scholarly journals Implemetasi Data Warehouse pada Bagian Pemasaran Perguruan Tinggi

2012 ◽  
Vol 3 (1) ◽  
pp. 315
Author(s):  
Eka Miranda ◽  
Rudy Rudy ◽  
Eli Suryani
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Data Warehouse ◽  
Ad Hoc ◽  
External Data ◽  
Study Results ◽  
Marketing Activities ◽  
Warehouse Design ◽  
Transactional Data ◽  
Support Decision Making

Transactional data are widely owned by higher education institutes, but the utilization of the data to support decision making has not functioned maximally. Therefore, higher education institutes need analysis tools to maximize decision making processes. Based on the issue, then data warehouse design was created to: (1) store large-amount data; (2) potentially gain new perspectives of distributed data; (3) provide reports and answers to users’ ad hoc questions; (4) perform data analysis of external conditions and transactional data from the marketing activities of universities, since marketing is one supporting field as well as the cutting edge of higher education institutes. The methods used to design and implement data warehouse are analysis of records related to the marketing activities of higher education institutes and data warehouse design. This study results in a data warehouse design and its implementation to analyze the external data and transactional data from the marketing activities of universities to support decision making.

scholarly journals Desain Data Warehouse pada Sistem Informasi Sumber Daya Manusia Sub-Sistem Rekrutmen

2012 ◽  
Vol 3 (1) ◽  
pp. 307
Author(s):  
Eka Miranda
Keyword(s):  
Decision Making ◽  
Resource Management ◽  
Data Warehouse ◽  
High Performance ◽  
Ad Hoc ◽  
Process Efficiency ◽  
Literature Study ◽  
Recruitment Activities ◽  
Warehouse Design ◽  
Transactional Data

Employees as a resource management are essential to improve the effectiveness of company’s performance and process efficiency. This paper discusses the implementation of data warehouse and its role in assisting the decision making related to recruitment activities undertaken by Human Resources Department. In this research built a data warehouse design to store large amounts of data and to gain potentially a new perspective of data distribution as well as to provide reports and solutions for users the ad hoc question and to analyze the transactional data. This study aims to design a data warehouse to support accurate decision making related to human resource management in order to create high performance productivity. The method used in this paper consists of: (1) data collection using interview and literature study related to employee recruitment and (2) data warehouse design derived from Teh Ying Wah et al. This research results in a data warehouse design and its implementation to analyze transactional data from the related activities of recruitment and employee management to support decision making.

scholarly journals Quality Assurance in Knowledge Data Warehouse

2017 ◽  
Author(s):  
Andysah Putera Utama Siahaan
Keyword(s):  
Decision Making ◽  
Knowledge Discovery ◽  
Data Warehouse ◽  
Noisy Data ◽  
Strategic Decision ◽  
Strategic Decision Making ◽  
Quality Of Information ◽  
External Data ◽  
Existing Data

Knowledge discovery is the process of adding knowledge from a large amount of data. The quality of knowledge generated from the process of knowledge discovery greatly affects the results of the decisions obtained. Existing data must be qualified and tested to ensure knowledge discovery processes can produce knowledge or information that is useful and feasible. It deals with strategic decision-making for an organization. Combining multiple operational databases and external data create the data warehouse. This treatment is very vulnerable to incomplete, inconsistent, and noisy data. Data mining provides a mechanism to clear this deficiency before finally stored in the data warehouse. This research tries to give technique to improve the quality of information in the data warehouse.

Data Warehouse Support for Policy Enforcement Rule Formulation

2019 ◽  
pp. 255-273
Author(s):  
Deepika Prakash
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Conversion Process ◽  
Tool Support ◽  
Policy Enforcement ◽  
Decision Making Problem ◽  
Operational Decision Making ◽  
Early Information ◽  
Support Decision Making ◽  
Policy Violation

It is believed that a data warehouse is for operational decision making. Recently, a proposal was made to support decision making for formulating policy enforcement rules that enforce policies. These rules are expressed in the WHEN-IF-THEN form. Guidelines are proposed to elicit two types of actions, triggering actions that cause the policy violation and the corresponding correcting actions. The decision-making problem is that of selecting the most appropriate correcting action in the event of a policy violation. This selection requires information. The elicited information is unstructured and is “early.” This work is extended by proposing a method to directly convert early information into its multi-dimensional form. For this, an early information mode is proposed. The proposed conversion process is a fully automated one. Further, the tool support is extended to accommodate the conversion process. The authors also apply the method to a health domain.

Using OLAP Tools for e-HRM

2011 ◽  
pp. 252-265
Author(s):  
Alysson Bolognesi Prado ◽  
Carmen Freitas ◽  
Thiago Ricardo Sbrici
Keyword(s):  
Decision Making ◽  
Human Resources ◽  
Data Warehouse ◽  
End Users ◽  
Web Portal ◽  
Business Information ◽  
On Line ◽  
Analytical Processing ◽  
Resource Data ◽  
Support Decision Making

In the growing challenge of managing people, Human Resources need effective artifacts to support decision making. On Line Analytical Processing is intended to make business information available for managers, and HR departments can now encompass this technology. This paper describes a project in which the authors built a Data Warehouse containing actual Human Resource data. This paper provides data models and shows their use through OLAP software and their presentation to end-users using a web portal. The authors also discuss the progress, and some obstacles of the project, from the IT staff’s viewpoint.

Data Warehouse Performance

2011 ◽  
pp. 580-585 ◽  
Author(s):  
Beixin ("Betsy") Lin ◽  
Yu Hong ◽  
Zu-Hsu Lee
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Business Intelligence ◽  
Performance Tuning ◽  
Data Warehouses ◽  
User Query ◽  
Data Volume ◽  
Predictive Data Mining ◽  
Support Decision Making ◽  
Over Time

A data warehouse is a large electronic repository of information that is generated and updated in a structured manner by an enterprise over time to aid business intelligence and to support decision making. Data stored in a data warehouse is non-volatile and time variant and is organized by subjects in a manner to support decision making (Inmon et al., 2001). Data warehousing has been increasingly adopted by enterprises as the backbone technology for business intelligence reporting and query performance has become the key to the successful implementation of data warehouses. According to a survey of 358 businesses on reporting and end-user query tools, conducted by Appfluent Technology, data warehouse performance significantly affects the Return on Investment (ROI) on Business Intelligence (BI) systems and directly impacts the bottom line of the systems (Appfluent Technology, 2002). Even though in some circumstances it is very difficult to measure the benefits of BI projects in terms of ROI or dollar figures, management teams are still eager to have a “single version of the truth,” better information for strategic and tactical decision making, and more efficient business processes by using BI solutions (Eckerson, 2003). Dramatic increases in data volumes over time and the mixed quality of data can adversely affect the performance of a data warehouse. Some data may become outdated over time and can be mixed with data that are still valid for decision making. In addition, data are often collected to meet potential requirements, but may never be used. Data warehouses also contain external data (e.g. demographic, psychographic, etc.) to support a variety of predictive data mining activities. All these factors contribute to the massive growth of data volume. As a result, even a simple query may become burdensome to process and cause overflowing system indices (Inmon et al., 1998). Thus, exploring the techniques of performance tuning becomes an important subject in data warehouse management.

scholarly journals Higher education and oil: The cost of credit and crude – A strategic corollary for industry and higher education

2020 ◽  
pp. 095042222095954
Author(s):  
Joseph M. Woodside
Keyword(s):  
Higher Education ◽  
Decision Making ◽  
Policy Development ◽  
Oil Industry ◽  
Organizational Outcomes ◽  
Lessons Learned ◽  
Cycle Model ◽  
Business Cycle Model ◽  
The Cost ◽  
Support Decision Making

The market shock that accompanied COVID-19 has the potential to significantly transform higher education. At the same time, it presents an opportunity for higher education to learn from industry and adopt successful policies and practices. This paper provides lessons learned from the oil industry which may help higher education institutions to successfully navigate disruption and improve organizational outcomes. A four-phase business cycle model is presented as a strategic corollary for industry and higher education to support decision-making and provide a mechanism for discussion and policy development.

scholarly journals Building a Diabetes Data Warehouse to Support Decision making in healthcare industry

2014 ◽  
Vol 16 (2) ◽  
pp. 138-143 ◽  
Author(s):  
Abubakar Ado ◽  
◽  
Ahmed Aliyu ◽  
Saifullahi Aminu Bello ◽  
Abdulra’uf Garba Sharifai ◽  
...  
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Healthcare Industry ◽  
Support Decision Making

Design and Implementation of a Data Warehouse to Support Decision-Making in a Health Environment

2018 ◽  
Vol 147 (3) ◽  
pp. 45-53
Author(s):  
Simon G. Cornejo ◽  
Karina Caro ◽  
Luis-Felipe Rodriguez ◽  
Roberto Aguilar A. ◽  
Cynthia B. Perez ◽  
...  
Keyword(s):  
Decision Making ◽  
Data Warehouse ◽  
Design And Implementation ◽  
Support Decision Making
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