Authored on: May 21, 2012
Advanced analytics should not live in a world separate from BI systems. Where BI systems provide the most value through dashboards, reports and tracking of performance metrics, advanced analytics can offer highly complementary technology.
To get the most out of advanced analytics, organizations need to include high-performance computing in their data management practices and technology deployments. High-performance computing can enable organizations to overcome performance bottlenecks and other challenges that inhibit the power and growth of advanced analytics and their consumption by BI users. High-performance data management options that are especially important to advanced analytics are in-database processing, in-memory processing, grid computing and stream processing.
Seven key steps toward aligning BI and analytics are discussed at length in this monograph. These steps include exploiting in-database processing to speed analysis, increasing the power of analytics with ELT, implementing data federation to reduce data movement, and managing in-memory processing to achieve higher performance. Other focus areas include achieving dynamic scalability by integrating grid computing with in-memory and in-database technology, employing workload management to align technology with analytic requirements, and using high-performance computing for real-time analytics and complex event processing.