Technology in the 21st Century: New Challenges and Opportunities

Although big data, big data analytics (BDA) and business intelligence have attracted growing attention of both academics and practitioners, a lack of clarity persists about how BDA has been applied in business and management domains. In reflecting on Professor Ayre's contributions, we want to extend his ideas on technological change by incorporating the discourses around big data, BDA and business intelligence. With this in mind, we integrate the burgeoning but disjointed streams of research on big data, BDA and business intelligence to develop unified frameworks. Our review takes on both technical and managerial perspectives to explore the complex nature of big data, techniques in big data analytics and utilisation of big data in business and management community. The advanced analytics techniques appear pivotal in bridging big data and business intelligence. The study of advanced analytics techniques and their applications in big data analytics led to identification of promising avenues for future research

Big data academic and learning analytics: connecting the dots for academic excellence in higher education

Purpose Although big data analytics have great benefits for higher education institutions, due to lack of sufficient evidence on how big data analytics investment can pay off, it is tough for HEIs practitioners to realize value from such adoption. The current study proposes a big data academic and learning analytics enabled business value model to explain big data analytics potential benefits and business value which can be obtained by developing such analytics capabilities in HEIs. Design/methodology/approach The study examined 47 case descriptions from 26 HEIs to investigate the causal association between the big data analytics current and potential benefits and business value creation path for big data academic and learning analytics success in higher education institutions. Findings The pressure of compliance with all legal & regulatory requirements and competition had pushed higher education institutions hard to adopt BDA tools. However, the study found out that application of risk & security and predictive analytics to higher education fields is still in its infancy. Using this theoretical model, our results provide new insights to higher education administrators on ways to create big data analytics capabilities for higher education institutions transformation and suggest an empirical foundation that can lead to more thorough analysis of big data analytics implementation. Originality/value A distinctive theoretical contribution of this study is its conceptualization of understanding business value from big data analytics in the typical setting of higher education. The study provides HEIs with an all-inclusive understanding of big data analytics and gives insights on how it helps to transform HEIs. The new perspectives associated with the big data academic and learning analytics enabled business value model will contribute to future research in this area

Applied business analytics approach to IT projects – Methodological framework

The design and implementation of a big data project differs from a typical business intelligence project that might be presented concurrently within the same organization. A big data initiative typically triggers a large scale IT project that is expected to deliver the desired outcomes. The industry has identified two major methodologies for running a data centric project, in particular SEMMA (Sample, Explore, Modify, Model and Assess) and CRISP-DM (Cross Industry Standard Process for Data Mining). More general, the professional organizations PMI (Project Management Institute) and IIBA (International Institute of Business Analysis) have defined their methods for project management and business analysis based on the best current industry practices. However, big data projects place new challenges that are not considered by the existing methodologies. The building of end-to-end big data analytical solution for optimization of the supply chain, pricing and promotion, product launch, shop potential and customer value is facing both business and technical challenges. The most common business challenges are unclear and/or poorly defined business cases; irrelevant data; poor data quality; overlooked data granularity; improper contextualization of data; unprepared or bad prepared data; non-meaningful results; lack of skill set. Some of the technical challenges are related to lag of resources and technology limitations; availability of data sources; storage difficulties; security issues; performance problems; little flexibility; and ineffective DevOps. This paper discusses an applied business analytics approach to IT projects and addresses the above-described aspects. The authors present their work on research and development of new methodological framework and analytical instruments applicable in both business endeavors, and educational initiatives, targeting big data. The proposed framework is based on proprietary methodology and advanced analytics tools. It is focused on the development and the implementation of practical solutions for project managers, business analysts, IT practitioners and Business/Data Analytics students. Under discussion are also the necessary skills and knowledge for the successful big data business analyst, and some of the main organizational and operational aspects of the big data projects, including the continuous model deployment

How can SMEs benefit from big data? Challenges and a path forward

Big data is big news, and large companies in all sectors are making significant advances in their customer relations, product selection and development and consequent profitability through using this valuable commodity. Small and medium enterprises (SMEs) have proved themselves to be slow adopters of the new technology of big data analytics and are in danger of being left behind. In Europe, SMEs are a vital part of the economy, and the challenges they encounter need to be addressed as a matter of urgency. This paper identifies barriers to SME uptake of big data analytics and recognises their complex challenge to all stakeholders, including national and international policy makers, IT, business management and data science communities. The paper proposes a big data maturity model for SMEs as a first step towards an SME roadmap to data analytics. It considers the ‘state-of-the-art’ of IT with respect to usability and usefulness for SMEs and discusses how SMEs can overcome the barriers preventing them from adopting existing solutions. The paper then considers management perspectives and the role of maturity models in enhancing and structuring the adoption of data analytics in an organisation. The history of total quality management is reviewed to inform the core aspects of implanting a new paradigm. The paper concludes with recommendations to help SMEs develop their big data capability and enable them to continue as the engines of European industrial and business success. Copyright © 2016 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft

Development of Business Analytics Curricula to Close Skills Gap for Job Demand in Big Data

This research paper reviews the external pressure for teaching Business Analytics to business administration students as a necessity in the current era of big data. The cause is obvious. Numerous organizations, businesses, end users are generating incredibly large volumes of data, and new technologies are providing means for creating, transmitting, processing and storing all these data. Consecutively, this is the reason of comprehensible need to find more efficient techniques to analyze data and make use of it as a part of business operations, which in turn cause huge demand for highly trained professionals in this area. New trends and business needs for big data and related technologies force universities to respond with creating business analytics related academic programs.  At some stage of planning and developing Business Analytics program a number of issues must be considered to satisfy the industries’ requirements. The results of a survey of BA curricula of academic programs and current needs of Saudi Arabia industry in have been used as a basis for development of the Business Analytics (BA) curricula presented in this paper. There is an urgent need for training graduate students to manipulate the enormous volumes of data and present results in comprehensible and concise form. Such a program should get students ready for entry to the job market with specialty in Business Analytics, broaden their knowledge of business, link their acquired knowledge to growing industries, and to prepare students to use results of big data analytics for development of business strategies. Graduates of Business Analytics program will apply their knowledge and skills of business analytics in their work in science, business, healthcare, engineering management, government and finance fields. Business schools in KSA are strongly encouraged to initiate appropriate master degree programs in the proposed BA curriculum to minimize the big data skills gap in Saudi Arabia Keywords: Business Analytics, Big Data, Curriculum Development, Surve

Business Intelligence and Big Data Analytics: An Overview

This research investigates the current status of big data business analytics and critical skills necessary to create business value. Business analytics refers to the skills, technologies, applications and practices for continuous iterative exploration and investigation of past business performance to provide actionable insights. Business analytics focuses on developing new insights and understanding of business performance based on data and statistical methods. Big data is used to characterize data sets that are large, diverse and rapidly-changing, as seen by ever- increasing numbers of organizations. Big data require database management systems with capabilities beyond those seen in standard SQL-based systems. According to Manyika et al. (2011), the projected demand for deep business analytical positions could exceed the supply produced with the current trend by 140,000 to 190,000 positions, in addition to the projected need of 1.5 million managers and analysts in dealing with big data business analytics in the United States. Specifically, the emphasis of this research is on how organizations are using big data business analytics and how business school in the United States and across the globe are designing their programs to fill in the talent gap, which leads to a more in-depth analysis on the graduate degree programs in the Greater New York Metropolitan area and potential applications in various industries

Big Data: The Structure and Value of Big Data Analytics

The term Big Data is intuitively appealing and increasingly well accepted in academics as well as practices. Firms readily see the possibility of new business value from big data and future business opportunities. Although they are good understanding what Big Data captures that conventional data do not, the journey for Big Data is difficult and deeply frustrating, as widely known, because of its volume, variety, and velocity. They also get stuck how to collect and analyze Big Data because how-to advice is scarce on this subject and mostly aimed at experts. As a result, Big Data Analytics are considered difficult to implement. The paper discusses that big data have business value and develop a model for measuring its value. We also attempt to design an implementing framework for big data collection as the first step for analytics. This paper can contribute to provide a guideline for studying big data analytics

Big Data Analytics on Cloud: challenges, techniques and technologies

These days it is known that Big Data Analytics is taking a huge attention from researchers and also from business. We all are witness of the data growth that every institution, company or even individuals store in order to use them in the future. There is a big potential to extract useful data from this Big Data that is stored usually in Cloud because sometimes there is not enough local space to store big amounts of data. There is a huge number of sectors where Big Data can be helpful including economic and business activities, public administration, national security, scientific researches in many areas, etc. This data in order to be used must get processed, usually by using Big Data Analytics Techniques. It is for sure that the future of business and technology will be relied on Big Data Analytics. This paper aims to show how big data is analyzed especially when it is deployed on cloud as well as the challenges, techniques and technologies that are used and can be used, in order to analyze Big Data on Cloud. We discuss and implement different methodologies of Big Data Analytics on Cloud

Blending big data analytics : review on challenges and a recent study

With the collection of massive amounts of data every day, big data analytics has emerged as an important trend for many organizations. These collected data can contain important information that may be key to solving wide-ranging problems, such as cyber security, marketing, healthcare, and fraud. To analyze their large volumes of data for business analyses and decisions, large companies, such as Facebook and Google, adopt analytics. Such analyses and decisions impact existing and future technology. In this paper, we explore how big data analytics is utilized as a technique for solving problems of complex and unstructured data using such technologies as Hadoop, Spark, and MapReduce. We also discuss the data challenges introduced by big data according to the literature, including its six V's. Moreover, we investigate case studies of big data analytics on various techniques of such analytics, namely, text, voice, video, and network analytics. We conclude that big data analytics can bring positive changes in many fields, such as education, military, healthcare, politics, business, agriculture, banking, and marketing, in the future. © 2013 IEEE