such as level of IT adoption or the degree of job specialization.,Guided by the resource-based view of the firm, organizational theory and social cognitive theory, an explanatory model is developed that helps to explain the apparent skills gap, and thus, to enhance the understanding towards the rationales behind the observed findings. One major limitation to be mentioned is that the data sample integrated into this study is restricted to member profiles of German controlling professionals from foremost large companies.,The insights provided in this study extend the ongoing debate in accounting literature and business media on the skills changes of the controlling and MA profession in the big data era. The originality of this study lies in its explicit attempt to integrate recent advances in data analytics to explore the self-reported competence supplies of controlling professionals based on a comprehensive set of semi-structured data. A theoretically founded explanatory model is proposed that integrates empirically validated findings from extant research across various disciplines.

Exploring the Relationship Between Data Science and Circular Economy: An Enhanced CRISP-DM Process Model

Abstract: To date, data science and analytics have received much attention from organizations seeking to explore how to use their massive volumes of data to create value and accelerate the adoption of Circular Economy (CE) concepts. The correct utilization of analytics with circular strategies may enable a step change that goes beyond incremental efficiency gains towards a more sustainable and circular economy. However, the adoption of such smart circular strategies by the industry is lagging, and few studies have detailed how to operationalize this potential at scale. Motivated by this, this study seeks to address how organizations can better structure their data understanding and preparation to align with overall business and CE goals. Therefore, based on the literature and a case study the relationship between data science and the CE is explored, and a generic process model is proposed. The proposed process model extends the Cross Industry Standard Process for Data Mining (CRISP-DM) with an additional phase of data validation and integrates the concept of analytic profiles. We demonstrate its application for the case study of a manufacturing company seeking to implement the smart circular strategy - predictive maintenance.

Management of ICCT underlying Technologies used for Digital Service Innovation

Abstract: Information Communication and Computation Technology (ICCT) also called as Digital Technology and is considered as a general purpose universal technology due to its ability to solve many problems in the human society related to basic needs, advanced wants, and dreamy desires. In this chapter, initially, we have identified various quality attributes of Digital Service and some prominent published works in digital service innovation. The important underlying technologies of ICCT which are emerging as technologies of 21st century including Artificial intelligence& robotics, Big data & business analytics, Cloud computing& storage, Digital marketing, 3D printing, Internet of Things, Online ubiquitous education, Quantum computing, Information storage technology, and Virtual & Augmented Reality are considered for possible innovations in such industries. The applications of ICCT underlying technologies in some of the prominent service industry sectors are identified and the management of ICCT underlying technology usage strategies for digital service innovation in tertiary sector industries are analysed.

Corporate Attitudes towards Big Data and Its Impact on Performance Management: A Qualitative Study

Abstract: This paper investigates the organizational challenges raised by Big Data and its impact on the business environment with a focus on performance management. We investigate managers’ perceptions, understanding, and attitudes relating to Big Data and its analytics, in terms of opportunities, extent, limitations, challenges, and implications, with specific reference to performance management. The research methodology we adopt is grounded theory: we develop a reflection guide based on research questions covering the impact and challenges of a data-driven culture on business, and the impact on performance management and the decision-making process. The results obtained from senior executives from 21 Romanian companies leads to a conceptual model that distils the major areas arising from the responses and the interrelationships between them. These reveal several key areas of managerial relevance and suggest fruitful action. In particular, we find that the most critical areas requiring intervention lie in the area of awareness and understanding, goal setting, assessing benefits and limitations, learning to trust data, and commitment to an embedded data-driven culture. In addition to changes within organizations themselves, there are also implications for other stakeholders, such as education providers.

How to turn managers into data-driven decision makers

Abstract: The purpose of this paper is to emphasize the need to engender a positive attitude toward business analytics in order for firms to more effectively transform into data-driven businesses, and for business schools to better prepare future managers.,This paper develops and validates a measurement instrument that captures the attitude toward business statistics, the foundation of business analytics. A multi-stage approach is implemented and the validation is conducted with a sample of 311 students from a business school.,The instrument has strong psychometric properties. It is designed so that it can be easily extrapolated to professional contexts and extended to the entire domain of business analytics.,As the advent of a data-driven business world will impact the way organizations function and the way individuals think, work, communicate and interact, it is crucial to engage a transdisciplinary dialogue among domains that have the expertise to help train and transform current and future professionals.,The contribution provides educators and organizations with a means to measure and monitor attitudes toward statistics, the most anxiogenic component of business analytics. This is a first step in monitoring and developing an analytics mindset in both managers and students.,By demonstrating how the advent of the data-driven business era is transforming the DNA and functioning of organizations, this paper highlights the key importance of changing managers’ and all employees’ (to a lesser extent) mindset and way of thinking.

Memetic Algorithms for Business Analytics and Data Science: A Brief Survey.

Abstract: This chapter reviews applications of Memetic Algorithms in the areas of business analytics and data science. This approach originates from the need to address optimization problems that involve combinatorial search processes. Some of these problems were from the area of operations research, management science, artificial intelligence and machine learning. The methodology has developed considerably since its beginnings and now is being applied to a large number of problem domains. This work gives a historical timeline of events to explain the current developments and, as a survey, gives emphasis to the large number of applications in business and consumer analytics that were published between January 2014 and May 2018.

From Digitalization to Data-Driven Decision Making in Container Terminals

Abstract: With the new opportunities emerging from the current wave of digitalization, terminal planning and management need to be revisited by taking a data-driven perspective. Business analytics, as a practice of extracting insights from operational data, assists in reducing uncertainties using predictions and helps to identify and understand causes of inefficiencies, disruptions, and anomalies in intra- and inter-organizational terminal operations. Despite the growing complexity of data within and around container terminals, a lack of data-driven approaches in the context of container terminals can be identified. In this chapter, the concept of business analytics for supporting terminal planning and management is introduced. The chapter specifically focuses on data mining approaches and provides a comprehensive overview on applications in container terminals and related research. As such, we aim to establish a data-driven perspective on terminal planning and management, complementing the traditional optimization perspective.