Objective

ABC Company has an OLTP database which keeps records on motor vehicle insurance information. This database contains detailed information in respect of drivers, vehicle and claim information.

Current database model has been designed for fast data entry and is sufficient for individual client’s specific information as well as fast transaction processing. Company critically needs to make comprehensive analysis like identification of contracts with a high loss ratio and low overall customer value.

Appropriate actions needed to be taken with high risk customers, such as premium adjustment, loss prevention measures and in some cases contract cancellations and reduce gross claim expenditures.

By making evidenced based management and informed decision, company will focus on profitable customers by lowering their premiums and overcoming competitive pressure. It will help company make better risk management and overall profitability for the company. ABC Company is in urgent need to utilize the existing data resources efficiently for better risk management and obtain competitive advantage in Auto Insurance Industry.

Recommended Solution;

ABC Company has decided to implement a Data Warehouse to leverage its data resources. ABC Company needs to reorganize the existing process of information delivery and to establish one single, unified and integrated data warehouse. A data warehouse is an integrated subject oriented, time-variant, non-volatile database that provides support for decision making.

In order to support decision making ABC Company decided to reorganize the data into Star Schema in Data warehouse. In effect, the star schema creates near equivalent of multidimensional database schema from the existing OLTP relational database [1]. It will help in advance data analysis for Risk management and overcoming competitive pressure.

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Structure of Star Schema

Star schema yield an easily implemented model for multidimensional data analysis while still preserving the relational structure on which the operation database is built. [3] The basic star schema has four components: facts, dimensions, attributes and attributes hierarchies. The STAR schema would most likely be a read-only database due to the widespread redundancy introduced into the model. [4]

Fact Table

ABC Company has a factual data in Claim Information such as date, location, type of accident, cause of accident, liability, recovery cost.[5] Fact tables contain the quantitative data or factual data about a business. This information is numerical, additive measurements and can consist of many columns and millions or billions of rows.

Dimensions

Claim Information facts can be analyzed by dimensions such as Driver, Location, Time, and Automotive. Dimension tables are usually smaller and hold descriptive data that reflects the dimensions.

Attributes

For example Driver name, Driver ID, gender, age group, race, and other attributes. Some of these attributes might relate to each other hierarchically.

Attribute Hierarchies

Provide top down data Aggregation, Drill down or roll up data analysis. For example in time dimension there are Attribute hierarchies such as day, week, month, quarter, and year. When decision maker want to see company yearly claim information, then they are using year hierarchy level, they can further drill down to quarter level sales quantity, as per there needs. Same as in Location dimension is data can be analyzed by Country, Region, Province City and town.

Benefits of Data warehouse to ABC Company

By organizing the ABC Company data around star scheme company can analyze information like what customers are high risk and what group of customers is profitable. What cities have more accidents ratios and what time of the year accident happens? What habit of drivers is may be considered high risk? What vehicles are considered low risk and so on?
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In addition to the internal information some external information like Auto Industry statistics can also be integrated into data warehouse.

By having answers to ad-hoc queries and in depth data analysis, ABC Company will be able to manage customer relations, smartly overcoming competitive pressure and most important of all is significantly improved risk management.

Data Warehouse will enable company have the business Intelligence for making strategic decision for Risk management and keep the company ahead of the competition and possibly diversify into new auto insurance products. [2]

References:

1- Data model overview

http://www.teradata.com/t/WorkArea/DownloadAsset.aspx?id=2332

2- The Benefits of Data Warehousing for Insurance Company Wolfgang Hofbauer, Mannheimer AG Holding, Mannheim, Germany

3- Database Systems By Peter Rob, Carlos Coronel, Keeley Crockett – Published by Thomson Learning; International Ed edition (12 Mar 2008)

4- Oracle Data Warehouse Tips by Burleson Consulting.

5- Class notes and research with Insurance companies.

In writing this post we followed the Funds Database example in SQL Server OLAP Developer’s Guide by William C Amo published year 2000, check with Amazon if this is available. We like this example because it elaborated the process of populating the fact table very well.

The sql statement was executed to populate fact 2 fact tables (Please see funds Star Schema picture)
as follows;
1- Investments
2- Dividends

Sql statement for populating fact table Investments
select a.manager_num, M.office_num, f.fund_cd,f.fundacctno,t.trandate,
t.amount from accounts a INNER join managers m on a.manager_num=m.manager_num
inner join fundaccounts f on a.acct_no = f.ACCT_NO
INNER JOIN TRANHISTORY T ON (F.FUND_CD = T.fUND_CD) And f.fundacctno = t.fundacctno
Where t.trantype = ‘invest’
—————————————————-
Sql statement for populating fact table Dividends
select a.manager_num, M.office_num, f.fund_cd,f.fundacctno,t.trandate,
t.amount from accounts a INNER join managers m on a.manager_num=m.manager_num
inner join fundaccounts f on a.acct_no = f.ACCT_NO
INNER JOIN TRANHISTORY T ON (F.FUND_CD = T.fUND_CD) And f.fundacctno = t.fundacctno
Where t.trantype <> ‘invest’

Creating four dimensions – Managers, Offices, Funds, and Accounts sions should be simple (Please see Funds OLTP Picture and Funds Star Schema), we will write more in our next post please feel free to add more o this topic.

Read similar topic

Abstract – Trends in business intelligence, e-commerce and remote access make it necessary and practical to store data in different ways on multiple systems with different operating systems. As business evolve and grow, they require efficient computerized solution to perform data update and to access data from diverse
enterprise business applications.

The objective of this paper is to demonstrate the capability of DTS [1] as a database solution for automatic data transfer and update in solving business problem. This DTS package is developed for the sales of variety of plants and eventually expanded into commercial supply and landscaping business.

Dimension data modeling is used in DTS package to extract, transform and load data from heterogeneous database systems such as MySQL, Microsoft Access and Oracle that consolidates into a Data Mart residing in SQL Server. Hence, the data transfer from various databases is scheduled to run automatically every quarter of the year to review the efficient sales analysis. Therefore, DTS is absolutely an attractive solution for automatic data transfer and update which meeting today’s business needs.

Please read the complete Paper

Source: Wikipedia.org

As a Business Intelligence (BI) application, the analytical data mart system enables the National Commercial bank’s credit card division to generate meaningful information about its credit card holders, which allows the bank to identify, segment and classify the most valuable members within a target group and better understand customers’ needs. The system also helps protect the bank against fraudulent transactions.

“As the competition in the banking industry has intensified in recent years, Chinese banks increasingly rely on Longtop’s BI applications to differentiate service offerings and improve customer satisfaction. Having developed similar solutions for other leading banks in China, we are very pleased to work with this customer to enhance its business intelligence capabilities,” commented Weizhou Lian, Chief Executive Officer of Longtop.

Read more

What fact, figures, statistics, and so forth do they need for effective decision making?

How should this information be sliced and diced for analysis? (dimensions)

What additional information can aid in finding exactly what is needed? (attributes)[1]

Source:

1- Delivering Business Intelligence with Sql Server 2008 by Brian Larson