As the financial industry rapidly develops in recent years, the industry faces complexity of financial infrastructure and diverse demands and needs of customers. Accordingly, volume of data to be managed by systems is increasing exponentially, requiring financial industry to expand hardware and heavily invest in management tools to address such problems. But, timely and proper analysis and application of data produced by diverse business systems still remain as difficult tasks of managers. These problems can be resolved by by implementing DW (data warehouse) system.

Direction of Installing DW

  1. Subject-Oriented Design
    Design subject-oriented system based on analysis made by analysts rather than designing function-oriented system
  2. Integrated Structure
    Build integrated structure with coherent data by extracting different data from existing diverse operating systems, and modifying data with different structure and features to be suitable for core theme.
  3. Design that reflects Notion of Time
    Need structure that can fully add temporal element to data and integrate information based on time
  4. Non-volatile Structural Infrastructure
    Integrated data should not be changed, large volume data can be stored, and they should be read-only data
DW · BI Consulting

While volume of data used at enterprises increases exponentially and most companies expect to process data in real time, performance of existing data warehouse systems is not satisfactory. And Gartner predicts that 75% of existing DW systems will become obsolete by 2016.

  1. Necessity of Next Generation DW system
    – Need to monitor results of data analysis in real time
    – Need to address in real time by the field to timely provide basic data needed for analyzing information by decision makers
    – Need to analyze whether proper costs are used for post processing results of analysis
  2. DW  System’s View by DataStreams
    – Real time data integration-oriented DW: It enables the users to address by rapidly analyzing data
    – High quality data-oriented DW: Essential for assuring accuracy of analyzed data and making clear decision
    – Integration of large volume data and data with nonidentical structure
System Architecture
Case Study
H Bank Implemented near-real-time data warehouse
Government agency Implemented data warehouse at Korea Workers’ Compensation & Welfare Service