IT Cost Reduction

IT Cost Reduction for Data & Analytics comes from two key areas: data acquisition efficiency and data storage optimization.

Data acquisition efficiency focus on reducing costs associated with making data available for enterprise reporting and analytics. Typical ETL processes are based on data movement through server-based ETL tools to data warehouses and data marts. Cost reduction opportunities exist because:

  • ETL data transformation consumes large amounts servers and licenses:
    • The cost and performance of current ETL can be improved using Hadoop, as shown in this example
      Infographics_data_3
  • (2) compute resources on massively parallel processing(MPP) databases:
    • 80% of total queries are data preparation (ETL/ELT);
    • 60% of CPU utilization is consumed by data preparation (ETL/ELT);
    • ETL/ELT processing is often wasted on unused data.

Data storage optimization focuses on right-sizing spend on expensive data storage. For data warehouses that have had a long life, we typically find that more than 50% of the data it contains is unused or dormant. Large data warehouses are costly, especially if built on expensive, proprietary hardware and software, so it can make sense to move unused and little-used data to Hadoop.

These IT cost reduction opportunities will introduce further savings by incorporating existing data marts and data warehouses into a Big Data & Advanced Analytics Architecture for the enterprise, further lowering the cost of data for the enterprise.