International Journal of Academic Research in Business and Social Sciences
Vol. 9, No. 14, Special Issue: Education 4.0: Future Learning. 2019, E-ISSN: 2222-6990
© 2019 HRMARS
Business Intelligence systems. The most popular data model of Data warehouse is
multidimensional model, which consists of a group of dimension tables and one fact table
according to the functional requirements (Kimball, Reeves, Ross, & Thornthwaite, 1998). The
purpose of a data warehouse is to ensure the appropriate data is available to the appropriate
end user at the appropriate time (Chau, Cao, Anson, & Zhang, 2003). Data warehouses are
based on multidimensional modeling. Using On-Line Analytical Processing tools, decision
makers navigate through and analyze multidimensional data (Prat, Comyn-Wattiau, & Akoka,
2011).
Data warehouse uses a data model that is based on multidimensional data model. This model
is also known as a data cube which allows data to be modeled and viewed in multiple
dimensions (Singhal, 2007). And the schema of a data warehouse lies on two kinds of
elements: facts and dimensions. Facts are used to memorize measures about situations or
events. Dimensions are used to analyze these measures, particularly through aggregations
operations(counting, summation, average, etc.) (Bhansali, 2009; J. Wang, 2009). Data Quality
(DQ) is the crucial factor in data warehouse creation and data integration. The data
warehouse must fail and cause a great economic loss and decision fault without insight
analysis of data problems (Yu, Xiao-yi, Zhen, & Guo-quan, 2009). The quality of data is often
evaluated to determine usability and to establish the processes necessary for improving data
quality. Data quality may be measured objectively or subjectively. Data quality is a state of
completeness, validity, consistency, timeliness and accuracy that make data appropriate for
a specific use (Manjunath, Hegadi, & Ravikumar, 2011).
Data quality has been defined as the fraction of performance over expectancy, or as the loss
imparted to society from the time a product is shipped (Besterfield, Besterfield-Michna,
Besterfield, & Besterfield-Sacre, n.d.)]. The believe was the best definition is the one found in
(Orr, 1998; Tayi & Ballou, 1998; R. Y. Wang & Strong, 1996): data quality is defined as "fitness
for use". The nature of this definition directly implies that the concept of data quality is
relative. For example, data semantics is different for each distinct user. The main purpose of
data quality is about horrific data - data which is missing or incorrect or invalid in some
perspective. A large term is that, data quality is attained when business uses data that is
comprehensive, understandable, and consistent, indulging the main data quality magnitude
is the first step to data quality perfection which is a method and able to understand in an
effective and efficient manner, data has to satisfy a set of quality criteria. Data gratifying the
quality criterion is said to be of high quality (Manjunath et al., 2011). This paper is divided
into seven sections. Section 1 introduction, Definition of Data Warehouse and The Quality of
Data Warehouse. Section 2 presents related work, Section 3 presents Data Warehouse
Creation and the main idea is that a Data warehouse database gathers data from an overseas
trading company databases. Section 4 describes Data Warehouse Design For this study, we
suppose a hypothetical company with many branches around the world, each branch has so
many stores and showrooms scattered within the branch location. Each branch has a
database to manage branch information. Section 5 describes our evaluation Study of Quality
Criteria for DW, which covers aspects related both to quality and performance of our
approach, and the obtained results, and work on compare between star schema and
snowflake schema. Section 6 provides conclusions. Finally, Section 7 describes open issues
and our planned future work.