Combining Business Intelligence And Stock Market Data: A Primer For Data Analytics And Business Intelligence

Business Intelligence (BI) has become indispensible to modern business decision-making. Organizations rely on BI to interpret the mass amounts of data circulating throughout the world. However, integration of BI into university business programs does not parallel industry demands. The purpose of this paper is to introduce an innovative business intelligence project tutorial for Information Systems (IS) education. The applied tutorial was designed to help students learn how to design and publish a report using SQL Server Reporting Services to analyze current stock market data. This tutorial exposes students to the decision-making power derived from raw data analysis and assists in development of business professionals who can maximize profitability through effective use of business intelligence

Big data warehouse framework for smart revenue management

Revenue Management’s most cited definitions is probably “to sell the right accommodation to the right customer, at the right time and the right price, with optimal satisfaction for customers and hoteliers”. Smart Revenue Management (SRM) is a project, which aims the development of smart automatic techniques for an efficient optimization of occupancy and rates of hotel accommodations, commonly referred to, as revenue management. One of the objectives of this project is to demonstrate that the collection of Big Data, followed by an appropriate assembly of functionalities, will make possible to generate a Data Warehouse necessary to produce high quality business intelligence and analytics. This will be achieved through the collection of data extracted from a variety of sources, including from the web. This paper proposes a three stage framework to develop the Big Data Warehouse for the SRM. Namely, the compilation of all available information, in the present case, it was focus only the extraction of information from the web by a web crawler – raw data. The storing of that raw data in a primary NoSQL database, and from that data the conception of a set of functionalities, rules, principles and semantics to select, combine and store in a secondary relational database the meaningful information for the Revenue Management (Big Data Warehouse). The last stage will be the principal focus of the paper. In this context, clues will also be giving how to compile information for Business Intelligence. All these functionalities contribute to a holistic framework that, in the future, will make it possible to anticipate customers and competitor’s behavior, fundamental elements to fulfill the Revenue Managemen

Business Intelligence: Development of a programmatic marketing campaign performance dashboard

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceBusiness intelligence is a discipline that has been around since the 1980s and has provided many innovative tools and technologies to support decision making and performance management. Its combination with business performance management led to the appearance of performance dashboards that bring key metrics into a single display. This transforms into more efficient and effective decision making. This report describes a business intelligence project carried out at GroupM, an established marketing company, world-leading in media investment. The aim of this project was to develop a programmatic marketing performance dashboard that would innovate the way the team would manage the campaigns. Previously, the marketers used a manual method that involved manually extracting reports from the different ad server platforms, manipulate the report structure to make it uniform, and then running an Excel file that would aggregate all these files into a single report, where they would be able to look at the data. This process consumed a lot the team’s time and so, this marketing performance dashboard allowed the marketers to access the data in an easier and timely fashion, permitting a more efficient campaign performance management. The project was developed using Salesforce Datorama, which allows for easy integration of data through its various native API connections as well as ingestion of raw excel/csv files through TotalConnect data streams. The Programmatic Marketing team provided an initial mock-up of the expected look of the dashboard, which served as the base of the data model that was created. This project was deemed as successful, and the team has already reported that the time spent on the daily monitorization of the performance of the campaigns has drastically decreased since using the dashboard

Data Classification and Its Application in Credit Card Approval

We are all now living in the information age. The amount of data being collected by businesses, companies and agencies is large. Recent advances in technologies to automate and improve data collection have increased the volumes of data. Lying hidden in all this data is potentially useful information that is rarely made explicit or taken advantage of. In this context, data mining has arisen as an important research area that helps to reveal the hidden useful information from the raw data collected. Many intensive researches have been conducted to enhance the capability of data mining solution in providing the intelligence so that different types of businesses can make informed decisions. This project demonstrates how data mining can address the need of business intelligence in the process of decision-making. An analysis on the field of data mining is done to show how data mining, especially data classification, can help in businesses such as targeted marketing, credit card approval, fraud detection, medical diagnosis, and scientific work. This project is involved with identification of the available algorithms used in data classification and the implementation of C4.5 decision tree induction algorithm in solving the data classifying task. Sample credit card approval dataset is used to demonstrate the functionality of a data mining solution prototype, which includes the typical tasks of a decision tree induction process: data selection, data preprocessing, decision tree induction, tree pruning, rules generation and validation. The result of this application using the sample credit card approval dataset includes a decision tree, a set of rules derived from the decision tree and its accuracy. These outputs help to identify the pattern of applicants who are more likely to be accepted or rejected. The set of rules can be used as part of the knowledge base in expert system or decision support system for financial institutions

Testing Data Vault-Based Data Warehouse

Data warehouse (DW) projects are undertakings that require integration of disparate sources of data, a well-defined mapping of the source data to the reconciled data, and effective Extract, Transform, and Load (ETL) processes. Owing to the complexity of data warehouse projects, great emphasis must be placed on an agile-based approach with properly developed and executed test plans throughout the various stages of designing, developing, and implementing the data warehouse to mitigate against budget overruns, missed deadlines, low customer satisfaction, and outright project failures. Yet, there are often attempts to test the data warehouse exactly like traditional back-end databases and legacy applications, or to downplay the role of quality assurance (QA) and testing, which only serve to fuel the frustration and mistrust of data warehouse and business intelligence (BI) systems. In spite of this, there are a number of steps that can be taken to ensure DW/BI solutions are successful, highly trusted, and stable. In particular, adopting a Data Vault (DV)-based Enterprise Data Warehouse (EDW) can simplify and enhance various aspects of testing, and curtail delays common in non-DV based DW projects. A major area of focus in this research is raw DV loads from source systems, keeping transformations to a minimum in the ETL process which loads the DV from the source. Certain load errors, classified as permissible errors and enforced by business rules, are kept in the Data Vault until correct values are supplied. Major transformation activities are pushed further downstream to the next ETL process which loads and refreshes the Data Mart (DM) from the Data Vault

Data warehousing & business intelligence to enhance the analysis of a worldwide IT consulting project.

This project has been set in an IT Consulting atmosphere. It has been developed an agile application that provides a global overview from any point of view. This has contributed to enhance internal analysis and has become a reference tool in decisión making. First, it has been carried out the study to lay down the project's scope and it has defined the functional specifications. Then, it has been necessary a period to acquire the knowledge and learn about the best technologies to achieve the goal. Testing has been taken into account before the app's release. The aim of this project has been to achieve a global service's overview and transform raw data into knowledge. In order to do that, it has been implemented a Data Warehouse to provide information to the Business Intelligence software, QlikView

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

Position paper on realizing smart products: challenges for Semantic Web technologies

In the rapidly developing space of novel technologies that combine sensing and semantic technologies, research on smart products has the potential of establishing a research field in itself. In this paper, we synthesize existing work in this area in order to define and characterize smart products. We then reflect on a set of challenges that semantic technologies are likely to face in this domain. Finally, in order to initiate discussion in the workshop, we sketch an initial comparison of smart products and semantic sensor networks from the perspective of knowledge technologies

Software como um Serviço: uma plataforma eficaz para oferta de sistemas holísticos de gestão da performance

This study main objective was to assess the viability of development of a Performance Management (PM) system, delivered in the form of Software as a Service (SaaS), specific for the hospitality industry and to evaluate the benefits of its use. Software deployed in the cloud, delivered and licensed as a service, is becoming increasingly common and accepted in a business context. Although, Business Intelligence (BI) solutions are not usually distributed in the SaaS model, there are some examples that this is changing. To achieve the study objective, design science research methodology was employed in the development of a prototype. This prototype was deployed in four hotels and its results evaluated. Evaluation of the prototype was focused both on the system technical characteristics and business benefits. Results shown that hotels were very satisfied with the system and that building a prototype and making it available in the form of SaaS is a good solution to assess BI systems contribution to improve management performance.O objetivo principal deste estudo é avaliar a viabilidade de desenvolvimento de um sistema de Gestão da Performance, entregue sob a forma de “Software como Serviço” (SaaS), específico para o setor hoteleiro, e também avaliar os benefícios de seu uso. O software implantado na cloud, entregue e licenciado como um serviço, é cada vez mais aceite num contexto de negócios. Todavia, não é comum que soluções de Business Intelligence (BI) sejam distribuídas neste modelo SaaS. No entanto, existem alguns exemplos de que isso se está a alterar. Para atingir o objetivo do estudo, foi utilizada Design Science Research como metodologia de pesquisa científica para desenvolvimento de um protótipo. Este protótipo foi implementado em quatro hotéis para que os seus resultados pudessem ser avaliados. A avaliação foi focada tanto nas características técnicas do sistema como nos benefícios para o negócio. Os resultados mostraram que os hotéis estavam muito satisfeitos com o sistema e que construir um protótipo e disponibilizá-lo sob a forma de SaaS é uma boa solução para avaliar a contribuição dos sistemas de BI para melhorar o desempenho da gestão.info:eu-repo/semantics/publishedVersio