Data Warehouse Success Lead towards Supply Chain Efficiency

Data warehouse management is crucial challenge due to which most of the supply chain companies are facing the issues of data management. These challenges effect adversely on data warehouse success and ultimately effect negatively on supply chain efficiency. Different studies are carried out by different researchers on the area of supply chain, however, these studies are missing with the element of data warehouse management. Therefore, the objective of the study is to examine the factors that influence on data warehouse success and supply chain efficiency. Data were collected from warehouse employees working in Indonesian supply chain companies. Results of the study shows that data warehouse success is based on various factors such as system quality, information quality, service quality and relationship quality. These factors have positive association with data warehouse success and data warehouse success increases the supply chain efficiency. Thus, companies should focus on these elements to promote data warehouse success. This study is helpful for practitioners to promote supply chain efficiency through data warehouse success

Practical data mining in a large utility company

We present in this paper the main applications of data mining techniques at Electricité de France, the French national electric power company. This includes electric load curve analysis and prediction of customer characteristics. Closely related with data mining techniques are data warehouse management problems: we show that statistical methods can be used to help to manage data consistency and to provide accurate reports even when missing data are present

Practical data mining in a large utility company

We present in this paper the main applications of data mining techniques at Electricité de France, the French national electric power company. This includes electric load curve analysis and prediction of customer characteristics. Closely related with data mining techniques are data warehouse management problems: we show that statistical methods can be used to help to manage data consistency and to provide accurate reports even when missing data are present

A family of experiments to validate measures for UML activity diagrams of ETL processes in data warehouses

In data warehousing, Extract, Transform, and Load (ETL) processes are in charge of extracting the data from the data sources that will be contained in the data warehouse. Their design and maintenance is thus a cornerstone in any data warehouse development project. Due to their relevance, the quality of these processes should be formally assessed early in the development in order to avoid populating the data warehouse with incorrect data. To this end, this paper presents a set of measures with which to evaluate the structural complexity of ETL process models at the conceptual level. This study is, moreover, accompanied by the application of formal frameworks and a family of experiments whose aim is to theoretical and empirically validate the proposed measures, respectively. Our experiments show that the use of these measures can aid designers to predict the effort associated with the maintenance tasks of ETL processes and to make ETL process models more usable. Our work is based on Unified Modeling Language (UML) activity diagrams for modeling ETL processes, and on the Framework for the Modeling and Evaluation of Software Processes (FMESP) framework for the definition and validation of the measures.In data warehousing, Extract, Transform, and Load (ETL) processes are in charge of extracting the data from the data sources that will be contained in the data warehouse. Their design and maintenance is thus a cornerstone in any data warehouse development project. Due to their relevance, the quality of these processes should be formally assessed early in the development in order to avoid populating the data warehouse with incorrect data. To this end, this paper presents a set of measures with which to evaluate the structural complexity of ETL process models at the conceptual level. This study is, moreover, accompanied by the application of formal frameworks and a family of experiments whose aim is to theoretical and empirically validate the proposed measures, respectively. Our experiments show that the use of these measures can aid designers to predict the effort associated with the maintenance tasks of ETL processes and to make ETL process models more usable. Our work is based on Unified Modeling Language (UML) activity diagrams for modeling ETL processes, and on the Framework for the Modeling and Evaluation of Software Processes (FMESP) framework for the definition and validation of the measures

The Assessment of Technology Adoption Interventions and Outcome Achievement Related to the Use of a Clinical Research Data Warehouse

Introduction: While funding for research has declined since 2004, the need for rapid, innovative, and lifesaving clinical and translational research has never been greater due to the rise in chronic health conditions, which have resulted in lower life expectancy and higher rates of mortality and adverse outcomes. Finding effective diagnostic and treatment methods to address the complex challenges in individual and population health will require a team science approach, creating the need for multidisciplinary collaboration among practitioners and researchers. To address this need, the National Institutes of Health (NIH) created the Clinical and Translational Science Awards (CTSA) program. The CTSA program distributes funds to a national network of medical research institutions, known as “hubs,” that work together to improve the translational research process. With this funding, each hub is required to achieve specific goals to support clinical and translational research teams by providing a variety of services, including cutting edge use of informatics technologies. As a result, the majority of CTSA recipients have implemented and maintain data warehouses, which combine disparate data types from a range of clinical and administrative sources, include data from multiple institutions, and support a variety of workflows. These data warehouses provide comprehensive sets of data that extend beyond the contents of a single EHR system and provide more valuable information for translational research. Although significant research has been conducted related to this technology, gaps exist regarding research team adoption of data warehouses. As a result, more information is needed to understand how data warehouses are adopted and what outcomes are achieved when using them. Specifically, this study focuses on three gaps: research team awareness of data warehouses, the outcomes of data warehouse training for research teams, and how to measure objectively outcomes achieved after training. By assessing and measuring data warehouse use, this study aims to provide a greater understanding of data warehouse adoption and the outcomes achieved. With this understanding, the most effective and efficient development, implementation, and maintenance strategies can be used to increase the return on investment for these resource-intensive technologies. In addition, technologies can be better designed to ensure they are meeting the needs of clinical and translational science in the 21st century and beyond. Methods: During the study period, presentations were held to raise awareness of data warehouse technology. In addition, training sessions were provided that focused on the use of data warehouses for research projects. To assess the impact of the presentations and training sessions, pre- and post-assessments gauged knowledge and likelihood to use the technology. As objective measurements, the number of data warehouse access and training requests were obtained, and audit trails were reviewed to assess trainee activities within the data warehouse. Finally, trainees completed a 30-day post-training assessment to provide information about barriers and benefits of the technology. Results: Key study findings suggest that the awareness presentations and training were successful in increasing research team knowledge of data warehouses and likelihood to use this technology, but did not result in a subsequent increase in access or training requests within the study period. In addition, 24% of trainees completed the associated data warehouse activities to achieve their intended outcomes within 30 days of training. The time needed for adopting the technology, the ease of use of data warehouses, the types of support available, and the data available within the data warehouse may all be factors influencing this completion rate. Conclusion: The key finding of this study is that data warehouse awareness presentations and training sessions are insufficient to result in research team adoption of the technology within a three-month study period. Several important implications can be drawn from this finding. First, the timeline for technology adoption requires further investigation, although it is likely longer than 90 days. Future assessments of technology adoption should include an individual’s timeline for pursuing the use of that technology. Second, this study provided a definition for outcome achievement, which was completion o

EU policies in data governance : the new challenge on the field of public administration.

Διπλωματική εργασία--Πανεπιστήμιο Μακεδονίας, Θεσσαλονίκη, 2019It is an analytic study in the new sector of policy and decision making of European Union. In the following project i will try to research and categorize the sectors( security, science, economy, environment, geopolicy, external policies) in which data governace effects the day to day work and life of european citizens, how it defines and involve with the constitutionals rules and laws of European Union's internal polices but also its external policies( USA , China, Russia). Furthermore, this study will show the progressively steps of EU in comparison with other developed countries and international organizations and also will examinate the policies of data governance, in private and public sector, across the globe

A Methodology Supporting the Design and Evaluating the Final Quality of Data Warehouses

The design and configuration of a data warehouse can be difficult tasks especially in the case of very large databases and in the presence of redundant information. In particular, the choice of which attributes have to be considered as dimensions and measures can be not trivial and it can heavily influence the effectiveness of the final system. In this article, we propose a methodology targeted at supporting the design and deriving information on the total quality of the final data warehouse. We tested our proposal on three real-word commercial ERP databases