Simplifying Big Data Analytics System with A Reference Architecture

The internet and pervasive technology like the Internet of Things (i.e. sensors and smart devices) have exponentially increased the scale of data collection and availability. This big data not only challenges the structure of existing enterprise analytics systems but also offer new opportunities to create new knowledge and competitive advantage. Businesses have been exploiting these opportunities by implementing and operating big data analytics capabilities. Social network companies such as Facebook, LinkedIn, Twitter and Video streaming company like Netflix have implemented big data analytics and subsequently published related literatures. However, these use cases did not provide a simplified and coherent big data analytics reference architecture as well as currently, there still remains limited reference architecture of big data analytics. This paper aims to simplify big data analytics by providing a reference architecture based on existing four use cases and subsequently verified the reference architecture with Amazon and Google analytics services

Big Data and Location Analytics II: Applications, Opportunities, and Challenges

Part II of the workshop on Big Data and Location Analytics will provide a thorough overview of location analytics solutions for Big Data. As organizations increasingly deploy powerful analytics solutions to better understand and analyze Big Data, the location component of Big Data is often left unexplored while making decisions. Location Analytics solutions seamlessly integrate Geographic Information Systems based analytical methods with enterprise BI platforms to deliver high impact analytics. Such solutions provide a unified view and real-time information about organizational assets, help to identify patterns in georeferenced data, and assist with evidence and data-driven decision-making. One such location analytics solution, built on a leading BI platform and developed by Esri, a renowned geotechnology organization will be demonstrated. Use cases and value addition aspects of location analytics will be presented by an industry keynoter. The topic is consistent with the “Blue Ocean IS Research” theme of this AMCIS conference

Table2Vec-automated universal representation learning of enterprise data DNA for benchmarkable and explainable enterprise data science.

Enterprise data typically involves multiple heterogeneous data sources and external data that respectively record business activities, transactions, customer demographics, status, behaviors, interactions and communications with the enterprise, and the consumption and feedback of its products, services, production, marketing, operations, and management, etc. They involve enterprise DNA associated with domain-oriented transactions and master data, informational and operational metadata, and relevant external data. A critical challenge in enterprise data science is to enable an effective 'whole-of-enterprise' data understanding and data-driven discovery and decision-making on all-round enterprise DNA. Accordingly, here we introduce a neural encoder Table2Vec for automated universal representation learning of entities such as customers from all-round enterprise DNA with automated data characteristics analysis and data quality augmentation. The learned universal representations serve as representative and benchmarkable enterprise data genomes (similar to biological genomes and DNA in organisms) and can be used for enterprise-wide and domain-specific learning tasks. Table2Vec integrates automated universal representation learning on low-quality enterprise data and downstream learning tasks. Such automated universal enterprise representation and learning cannot be addressed by existing enterprise data warehouses (EDWs), business intelligence and corporate analytics systems, where 'enterprise big tables' are constructed with reporting and analytics conducted by specific analysts on respective domain subjects and goals. It addresses critical limitations and gaps of existing representation learning, enterprise analytics and cloud analytics, which are analytical subject, task and data-specific, creating analytical silos in an enterprise. We illustrate Table2Vec in characterizing all-round customer data DNA in an enterprise on complex heterogeneous multi-relational big tables to build universal customer vector representations. The learned universal representation of each customer is all-round, representative and benchmarkable to support both enterprise-wide and domain-specific learning goals and tasks in enterprise data science. Table2Vec significantly outperforms the existing shallow, boosting and deep learning methods typically used for enterprise analytics. We further discuss the research opportunities, directions and applications of automated universal enterprise representation and learning and the learned enterprise data DNA for automated, all-purpose, whole-of-enterprise and ethical machine learning and data science

Business analytics-based enterprise information systems

Big data analytics and business analytics are a disruptive technology and innovative solution for enterprise development. However, what is the relationship between business analytics, big data analytics, and enterprise information systems (EIS)? How can business analytics enhance the development of EIS? How can analytics be incorporated into EIS? These are still big issues. This article addresses these three issues by proposing ontology of business analytics, presenting an analytics service-oriented architecture (ASOA) and applying ASOA to EIS, where our surveyed data analysis showed that the proposed ASOA is viable for developing EIS. This article then examines incorporation of business analytics into EIS through proposing a model for business analytics service-based EIS, or ASEIS for short. The proposed approach in this article might facilitate the research and development of EIS, business analytics, big data analytics, and business intelligence

Business analytics-based enterprise information systems

Big data analytics and business analytics are a disruptive technology and innovative solution for enterprise development. However, what is the relationship between business analytics, big data analytics, and enterprise information systems (EIS)? How can business analytics enhance the development of EIS? How can analytics be incorporated into EIS? These are still big issues. This article addresses these three issues by proposing ontology of business analytics, presenting an analytics service-oriented architecture (ASOA) and applying ASOA to EIS, where our surveyed data analysis showed that the proposed ASOA is viable for developing EIS. This article then examines incorporation of business analytics into EIS through proposing a model for business analytics service-based EIS, or ASEIS for short. The proposed approach in this article might facilitate the research and development of EIS, business analytics, big data analytics, and business intelligence

Examining the Relationship Between Enterprise Resource Planning (ERP) Implementation: The Role ofBig Data Analytics Capabilities and Firm Performance

Enterprise Resource Planning (ERP) implementation continues to hold attraction from information systems enthusiasts. Perhaps due to the rising budget dedicated to the implementation in many an organization in recent times. However, understanding the critical role that ERP implementation plays in Big Data Analytics Capabilities and firm performance is lacking sufficient treatment in the literature. By applying quantitative research techniques in a case study research orientation through the use of resource-based view theoretical insights, the study takes on three key hypotheses: That ERP implementation has a positive relationship with organizational big data analytics capabilities; Big data analytics capability has a positive effect on firm performance and ERP implementation is positively related to organizational performance. Using Partial Least Squared Structural Equation Model (PLS-SEM)data analysis techniques the study established a direct link between big data analytics capabilities and firm performance, and that ERP has a direct positive and significant effect on big data analytics capabilities. Lastly, it is the claim of this study that big data analytics capabilities have a direct positive and significant effect on firm performance. Part of the implications of the study highlights the need for a qualitative or even mixed method research undertakings to broaden the frontiers of our understanding in terms of ERP implementation and big data analytics capabilities in similar organizational contexts

Supply Chain Management and Big Data Analytics (SCMBDA): Perception to SCM Business

The standards of big data and analytics are being very much buildup with the aid of commercial enterprise executives, media, software program providers and consultant executive. But it is not simply buildup, as some groups are genuinely utilizing big data and analytics in real life experience. Big data and analytics in Supply Chain Management (SCM) has found discovered becoming alluring because of its unpredictability and the extraordinary part. This research proposal highlighting how ?Big Data analytics can be used most productively in managing the supply chain.? They can be utilized to evaluate ?what happened, why it happened, and to develop a plan for change. Based on pre-defined business rules, they can identify where an action is needed, they can help to prepare more accurate forecasts,? and, primarily, they are able to help to determine the best course of motion with WHAT-IF analysis. Materials and Methods used in research proposal describe the promising field of big data analytics in SCM, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the research problem. Possible outcome covered in research proposal how the SCM area can be affected by these new propensities and advancements

Are Malaysian companies ready for the big data economy? A business intelligence model approach

The next big phenomenon within the management accounting practices would be the big data economy.The phenomenon concerns the production of a large stream of data from diverse sources, analyzing these big data so as to provide important insights for better decision making. This paper attempts to evaluate the readiness of Malaysian companies to take advantage of big data by using the Enterprise Business Intelligence Maturity Model (EBIMM) as the evaluation tool.Data were collected from 132 Malaysian large scale enterprises using the EBIMM questionnaire.The results indicated that Malaysian companies are relatively ready for the big data economy.Up to 82% of the organizations surveyed attained the Defined level of maturity and had a decent level of capabilities and competencies to capture the benefits of big data analytics. However, none of the organizations reached the Optimizing level indicating that more investments in technology, talents and culture are required to enable Malaysia to become the regional hub for big data analytics

A survey on context awareness in big data analytics for business applications

The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics by businesses is facilitating such change at an even faster rate in much complicated ways. The potential benefits of embedding contextual information into an application are already evidenced by the improved outcomes of the existing context-aware methods in those applications. Since big data is growing very rapidly, context awareness in big data analytics has become more important and timely because of its proven efficiency in big data understanding and preparation, contributing to extracting the more and accurate value of big data. Many surveys have been published on context-based methods such as context modelling and reasoning, workflow adaptations, computational intelligence techniques and mobile ubiquitous systems. However, to our knowledge, no survey of context-aware methods on big data analytics for business applications supported by enterprise level software has been published to date. To bridge this research gap, in this paper first, we present a definition of context, its modelling and evaluation techniques, and highlight the importance of contextual information for big data analytics. Second, the works in three key business application areas that are context-aware and/or exploit big data analytics have been thoroughly reviewed. Finally, the paper concludes by highlighting a number of contemporary research challenges, including issues concerning modelling, managing and applying business contexts to big data analytics. © 2020, Springer-Verlag London Ltd., part of Springer Nature

The Transformation of Accounting Information Systems Curriculum in the Last Decade

Accounting information systems (AIS) are an extremely important component of accounting and accounting education. The purpose of the current study is to examine the transformation of accounting information systems (AIS) curriculum in the last decade. The motivation for this research comes from the vast advances made in the world of information technology (IT) and information systems (IS). The specific research questions addressed in the current study are: (1) how has AIS curriculum changed in the 18 years since SOX? (2) How has AIS curriculum adjusted in recent years with the emergence of the new hot-button topic big data/data analytics? Overall, this study finds that the core of AIS curriculum has not significantly changed over the last decade. However, more emphasis is being placed on topics such as enterprise wide systems/ERP, IT audits, computer fraud, and transaction-processing. Related, several new topical coverages have been introduced such as business analysts and big data/data analytics. The key contribution of this paper is to provide accounting students and accounting educators with useful information regarding the most significant shifts in AIS over the last decade and insight into the most valuable current AIS topics