Use SAS® to get more power out of your database
Move key components of BI, analytics and data integration processes from the server or desktop to inside the database and help shorten your time to intelligence
Whether it's through advancements in data transfer, parallel processing or grid computing, SAS has a strong history of harnessing new technologies to manage complex computations and to increase the performance and scalability of high-volume data processing.
Today, as data volumes continue to double every 15 to 18 months in most organizations, the ability to prepare, process and streamline analytics in time to make an impact on daily operations is more important than ever before.
The latest way to improve the timelines of data processing is a solution from SAS that leverages database technology to reduce data movement and streamline analytic processes. The solution will embed core data transformation, analytic and business intelligence (BI) applications from SAS in the enterprise data warehouse engine.
Once these advanced capabilities are in place inside the database, businesses can reduce data movement and leverage the computational power in the database engine to increase analytical exploration and action.
Technological advancements make it possible
What has changed? Databases have become more scalable and parallelized. They are becoming more like "data grid appliances" that can handle distributed computations across systems. This change, in turn, better supports the heavy CPU demand required for running analyses alongside data transformations.
The benefits of receiving the power of SAS while significantly reducing the movement of data?
SAS has always been vendor-neutral and platform-agnostic, giving customers greater flexibility and supporting their investments in legacy systems. Now, the company has gone a step further by announcing plans to support research and development efforts to develop deeper integration of its BI, analytics and data integration software to execute key elements within database engines. This advances SAS' goal to provide a flexible, cost-effective platform to maximize enterprise data warehousing investments for in-database analytics and BI.
Banking customer realizes benefits of in-database processing
Now that SAS predictive models are integrated within the Teradata engine, analysts can identify and address unusual or suspect patterns or anomalies more quickly. The new in-database solution also speeds the process of refining detection measures to more accurately report exceptions throughout the enterprise.
The bank's achievements reflect the dual goals of SAS' in-database effort:
Putting SAS Analytics into the database accelerates the analytic process, addresses governance regulations and speeds the delivery of BI to all levels of an organization. Blending the SAS strengths of BI and analytics with the core strengths of databases will make BI and analytics more pervasive throughout the organization.
Of course, this level of product integration depends on heavy partnership investments. Currently, SAS is identifying key database vendors as partners for this initiative, in a process that will be driven by market demand, customer direction and input.
It boils down to this simple equation: Less data movement = faster analytics, and faster analytics = faster delivery of real-time BI throughout an enterprise.
SAS partners with Teradata for in-database initiative
This story appears in the First Quarter 2008 issue of