Business Intelligence Tools Research Articles

Business Intelligence Adoption in Developing Economies: A Case Study of Ghana

In this era of competitive market and ever sharply changing market trends, there is a need for timely and integrated engagement of information systems for effective decision making. Developed countries and developed economies are evolving their information systems by aligning them to incorporate adaptive approaches to business models in realtime with forecasting capabilities. This has seen changes leading to the adoption of service-based system integration to big data platforms. Developing economies and developing countries, to some extent, incorporate business intelligence techniques in evaluating their data to make informed decisions. In our work, we considered the banking sector in developing economies with focus on Ghana as a case study in assessing the impact of the adoption of business intelligence tools with respect to its usefulness in the competitive emerging financial market. At the end of our study, those who adapted the approaches in business intelligence have experienced effective competitive advantage and dominance in the market and have excelled in their product delivery to customer satisfaction. General Terms Information Systems, Business Intelligence, Information Technology Management, Market Intelligence

Possibilities of Usage of Strategic Business Intelligence Systems Based on Databases in Agile Manufacturing

Organizations must have the ability of responding quickly to predictable and unpredictable changes in the market. Organizations’ ability to adapt to the fast changing conditions depending on the differing requirements of the customers, depend to a great extend on their ability to become agile. Since change and ambiguity always exist and always will in future, the necessity of the organizations to understand these changes in environment conditions quickly and to be able to respond gains importance every passing day. This requirement provides the wide spreading of agile manufacturing philosophy in organizations. Organizations, due to agile manufacturing, will be able to have the chance of turning the ambiguities in the environment to possibilities. Today, accessing correct and more detailed information became possible however another problem has arisen. This problem is the management of large numerical data stacks and their becoming meaningful. The work of turning raw data into information or making them meaningful can be made with data warehouse and business intelligence tools (data mining, inquiry-reporting, OLAP). At the stage of quickly accessing and using data which will form the basis of agile manufacturing work, creation of data warehouses and using them by integrating into organization information system will be useful. In this work, information is tried to be provided with the literature scanning about the possibility of usage of data warehouse and data base architecture during agile manufacturing process and with business intelligence tools.

Flexible Data Warehouse Parameters: Toward Building an Integrated Architecture

Clinical databases have gathered a huge amount of information about patients and their medical conditions. Relationships and patterns within this data could provide new medical knowledge. Thus it is a difficult task focusing to model a data warehouse, very often, into internal structures and implementation applications. The aim of is project is to find the parameters of medical data warehouse which will be able to construct a flexible framework by analyze the approaches of data warehouse architectures and compare them regarding the cost and integrity. The proposed parameters may assist in achieving continual access to the common data warehouse. The important consideration, however, is that the clinical data record should contain not only longitudinal health summary information but also be used for business intelligence tool such as data mining and OLAP

Fine-Grained Knowledge Sharing in Collaborative Environments

In collaborative environments, members may try to acquire similar information on the web in order to gain knowledge in one domain. For example, in a company several departments may successively need to buy business intelligence software and employees from these departments may have studied online about different business intelligence tools and their features independently. It will be productive to get them connected and share learned knowledge. We investigate fine-grained knowledge sharing in collaborative environments. We propose to analyze members’ web surfing data to summarize the fine-grained knowledge acquired by them. A two-step framework is proposed for mining fine-grained knowledge: (1) web surfing data is clustered into tasks by a nonparametric generative model; (2) a novel discriminative infinite Hidden Markov Model is developed to mine fine-grained aspects in each task. Finally, the classic expert search method is applied to the mined results to find proper members for knowledge sharing. Experiments on web surfing data collected from our lab at UCSB and IBM show that the fine-grained aspect mining framework works as expected and outperforms baselines. When it is integrated with expert search, the search accuracy improves significantly, in comparison with applying the classic expert search method directly on web surfing data.

Optimization of common table expressions in MPP database systems

Big Data analytics often include complex queries with similar or identical expressions, usually referred to as Common Table Expressions (CTEs). CTEs may be explicitly defined by users to simplify query formulations, or implicitly included in queries generated by business intelligence tools, financial applications and decision support systems. In Massively Parallel Processing (MPP) database systems, CTEs pose new challenges due to the distributed nature of query processing, the overwhelming volume of underlying data and the scalability criteria that systems are required to meet. In these settings, the effective optimization and efficient execution of CTEs are crucial for the timely processing of analytical queries over Big Data. In this paper, we present a comprehensive framework for the representation, optimization and execution of CTEs in the context ofOrca-- Pivotal's query optimizer for Big Data. We demonstrate experimentally the benefits of our techniques using industry standard decision support benchmark.

Augmenting Predictive Modeling Tools with Clinical Insights for Care Coordination Program Design and Implementation.

Context:The Center for Medicare and Medicaid Innovation (CMMI) awarded Denver Health’s (DH) integrated, safety net health care system $19.8 million to implement a “population health” approach into the delivery of primary care. This major practice transformation builds on the Patient Centered Medical Home (PCMH) and Wagner’s Chronic Care Model (CCM) to achieve the “Triple Aim”: improved health for populations, care to individuals, and lower per capita costs.Case description:This paper presents a case study of how DH integrated published predictive models and front-line clinical judgment to implement a clinically actionable, risk stratification of patients. This population segmentation approach was used to deploy enhanced care team staff resources and to tailor care-management services to patient need, especially for patients at high risk of avoidable hospitalization. Developing, implementing, and gaining clinical acceptance of the Health Information Technology (HIT) solution for patient risk stratification was a major grant objective.Findings:In addition to describing the Information Technology (IT) solution itself, we focus on the leadership and organizational processes that facilitated its multidisciplinary development and ongoing iterative refinement, including the following: team composition, target population definition, algorithm rule development, performance assessment, and clinical-workflow optimization. We provide examples of how dynamic business intelligence tools facilitated clinical accessibility for program design decisions by enabling real-time data views from a population perspective down to patient-specific variables.Conclusions:We conclude that population segmentation approaches that integrate clinical perspectives with predictive modeling results can better identify high opportunity patients amenable to medical home-based, enhanced care team interventions.

Merging Balanced Scorecards and Business Intelligence Techniques: An Applied case on the IT Subsidiary Company in the Egyptian Civil Aviation

A very important challenge for organizations is to have an integrated measurement framework that effectively defines the performance measures and connect them with the organizations’ strategies, then to analyze and report the results efficiently. Here comes the role of the Business Intelligence (BI) as an essential key factor that presents and translates the measurement results, for the performance measurement is one of the BI techniques. This paper focuses on adopting the Balanced Scorecard (BSC) as a key framework for performance measurement to manage and measure the organizations’ businesses. Merging both BI and BSC approaches gives a very strong roadmap to identify strategic goals, measures and take the right actions according to the measurement results. In this paper, the BSC model was applied along with some BI tools such as dashboards, analytical charts and performance measures on the IT arm for the civil aviation in Egypt to define clear connections between strategic objectives, the business processes by which the strategies are executed, and the Key Performance Indicators (KPIs) that measure business performance. Moreover, the current system was fully evaluated, and suggested corrective actions were reported to the strategic management, and a quick look is provided for how the BSC results can be presented using the BI techniques. General Terms BI Tools, SPM, Performance measurement results interpretation, performance patterns.

Quality of Big Data in health care.

The current trend in Big Data analytics and in particular health information technology is toward building sophisticated models, methods and tools for business, operational and clinical intelligence. However, the critical issue of data quality required for these models is not getting the attention it deserves. The purpose of this paper is to highlight the issues of data quality in the context of Big Data health care analytics. The insights presented in this paper are the results of analytics work that was done in different organizations on a variety of health data sets. The data sets include Medicare and Medicaid claims, provider enrollment data sets from both public and private sources, electronic health records from regional health centers accessed through partnerships with health care claims processing entities under health privacy protected guidelines. Assessment of data quality in health care has to consider: first, the entire lifecycle of health data; second, problems arising from errors and inaccuracies in the data itself; third, the source(s) and the pedigree of the data; and fourth, how the underlying purpose of data collection impact the analytic processing and knowledge expected to be derived. Automation in the form of data handling, storage, entry and processing technologies is to be viewed as a double-edged sword. At one level, automation can be a good solution, while at another level it can create a different set of data quality issues. Implementation of health care analytics with Big Data is enabled by a road map that addresses the organizational and technological aspects of data quality assurance. The value derived from the use of analytics should be the primary determinant of data quality. Based on this premise, health care enterprises embracing Big Data should have a road map for a systematic approach to data quality. Health care data quality problems can be so very specific that organizations might have to build their own custom software or data quality rule engines. Today, data quality issues are diagnosed and addressed in a piece-meal fashion. The authors recommend a data lifecycle approach and provide a road map, that is more appropriate with the dimensions of Big Data and fits different stages in the analytical workflow.

A business intelligence approach using web search tools and online data reduction techniques to examine the value of product-enabled services

This article summarizes the results of an empirical study focusing on the value of product-enabled services in intensive R&D spenders. The focus is on product-driven firms for which new service development is expected to be particularly promising but also quite challenging. Part of the motivation is based on the fact that existing studies on the value attributes of hybrid offerings are mostly conceptual and need to be further substantiated through more systematic empirical studies. The research includes two samples with a total of 83 product-driven firms selected among the top R&D spenders in Canada and Europe. It adopts an innovative methodology based on online textual data that could be implemented in advanced business intelligence tools aiming at the facilitation of innovation, marketing and business decision making. Combinations of keywords referring to different aspects of service value were designed and used in a web search resulting in the frequency of their use on companies’ websites. Principal component analysis was applied to identify distinctive groups of keyword combinations that were interpreted in terms of specific service value attributes. Finally, the firms were classified by means of K-means cluster analysis in order to identify the firms with a high degree of articulation of their service value attributes. This work articulates a relatively simple and intuitive method for quantitative and qualitative semantic analysis of online textual data that is similar to latent semantic analysis but could be used as part of more user-friendly expert system solutions and business intelligence tools based on easily accessible business statistics packages. The results show that the main service value attributes of the Canadian firms are: better service effectiveness, higher market share, higher service quality, and customer satisfaction. The service value attributes for the European firms include, among others, product added-value, product modernization and optimization of customer time and efforts. Canadian firms focus on collaboration and co-creation with suppliers and customers for the sake of product–service innovation as a competitive advantage on the marketplace. On the other hand, the focus of EU firms on innovative hybrid offerings is not explicitly related to business differentiation and competitiveness.

Business Intelligence Systems for Analyzing University Students Data

Abstract : Globalization and ICT rapid development have led to strong competition between educational institutions. Advanced analytical technologies, including Business Intelligence (BI) tools, are implemented at universities worldwide for analyzing data and getting profound knowledge of the students, their individual learning characteristics and specific educational needs. This paper presents an example of BI implementation for student data analysis.

The Influence of Retailer Enterprise Marketing Information System on Bullwhip Effect

This study aims to investigate the influence of utilizing enterprise level marketing information system in the form of adopting a combination of software tools on the reduction of bullwhip effect. In order to formulate our research hypothesis, we revised the literature extensively. The empirical study findings indicated a positive influence and relationship between the Enterprise Marketing Information System in the form of ERP software, Social Media Monitoring Tools, Business Intelligence Tools and Reduction in Bullwhip Effect.

The Impact of RFID on Pharmaceutical Supply Chains: India, China and Europe Compared

This paper analyzes the impact of Radio Frequency Identification (RIFD) on pharmaceutical supply chains in Asia and Europe, focusing particularly on India, China, France and the UK. As the global pharmaceutical industry is rapidly expanding due to the entry of generic drugs coupled with the prevalence of business process outsourcing activities among companies there is a heightened risk of counterfeit drugs becoming more easily available in the market place. The application of RFID, both as a security measure and as a device for optimizing supply chain efficiency, will bring about a significant change in the functioning of the industry's distribution network. The present paper discusses several applications of RFID in regard to both the Asian and European pharmaceutical supply chains and points to its potential use as a Business Intelligence (BI) tool. RFID, used as a modern BI tool, provides several advantages to the enterprise, among the most prominent of which is the transformation of raw RFID data into vital business information. Through this mechanism a company can be better positioned to efficiently analyze customer needs and buyer behavior, as well as forecast sales and improve potential profit margins.

Opinion-Mining Methodology for Social Media Analytics

Social media have emerged as new communication channels between consumers and companies that generate a large volume of unstructured text data. This social media content, which contains consumers’ opinions and interests, is recognized as valuable material from which businesses can mine useful information; consequently, many researchers have reported on opinion-mining frameworks, methods, techniques, and tools for business intelligence over various industries. These studies sometimes focused on how to use opinion mining in business fields or emphasized methods of analyzing content to achieve results that are more accurate. They also considered how to visualize the results to ensure easier understanding. However, we found that such approaches are often technically complex and insufficiently user-friendly to help with business decisions and planning. Therefore, in this study we attempt to formulate a more comprehensive and practical methodology to conduct social media opinion mining and apply our methodology to a case study of the oldest instant noodle product in Korea. We also present graphical tools and visualized outputs that include volume and sentiment graphs, time-series graphs, a topic word cloud, a heat map, and a valence tree map with a classification. Our resources are from public-domain social media content such as blogs, forum messages, and news articles that we analyze with natural language processing, statistics, and graphics packages in the freeware R project environment. We believe our methodology and visualization outputs can provide a practical and reliable guide for immediate use, not just in the food industry but other industries as well.

Towards New Probabilistic Assumptions in Business Intelligence

Abstract One of the main assumptions of mathematical tools in science is represented by the idea of measurability and additivity of reality. For discovering the physical universe additive measures such as mass, force, energy, temperature, etc. are used. Economics and conventional business intelligence try to continue this empiricist tradition and in statistical and econometric tools they appeal only to the measurable aspects of reality. However, a lot of important variables of economic systems cannot be observable and additive in principle. These variables can be called symbolic values or symbolic meanings and studied within symbolic interactionism, the theory developed since George Herbert Mead and Herbert Blumer. In statistical and econometric tools of business intelligence we accept only phenomena with causal connections measured by additive measures. In the paper we show that in the social world we deal with symbolic interactions which can be studied by non-additive labels (symbolic meanings or symbolic values). For accepting the variety of such phenomena we should avoid additivity of basic labels and construct a new probabilistic method in business intelligence based on non-Archimedean probabilities.

A Map for Clinical Laboratories Management Indicators in the Intelligent Dashboard.

Introduction:management challenges of clinical laboratories are more complicated for educational hospital clinical laboratories. Managers can use tools of business intelligence (BI), such as information dashboards that provide the possibility of intelligent decision-making and problem solving about increasing income, reducing spending, utilization management and even improving quality. Critical phase of dashboard design is setting indicators and modeling causal relations between them. The paper describes the process of creating a map for laboratory dashboard.Methods:the study is one part of an action research that begins from 2012 by innovation initiative for implementing laboratory intelligent dashboard. Laboratories management problems were determined in educational hospitals by the brainstorming sessions. Then, with regard to the problems key performance indicators (KPIs) specified.Results:the map of indicators designed in form of three layered. They have a causal relationship so that issues measured in the subsequent layers affect issues measured in the prime layers.Conclusion:the proposed indicator map can be the base of performance monitoring. However, these indicators can be modified to improve during iterations of dashboard designing process.

MIND – Semantic Based Knowledge Visualization

In what has come to be known as the ‘Knowledge Era’, economy relies more than ever on knowledge-based activities. The need to capture, integrate and capitalize on knowledge within organizations, in an efficient and effective way has become crucial to maintaining a competitive edge in the market. Knowledge, Information and Data have always been of importance in human history, however, as technology advances and knowledge becomes more readily available, the requirement of providing knowledge workers with relevant knowledge inline and online to their ongoing activities has become a significant modern industrial and academic challenge. Whilst in the past the challenge was to maximize benefit retrieved from data (i.e. ERP, CRP solutions) and later from information (Business Intelligence (BI) tools) the new task is to provide equivalent knowledge solutions. This paper will present MIND, a multipurpose and user friendly, semantic analysis and knowledge visualization tool. MIND is applicable to a wide spectrum of applications, three of which will be presented in this paper, knowledge community mapping, CAD part profiling and an advanced BI application.

AN IN-MEMORY APPROACH TO SENTIMENT ANALYSIS BASED ON SAP HANA

Sentiment analysis (SA) is one of a number of fields in which big requires processing. The term refers to the feelings, beliefs, emotions, and opinions that people express in relation to certain matters in written form, more often than not in online media such as social networks. Sentiment analysis can provide enterprises with valuable insights into, for example, how customers regard their products. When sentiments are collected from social networks over a period of time, the amount of data can indeed be big. This paper describes and evaluates a lexicon-based SA solution, implemented as a prototype using SAP HANA. Sentiments are collected from Facebook and Google+, using a combination of tools including Microsoft Excel and the Power Query add-on to Excel. As HANA employs in-memory database technology, the calculation of values will be very fast. As a result of the analysis, five categories (from strong negative to strong positive), plus a category sentiment not found, are filled with individual sentiments and, with the help of business intelligence tools, evaluated as well. SA results are made available on a user interface created using SAP's current interface technology, SAPUI5.

Comparing fuzzy AHP and fuzzy TOPSIS for evaluation of business intelligence vendors

Article history: Received July 10, 2014 Accepted December 31, 2014 Available online January 3 2015 The main objective of this study is to identify the most important criteria and indicators in selection of business intelligence vendors, and ranking the vendors of such tools using Fuzzy Analytical hierarchy Process (FAHP) and Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS), to compare results of these two methods and to provide appropriate solutions for the sample company, namely National Iranian Oil Company (NIOC). Spearman's rank correlation test was used for comparing the methods and determining their correlation. A strong positive correlation was observed between the ranks of business intelligence tools at the significance level of 0.05 in both methods. The results of the ranking by means of FAHP method show that IBM Company was the best one, followed by Oracle, SAS, QlikTech, SAP and Microsoft. However, based on the FTOPSIS method, Oracle was the leading company, followed by IBM, SAS and SAP and finally Microsoft. . Growing Science Ltd. All rights reserved 5 © 201

A System Supporting the Analysis of Motorway Traffic Accidents

This work presents a business intelligence tool for monitoring traffic accidents on motorways and supporting decisions relevant to road safety. The system manages information on road characteristics, traffic accidents and traffic volumes and produces reports for monitoring the evolution of key performance indicators for road safety supporting decisions on actions for risk mitigation and safety improvements for road users. The paper illustrates the different types of analyses performed by the system. Pattern based analysis is used to evaluate safety performance indicators for the road sections matching defined patterns. Two different road segmentation algorithms, used to identify the most critical road sections according to various severity indicators, are presented and discussed. Differential analysis compares the value of selected severity indicators before and after the implementation of an intervention on a road. Finally, a graphical user interface allows the accident locations to be visualized and accidents with specific characteristics to be highlighted. The system was evaluated on the data collected between 2009 and 2011 for the A15 motorway in Italy, connecting Parma to La Spezia.

The role of business intelligence in decision process modeling

Decision making is a very significant and complex function of management that requires methods and techniques that simplify the process of choosing the best alternative. In modern business, the challenge for managers is to find the alternatives for improving the decision-making process. Decisions directly affect profit generation and positioning of the company in the market. It is well-known that people dealt with the phenomenon of decision making in each phase of the development of society, which has triggered the need to learn more about this process. The main contribution of this paper is to show the significance of business intelligence tools and techniques as support to the decision making process of managers. Research results have shown that business intelligence plays an enormous role in modern decision process modeling.