Review of modern business intelligence and analytics in 2015: How to tame the big data in practice?: Case study - What kind of modern business intelligence and analytics strategy to choose?

The objective of this study was to find out the state of art architecture of modern business intelligence and analytics. Furthermore the status quo of business intelligence and analytics' architecture in an anonymous case company was examined. Based on these findings a future strategy was designed to guide the case company towards a better business intelligence and analytics environment. This objective was selected due to an increasing interest on big data topic. Thus the understanding on how to move on from traditional business intelligence practices to modern ones and what are the available options were seen as the key questions to be solved in order to gain competitive advantage for any company in near future. The study was conducted as a qualitative single-case study. The case study included two parts: an analytics maturity assessment, and an analysis of business intelligence and analytics' architecture. The survey included over 30 questions and was sent to 25 analysts and other individuals who were using a significant time to deal with or read financial reports like for example managers. The architecture analysis was conducted by gathering relevant information on high level. Furthermore a big picture was drawn to illustrate the architecture. The two parts combined were used to construct the actual current maturity level of business intelligence and analytics in the case company. Three theoretical frameworks were used: first framework regarding the architecture, second framework regarding the maturity level and third framework regarding reporting tools. The first higher level framework consisted of the modern data warehouse architecture and Hadoop solution from D'Antoni and Lopez (2014). The second framework included the analytics maturity assessment from the data warehouse institute (2015). Finally the third framework analyzed the advanced analytics tools from Sallam et al. (2015). The findings of this study suggest that modern business intelligence and analytics solution can include both data warehouse and Hadoop components. These two components are not mutually exclusive. Instead Hadoop is actually augmenting data warehouse to another level. This thesis shows how companies can evaluate their current maturity level and design a future strategy by benchmarking their own actions against the state of art solution. To keep up with the fast pace of development, research must be continuous. Therefore in future for example a study regarding a detailed path of implementing Hadoop would be a great addition to this field

BI (XML) Publisher Conversion from Third Party Software in E-Business Suite: An ERP (Enterprise Resource Planning) Reporting Framework Conversion Model

Oracle Business Intelligence Publisher (BI Publisher) is an enterprise reporting framework to develop, manage, and deliver all types of highly formatted documents. It eliminates the need for costly point solutions. End users can easily design report layouts directly in a Web browser or using familiar desktop tools, dramatically reducing the time and cost needed to develop and maintain reports. In addition, it is extremely efficient and highly scalable because it can generate tens of thousands of documents per hour with minimal impact to transactional systems. Furthermore, it is “a template-based publishing solution delivered with the Oracle E-Business Suite, PeopleSoft, Enterprise and JD Edwards EnterpriseOne” (“Business Intelligence Publisher Core Components Guide,” 2008). Today, many companies who are using third-party ERP reports framework want to convert their reporting framework to Oracle BI Publisher framework because they want to reduce the overall cost of development, customization, and ongoing maintenance of their ERP reports. However, converting third party software to BI Publisher is not easy. It is technically challenging, can be costly, and can even fail if the project is lacking a thorough plan and careful implementation. This paper exhibits a case study and constructs a step-by-step conversion model for others to follow. The intended audiences are those companies who are planning on converting their reporting framework to BI Publisher in the Oracle EBS (E-Business Suite) environment

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

Chemical enterprise model and decision-making framework for sustainable chemical product design

The chemical product substitution process is undertaken by chemical industries for complying with regulations, like REACH in Europe. Initially devoted to chemists, chemicals substitution is nowadays a complex process involving corporate, business and engineering stakeholders across the chemical enterprise for orienting the search toward a sustainable solution. We formalize a decision making process framework dedicated to the sustainable chemical product design activity in an industrial context. The framework aims at improving the sharing of information and knowledge and at enabling a collaborative work across the chemical enterprise stakeholders at the strategic, tactical and operational levels. It is supported by information and communication technologies (ICT) and integrates a computer aided molecular design tool. During the initial intelligence phase, a systemic analysis of the needs and usages enables to define the product requirements. In the design phase, they are compiled with the help of a facilitator to generate the input file of a computer aided product design tool. This multiobjective tool is designed to find mixtures with molecular fragments issued from renewable raw materials, and is able to handle environment-health and safety related properties along with process physicochemical properties. The final choice phase discusses the solution relevancy and provides feedback, before launching the product manufacturing. The framework is illustrated by the search of a bio-sourced water–solvent mixture formulation for lithographic blanket wash used in printing industry. The sustainability of the solution is assessed by using the sustainability shades metho

Boundary resource interactions in solution networks

Purpose – This study aims to explore the interactions between two different and potentially complementary boundary resources in coordinating solution networks in a digital platform context: boundary spanners (those individuals who span interorganizational boundaries) and boundary interfaces (the devices that help coordinate interfirm relationships, e.g. electronic data interchanges, algorithms or chatbots). Design/methodology/approach – The authors conducted a multiple case study of three firms using digital platforms to coordinate solution networks in the information communication technology and lighting facility industries. Data were collected from 30 semi-structured interviews, which are complemented by secondary data. Findings – As task complexity increases, smarter digital interfaces are adopted. When the intelligence level of interfaces is low or moderate, they are only used as tools by boundary spanners or to support boundary spanners’ functions. When the intelligence level of interfaces is high or very high, boundary spanners design the interfaces and let them perform tasks autonomously. They are also sometimes employed to complement interfaces’ technological limitations and customers’ limited user ability. Research limitations/implications – The industry contexts of the cases may influence the results. Qualitative case data has limited generalizability. Practical implications – This study offers a practical tool for solution providers to effectively deploy boundary employees and digital technologies to offer diverse customized solutions simultaneously. Originality – This study contributes to the solution business literature by putting forward a framework of boundary resource interactions in coordinating solution networks in a digital platform context. It contributes to the boundary spanning literature by revealing the shifting functions of boundary spanners and boundary interfaces

CFBM - A Framework for Data Driven Approach in Agent-Based Modeling and Simulation

Recently, there has been a shift from modeling driven approach to data driven approach in Agent Based Modeling and Simulation (ABMS). This trend towards the use of data-driven approaches in simulation aims at using more and more data available from the observation systems into simulation models [1, 2]. In a data driven approach, the empirical data collected from the target system are used not only for the design of the simulation models but also in initialization, evaluation of the output of the simulation platform. That raises the question how to manage empirical data, simulation data and compare those data in such agent-based simulation platform. In this paper, we first introduce a logical framework for data driven approach in agent-based modeling and simulation. The introduced framework is based on the combination of Business Intelligence solution and a multi-agent based platform called CFBM (Combination Framework of Business intelligence and Multi-agent based platform). Secondly, we demonstrate the application of CFBM for data driven approach via the development of a Brown Plant Hopper Surveillance Models (BSMs), where CFBM is used not only to manage and integrate the whole empirical data collected from the target system and the data produced by the simulation model, but also to initialize and validate the models. The successful development of the CFBM consists not only in remedying the limitation of agent-based modeling and simulation with regard to data management but also in dealing with the development of complex simulation systems with large amount of input and output data supporting a data driven approach

Impact of Business Intelligence Solutions on Export Performance of Software Firms in Emerging Economies

The article is written with the aim of understanding how well software firms in emerging economies perform when exporting their goods. Focusing on Paraguay as a representative context, a multiple-case-study research design was adopted using different sources of evidence, including 15 in-depth interviews with founders, shareholders, and CEOs. The data were analyzed using grounded theory in order to develop patterns and categories, and to understand differences and regularities. The revised Uppsala internationalization process model was used as a theoretical framework. This article highlights the experts’ views of the impact of business intelligence on the export performance of software firms in Paraguay. Although only a few of the interviewees currently use business intelligence solutions to support international strategic decision-making processes, most of them reveal a desire to use them because they expect it will have a positive impact on export performance and international competitiveness. The main factors for selecting a business intelligence solution are transparency of cost and benefits, excellent client service, and an attractive pricing model. The study results apply to all stakeholders who support the impact of business intelligence systems on the export performance of software firms in emerging economies. The article fulfils an identified need and call for research to study the use and impact of business intelligence on the way an emerging country’s exportation of goods actually performs, and the ability of its software firms to globalize successfully