Big data environments help organizations capture, manage, process and analyze large amounts of data from both new data formats, as well as traditional formats, in real time. When managing the data lifecycle of big data, organizations should consider the volume, velocity and complexity of big data:

  • Volume: Every day 2.5 quintillion bytes of data are generated, which can impact the total cost of ownership for data warehouses and other big data environments if data growth is not managed appropriately
  • Velocity: Big data environments support time-sensitive processes, such as analyzing 500 million daily call detail records in real-time to predict customer churn faster
  • Complexity: Organizations capture a variety of data formats and comb through massive data sources in real-time to analyze and identify patterns within the data – e.g. identify fraud for credit card customers, identify financial trends for investment organizations, predict power consumption for energy companies.

As organizations look to big data environments to analyze and manage critical business decisions, they will face significant challenges related to data lifecycle management:

  • Archive data into Hadoop
    • Leverage analytics to make informed decisions across structured data, including archived data, and unstructured data
    • Optimize storage, maintenance and licensing costs by migrating rarely used data to Hadoop
  • Growing amounts of data outpacing the capabilities of the data warehouse and other big data systems/li>
  • Creating realistic test data for testing data warehouse environments
  • Preventing the exposure of confidential data in both production and non-production environments

Reduce costs, improve performance and reduce the risks associated with managing big data environments

InfoSphere Optim solutions provide proven data lifecycle management capabilities, maximizing the business value of data warehouse and big data environments through managing data growth, lowering TCO and meeting data retention compliance.

  • Archive data into Hadoop using InfoSphere Optim
    • Query-able access to data: Leverage analytics to make informed decisions across structured data, including archived data, and unstructured data
    • Optimize storage, maintenance and licensing costs by migrating rarely used data to Hadoop
    • Leverage governance and policy driven archiving
  • Reduce total cost of ownership of data warehouses – like IBM Netezza – by intelligently archiving and compressing historical data
  • Increase data warehouse performance by archiving dormant data, leveraging a multi-temperature storage strategy
  • Automate the creation of realistic “right-sized” test data to reduce the number of defects caught late in the test cycle
  • Refresh test data speeding the testing and delivery of the data warehouse
  • Mask sensitive data – for both production and non-production environments – for compliance and protection
  • Support data retention needs, as well as legal hold requirements within the data warehouse
  • Use workload capture and replay to establish realistic testing scenarios

Products

IBM InfoSphere Optim Archive

Manage data growth effectively to reduce costs and improve performance

IBM InfoSphere Optim Test Data Management

Optimize and automate test data management to reduce cost, accelerate application delivery and lower risk

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