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

A systematic literature review on hospitality analytics

With the growth of data generated by all systems involved in a hotel, terms like big data and business analytics (BA) gain strength within the hotel industry. Business analytics can be used in hospitality management to increase business knowledge and to improve the decision-making process. This study's main questions are: RQ1 – Which are the main research attributes studied in the past two decades related to analytics in the hospitality sector? RQ2 – What are the main differences between business intelligence and business analytics? RQ3 – What are the main trends in business analytics? RQ4 – Which are the main business intelligence perceptions and beliefs? To answer these research questions, this article provides a literature review to systematize the research made in business analytics information systems in the hospitality industry. The results can help identify different research attributes and the most relevant theories developed in the past two decades related to business analytics tools.info:eu-repo/semantics/publishedVersio

Business Intelligence and Big Data in Higher Education: Status of a Multi-Year Model Curriculum Development Effort for Business School Undergraduates, MS Graduates, and MBAs

Business intelligence (BI), “big data”, and analytics solutions are being deployed in an increasing number of organizations, yet recent predictions point to severe shortages in the number of graduates prepared to work in the area. New model curriculum is needed that can properly introduce BI and analytics topics into existing curriculum. That curriculum needs to incorporate current big data developments even as new dedicated analytics programs are becoming more prominent throughout the world. This paper contributes to the BI field by providing the first BI model curriculum guidelines. It focuses on adding appropriate elective courses to existing curriculum in order to foster the development of BI skills, knowledge, and experience for undergraduate majors, master of science in business information systems degree students, and MBAs. New curricula must achieve a delicate balance between a topic’s level of coverage that is appropriate to students’ level of expertise and background, and it must reflect industry workforce needs. Our approach to model curriculum development for business intelligence courses follows the structure of Krathwohl’s (2002) revised taxonomy, and we incorporated multi-level feedback from faculty and industry experts. Overall, this was a long-term effort that resulted in model curriculum guidelines

Big Data Management in United States Hospitals: Benefits and Barriers

Big Data has been considered as an effective tool to reduce healthcare costs by eliminating adverse events and reducing readmissions in hospitals. The purpose of this study was to examine the emergence of Big Data in the United Sates healthcare industry, to evaluate hospital’s ability to effectively make use of complex information, and to predict the potential benefits hospitals might realize if they are successful. The findings of the research suggest that there were a number of benefits expected by hospitals when using Big Data analytics, including cost savings and business intelligence. In addition, hospitals have recognized that there have been challenges including lack of experience and cost of developing the analytics. Many hospitals will need to invest the expense of acquiring adequate personnel with experience in Big Data analytics and data integration. The findings of this study suggest that the adoption, implementation, and utilization of Big Data technology will have a profound positive impact among healthcare providers

Identifying smart design attributes for Industry 4.0 customization using a clustering Genetic Algorithm

Industry 4.0 aims at achieving mass customization at a mass production cost. A key component to realizing this is accurate prediction of customer needs and wants, which is however a challenging issue due to the lack of smart analytics tools. This paper investigates this issue in depth and then develops a predictive analytic framework for integrating cloud computing, big data analysis, business informatics, communication technologies, and digital industrial production systems. Computational intelligence in the form of a cluster k-means approach is used to manage relevant big data for feeding potential customer needs and wants to smart designs for targeted productivity and customized mass production. The identification of patterns from big data is achieved with cluster k-means and with the selection of optimal attributes using genetic algorithms. A car customization case study shows how it may be applied and where to assign new clusters with growing knowledge of customer needs and wants. This approach offer a number of features suitable to smart design in realizing Industry 4.0

Data Analytics

This chapter sets out to illustrate the dictum that there is (almost) nothing new under the sun. More specifically, its goal is to make the unfamiliar familiar within the field of data analytics. The need for such a treatment can be gauged from the plethora of terms currently vying for attention in the contemporary data analysis landscape, which can be puzzling even for seasoned researchers. These terms include: data mining, data science, data analytics, machine learning, deep learning, neural networks, and artificial intelligence. Hybrid terms such as ‘big data analytics’ are also emerging. As for the current front-runner term, data analytics, the evidence provided by the number of search engine hits reveals multiple competing versions subdivided by application domains, ranging from business analytics and crime analytics, to performance analytics, visual analytics, and many more. There is also an emerging software sub-industry providing tools for data analytics, many of which are named after the company which originally developed them

Effective modelling for predictive analytics in data science

Predictive analytics includes many statistical and other empirical methods that create various data predictions as well as different methods for assessing predictive power. Predictive analytics not only helps in creating practically useful models but also plays an important role in building new theory for further study and research. Today, the use of available data to extract inferences and predictions by using predictive analytics has grown in the industry from being a small department in large companies to being an active component in most mid to large sized organizations. This paper addresses to reduce a particularly large gap of, the nearabsence of empirical or factual predictive analytics in the mainstream research going on in this field by analyzing the issues faced in predictive modelling by the empirical determination of data with its experimental facts for latency pattern.Keywords: Predictive Analytics, Big Data, Business Intelligence, Project Planning

Using Data Analytics to Derive Business Intelligence: A Case Study

The data revolution experienced in recent times has thrown up new challenges and opportunities for businesses of all sizes in diverse industries. Big data analytics is already at the forefront of innovations to help make meaningful business decisions from the abundance of raw data available today. Business intelligence and analytics has become a huge trend in todays IT world as companies of all sizes are looking to improve their business processes and scale up using data driven solutions. This paper aims to demonstrate the data analytical process of deriving business intelligence via the historical data of a fictional bike share company seeking to find innovative ways to convert their casual riders to annual paying registered members. The dataset used is freely available as Chicago Divvy Bicycle Sharing Data on Kaggle. The authors used the RTidyverse library in RStudio to analyse the data and followed the six data analysis steps of ask, prepare, process, analyse, share, and act to recommend some actionable approaches the company could adopt to convert casual riders to paying annual members. The findings from this research serve as a valuable case example, of a real world deployment of BIA technologies in the industry, and a demonstration of the data analysis cycle for data practitioners, researchers, and other potential users

Is it too early to learn lessons from the developed world on the potential of big data technology: Harnessing and nurturing intuition in organisational decision makers

YesAs big data (BD) and data analytics gain significance in Europe, the industry expects that executives will eventually move towards evidence based decision making, and consequently would build sustainable competitive advantages for their organisations. Therefore, the lessons learned from experiences of European executives can be key for human development and also economic development. However, it also seems that in some cases decision makers in Europe seem to not use business intelligence systems at all. Since, executives are intelligent human beings with credible and proven expertise, it seems to raise a question mark on effectiveness of business intelligence systems, and the potential it has in human and economic development. Furthermore, repeated evaluation of literature pointed out that ultimately executives in Europe make decisions by intuition, and this leads to the question whether big data would ever replace intuition. In this paper, the mind-sets of executives about application and limitations of big data have been explored, by taking into account the cognitive factors in decision making. By using this, it is evaluated whether BD technologies can use to accelerate intuition development of the executives, and consequently lead to faster and sustainable economic development in the developing world

Challenges and drivers for data mining in the AEC sector

Purpose: This paper explores the current challenges and drivers for data mining in the AEC sector. Design/methodology/approach: Following a comprehensive literature review, the data mining concept was investigated through a workshop with industry experts and academics. Findings: The results showed that the key drivers for using data mining within the AEC sector is associated with the sustainability, process improvement, market intelligence, cost certainty and cost reduction, performance certainty and decision support systems agendas in the sector. As for the processes with the greatest potential for data mining application, design, construction, procurement, forensic analysis, sustainability and energy consumption and reuse of digital components were perceived as the main process areas. While the key challenges were perceived as being, data issues due to the fragmented nature of the construction process, the need for a cultural change, IT systems used in silos, skills requirements and having clearly defined business goals. Originality/value: With the increasing abundance of data, business intelligence and analytics and its related concepts, data mining and big data have captured the attention of practitioners and academics for the last 20 years. On the other hand, and despite the growing amount of data in its business context, the AEC sector still lags behind in utilising those concepts in its end products and daily operations with limited research conducted to explore those issues at the sector level. This paper investigates the main opportunities and barriers for Data Mining in the AEC sector with a practical focus. Keywords: Business analytics, Data Mining, Data Analytics, AEC, Facilities Managemen