Managing the implementation of business intelligence systems : a critical success factors framework

The implementation of a BI system is a complex undertaking requiring considerable resources. Yet there is a limited authoritative set of CSFs for management reference. This article represents a first step of filling in the research gap. The authors utilized the Delphi method to conduct three rounds of studies with 15 BI system experts in the domain of engineering asset management organizations. The study develops a CSFs framework that consists of seven factors and associated contextual elements crucial for BI systems implementation. The CSFs are committed management support and sponsorship, business user-oriented change management, clear business vision and well-established case, business-driven methodology and project management, business-centric championship and balanced project team composition, strategic and extensible technical framework, and sustainable data quality and governance framework. This CSFs framework allows BI stakeholders to holistically understand the critical factors that influence implementation success of BI systems.

An empirical study of business intelligence impact on corporate performance management

Business intelligence technologies have received much attention recently from both academics and practitioners. However, the impact of business intelligence (BI) on corporate performance management (CPM) has not yet been investigated. To address this gap, we conducted a large-scale survey collecting data from 337 senior managers. Partial least square method was employed to analyse the survey data. Findings suggest that the more effective the BI implementation, the more effective the CPM-related planning and analytic practices. Interestingly, size and industry sector do not influence the relationships between BI effectiveness and the CPM. This research offers a number of implications for theory and practice.

Business Intelligence: comparação de ferramentas

Cada vez mais o tempo acaba sendo o diferencial de uma empresa para outra. As empresas, para serem bem sucedidas, precisam da informação certa, no momento certo e para as pessoas certas. Os dados outrora considerados importantes para a sobrevivência das empresas hoje precisam estar em formato de informações para serem utilizados. Essa é a função das ferramentas de “Business Intelligence”, cuja finalidade é modelar os dados para obter informações, de forma que diferencie as ações das empresas e essas consigam ser mais promissoras que as demais. “Business Intelligence” é um processo de coleta, análise e distribuição de dados para melhorar a decisão de negócios, que leva a informação a um número bem maior de usuários dentro da corporação. Existem vários tipos de ferramentas que se propõe a essa finalidade. Esse trabalho tem como objetivo comparar ferramentas através do estudo das técnicas de modelagem dimensional, fundamentais nos projetos de estruturas informacionais, suporte a “Data Warehouses”, “Data Marts”, “Data Mining” e outros, bem como o mercado, suas vantagens e desvantagens e a arquitetura tecnológica utilizada por estes produtos. Assim sendo, foram selecionados os conjuntos de ferramentas de “Business Intelligence” das empresas Microsoft Corporation e Oracle Corporation, visto as suas magnitudes no mundo da informática.

Estratégia para extração, transformação e armazenamento em Data Warehouse ativo baseada em políticas configuráveis de propagação de dados

Pós-graduação em Ciência da Computação - IBILCE

Pervasive Business Intelligence

In the last few years, a new generation of Business Intelligence (BI) tools called BI 2.0 has emerged to meet the new and ambitious requirements of business users. BI 2.0 not only introduces brand new topics, but in some cases it re-examines past challenges according to new perspectives depending on the market changes and needs. In this context, the term pervasive BI has gained increasing interest as an innovative and forward-looking perspective. This thesis investigates three different aspects of pervasive BI: personalization, timeliness, and integration. Personalization refers to the capacity of BI tools to customize the query result according to the user who takes advantage of it, facilitating the fruition of BI information by different type of users (e.g., front-line employees, suppliers, customers, or business partners). In this direction, the thesis proposes a model for On-Line Analytical Process (OLAP) query personalization to reduce the query result to the most relevant information for the specific user. Timeliness refers to the timely provision of business information for decision-making. In this direction, this thesis defines a new Data Warehuose (DW) methodology, Four-Wheel-Drive (4WD), that combines traditional development approaches with agile methods; the aim is to accelerate the project development and reduce the software costs, so as to decrease the number of DW project failures and favour the BI tool penetration even in small and medium companies. Integration refers to the ability of BI tools to allow users to access information anywhere it can be found, by using the device they prefer. To this end, this thesis proposes Business Intelligence Network (BIN), a peer-to-peer data warehousing architecture, where a user can formulate an OLAP query on its own system and retrieve relevant information from both its local system and the DWs of the net, preserving its autonomy and independency.

Progettazione e prototipazione di un sistema di Social Business Intelligence con Hadoop Impala

Il presente elaborato ha come oggetto l’analisi delle prestazioni e il porting di un sistema di SBI sulla distribuzione Hadoop di Cloudera. Nello specifico è stato fatto un porting dei dati del progetto WebPolEU. Successivamente si sono confrontate le prestazioni del query engine Impala con quelle di ElasticSearch che, diversamente da Oracle, sfrutta la stessa componente hardware (cluster).

Applying model-driven engineering to the development of Rich Internet Applications for Business Intelligence

Business Intelligence (BI) applications have been gradually ported to the Web in search of a global platform for the consumption and publication of data and services. On the Internet, apart from techniques for data/knowledge management, BI Web applications need interfaces with a high level of interoperability (similar to the traditional desktop interfaces) for the visualisation of data/knowledge. In some cases, this has been provided by Rich Internet Applications (RIA). The development of these BI RIAs is a process traditionally performed manually and, given the complexity of the final application, it is a process which might be prone to errors. The application of model-driven engineering techniques can reduce the cost of development and maintenance (in terms of time and resources) of these applications, as they demonstrated by other types of Web applications. In the light of these issues, the paper introduces the Sm4RIA-B methodology, i.e., a model-driven methodology for the development of RIA as BI Web applications. In order to overcome the limitations of RIA regarding knowledge management from the Web, this paper also presents a new RIA platform for BI, called RI@BI, which extends the functionalities of traditional RIAs by means of Semantic Web technologies and B2B techniques. Finally, we evaluate the whole approach on a case study—the development of a social network site for an enterprise project manager.

Empowering global software development with business intelligence

Context: Global Software Development (GSD) allows companies to take advantage of talent spread across the world. Most research has been focused on the development aspect. However, little if any attention has been paid to the management of GSD projects. Studies report a lack of adequate support for management’s decisions made during software development, further accentuated in GSD since information is scattered throughout multiple factories, stored in different formats and standards. Objective: This paper aims to improve GSD management by proposing a systematic method for adapting Business Intelligence techniques to software development environments. This would enhance the visibility of the development process and enable software managers to make informed decisions regarding how to proceed with GSD projects. Method: A combination of formal goal-modeling frameworks and data modeling techniques is used to elicitate the most relevant aspects to be measured by managers in GSD. The process is described in detail and applied to a real case study throughout the paper. A discussion regarding the generalisability of the method is presented afterwards. Results: The application of the approach generates an adapted BI framework tailored to software development according to the requirements posed by GSD managers. The resulting framework is capable of presenting previously inaccessible data through common and specific views and enabling data navigation according to the organization of software factories and projects in GSD. Conclusions: We can conclude that the proposed systematic approach allows us to successfully adapt Business Intelligence techniques to enhance GSD management beyond the information provided by traditional tools. The resulting framework is able to integrate and present the information in a single place, thereby enabling easy comparisons across multiple projects and factories and providing support for informed decisions in GSD management.

Design and development of agent-based procurement system to enhance business intelligence

The purpose of this research is to propose a procurement system across other disciplines and retrieved information with relevant parties so as to have a better co-ordination between supply and demand sides. This paper demonstrates how to analyze the data with an agent-based procurement system (APS) to re-engineer and improve the existing procurement process. The intelligence agents take the responsibility of searching the potential suppliers, negotiation with the short-listed suppliers and evaluating the performance of suppliers based on the selection criteria with mathematical model. Manufacturing firms and trading companies spend more than half of their sales dollar in the purchase of raw material and components. Efficient data collection with high accuracy is one of the key success factors to generate quality procurement which is to purchasing right material at right quality from right suppliers. In general, the enterprises spend a significant amount of resources on data collection and storage, but too little on facilitating data analysis and sharing. To validate the feasibility of the approach, a case study on a manufacturing small and medium-sized enterprise (SME) has been conducted. APS supports the data and information analyzing technique to facilitate the decision making such that the agent can enhance the negotiation and suppler evaluation efficiency by saving time and cost.

Actionable pattern analysis in computing and business intelligence systems

With advances in science and technology, computing and business intelligence (BI) systems are steadily becoming more complex with an increasing variety of heterogeneous software and hardware components. They are thus becoming progressively more difficult to monitor, manage and maintain. Traditional approaches to system management have largely relied on domain experts through a knowledge acquisition process that translates domain knowledge into operating rules and policies. It is widely acknowledged as a cumbersome, labor intensive, and error prone process, besides being difficult to keep up with the rapidly changing environments. In addition, many traditional business systems deliver primarily pre-defined historic metrics for a long-term strategic or mid-term tactical analysis, and lack the necessary flexibility to support evolving metrics or data collection for real-time operational analysis. There is thus a pressing need for automatic and efficient approaches to monitor and manage complex computing and BI systems. To realize the goal of autonomic management and enable self-management capabilities, we propose to mine system historical log data generated by computing and BI systems, and automatically extract actionable patterns from this data. This dissertation focuses on the development of different data mining techniques to extract actionable patterns from various types of log data in computing and BI systems. Four key problems—Log data categorization and event summarization, Leading indicator identification , Pattern prioritization by exploring the link structures , and Tensor model for three-way log data are studied. Case studies and comprehensive experiments on real application scenarios and datasets are conducted to show the effectiveness of our proposed approaches.

Using business intelligence to support the process of organizational sensemaking

Making sense of an organization overwhelmed with data becomes a problem for decision makers at all levels of business planning and operation. Although scholars have suggested several technological solutions such as business intelligence as being useful in helping busy executives to make decisions, we still know little about assisting business stakeholders in the process of understanding their organizational complexity before such decisions could even be formulated. In this paper, we investigate the opportunities in using BI technologies to make sense of a business environment. We analyze the views and opinions of developers, analysts, consultants, and users of business intelligence, who are experienced in using the technology beyond decision making to support organizational sensemaking. Our results highlight the need for creating and maintaining individual; and organizational identity and enacting this identity on the business and its environment.

Il passaggio dalla Business Intelligence all'Advanced Analytics: individuazione e analisi di un modello in merito all'influenza del lobbying nel sistema economico

I Big Data stanno guidando una rivoluzione globale. In tutti i settori, pubblici o privati, e le industrie quali Vendita al dettaglio, Sanità, Media e Trasporti, i Big Data stanno influenzando la vita di miliardi di persone. L’impatto dei Big Data è sostanziale, ma così discreto da passare inosservato alla maggior parte delle persone. Le applicazioni di Business Intelligence e Advanced Analytics vogliono studiare e trarre informazioni dai Big Data. Si studia il passaggio dalla prima alla seconda, mettendo in evidenza aspetti simili e differenze.

Business intelligence em sistemas de apoio à gestão de frotas: Análise de Tecnologias e metodologias

O objecto de estudo desta tese de mestrado surgiu da necessidade de dar resposta a uma proposta para uma solução de business intelligence a pedido de um cliente da empresa onde até à data me encontro a desempenhar funções de analista programador júnior. O projecto consistiu na realização de um sistema de monitorização de eventos e análise de operações, portanto um sistema integrado de gestão de frotas com módulo de business intelligence. Durante o decurso deste projecto foi necessário analisar metodologias de desenvolvimento, aprender novas linguagens, ferramentas, como C#, JasperReport, visual studio, Microsoft SQL Server entre outros. ABSTRACT: Business Intelligence applied to fleet management systems - Technologies and Methodologies Analysis. The object of study of this master's thesis was the necessity of responding to a proposal for a business intelligence solution at the request of a client company where so far I find the duties of junior programmer. The project consisted of a system event monitoring and analysis of operations, so an integrated fleet management with integrated business intelligence. During the course of this project was necessary to analyze development methodologies, learn new languages, tools such as C #, JasperReports, visual studio, Microsoft Sql Server and others.