Improving the Efficiency of BI Report Generation for Data Warehousing Projects

This project has been implemented to replace the waterfall model of development with a new agile model of development for data warehousing projects in an organization. Some of the problems associated with waterfall model of development include high cost, late delivery of business value, inability to adapt to the changing requirements etc. The main goals of this project were to reduce the project cost, improve the quality of reports to increase user adoption, and make the process more time efficient. The stated goals were achieved by implementing agile model of development to the data warehousing projects, and were validated by the analysis of the data collected throughout the process of project execution

Business Intelligence systems development in hospitals using an Agile Project Management approach

"Measure to manage" is a widely used expression to demonstrate that good governance must necessarily go through obtaining good data and information. These will allow managers to know the past and the momentum of the business and also to predict, estimate and take the best-informed decisions. The greater the complexity of the business, the greater this need. Healthcare units, specifically hospitals, are organizations that, due to their function and diversity of areas, are considered one of the most complex. In this context, projects for the development of business intelligence solutions, with huge impact and scope, undergo the need for continuous improvement and incremental evolution. Agile methods, by their nature and principles, are suitable to fulfil this need. The purpose of this dissertation is to support future research towards better models with agile tools to develop business intelligence system in hospitals and, manly, to understand how can Agile methodology improve a Business Intelligence System Implementation. This will be done mainly through bibliographical research on the covered topics, namely, Hospitals, Business Intelligence, Agile and Project Management. The expect results will be some clear practical guidelines, that any IT Project Manager could use for an efficient Business Intelligence System implementation using an Agile methodology. This will be done with the presentation of two use cases, from implementations in two hospitals in Portugal, where the Agile proposed model could be used to improve the outcomes of the projects. For that a deep analysis of the various phases of Business Intelligence development was carried out on the basis of information obtained in the literature and on the basis of information obtained in the practical development of Business Intelligence implementation projects. In the end it can be seen that the application of Agile can bring enormous benefits to the development of this kind of project, as, in addition to the advantages listed and widely known about Agile, it can help intensively to bring together and involve all the stakeholders of a project in a common goal of success and effectiveness

Creating business value from big data and business analytics : organizational, managerial and human resource implications

This paper reports on a research project, funded by the EPSRC’s NEMODE (New Economic Models in the Digital Economy, Network+) programme, explores how organizations create value from their increasingly Big Data and the challenges they face in doing so. Three case studies are reported of large organizations with a formal business analytics group and data volumes that can be considered to be ‘big’. The case organizations are MobCo, a mobile telecoms operator, MediaCo, a television broadcaster, and CityTrans, a provider of transport services to a major city. Analysis of the cases is structured around a framework in which data and value creation are mediated by the organization’s business analytics capability. This capability is then studied through a sociotechnical lens of organization/management, process, people, and technology. From the cases twenty key findings are identified. In the area of data and value creation these are: 1. Ensure data quality, 2. Build trust and permissions platforms, 3. Provide adequate anonymization, 4. Share value with data originators, 5. Create value through data partnerships, 6. Create public as well as private value, 7. Monitor and plan for changes in legislation and regulation. In organization and management: 8. Build a corporate analytics strategy, 9. Plan for organizational and cultural change, 10. Build deep domain knowledge, 11. Structure the analytics team carefully, 12. Partner with academic institutions, 13. Create an ethics approval process, 14. Make analytics projects agile, 15. Explore and exploit in analytics projects. In technology: 16. Use visualization as story-telling, 17. Be agnostic about technology while the landscape is uncertain (i.e., maintain a focus on value). In people and tools: 18. Data scientist personal attributes (curious, problem focused), 19. Data scientist as ‘bricoleur’, 20. Data scientist acquisition and retention through challenging work. With regards to what organizations should do if they want to create value from their data the paper further proposes: a model of the analytics eco-system that places the business analytics function in a broad organizational context; and a process model for analytics implementation together with a six-stage maturity model

Complementing Measurements and Real Options Concepts to Support Inter-iteration Decision-Making in Agile Projects

Agile software projects are characterized by iterative and incremental development, accommodation of changes and active customer participation. The process is driven by creating business value for the client, assuming that the client (i) is aware of it, and (ii) is capable to estimate the business value, associated with the separate features of the system to be implemented. This paper is focused on the complementary use of measurement techniques and concepts of real-option-analysis to assist clients in assessing and comparing alternative sets of requirements. Our overall objective is to provide systematic support to clients for the decision-making process on what to implement in each iteration. The design of our approach is justified by using empirical data, published earlier by other authors

A review and future direction of agile, business intelligence, analytics and data science

Agile methodologies were introduced in 2001. Since this time, practitioners have applied Agile methodologies to many delivery disciplines. This article explores the application of Agile methodologies and principles to business intelligence delivery and how Agile has changed with the evolution of business intelligence. Business intelligence has evolved because the amount of data generated through the internet and smart devices has grown exponentially altering how organizations and individuals use information. The practice of business intelligence delivery with an Agile methodology has matured; however, business intelligence has evolved altering the use of Agile principles and practices. The Big Data phenomenon, the volume, variety, and velocity of data, has impacted business intelligence and the use of information. New trends such as fast analytics and data science have emerged as part of business intelligence. This paper addresses how Agile principles and practices have evolved with business intelligence, as well as its challenges and future directions

Implementation of a data virtualization layer applied to insurance data

This work focuses on the introduction of a data virtualization layer to read and consolidate data from heterogeneous sources (Hadoop system, a data mart and a data warehouse) and provide a single point of data access to all data consumers

An Exploration of the Use of Gamification in Agile Software Development

Although Project Management has existed for many millennia, software project management is relatively new. As a discipline, software project management is considered difficult. The reasons for this include that software development is non-deterministic; opaque and delivered under ever-increasing time pressure in a volatile environment. Evolving from Incremental and Iterative Development (IID), Agile methodologies have attempted to address these issues by focusing on frequent delivery; working closely with the customer; being responsive to change and preferring working software to extensive documentation. This focus on delivery rather than documentation has sometimes been misrepresented as no documentation, which has led to a shortfall in project metrics. Gamification has its roots in motivation. The aim of gamification is to persuade users to behave in a manner set out by the designer of the gamification. This is achieved by adding game mechanics or elements from games into non-game applications. This dissertation examines the use of gamification in Agile projects and includes an empirical experiment that examines the use of gamification on Agile project tracking. Project tracking is an element of software engineering that acts as a de-motivator for software engineers. Software Engineers are highly motivated by independence and growth, while project tracking is seen as boring work. The dissertation experiment identifies a methodology for applying gamification experiments and then implements an experiment. The result was an overall improvement in project tracking. The experiment needs to be expanded to be run over a longer period of time and a more varied group of development teams

Evolving a software development methodology for commercial ICTD projects

This article discusses the evolution of a “DistRibuted Agile Methodology Addressing Technical Ictd in Commercial Settings” (DRAMATICS) that was developed in a global software corporation to support ICTD projects from initial team setup through ICT system design, development, and prototyping, to scaling up and transitioning, to sustainable commercial models. We developed the methodology using an iterative Action Research approach in a series of commercial ICTD projects over a period of more than six years. Our learning is reflected in distinctive methodology features that support the development of contextually adapted ICT systems, collaboration with local partners, involvement of end users in design, and the transition from research prototypes to scalable, long-term solutions. We offer DRAMATICS as an approach that others can appropriate and adapt to their particular project contexts. We report on the methodology evolution and provide evidence of its effectiveness in the projects where it has been used

Improving Big Data Processing Time

The process of storing and processing massive amounts of data (big data) in a traditional database is expensive and consumes a lot of time to obtain desired results. This project has been implemented to solve these problems faced by an organization, with the implementation of Hadoop framework that stores huge data sets on distributed clusters and performs parallel data processing to achieve results quickly. It uses commodity hardware to store the data making it cost effective and provides data security by replicating the data sets. The main goals of the project were to improve the performance of processing huge data sets, reduce long term data storage costs and provide a platform that supports ad hoc analysis and provides real-time insights. The project was structured to follow agile model of software development and the data was collected and analyzed after the execution of the project. The results obtained by the analysis of data aided in arriving to the conclusion and validating that the stated goals were achieved