11st Cuts Task Deployment Time from Eight to Nine Hours to Two Hours, Reduces Storage Use by 500%
 
 

11st Cuts Task Deployment Time from Eight to Nine Hours to Two Hours, Reduces Storage Use by 500%

  • Oracle Customer:  11st
    Location:  Seoul, Republic of Korea
    Industry:  Retail
    Employees:  600
    Annual Revenue:  $1 to $5 Billion

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In 2008, Korea’s largest mobile communications service provider SK Telecom launched 11st, an online shopping site. 11st has grown significantly in the past three years, becoming the first company to earn revenue of US$2.6 billion in the shortest time in the history of the e-commerce industry in South Korea.

 
 

 
 

Challenges

A word from 11st

  • “The ability to react quickly to daily changes in the fast-moving e-commerce market is critical to success. Oracle Exadata Database Machine offers fast transaction processing times and data compression features that enable us to analyze customer transactions in a timely manner and take action if we spot any emerging sales trends.” – JangWon Park, Manager, 11st

  • Implement a data processing platform that could support a rise in the number of online shoppers, by increasing data throughput and transaction processing speeds
  • Complete sales reports on the previous day’s best-selling product categories and top customers in a short amount of time to ensure the data warehouse is fully operational before the start of business hours
  • Enable easy analysis of large volumes of data, such as customers’ shopping habits and bestselling products, over a long-term period

Solutions

Oracle Product and Services

  • Implemented Oracle Exadata Database Machine X2-2 HC Quarter Rack to take advantage of faster processing times and data compression features
  • Shortened the time taken to complete tasks, such as the previous day’s sales reports, from eight to nine hours in the past, to two hours
  • Processed 100 million shopping transactions in a day, as Oracle’s compression and partitioning features and data transfer specifications enhance data processing speeds
  • Enabled fast reporting and analysis of six-month-old transactions, by partitioning data into daily, weekly, and monthly segments and applying different compression methods
  • Improved performance at low cost and with minimal man hours as the existing database system did not need to be changed
  • Reduced storage needs by 400% to 500% (from 20TB. to 25TB to 5TB) by compressing customer and transaction data
  • Optimized resource allocation among the many concurrent database sessions with Oracle Database Resource Manager

Resources