Practices that organizations employ to enhance business intelligence agility

In today's rapidly changing business environment, organizations strive to be agile in order to accommodate changes and seize opportunities. Since organizations use information system as a tool to serve their needs, it is important for these systems also to be agile. One prominent type of such systems is business intelligence, which provides organizations with information to gain and retain competitive advantage. This thesis focuses on business intelligence agility, which is widely discussed in practice however not extensively covered in information systems literature. Therefore, this thesis seeks to identify the practices employed by organizations to enhance business intelligence agility. To find the answer to the research question this thesis first compiles a theoretical framework on business intelligence, information systems agility in general and business intelligence agility in specific using academic literature and market white papers. This compiled framework is comprised of four enabling factors 1) sensing business changes, 2) development approach, 3) IT governance, and 4) technical factors. This thesis conducts a qualitative research based on semi-structured interviews with business intelligence experts. Based on analysis of the empirical data this thesis identified a set of practices organized in terms of the enabling factors. The practices in sensing business changes are enabling business staff to sense changes and incorporating business staff feedback into data requirements. Regarding development approach, this thesis identifies the practices as applying an iterative development approach, building collaborative team of skilled members, enabling a centric role of business staff, reducing use of approval documents and learning from each project. In IT governance, applying a centralized or decentralized development were the two practices. Regarding practices in technical factors, this thesis identifies integrating data through either building an enterprise-wide data warehouse or applying an appropriate modeling approach while managing multiple data warehouses, using multiple front-end applications, and adopting cloud business intelligence. The findings of this thesis provide organizations with a pool of practices that can be used to enhance business intelligence agility

BUSINESS INTELLIGENCE FOR BUSINESS PROCESSES: THE CASE OF IT INCIDENT MANAGEMENT

IT service desks have become an integral part of intra-enterprise ecosystems, keeping IT hardware and software services within the company running. Business Intelligence methods have an enormous potential to support IT helpdesk employees by making implicit knowledge explicit, accelerating business processes throughout the entire company, and retaining the knowledge of experienced employees upon retirement. In this paper, we investigate these benefits by showing how analytics can automate the assignment of helpdesk tasks, enable early warning mechanisms for accumulated incidents, and enhance knowledge sharing among helpdesk users. For this purpose, we use a combination of topic modeling and predictive analytics, which is applied to an extensive dataset of support tickets from a global automotive supplier. Our approach identifies relevant topics and assigns these to helpdesk tickets, thereby decoding implicit knowledge into formal rules and business processes

A Maturity Model of Data Modeling in Self-Service Business Intelligence Software

Although Self-Service Business Intelligence (SSBI) is continually being adopted in various industries, there is a lack of research focused on data modeling in SSBI. This research aims to fill that research gap and propose a maturity model for SSBI data modeling which is generalizeable between different software and applicable for users of all technical backgrounds. Through extensive literature review, a five-tier maturity model was proposed, explained, and instantiated in PowerBI and Tableau. The testing of the model was found to be simple and intuitive, and the research concludes that the model is applicable to enterprise SSBI environments. This research is limited to SSBI software and does not consider architecture specifications such as data warehouse or hardware limitations, and could be expanded in future research to include those considerations

Toward Business Integrity Modeling and Analysis Framework for Risk Measurement and Analysis

Financialization has contributed to economic growth but has caused scandals, misselling, rogue trading, tax evasion, and market speculation. To a certain extent, it has also created problems in social and economic instability. It is an important aspect of Enterprise Security, Privacy, and Risk (ESPR), particularly in risk research and analysis. In order to minimize the damaging impacts caused by the lack of regulatory compliance, governance, ethical responsibilities, and trust, we propose a Business Integrity Modeling and Analysis (BIMA) framework to unify business integrity with performance using big data predictive analytics and business intelligence. Comprehensive services include modeling risk and asset prices, and consequently, aligning them with business strategies, making our services, according to market trend analysis, both transparent and fair. The BIMA framework uses Monte Carlo simulation, the Black–Scholes–Merton model, and the Heston model for performing financial, operational, and liquidity risk analysis and present outputs in the form of analytics and visualization. Our results and analysis demonstrate supplier bankruptcy modeling, risk pricing, high-frequency pricing simulations, London Interbank Offered Rate (LIBOR) rate simulation, and speculation detection results to provide a variety of critical risk analysis. Our approaches to tackle problems caused by financial services and the operational risk clearly demonstrate that the BIMA framework, as the outputs of our data analytics research, can effectively combine integrity and risk analysis together with overall business performance and can contribute to operational risk research

Intelligence approach in improving business processes

Nowadays, business intelligence (BI) has the top-most priority for the contemporary enterprises. The aim of this paper is to emphasize the advantages of this computer based approach in improving business processes. Data analytics and modeling of sales prediction system for enterprise is realized with artificial neural networks (ANNs). For the purpose of this research are created over 100 artificial neural networks. Three types of artificial neural networks are designed and evaluated: Function fitting neural networks, Focused Time-delay neural networks and NARX (Non-linear autoregressive) neural networks. Also were performed simulations with different architectures that differ to the number of delays and the number of neurons in the hidden layer. The network prediction performance was evaluated with Average Percentage Error (APE) and Root Mean Square Error (RMSE). The obtained results show big accuracy in prediction of the product sale. The precise prediction has influence to optimization of most of the business processes such as: supply of raw materials, organization of production process, staff scheduling, plan the electricity demand, cost reduction etc

Introduction to the thematic issue on Intelligent systems, applications and environments for the industry of the future

Recent advances in the area of ubiquitous computing, ambient intelligence and intelligent environments are making inroads in business-oriented application domains. This issue of JAISE addresses core topics on the design, use and evaluation of smart applications and systems for the factory of the future, an emerging trend perhaps better known as Industry 4.0. The digital transformation in the enterprise envisioned by Industry 4.0 will entwine the cyber-physical world and real world of manufacturing to deliver networked production with enhanced process transparency. Production systems, data analytics and cloud-enabled business processes will interact directly with customers to realize the ambitious goal of single lot individualized manufacturing. This thematic issue features a survey and 5 research articles which address the modeling, designing, implementation, assessment and management of intelligent systems, applications and environments that will shape and advance the smart industry of the future.status: publishe

The necessities for building a model to evaluate Business Intelligence projects- Literature Review

In recent years Business Intelligence (BI) systems have consistently been rated as one of the highest priorities of Information Systems (IS) and business leaders. BI allows firms to apply information for supporting their processes and decisions by combining its capabilities in both of organizational and technical issues. Many of companies are being spent a significant portion of its IT budgets on business intelligence and related technology. Evaluation of BI readiness is vital because it serves two important goals. First, it shows gaps areas where company is not ready to proceed with its BI efforts. By identifying BI readiness gaps, we can avoid wasting time and resources. Second, the evaluation guides us what we need to close the gaps and implement BI with a high probability of success. This paper proposes to present an overview of BI and necessities for evaluation of readiness. Key words: Business intelligence, Evaluation, Success, ReadinessComment: International Journal of Computer Science & Engineering Survey (IJCSES) Vol.3, No.2, April 201

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page