Business Intelligence: Development of a performance monitoring dashboard in a pharmaceutical company

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsBusiness intelligence is a concept that has been around for over 150 years. Although, only for the past 10 years has it become an idea wildly known in the technology world. Now, it is a tool used to help organizations handle big amounts of data and transform it into real-time information and ultimately make better and more effective decisions. In this context, the report describes my internship experience working as a Business Intelligence consultant at SDG. Which is an established consulting firm with offices in almost every continent. SDG helps companies handle their most important challenges as well as discover new opportunities with the use of advanced analytics and data-driven business models. The report specifies one of the projects that was accomplished during my time at SDG, with the role of developer. The goal of this project was to develop a dashboard using Qlik that would help two pharmaceutical companies measure the performance of a new drug. To do so the necessary data sources were provided from both sides of the company, and using an ETL approach, the data was integrated and ready to be used in the project. The companies provided an initial mock-up of the expected visualizations, and with some changes considering the initial discoveries, the data model was created. Following, the KPI’s were defined and implemented in the dashboard’s visualizations. The project was deemed successful, and it fulfilled the clients’ expectations

Integrating star and snowflake schemas in data warehouses

A fundamental issue encountered by the research community of data warehouses (DWs) is the modeling of data. In this paper, a new design is proposed, named the starnest schema, for the logical modeling of DWs. Using nested methodology, data semantics can be explicitly represented. Part of the design involves providing a translation mechanism from the star/snowflake schemas to a nested representation. The novel schema proposed in this paper is accomplished by converting the fact-dimension schema to a fact-nested dimension schema. The transformation of the denormalized dimension tables to nested dimension tables increases the efficiency of query execution by reducing the number of tuples accessed for query retrieval since dimensional attributes can be used directly in the Group-by clause. In order to facilitate the implementation of the proposed approach, specific algorithms are built based on the starnest schema

MLED_BI: A Novel Business Intelligence Design Approach to Support Multilingualism

With emerging markets and expanding international cooperation, there is a requirement to support Business Intelligence (BI) applications in multiple languages, a process which we refer to as Multilingualism (ML). ML in BI is understood in this research as the ability to store descriptive content (such as descriptions of attributes in BI reports) in more than one language at Data Warehousing (DWH) level and to use this information at presentation level to provide reports, queries or dashboards in more than one language. Design strategies for data warehouses are typically based on the assumption of a single language environment. The motivations for this research are the design and performance challenges encountered when implementing ML in a BI data warehouse environment. These include design issues, slow response times, delays in updating reports and changing languages between reports, the complexity of amending existing reports and the performance overhead. The literature review identified that the underlying cause of these problems is that existing approaches used to enable ML in BI are primarily ad-hoc workarounds which introduce dependency between elements and lead to excessive redundancy. From the literature review, it was concluded that a satisfactory solution to the challenge of ML in BI requires a design approach based on data independence the concept of immunity from changes and that such a solution does not currently exist. This thesis presents MLED_BI (Multilingual Enabled Design for Business Intelligence). MLED_BI is a novel design approach which supports data independence and immunity from changes in the design of ML data warehouses and BI systems. MLED_BI extends existing data warehouse design approaches by revising the role of the star schema and introducing a ML design layer to support the separation of language elements. This also facilitates ML at presentation level by enabling the use of a ML content management system. Compared to existing workarounds for ML, the MLED_BI design approach has a theoretical underpinning which allows languages to be added, amended and deleted without requiring a redesign of the star schema; provides support for the manipulation of ML content; improves performance and streamlines data warehouse operations such as ETL (Extract, Transform, Load). Minor contributions include the development of a novel BI framework to address the limitations of existing BI frameworks and the development of a tool to evaluate changes to BI reporting solutions. The MLED_BI design approach was developed based on the literature review and a mixed methods approach was used for validation. Technical elements were validated experimentally using performance metrics while end user acceptance was validated qualitatively with end users and technical users from a number of countries, reflecting the ML basis of the research. MLED_BI requires more resources at design and initial implementation stage than existing ML workarounds but this is outweighed by improved performance and by the much greater flexibility in ML made possible by the data independence approach of MLED_BI. The MLED_BI design approach enhances existing BI design approaches for use in ML environments