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.

Extending the understanding of critical success factors for implementing business intelligence systems

Extant studies suggest implementing a business intelligence (BI) system is a costly, resource-intensive and complex undertaking. Literature draws attention to the critical success factors (CSFs) for implementation of BI systems. Leveraging case studies of seven large organizations and blending them with Yeoh and Koronios's (2010) BI CSFs framework, our empirical study gives evidence to support this notion of CSFs and provides better contextual understanding of the CSFs in BI implementation domain. Cross-case analysis suggests that organizational factors play the most crucial role in determining the success of a BI system implementation. Hence, BI stakeholders should prioritize on the organizational dimension ahead of other factors. Our findings allow BI stakeholders to holistically understand the CSFs and the associated contextual issues that impact on implementation of BI systems.

Utilização de business intelligence para gestão de área operacional de agências bancárias : um estudo de caso

A eficiência operacional nos bancos é um fator que vem ganhando importância em função da evolução no cenário econômico, apontando para maior competitividade. Nesse contexto, a gestão operacional agências torna-se cada vez mais relevante. Entretanto, a atividade de gerenciar milhares de agências, com necessidade de agilidade na tomada de decisões, mostra-se complexa. Nesse sentido, o Business Intelligence se apresenta como uma solução para otimizar a atividade de gestão, adicionando inteligência ao negócio. Não obstante, questões práticas de implementação e uso são desafios para unir BI e gestão de agências bancárias. Este trabalho analisa a aplicação de Business Intelligence para a gestão operacional de agências bancárias em busca de práticas relevantes. O método de pesquisa utilizado é o estudo de caso, aplicado em uma grande instituição financeira nacional. Por meio de consulta a documentações, entrevistas com Gerentes Regionais e Equipe de Projeto buscou-se verificar proposições que foram depreendidas da revisão da literatura sob dois aspectos: implementação e utilização da solução de BI. Como resultado, foram confirmadas as proposições apontando para importância do apoio da organização e alinhamento ao negócio para uma implementação bem sucedida, além da constatação que BI não pode ser tratado apenas como uma ferramenta, na verdade além da parte técnica, envolve processos e negócios. Com relação à utilização, foi verificado que BI traz mais qualidade à informação, melhora o suporte ao processo de tomada de decisão e trás benefícios intangíveis e tangíveis para a gestão operacional de agências bancárias, como aumento da produtividade, redução de custos e riscos e melhor atendimento ao cliente.

A relação entre business intelligence e o processo de inovação no mercado de cartões de crédito brasileiro : um estudo de caso

As ferramentas de Business Intelligence se tornaram elemento importante no contexto organizacional em função de fornecerem às empresas informações necessárias para o processo decisório. Para garantir vantagem competitiva perante os concorrentes, as empresas buscam inovar. O processo atrelado ao fenômeno da inovação é complexo e depende de uma série de fatores, tais como regulação, pressão do consumidor e tecnologia. O mercado de cartões de crédito no Brasil cresce em ritmo acelerado, com recente concentração em poucos competidores e grande variedade de produtos ofertados. Assim dito, a presente pesquisa busca analisar de que maneira o ferramental de BI se relaciona com o processo de inovação no setor de cartões de crédito brasileiro. O método de pesquisa escolhido foi o estudo de caso, realizado em uma empresa emissora de cartões de crédito com tradição no mercado. A partir de entrevistas semi-estruturadas com executivos de diversas áreas, de coleta de documentação existente e estudo das ferramentas de BI aplicadas no caso, propõe-se uma matriz da relação entre as ferramentas de BI e inovação. Como resultado conclui-se que as ferramentas de BI podem influenciar diretamente o processo de inovação, definindo novos atributos de preço, segmentos e programas de incentivo, ou indiretamente, servindo somente como insumo para discussões, identificação de gaps e formulação de estratégias, tendo uma relação indireta com o processo de inovação.

Realizzazione di un sistema di Social Business Intelligence basato sul motore SPSS

Realizzazione di un sistema di Social Business Intelligence basato sul motore SPSS.

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.

Studio e implementazione di una soluzione di business intelligence a supporto dell'analisi economico finanziaria

Progetto di tesi svolto in azienda. Studio dei principali concetti di Business Intelligence (BI) e degli strumenti per la BI. Confronto tra i principali vendor nel mercato dell'analisi dei dati e della Business Intelligence. Studio e reigegnerizzazione di un modello per l'analisi economico finanziaria dei fornitori/clienti di un'azienda. Realizzazione di un prototipo del modello utilizzando un nuovo strumento per la reportistica: Tableau. Il prototipo si basa su dati economici finanziari estratti da banche dati online e forniti dall'azienda cliente. Implementazione finale del database e di un flusso automatico per la riclassificazione dei dati di bilancio.

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.

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.

La Business Intelligence per il monitoraggio delle vendite: il caso Ducati Motor Holding

“La Business Intelligence per il monitoraggio delle vendite: il caso Ducati Motor Holding”. L’obiettivo di questa tesi è quello di illustrare cos’è la Business Intelligence e di mostrare i cambiamenti verificatisi in Ducati Motor Holding, in seguito alla sua adozione, in termini di realizzazione di report e dashboard per il monitoraggio delle vendite. L’elaborato inizia con una panoramica generale sulla storia e gli utilizzi della Business Intelligence nella quale vengono toccati i principali fondamenti teorici: Data Warehouse, data mining, analisi what-if, rappresentazione multidimensionale dei dati, costruzione del team di BI eccetera. Si proseguirà mediante un focus sui Big Data convogliando l’attenzione sul loro utilizzo e utilità nel settore dell’automotive (inteso nella sua accezione più generica e cioè non solo come mercato delle auto, ma anche delle moto), portando in questo modo ad un naturale collegamento con la realtà Ducati. Si apre così una breve overview sull’azienda descrivendone la storia, la struttura commerciale attraverso la quale vengono gestite le vendite e la gamma dei prodotti. Dal quarto capitolo si entra nel vivo dell’argomento: la Business Intelligence in Ducati. Si inizia descrivendo le fasi che hanno fino ad ora caratterizzato il progetto di Business Analytics (il cui obiettivo è per l'appunto introdurre la BI i azienda) per poi concentrarsi, a livello prima teorico e poi pratico, sul reporting sales e cioè sulla reportistica basata sul monitoraggio delle vendite.

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.

Supporting business intelligence usage: an integrated framework with automatic weighting

This paper presents an integrated framework that comprises an automatic weighting method for assessing data quality (DQ) of the framework so as to better support the business intelligence (BI) usage. Specifically, we utilize business process modeling (BPM) notation and information product map and frame them into a hierarchical mapping structure. Furthermore, we develop and demonstrate an automatic weight-assignment method for evaluating critical dimensions (i.e., completeness and accuracy) of DQ of the integrated framework. Through a design science paradigm, the effectiveness of the framework and the associated DQ weighting method has been rigorously validated by faculty management users of a university. The framework together with the DQ weighting method builds user confidence by enhancing the traceability of a BI product. The automatic DQ weight assignment also provides better time efficiency because the weight of each data attribute is determined automatically based on its usage on the BI dashboard.

Using business intelligence to support the process of organizational sensemaking.

Morteza’s thesis investigated the opportunities in using BI technologies to make sense of a business environment. The results of his research highlighted the need for creating and maintaining an identity for Business Intelligence at both individual and organizational level and enacting this identity on the business and its environment.