DATAWAREHOUSING A TOOL FOR BUILDING RETAIL BANKING BRANDS

PRESENTED BY:-YOGESH KUSHAWAH11BSP1719

ABSTRACT:

Data warehousing is an emerging technique of data base management which can act as a strategic differentiator for data intensive sectors like retail banking. The Presentation starts with briefly explaining the concept of data warehousing and points out the areas in retail banking which can be most benefited from data warehousing. The Presentation also proposes data warehouse architecture to support retail banking activity. Finally critical issues related to management of the data warehouse which will affect the usefulness and functioning of the bank has been discussed. The Presentation is concluded with a futuristic outlook of data warehousing.

LEVERAGING RETAIL BANKING BRANDS USING DATA WAREHOUSING

Banking is an extensively data intensive sector. Large amounts of data pertaining to various transactions are generated. This large quantity of data regarding individual customers saving, earning and spending pattern can speak volumes about an individual’s taste and preferences, habits, family and his personality in general. But most part of these data is locked up in files and computer systems and is exceedingly difficult to get it.

Recently a significant concept that has emerged, which addresses to this problem is ‘data warehousing’. Data warehousing has grown out of being just a technique of providing efficient, precise and flexible data management tool to an organization’s most valuable and critical asset.

Bill Inmon (1992) is widely credited for popularizing the data warehouse concept and terminology. Also known as the ‘Father of Data warehousing’, defines a data warehouse as follows:

“ A (data) warehouse is a subject oriented, integrated, time-variant and non-volatile collection of data in support of management decision-making process “

Subject- oriented: Data that gives information about a particular subject instead of about a company’s on going operations.

Integrated: Data that is gathered in to the data warehouse from a variety of sources and merged in to a coherent whole.

Time Variant:All data in the data warehouse are identified with a particular time period.

Non Volatile:Data is stable in a data warehouse. More data is added, but data is never removed. This enables management to gain a consistent picture of the business. It is clear from the definitions given above, that data warehousing largely deals with storing large and varied data that has been collected from different sources, over a period of time and can be retrieved for future analysis.

The obvious question that comes in to one’s mind is ‘how is data warehousing different from conventional data base management system (DBMS)’?

(1) Data warehouse is a large collection of data, which has already been executed.

(2) All the data that is stored is in reference to a particular time frame.

(3) Data is fed in to the data warehouse through multiple sources.

(4) As data is fed in to the data warehouse from various sources, all data are first converted in to a common platform. This enables joining of data for query and analysis.

(5) Data can be recalled from the data warehouse using various front-end tools, as the user requires.

(6) Data is updated (New data is fed) in the data warehouse periodically.

Areas in which Data warehousing techniques will support banking activities:

• Customer Profiling

• Identifying High-end Users

• Fraud Detection

• Improved Underwriting

• Quality Control

• Business Forecasting

• Strategic Planning

Strategizing and Designing a data warehouse for retail banking:

Operational Database:Operational database collects data pertaining to the day to day transactions that a bank makes. Operational data in banks are generated in terms of routine transactions such as cheque clearance, withdrawals and deposits.

Information Access Layer:This layer contains the tools which users require to analyze the data. Some commonly used tools that are in use are Excel, lotus 1-2- 3, Access, statistical analysis system etc.

Data Access Layer: This is the portion which contains software which creates the interface between the operational database and the information access layer.

Data Directory (Metadata) Layer: Meta-Data is the data about the data within the enterprise. In order to provide universal access to end users, some kind of a directory has to be built. The key issue here is that a functional data warehouse collects data from different sources (internal as well as external).

Data Warehouse (Physical) Layer: This is the main database where the data is stored. This is stored such that data is easy to access and is highly flexible.

Data staging Layer:Data staging involves replication (making copies), selecting, editing, summarizing and loading the data warehouse with data from operational/or external databases.

Application Messaging Layer: Application messaging is also called ‘middleware’ as this involves the networking and the various protocols which govern them. Application messaging in general is related to the transportation of information around the organization. Application messaging can also be used to collect transactions or messages and deliver them to certain location at certain time.

Process Management Layer: Process management layer is responsible for updating and maintenance of the data warehouse. For retail banks this is a very important component as this involves maintenance of metadata. With every change in the external environment new variety of data is generated.

Managing data warehouse for brand differentiation:

Standing out in the crowd

Internal marketing

Employee Training cost

New services and NPD

Increasing Penetration

E-Banking and security issues Channel Integration

DATA WAREHOUSING – EFFICIENT TOOL FOR CRM

Fig:- growth of data warehouse in banking sector

REFERENCES:

1- ‘Data warehousing technology’ ©Copyright 1996 by The Ken Orr Institute; revised edition, 2000

2- Gupta, Vivek R (1997), An Introduction to Data warehousing, http://www.system-service.com

3- As quoted by Mishra AK, ‘Internet Banking in India’, www.banknetindia.com

4- Kotler Phillip (1999), ‘Marketing Management’, Millenium Edition, pg 22, PHI (Indian reprint)

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