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.

There are many fundamental differences between a data warehouse and a data mart. Some of the differences are as follows:

Is building an enterprise data warehouse (EDW) the best path to business intelligence (BI)? It’s a perennially vexing question that — thanks to a couple of recent trends in BI and data warehousing (DW) — has taken on new life. The value of the full-fledged EDW seems unassailable. Over the last half-decade, however, some of the biggest EDW champions have moderated their stances, such that they now both countenance the existence of alternatives and, under certain very special conditions, are even willing to admit they’re useful. The result is that although the EDW is still seen as the Holy Grail of data warehousing, departmental (and even enterprise) data marts are now countenanced as well.

Read more

MUMBAI, INDIA: Having engineered technology around enterprise data cloud solutions for large-scale data warehousing and analytics, US-based Greenplum is now eying the India market aggressively. While in 2008 the vendor focused on setting up a technical team in India, Greenplum has extensive plans to set-up its marketing team in India this year.

Elaborating on the company’s business model, Keith Budge, VP & GM—Asia Pacific & Japan, Greenplum stated, “We are aiming at having a direct presence in India and will be appointing a host of system integrator and reseller partners in the country.”

Budge further elaborated that Greenplum technology enables the customer to perform high-scale data warehousing and analytics, and hence customers demand for building data warehouse application depending upon the need .

Greenplum is looking at appointing SIs who specialize in various market verticals, including banking, retail, manufac-turing among others. While Wipro, HCL and TCS will be Greenplum’s tier-1 partner, the company is in the process of building an ecosystem of partners that will include tier-2 SIs and distributors.

Talking about enterprise data cloud computing, Keith mentioned, “While virtualization has taken over the IT industry, the enterprise data cloud computing will be the next trend that will be followed. It allows customers to utilize and access data depending upon their needs. Our partners will require a lot of support in terms of technical implementation and understanding business processes of our the customers and can generate revenue by providing such services.”

Throwing light on its go-to-market strategy, Kalyan Roy, Director—Sales, India & SA, Greenplum pointed, “We will have a full-fledged sales team backed by support team in India. Besides we are also looking at appointing regional partners. Greenplum may also look at expanding its R&D in India and has already put a product technical support team in Mumbai, that will offer a 24×7 support.”

data mart approach’s focus on a particular subject or department to help management make strategic decision about their business is similar to Evidenced based Management’s approach’s defining the objective and information needs, based on that collecting data and analyzing and turning that data into insights and presenting that into insights.

Instead of focusing on collecting everything that is easily measured and counted, organization’s need to be more systematic and selective about the information they are gathering.[1]

Source:

1- Moving from data to insights by Bernard Marr – Management by Certified Management Accounting Canada -June/July 2009.

Staff must agree on exactly what constitutes a “customer” or a “partner,” and how to resolve any disagreements across business units. Departments and divisions need to agree on hierarchies of customers and products and how to resolve duplicate records across sources.[1]

Source:
1- Demystifying Master Data Management by CIO.com

SDLC outlines how software is designed, built and deployed. There multiple versions and approaches to the SDLC that are in use today.

A company may opt to use a single SDLC for every project that they run, or may deploy a different SDLC process for different types of projects they deploy.

Many professional service focused organizations have created proprietary SDLC which is part of their overall value offering. (e.g Accenture and Teradata have both developed SDLC’s for delivering Data warehousing, data mart / Business Intelligence projects.

Bottom Up approach
Faster and easier implementation of manageable pieces of information. Inherently incremental and important data marts can be scheduled first as per end user requirements. Facilitate project team to learn and grow. Each data mart has narrow scope, data redundancy and inconsistency may be present.

The best approach
Best approach is the basic understanding of information needs of an organization. That must include both present and long-term information requirements. In view of that a careful implementation and monitoring is critical.

We tried our best to elaborate some terms used in Business intelligence, Data Warehousing and Data Mart.
If readers have some more to add / please feel free to submit in comments so it can help other readers and correct ourselves.

Data Mart Structure

The data we used for business intelligence can be divided into four
categories: measures, dimensions, attributes, hierarchies.The four type of data help us to define the structure of data mart.[1]

Measures

Measure is numeric quantity expressing some aspect organization’s
performance. Measures are the facts and also known as fact table.[1]

Dimension

Dimension is categorization used to spread out aggregated measure to reveal its constituent parts.[1] dimensions include time (or date), customer, product, geography, lab type, campus, patient, promotions, gender (and other demographics), and so forth. Each dimension is associated with the facts / measures to which it relates via the linkages / joins between the table(s) housing the dimension (the dimension table) and the fact table.[2]

Attributes
For example a member of the Patient dimension, within the Analysis Services implementation for a healthcare provider, might contain information such as patient name, patient ID, gender, age group, race, and other attributes. Some of these attributes might relate to each other hierarchically, and, as we shall see in subsequent articles of this subseries (as well as within other of my articles), multiple hierarchies of this sort are common in real-world dimensions.[2]
Hierarchies

These are created from the attributes of dimesions. For example In Dimension Geography there are attributes named as Country-State-City-Postal Code.[3]

Source

1- Delivering Business Intelligence with Sql Server 2008 by brian Larson
2- Database Journal by Internet.com
3- Sql Server Analysis Services 2008 with MDX