Leveraging social network data for analytical CRM strategies : the introduction of social BI

The skyrocketing trend for social media on the Internet greatly alters analytical Customer Relationship Management (CRM). Against this backdrop, the purpose of this paper is to advance the conceptual design of Business Intelligence (BI) systems with data identified from social networks. We develop an integrated social network data model, based on an in-depth analysis of Facebook. The data model can inform the design of data warehouses in order to offer new opportunities for CRM analyses, leading to a more consistent and richer picture of customers? characteristics, needs, wants, and demands. Four major contributions are offered. First, Social CRM and Social BI are introduced as emerging fields of research. Second, we develop a conceptual data model to identify and systematize the data available on online social networks. Third, based on the identified data, we design a multidimensional data model as an early contribution to the conceptual design of Social BI systems and demonstrate its application by developing management reports in a retail scenario. Fourth, intellectual challenges for advancing Social CRM and Social BI are discussed.

Enterprise systems curricula for Asia-Pacific region : a vision for 2020

Enterprise Systems (ES) have emerged as possibly the most important and challenging development in the corporate use of information technology in the last decade. Organizations have invested heavily in these large, integrated application software suites expecting improvments in; business processes, management of expenditure, customer service, and more generally, competitiveness, improved access to better information/knowledge (i.e., business intelligence and analytics). Forrester survey data consistently shows that investment in ES and enterprise applications in general remains the top IT spending priority, with the ES market estimated at $38 billion and predicted to grow at a steady rate of 6.9%, reaching $50 billion by 2012 (Wang & Hamerman, 2008). Yet, organizations have failed to realize all the anticipated benefits. One of the key reasons is the inability of employees to properly utilize the capabilities of the enterprise systems to complete the work and extract information critical to decision making. In response, universities (tertiary institutes) have developed academic programs aimed at addressing the skill gaps. In parallel with the proliferation of ES, there has been growing recognition of the importance of Teaching Enterprise Systems at tertiary education institutes. Many academic papers have discused the important role of Enterprise System curricula at tertiary education institutes (Ask, 2008; Hawking, 2004; Stewart, 2001), where the teaching philosophises, teaching approaches and challenges in Enterprise Systems education were discussed. Following the global trends, tertiary institutes in the Pacific-Asian region commenced introducing Enterprise System curricula in late 1990s with a range of subjects (a subject represents a single unit, rather than a collection of units; which we refer to as a course) in faculties / schools / departments of Information Technology, Business and in some cases in Engineering. Many tertiary educations commenced their initial subject offers around four salient concepts of Enterprise Systems: (1) Enterprise Systems implementations, (2) Introductions to core modules of Enterprise Systems, (3) Application customization using a programming language (e.g. ABAP) and (4) Systems Administration. While universities have come a long way in developing curricula in the enterprise system area, many obstacles remain: high cost of technology, qualified faculty to teach, lack of teaching materials, etc.

Learning domain-specific sentiment lexicons for predicting product sales

Generic sentiment lexicons have been widely used for sentiment analysis these days. However, manually constructing sentiment lexicons is very time-consuming and it may not be feasible for certain application domains where annotation expertise is not available. One contribution of this paper is the development of a statistical learning based computational method for the automatic construction of domain-specific sentiment lexicons to enhance cross-domain sentiment analysis. Our initial experiments show that the proposed methodology can automatically generate domain-specific sentiment lexicons which contribute to improve the effectiveness of opinion retrieval at the document level. Another contribution of our work is that we show the feasibility of applying the sentiment metric derived based on the automatically constructed sentiment lexicons to predict product sales of certain product categories. Our research contributes to the development of more effective sentiment analysis system to extract business intelligence from numerous opinionated expressions posted to the Web

Augmenting analytical CRM strategies with social BI

Large communities built around social media on the Internet offer an opportunity to augment analytical customer relationship management (CRM) strategies. The purpose of this paper is to provide direction to advance the conceptual design of business intelligence (BI) systems for implementing CRM strategies. After introducing social CRM and social BI as emerging fields of research, the authors match CRM strategies with a re-engineered conceptual data model of Facebook in order to illustrate the strategic value of these data. Subsequently, the authors design a multi-dimensional data model for social BI and demonstrate its applicability by designing management reports in a retail scenario. Building on the service blueprinting framework, the authors propose a structured research agenda for the emerging field of social BI.

A data analytics case study assessing factors affecting pavement deflection values

Road networks are a national critical infrastructure. The road assets need to be monitored and maintained efficiently as their conditions deteriorate over time. The condition of one of such assets, road pavement, plays a major role in the road network maintenance programmes. Pavement conditions depend upon many factors such as pavement types, traffic and environmental conditions. This paper presents a data analytics case study for assessing the factors affecting the pavement deflection values measured by the traffic speed deflectometer (TSD) device. The analytics process includes acquisition and integration of data from multiple sources, data pre-processing, mining useful information from them and utilising data mining outputs for knowledge deployment. Data mining techniques are able to show how TSD outputs vary in different roads, traffic and environmental conditions. The generated data mining models map the TSD outputs to some classes and define correction factors for each class.

Course quality assurance at a glance

From 2014, QUT will be adopting a life-cycle approach to Course Quality Assurance informed by a wider and richer range of historic, ‘live’ and ‘predictive’ course data. Key data elements continue to be grouped according to the three broad categories – Viability, Quality of Learning Environment and Outcomes – and are further supported with analytic data presented within tables and charts. Course Quality Assurance and this Consolidated Courses Performance Report illuminate aspects of courses from a data evidence base highlighting the strengths and weaknesses of our courses. It provides the framework and tools to achieve QUT's commitment to excellent graduate outcomes by drawing attention and focus to the quality of our courses and providing a structured approach for bringing about change. Our portfolio of courses forms a vital part of QUT, generating almost $600 million in 2013 alone. Real world courses are fundamental to the strength of the Institution; they are what our many thousands of current and future students are drawn to and invest their time and aspirations in. As we move through a period of some regulatory and deregulatory uncertainty, there is a greater need for QUT to monitor and respond to the needs and expectations of our students. The life-cycle approach, with its rich and predicative data, provides the best source of evidence we have had, to date, to assure the quality of our courses and their relevance in a rapidly changing higher education context.

Organizational competencies and dynamic Accounting Information System capability: Impact on AIS processes and firm performance

Accounting information systems (AIS) capture and process accounting data and provide valuable information for decision-makers. However, in a rapidly changing environment, continual management of the AIS is necessary for organizations to optimise performance outcomes. We suggest that building a dynamic AIS capability enables accounting process and organizational performance. Using the dynamic capabilities framework (Teece 2007) we propose that a dynamic AIS capability can be developed through the synergy of three competencies: a flexible AIS, having a complementary business intelligence system and accounting professionals with IT technical competency. Using survey data, we find evidence of a positive association between a dynamic AIS capability, accounting process performance, and overall firm performance. The results suggest that developing a dynamic AIS resource can add value to an organization. This study provides guidance for organizations looking to leverage the performance outcomes of their AIS environment.

Visualização dos dados estatísticos da Uerj: proposta de dashboards baseados no trabalho de Jacques Bertin

Os painéis de gráficos estatísticos conhecidos como dashboards são utilizados comumente naárea de Business Intelligence (BI) para a visualização de grandes sistemas organizados de dados. A presente dissertação propõe embasar o projeto de dashboards pelas teorias de Jacques Bertin, formuladas nas obras Sémiologie Graphique e La Graphique et le Traitement Graphique de linformation. Considerando este referencial, e ainda parâmetros do design de informação e da visualização de dados, foram desenvolvidos dashboards que apresentam dados sobre a política de reserva de vagas da Universidade do Estado do Rio de Janeiro, sistematizados pelo projeto de BI dessa instituição. O objetivo foi não apenas o de atender aos requisitos convencionais de um dashboard, mas sobretudo o de apresentar outras perspectivas informativas. Nesse sentido, investigam-se as especificidades dos métodos de Bertin e sua expansão para o domínio dos sistemas interativos.

商务智能中数据分析代理(DAA)模式的研究与实践

在介绍了数据分析代理的概念后,提出了数据分析代理模式的体系结构,讨论了在不同类型企业中数据分析代理的具体应用模式企业内代理模式和企业外代理模式,对比分析了数据分析传统模式和代理模式二者之间特点,最后举例说明了数据分析代理模式在企业中的具体实践。

商业智能在银联业务分析系统中的应用研究

随着国内金融行业的逐步开放,中国银联也面临着跨国银行卡组织的激烈竞争，跨国银行卡组织拥有先进的IT技术和经营管理经验，和中国银联相比，具有很大竞争优势。银联为了积极面对跨国银行卡组织的挑战，成为一个知名品牌，必须加快信息化建设，转变经营观念，从传统的以业务为中心转移到以客户为中心，而商业智能技术——数据仓库和数据挖掘正是银联信息化建设的重要保障。 本文首先分析了银联的实际业务需求，结合银联的具体业务特点，设计与实现了银联的数据仓库系统，着重对数据仓库技术在银联中的应用现状进行了详细表述；该系统采用总线式的设计架构，有很好的一致性和可扩展性；系统采用维度建模方法进行数据仓库的逻辑设计，维度建模方法能很好地提高系统查询性能，在逻辑设计基础上本文也进行了数据仓库的物理设计。同时本文也详细介绍了数据仓库的重点部分——ETL系统的设计和实现，该ETL系统采用模块化的设计方法，采用元数据作为驱动方式，加强了调度管理和监控的功能，使该ETL工具更具智能性和更好的适应性。 本文在完成银联数据仓库系统建设的基础上，详细分析了银联业务系统要实现的OLAP分析目标，介绍了数据挖掘技术在银联客户分类中的应用，首次尝试在银联数据仓库系统中构建客户分类模型。在客户分类模型的构建中，我们首先采用聚类技术进行客户群分类，然后使用改进的SLIQ算法构建分类模型，本文对SLIQ算法中的符号型属性处理方法及其剪枝算法进行改进，并对结果进行对比分析，得到了一个较为合理的客户分类模型，取得了很好的应用效果，从而为银联数据仓库系统开发应用提供了可借鉴的操作思路。

Design principles for an enterprise systems chartering method

Our research follows a design science approach to develop a method that supports the initialization of ES implementation projects – the chartering phase. This project phase is highly relevant for implementation success, but is understudied in IS research. In this paper, we derive design principles for a chartering method based on a systematic review of ES implementation literature and semi-structured expert interviews. Our analysis identifies differences in the importance of certain success factors depending on the system type. The proposed design principles are built on these factors and are linked to chartering key activities. We specifically consider system-type-specific chartering aspects for process-centric Business Intelligence & Analytics (BI&A) systems, which are an emerging class of systems at the intersection of BI&A and business process management. In summary, this paper proposes design principles for a chartering method – considering specifics of process-centric BI&A.

Economics of new oncology drug development.

PURPOSE: Review existing studies and provide new results on the development, regulatory, and market aspects of new oncology drug development. METHODS: We utilized data from the US Food and Drug Administration (FDA), company surveys, and publicly available commercial business intelligence databases on new oncology drugs approved in the United States and on investigational oncology drugs to estimate average development and regulatory approval times, clinical approval success rates, first-in-class status, and global market diffusion. RESULTS: We found that approved new oncology drugs to have a disproportionately high share of FDA priority review ratings, of orphan drug designations at approval, and of drugs that were granted inclusion in at least one of the FDA's expedited access programs. US regulatory approval times were shorter, on average, for oncology drugs (0.5 years), but US clinical development times were longer on average (1.5 years). Clinical approval success rates were similar for oncology and other drugs, but proportionately more of the oncology failures reached expensive late-stage clinical testing before being abandoned. In relation to other drugs, new oncology drug approvals were more often first-in-class and diffused more widely across important international markets. CONCLUSION: The market success of oncology drugs has induced a substantial amount of investment in oncology drug development in the last decade or so. However, given the great need for further progress, the extent to which efforts to develop new oncology drugs will grow depends on future public-sector investment in basic research, developments in translational medicine, and regulatory reforms that advance drug-development science.