The Decision Maker

Data Warehousing for Community Financial Institutions: Three Necessary Components

Posted by Aaron Wang on Nov 10, 2013 11:16:02 PM

A data warehouse is a database used for reporting and data analysis. It is a central repository of data which is created by integrating data from one or more different sources. Data warehouses store current and historical data and are used for creating trending reports for various levels of management.

The three necessary components for a successful Business Intelligence (BI)/DW initiative:

1. Strategic Initiative – Leading community financial institutions (CFI) are developing Business Intelligence (BI) functions to better understand their data to improve financial performance and enhance customer/ member services. The BI initiative cannot succeed without C-level support and strategic priority. Many failed BI/DW projects can be traced backed to a lack of c-level support. How Companies Like Amazon Make You Love Them - "They’re using that data to build our (customer) relationship."

2. Enterprise Integrated Data – Unlike a database, a data warehouse contains data from most or all of an organization's operational systems and these data are made consistent. Integrated data provides users with a unified view platform for business analysis, to be applied consistently across the enterprise. CFI’s use many applications to process customer/member transactions, and reporting and analyzing from the silos of data is challenging. Integrated data is important in any BI/DW initiative. Does your organization have a data analytics platform to properly measure trends and transactions? As Peter Drucker states: “What gets Measures Gets Managed”.

3. Effective Data Architecture – The main purpose of any BI/DW is to have the ability to analyze data at the transaction level across multiple applications, and to provide effective reporting. The data in the data warehouse is organized so that all the data elements relating to the same member/customer or transaction are linked. BI architecture begins with the extraction process from source systems. The data is staged and then consolidated into a data warehouse environment. A properly designed data structure will limit performance and scalability issues. Poor planning and bad design, often lead to ongoing, unnecessary spending and, even worse, unsuccessful projects.

Topics: Credit Unions, Data Integration, Data Analytics