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Noetix Analytics is a packaged analytics solution designed to provide business users with strategic reporting for trending and analysis based on information from multiple data sources.

Organizations can use Noetix Analytics in conjunction with their existing business intelligence and reporting tools from Noetix, Oracle, IBM Cognos, and SAP Business Objects. Business professionals gain visibility to the historical data they need, when they need it.

KEY FEATURES

Noetix Analytics features and benefits include:

• Enterprise data model for strategic reporting.
• Integration of Oracle E-Business Suite, third-party, and custom applications data.
• Pre-built operational framework for loading and refreshing data.
• Flexible scheduling defined by the business user to ensure data is delivered when needed.
• Optimized performance and scalability for handling large data volumes within short maintenance windows.

A core component of Noetix Analytics is the enterprise data model—which includes an operational data store (ODS) and subject area data marts—all based on a set of common dimensions.

The ODS provides information from multiple transaction systems in a format which is more efficient and effective for business users to navigate compared to the source transaction systems.

The data marts consist of an integrated set of star schemas designed for analysis, enabling the strategic reporting expected of an enterprise data warehouse.

Noetix has created ETL maps designed and optimized to extract and load data from Oracle E-Business Suite. Open interfaces are provided to integrate additional third-party, legacy, and custom applications. Specialized configuration options support advanced requirements such as slowly changing dimensions.

In conjunction with the ETL maps, a layer of database views is used to facilitate data extraction from Oracle E-Business Suite. The views shield the ETL maps from changes in the database tables introduced by new EBS releases, streamlining the upgrade process. The views are also designed to process both full and incremental extractions, enabling a single set of ETL maps to be used for both initial loads and incremental updates to the data warehouse resulting in a dramatically simplified architecture to maintain and support.