Alignment between data warehouse design and business strategy

Data warehouse has become a very important tool for supporting the corporate executives in making important decisions in a highly competitive business environment. Data warehouse enables the top management to acquire and analyze integrated data extracted globally across the enterprise. Many organizations are sitting on vast amount of data already accumulated in their operational database. The huge data repository with potential strategic business information can be analysed comprehensively only through the usage of data warehouse system. The main objective of this paper is to systematically review the development and adoption of data warehouse design in business environment and the different models to assess the alignment between information technology strategy and business strategy

CLINICAL DATA WAREHOUSE: A REVIEW

Clinical decisions are crucial because they are related to human lives. Thus, managers and decision makers inthe clinical environment seek new solutions that can support their decisions. A clinical data warehouse (CDW) is animportant solution that is used to achieve clinical stakeholders’ goals by merging heterogeneous data sources in a centralrepository and using this repository to find answers related to the strategic clinical domain, thereby supporting clinicaldecisions. CDW implementation faces numerous obstacles, starting with the data sources and ending with the tools thatview the clinical information. This paper presents a systematic overview of purpose of CDWs as well as the characteristics;requirements; data sources; extract, transform and load (ETL) process; security and privacy concerns; design approach;architecture; and challenges and difficulties related to implementing a successful CDW. PubMed and Google Scholarare used to find papers related to CDW. Among the total of 784 papers, only 42 are included in the literature review. Thesepapers are classified based on five perspectives, namely methodology, data, system, ETL tool and purpose, to findinsights related to aspects of CDW. This review can contribute answers to questions related to CDW and providerecommendations for implementing a successful CDW

Using Semantic Web technologies in the development of data warehouses: A systematic mapping

The exploration and use of Semantic Web technologies have attracted considerable attention from researchers examining data warehouse (DW) development. However, the impact of this research and the maturity level of its results are still unclear. The objective of this study is to examine recently published research articles that take into account the use of Semantic Web technologies in the DW arena with the intention of summarizing their results, classifying their contributions to the field according to publication type, evaluating the maturity level of the results, and identifying future research challenges. Three main conclusions were derived from this study: (a) there is a major technological gap that inhibits the wide adoption of Semantic Web technologies in the business domain;(b) there is limited evidence that the results of the analyzed studies are applicable and transferable to industrial use; and (c) interest in researching the relationship between DWs and Semantic Web has decreased because new paradigms, such as linked open data, have attracted the interest of researchers.This study was supported by the Universidad de La Frontera, Chile, PROY. DI15-0020. Universidad de la Frontera, Chile, Grant Numbers: DI15-0020 and DI17-0043

Building an Effective Health Insurance Exchange Website

Offers lessons and resources from Massachusetts about teams and partnerships, vendors, stakeholder input, system requirements, and ongoing improvement to help states plan, build, and implement Web sites for health insurance exchanges

The Global Crisis as Digital Transformation Motivator: from Lifecycle Optimization to Efficient Implementation Series

It is generally known that software system development lifecycle (SSDL) should be managed adequately. The global economy crisis and subsequent depression have taught us certain lessons on the subject, which is so vital for digital transformation, for Industry 4.0. The paper presents the adaptive methodology of enterprise SSDL, which allows to avoid local crises while producing large-scale software. The methodology is based on extracting common ERP module level patterns and applying them to series of heterogeneous implementations. The approach includes a lifecycle model, which extends conventional spiral model by formal data representation/management models and DSL-based low-level CASE tools supporting the formalisms. The methodology has been successfully implemented as a series of portal-based ERP systems in ITERA oil-and-gas corporation, and in a number of trading/banking enterprise smart applications for other enterprises. Semantic network-based air traffic planning system, and a 6D-model-driven nuclear power plant construction support system are currently in progress

Implementing data-driven decision support system based on independent educational data mart

Decision makers in the educational field always seek new technologies and tools, which provide solid, fast answers that can support decision-making process. They need a platform that utilize the students’ academic data and turn them into knowledge to make the right strategic decisions. In this paper, a roadmap for implementing a data driven decision support system (DSS) is presented based on an educational data mart. The independent data mart is implemented on the students’ degrees in 8 subjects in a private school (Al-Iskandaria Primary School in Basrah province, Iraq). The DSS implementation roadmap is started from pre-processing paper-based data source and ended with providing three categories of online analytical processing (OLAP) queries (multidimensional OLAP, desktop OLAP and web OLAP). Key performance indicator (KPI) is implemented as an essential part of educational DSS to measure school performance. The static evaluation method shows that the proposed DSS follows the privacy, security and performance aspects with no errors after inspecting the DSS knowledge base. The evaluation shows that the data driven DSS based on independent data mart with KPI, OLAP is one of the best platforms to support short-to-long term academic decisions

A Prototyped NL-Based Approach for the Design of Multidimensional Data Warehouse

Organizations are more and more interested in the Data Warehouse (DW) technology and data analytics to base their decision-making processes on scientific arguments instead of intuition. Despite the efforts invested, the DW design issue remains a great challenging research domain. The design quality of the DW depends on several aspects, as the requirement gathering. In this context, we propose a Natural Language (NL) based design approach, which is twofold, first, it facilitates the involvement of the decision-makers in the DW design process; indeed, NL can encourage the decision-makers to express their requirements as English-like sentences conform to NL-templates. Secondly, our approach aims to generate semi-automatically a DW schema from a set of requirements gathered as analytical queries compliant to the NL-templates. This design approach relies on (i) two easy-to-use NL-templates to specifying the analysis components, and (ii) a set of five heuristic rules for extracting the multidimensional concepts from the requirements. We demonstrate the feasibility of our approach by developing the prototype Natural Language Decisional Requirements to DW Schema (NLDR2DWS)

Implementation of a business intelligence system in a public institution

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceTechnology is capable to support companies to reach their goals by making use data to take advantages from its analysis. Concerning public institutions, it is meaningful to deliver high-quality services and products to society, and technology may lead the organizations to a more efficient service provision. The purpose of this project is the implementation of a business intelligence system in Consultoria Jurídica da União (CJU) [Consultancy Office], the institution responsible for analyzing bidding processes in Brazil. The solution proposed by this work aims to store the business data and provide an analytical tool to display information in dashboards to provide insights to stakeholders, analyzing data trends and tendencies, preventing future unnecessary events, identifying best practices, to finally improve the public tenders to a better application of public funds and provide better services to society. To reach this project main objective, the technology that surrounds the BI system to be implemented includes the development of a scalable data warehouse to store the organization data and its schema modelling, the extraction-transform-load method to populate the data warehouse tables, and create the analytical tool, named dashboard, to answer the business needs providing the institution information. This business intelligence system intends to improve the legal bidding process in public agencies by making use of technology

Dashboards in smart city’s sustainability performance measurement through business intelligence

Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligencePeople’s concentration in urban areas is causing our society significant challenges because of a high populational density in mega-cities. These mega-cities cannot meet and balance their inhabitants’ needs, making it hard to develop an economy to increase their quality of life and improve cities’ surrounding environment and social communities. For cities to grow, considering the three pillars proposed by the sustainable development concept, which traces back to 1980 and supported by OECD, these cities building must meet today's society’s needs without risking future generations’ needs. Following the smart city concept means that decisions taken now must consider the impact on the economy, environment, and society altogether to avoid putting at risk the needs of today’s society, especially its future generations’ well-being. OECD expects this concept to change society’s view on its relationship with the world, hoping the community understands that our planet is an ecosystem that provides vital services. These critical services comprise food, clean water, oxygen, bacterial waste processing, citing a few, and conclude that its survival depends on the environment. The smart city concept aims to address these issues through the simultaneous management of these three pillars and is gaining strength with the latest technological development because it leverages information and communication technologies to collect data to monitor cities’ growth. Besides, smart cities can play a vital role in the world’s climate change by reducing carbon footprint and the usage of cities’ non-renewable energy sources while socially developing its communities and promoting equity between its inhabitants. However, for smart cities to realize all the benefits it proposes, the data collected must support informed decisions. This master project uses business intelligence methods, technologies, and tools to create a strategic performance dashboard using a correlational study based on data made available at European Commission’s Eurostat portal. Business performance management principles guide the strategic dashboard creation to monitor smart city strategic performance under the light of the triple bottom line concept