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.

 
• . University Degree required, preferably in Business/Marketing and 2-3
  years of Business to Business experience in a marketing role, preferably
  in similar technology/industry.
  . Strong project management and organizational skills
  . Strong business skills (knowledge of economics and market dynamics).
  . Strong analytical, communication, interpersonal, negotiation,
  decision making and presentation skills.
  . Ability to multitask and thrive in a fast paced environment with a
  great attention to detail and accuracy
  . In depth knowledge of Microsoft Excel

 

* Distinct lengths of string values in a column and the percentage of rows in the table that each length represents. Example: Profile of a column of US State codes, which should be two characters, shows values longer than 2 characters.

* Percentage of null values in a column. Example: Profile of a Zip Code/Postal code column shows a high percentage of missing codes.

* Percentage of regular expressions that occur in a column. Example: A pattern profile of a phone number column shows numbers entered in three different formats: (919)674-9999, [919]6749988, and 9199018888.

* Minimum, maximum, average, and standard deviation for numeric columns; and minimum and maximum for date/time columns. Example: Profile for an Employee birthdate column shows the maximum value is in the future.

* Distinct values in a column and percentage of rows in the table that each value represents. Example: A profile of a U.S State column contains more than 50 distinct values.

* Candidate key column for a selected table. Example: Profile shows duplicate values in a potential key column.

* Dependency of values in one column to values in another column or columns. Example: Profile shows that two or more values in the State field have the same value in the Zip Code field.

* Value inclusion between two or more columns. Example: Some values in the ProductID column of a Sales table have no corresponding value in the ProductID column of the Products table.

[edit]
Key steps undertaken during Data profiling

Following are some of the key steps that are generally employed during data profiling process:-

1. Use of analytical and statistical tools to outline the quality of data structure and data organization by determining various frequencies and ranges of key data element within data sources.
2. Applying Numerical analysis techniques to determine the scope of numeric data within data sources.
3. Identifying multiple coding schemes and different spellings used in the data content
4. Identifying data patterns and data formats and making note of the variation in the datatypes and data formats being used within data sources
5. Identifying duplicacy in the data content such as in name, address or other pertinent information
6. Decipheing and validating redundant data within the data sources.
7. Making note of primary and foreign key relationships and studying their impact on data organization and data retreival
8. Making validation trials by following specific business rules on data records across the data sources

Source

In another example,

The chart below shows an example of a composite key.

• Employee_id and Project_id are primary keys in the related tables: employee and project.
• When Employee_id and Project_id are put into the Billable_hours table, they become foreign keys.
• Employee_id and Project_id are combined to make one composite key that would identify each instance of billable hours.

The process of performing a variety of tests on a system to explore functionality or to identify problems. System testing is usually required before and after a system is put in place. A series of systematic procedures are referred to while testing is being performed. These procedures tell the tester how the system should perform and where common mistakes may be found. Testers usually try to “break the system” by entering data that may cause the system to malfunction or return incorrect information. For example, a tester may put in a city in a search engine designed to only accept states, to see how the system will respond to the incorrect input.

Source; BusinessDictionary.com

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

1. Hyperion Essbase
2. Seagate Holos and Seagate INFO/Crystal Info
3. IBM DB2 OLAP Server
4. Informix MetaCube
5. Microsoft SQL Server 2007 and above
6. OLEDB for OLAP Sources(Oor other “Open OLAP Sources)

· As 1995 approached, Bank of Montreal was anticipating a considerable increase in competition from US-based card issuers. Card issuers in the United States enjoyed the twin benefits of scale and skill—larger scale of marketing budgets and greater skill in information marketing. The bank understood that it might never be able to match the budget capabilities of the US issuers, but it could work to augment its marketing skill.

· One of the insights Bank of Montreal gained early in its efforts to construct the data mart was that the skills required for model building and sophisticated analytical work are not typically found among bank employees. The bank was forced to recruit and hire people with highly specialized skills. The bank also realized that motivating and retaining these analysts would require different incentive plans. Bank of Montreal needed to address cultural as well as technology issues in building their credit card data mart.

· While the credit card division was building its data mart, Bank of Montreal was also building a Bank Information Warehouse (BIW). While the data mart represented a “bottom-up” initiative, the BIW is a “top-down” approach to customer relationship management (CRM), and the integration of the two projects will be a challenge for the bank.

Source: Sybase