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The aim of this paper is to discuss how we build or data warehouse and OLAP cube for Banking
Products. As top level management want to make strategical decisions we have prepared this
data warehouse. To develop the data warehouse there were 4 members assigned.
Banking Products keeps the data about the customers, branch and the products of the bank.
These are the products of the “HAND” Bank. The purpose of this data warehouse is to extract
all the data from the transactional system and store it in the data warehouse. . While the
concept of a data warehouse sounds quite simple, in reality it is almost completely opposite. To
store the data in the data warehouse first the data should be transformed to the common
format then cleansing should be done to allow it to generate queries and reports to take some
strategical decisions.
Case Study: Banking Products
1. Business Scenario:
“HAND” Bank was established in 2009. It has around more than 20 branches in all
around the United States. The Headquarter of this branch is in the Houston. The bank
managers in each branch want to know to whom the credit card and loan should be
given if they are applying for the same. They also want to find out most profitable
customer, profitable branch etc. All the data of the customers are stored in the
transactional system from where the bank managers cannot make any decisions. So, for
that we require to build the datawarehouse to generate some strategical reports.
2. Why Datawarehouse?
HAND Bank’s managers need some useful strategical information to take decisions. The
idea of the data warehouse is to extract the data from the transactional system and
then transform the data into the common format using mapping concept. Then to load
the data in the datawarehouse. This what the Bank managers are looking for. Managers
need to query the datawarehouse and then generate the reports such as credit card and
loan approval. This is what exactly the datawarehouse tools provide. The data in the
datawarehouse is stored in such a way that managers will gets the strategical
information of the customers.
3. Methodology:
Initially we did not have a clear idea about the datawarehouse for the bank. So, first we
developed the transactional database so that we can extract into the datawarehouse.
We used the bottom up approach to build the datawarehouse. We prepared first the
small data marts to prepare the entire datawarehouse. Bottom-up approach is flexible
and as this approach help to learn the building of datawarehouse very efficiently we
used this approach. Each and every team member learned building of the
datawarehouse. To prepare the transactional database we developed the mappings
between the tables.
4. Dimensional Modelling and defining Data Structure:
Dimensional modeling is the design concept used by many data warehouse designers to
build their data warehouse. Dimensional model is the underlying data model used by
many of the commercial OLAP products available today in the market. In this model, all
data is contained in two types of tables called Fact Table and Dimension Table. In a
Dimensional Model, Fact table contains the measurements or metrics or facts of
business processes. the measurements, the only other things a fact table contains are
foreign keys for the dimension tables. In a Dimensional Model, context of the
measurements are represented in dimension tables. the Dimension Attributes are used
in report labels, and query constraints. The dimension attributes also contain one or
more hierarchical relationships.Before designing your data warehouse, you need to
decide what this data warehouse contains. In computing, the star schema (also called
star-join schema, data cube, or multi-dimensional schema) is the simplest style of data
warehouse schema. The star schema consists of one or more fact tables referencing any
number of dimension tables. The star schema is an important special case of the
snowflake schema, and is more effective for handling simpler queries.
Above figure is the star schema ofour project. Here Customer, Product, Branch and Time are
the Dimension tables and Bank_fact is the fact table. From the star schema it is clear that the
managers can find the information about the account balances of the customers corresponding
the customer personal fields, branch name and product name. Following is the fact table data
and the relationships between them.
Fact Table
Then we went further to import the access database into SQL SERVER 2008.
5. Implementation in SQL SERVER 2008:
We first imported the Access database into the sql server. Then we established the
relationships between the tables. Then we generated the cube. An OLAP cube for online
analytical processing is a data structure that allows fast analysis of data. It can also be
defined as the capability of manipulating and analyzing data from multiple perspectives.
The arrangement of data into cubes overcomes some limitations of relational databases.
A slice is a subset of a multi-dimensional array corresponding to a single value for one or
more members of the dimensions not in the subset. The dice operation is a slice on
more than two dimensions of a data cube. Drilling down or up is a specific analytical
technique whereby the user navigates among levels of data ranging from the most
summarized to the most detailed. A roll-up involves computing all of the data
relationships for one or more dimensions. All these are the operations which can be
done on the data of the cube.
Cube Implementation
6. Browsing the cube:
The Cube Browser is a tool provided within the Analysis Services to display the results of
the cube process without the need to add external software. It is a relatively useful data
analysis tool that can display the dimension data as required by the end user. The user
can drill up and drill down and check the accuracy, completeness and effectiveness of
the model design at the Analysis services level. We dragged the attributes from the
dimension tables and the measures from the left panel to generate the reports.
Following are some of the reports which we have developed.
Report for Credit Card and Loan Approval
Report for the Most profitable customer
Report for Most Profitable Branch
7. Conclusion:
There are no employees whose checking account balance went negative.
Credit card service is offered to “Sahil Gandhi”.
Most profitable customer is “Nikhil Dabholkar”.
Most profitable branch is “New Jersey” branch.
8. References: