Semantic integrity in data warehousing : a framework for understanding : a thesis presented in partial fulfilment of the requirements for the degree of Masters of Business Studies in Information Systems at Massey University, Palmerston North, New Zealand

Data modelling has gathered an increasing amount of attention by data warehouse developers as they come to realise that important implementation decisions such as data integrity, performance and meta data management, depend on the quality of the underlying data model. Not all organisations model their data but where they do, Entity-Relationship (E-R) modelling, or more correctly relational modelling, has been widely used. An alternative, dimensional modelling, has been gaining acceptance in recent years and adopted by many practitioners. Consequently, there is much debate over which form of modelling is the most appropriate and effective. However, the dimensional model is in fact based on the relational model and the two models are not so different that a debate is necessary. Perhaps, the real focus should be on how to abstract meaning out of the data model. This research explores the importance of semantic integrity during data warehouse design and its impact on the successful use of the implemented warehouse. This has been achieved through a detailed case study. Consequently, a conceptual framework for describing semantic integrity has been developed. The purpose of the framework is to provide a theoretical basis for explaining how a data model is interpreted through the meaning levels of understanding, connotation and generation, and also how a data model is created from an existing meaning structure by intention, generation and action. The result of this exploration is the recognition that the implementation of a data warehouse may not assist with providing a detailed understanding of the semantic content of a data warehouse

Examining Quality Factors Influencing the Success of Data Warehouse

Increased organizational dependence on data warehouse (DW) systems has drived the management attention towards improving data warehouse systems to a success. However, the successful implementation rate of the data warehouse systems is low and many firms do not achieve intended goals. A recent study shows that improves and evaluates data warehouse success is one of the top concerns facing IT/DW executives. Nevertheless, there is a lack of research that addresses the issue of the data warehouse systems success. In addition, it is important for organizations to learn about quality needs to be emphasized before the actual data warehouse is built. It is also important to determine what aspects of data warehouse systems success are critical to organizations to help IT/DW executives to devise effective data warehouse success improvement strategies. Therefore, the purpose of this study is to further the understanding of the factors which are critical to evaluate the success of data warehouse systems. The study attempted to develop a comprehensive model for the success of data warehouse systems by adapting the updated DeLone and McLean IS Success Model. Researcher models the relationship between the quality factors on the one side and the net benefits of data warehouse on the other side. This study used quantitative method to test the research hypotheses by survey data. The data were collected by using a web-based survey. The sample consisted of 244 members of The Data Warehouse Institution (TDWI) working in variety industries around the world. The questionnaire measured six independent variables and one dependent variable. The independent variables were meant to measure system quality, information quality, service quality, relationship quality, user quality, and business quality. The dependent variable was meant to measure the net benefits of data warehouse systems. Analysis using descriptive analysis, factor analysis, correlation analysis and regression analysis resulted in the support of all hypotheses. The research results indicated that there are statistically positive causal relationship between each quality factors and the net benefits of the data warehouse systems. These results imply that the net benefits of the data warehouse systems increases when the overall qualities were increased. Yet, little thought seems to have been given to what the data warehouse success is, what is necessary to achieve the success of data warehouse, and what benefits can be realistically expected. Therefore, it appears nearly certain and plausible that the way data warehouse systems success is implemented in the future could be changed

An Exploratory Investigation of System Success Factors in Data Warehousing

Despite the increasing role of the data warehouse as a strategic information source for decision makers, academic research has been lacking, especially from an organizational perspective. An exploratory study was conducted to improve general understanding of data warehousing issues from the perspective of IS success. For this, the effect of variables pertaining to system quality, information quality, and service quality on user satisfaction for the data warehouse was studied. Additional characterization was made on data warehouse users, their organizational tasks, and data warehouse usage. Empirical data were gathered at a large enterprise from three different information sources: a survey, unstructured group interviews with end-users, and informal interviews with an IT manager who was in charge of the data warehouse. Data analysis showed that user satisfaction with the data warehouse was significantly affected by such system quality factors as data quality, data locatability, and system throughput. Interviews also supported the existence of system design and management issues that have to be addressed to optimize the utility of the data warehouse as an effective decision support environment. In the meantime, data analysis indicated that first-line (or lower) and middle managers were the main users of the system. Managers and knowledge workers were taking advantage of the system to perform complex tasks, to support decision making, and to seek information critical for enhanced productivity. The group interviews revealed additional benefits of the data warehouse and major roadblocks in its successful usage

An ETL Metadata Model for Data Warehousing

Metadata is essential for understanding information stored in data warehouses. It helps increase levels of adoption and usage of data warehouse data by knowledge workers and decision makers. A metadata model is important to the implementation of a data warehouse; the lack of a metadata model can lead to quality concerns about the data warehouse. A highly successful data warehouse implementation depends on consistent metadata. This article proposes adoption of an ETL (extracttransform-load) metadata model for the data warehouse that makes subject area refreshes metadata-driven, loads observation timestamps and other useful parameters, and minimizes consumption of database systems resources. The ETL metadata model provides developers with a set of ETL development tools and delivers a user-friendly batch cycle refresh monitoring tool for the production support team

Perancangan Data Warehouse Dan Penerapan Data Mining Di Bidang Akademik Pada Institut Informatika Dan Bisnis Darmajaya

Higher education institution must be able to well perform processes of evaluation, planning and management in order to win the competition in this globalization era. To support any effort of the aforementioned, the institution needs qualified and sufficient information supports so that it can probe and predict any potential strength which existed. Development data warehouse and data mining is kinds of solution alternatives which can be done to help organization in finding and understanding hidden patterns from the data provided. Data warehouse is a collection of integrated databases which is used to support the process of decision making. Data mining is a kind of analysis tool which is used to extract any information provided in the data warehouse. The research discussed a problem in designing data warehouse and applying data mining to support the academic system at IBI Darmajaya in representing potential information required for better academic services to learners. The first executed steps was establishing the data warehouse of IBI Darmajaya, then an analysis was conducted towards all saved data in the data warehouse by using data mining techniques. The results of this research is a data warehouse that can represent information to support the evaluation process and acceptance of new students campaign planning to the potential areas and school, advertising media that will be used, monitoring of students' academic status, evaluation and planning of students' study plans, and performance evaluation of study program within the aspects of alumni quality and length of study. In addition, this research also result the application of data mining for finding the rules that used to driving and directing the students enthusiast and study program selection for prospective new students

Data Profiling in Cloud Migration: Data Quality Measures while Migrating Data from a Data Warehouse to the Google Cloud Platform

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsIn today times, corporations have gained a vast interest in data. More and more, companies realized that the key to improving their efficiency and effectiveness and understanding their customers’ needs and preferences better was reachable by mining data. However, as the amount of data grow, so must the companies necessities for storage capacity and ensuring data quality for more accurate insights. As such, new data storage methods must be considered, evolving from old ones, still keeping data integrity. Migrating a company’s data from an old method like a Data Warehouse to a new one, Google Cloud Platform is an elaborate task. Even more so when data quality needs to be assured and sensible data, like Personal Identifiable Information, needs to be anonymized in a Cloud computing environment. To ensure these points, profiling data, before or after it migrated, has a significant value by design a profile for the data available in each data source (e.g., Databases, files, and others) based on statistics, metadata information, and pattern rules. Thus, ensuring data quality is within reasonable standards through statistics metrics, and all Personal Identifiable Information is identified and anonymized accordingly. This work will reflect the required process of how profiling Data Warehouse data can improve data quality to better migrate to the Cloud

Diseño e implementación de una solución de Business Intelligence para el análisis y estudio de pruebas atléticas

[Resumen]: El foco central de este trabajo es la creación de un data warehouse para el análisis de datos de pruebas de atletismo. Para conseguir este objetivo se diseña un sistema complejo que realiza todo el proceso completo desde la obtención de los datos en origen hasta su introducción en el data warehouse. Se integran diferentes fuentes de datos y se aplican diversos procesos ETL con los que garantizar la calidad de los datos, facilitando así su análisis. Una vez completado el data warehouse permitirá realizar consultas personalizadas, generar informes, analizar tendencias y comparar marcas. En resumen, este data warehouse beneficiará a entrenadores, atletas, clubes y federaciones brindándoles una mejor comprensión del rendimiento atlético.[Abstract]: The central focus of this work is the creation of a data warehouse for the analysis of track and fields events data. In order to accomplish this goal, a complex system is designed to handle the entire process, starting from data acquisition at the source and concluding with its introduction into the data warehouse. Different data sources are integrated, and various ETL processes are applied to ensure data quality, thus facilitating its analysis. Once the data warehouse is completed, it will allow for personalized queries, reports and dahboards generation, trend analysis, and performance comparisons. In summary, this data warehouse will benefit coaches, athletes, clubs, and federations by providing them with a better understanding of athletic performance.Traballo fin de grao (UDC.FIC). Enxeñaría Informática. Curso 2022/202

Strategic alignment in data warehouses : two case studies

This research investigates the role of strategic alignment in the success of data warehouse implementation. Data warehouse technology is inherently complex, requires significant capital investment and development time. Many organizations fail to realize the full benefits from it. While failure to realize benefits has been attributed to numerous causes, ranging from technical to organizational reasons, the underlying strategic alignment issues have not been studied. This research confirms, through two case studies, that the successful adoption of the data warehouse depends on its alignment to the business plans and strategy. The research found that the factors that are critical to the alignment of data warehouses to business strategy and plans are (a) joint responsibility between data warehouse and business managers, (b) alignment between data warehouse plan and business plan, (c) business user satisfaction, (d) flexibility in data warehouse planning and (e) technical integration of the data warehouse. In the case studies, the impact of strategic alignment was visible both at implementation and use levels. The key findings from the case studies are that a) Senior management commitment and involvement are necessary for the initiation of the data warehouse project. The awareness and involvement of data warehouse managers in corporate strategies and a high level of joint responsibility between business and data warehouse managers is critical to strategic alignment and successful adoption of the data warehouse. b) Communication of the strategic direction between the business and data warehouse managers is important for the strategic alignment of the data warehouse. Significant knowledge sharing among the stakeholders and frequent communication between the data warehouse managers and users facilitates better understanding of the data warehouse and its successful adoption. c) User participation in the data warehouse project, perceived usefulness of the data warehouse, ease of use and data quality (accuracy, consistency, reliability and timelines) were significant factors in strategic alignment of the data warehouse. d) Technology selection based on its ability to address business and user requirements, and the skills and response of the data warehousing team led to better alignment of the data warehouse to business plans and strategies. e) The flexibility to respond to changes in business needs and flexibility in data warehouse planning is critical to strategic alignment and successful adoption of the data warehouse. Alignment is seen as a process requiring continuous adaptation and coordination of plans and goals. This research provides a pathway for facilitating successful adoption of data warehouse. The model developed in this research allows data warehouse professionals to ensure that their project when implemented, achieve the strategic goals and business objectives of the organization

Content Versus Structure in Information Environments: A Longitudinal Analysis of Website Preferences

From the prospective traveler surfing the web for cheap vacations to executives analyzing market trends with a data warehouse, at home and at work, people are confronted with increasingly richer information environments. This study is an attempt at modeling the behavior over time of the “information consumer” (web surfer or executive) in such environments. The objective is to gain a better understanding of how to design the technologies that support and enhance the interaction with these information environments. Two key design variables for information environments are examined: content quality and structural quality. Drawing on research in human-computer interaction and ecological psychology, a behavioral model is developed in which it is postulated that the importance of structural quality will diminish with time, whereas content quality will increaseinimportance. Atwo-stagemethodologyisemployedwhichcombinesalongitudinalexperimentwith a cross-sectional survey. Both the survey and experiment are conducted in the context of informational websites. The experiment provided 178 undergraduates with repeated exposure over several weeks to eight custom-built websites, manipulated to vary in content quality and structural quality for which their preferences (and associated rationales) were elicited at three time points over the course of the experiment. Additionally, 163 of the undergraduates also completed a survey providing data about the effect of content and structure on usage behavior for sites for which they had mature experience. Preliminary results of the experimental data support the hypotheses. The research has potentially significant implications for the design of information environments

Business intelligence in modern banking

Business intelligence represents the process of collecting all the available and important external data and their transformation into useful ones that help each bank management with making business decisions. In modern banking, the system of business intelligence enables multimedia analyze, on-line analytic data processing as wel as Data Mining which can be used by bank man-agers in order to get and learn important trends that are “hidden” in big data bases. Apart the others, integral parts of business intelligence are Data Warehouse, executive and informational systems, on-line analityc data processing and Balanced Scorecard (BSC) implementation. Among the most important goals of business intelligence is identifcation and anticipation of real favorites and bad circumstances in business bank environment. Quality architecture of the environment of bank systems for support should include the trinty: Data Warehouse, OLAP and Data Mining. Business intelligence values should be observed from the point of modern understanding of managing and making decisions. Business banks which are able to manage their data resources, information and knowledge are more successful than their competitors. Business banks have a lot of information resources, but real challenge is to know to collect the information in a defnite time period, from the appropriate category of clients. The main idea of CRM is not any more going in for products and services but for their clients. Today it has become possible by development of data bases where saved data about specifc clients are put, as well as software that enables optimal usage of those data. Studying the clients represents the base of CRM and it is the information of bank client inte-raction that results in the possibility for making stabile profitable relations with clients. The concept of electric business intelligence as its main support has a signifcant importance for developing of CRM in business banking. Therefore, business banks, which are oriented to traditional managing way, become uncompetitive in a very complex capital of bank market