Data Models

Live Access to Your Financial Data

The only complete solution for Oracle Cloud ERP Financials reporting, GLSense provides built-in Microsoft Excel integration with leading ERP software. Directly integrate your ERP financial system with Excel to build financial statements and expedite your reconciliation and close processes.

Orbit Analytics Integration

Real Time Drilldown to Your Subledger Data

Improve your reconciliation process and achieve a faster period close with complete drilldown from balances to subledger with hundreds of pre-built reports.

Data Security and Governance at Orbit Analytics

Simple and Secure Report Distribution

Do everything inside Excel. Fast, easy distribution of financial reports without switching between applications.

For Cloud Financials, EBS, and NetSuite

Build financial statements from live data in Oracle Cloud Financials, Oracle EBS Financial reporting, NetSuite, and more.

Data Models increase data consistency

In the context of reporting and analytics, data models give the most lineage in terms of empowering business users, also enforcing data security in the process. Additionally, data models help in reducing maintenance efforts, increasing data lineage, and ensures data consistency by the abstraction of complex formulas across domains. All of this put together empowers the business user to build the reports they need.

Orbit’s three-layer architecture for designing data models includes a physical layer (registrations of database objects, such as tables and views), a logical layer (capturing relations between objects), and a presentation layer ( Abstracting complexity). This sophisticated metadata layering design allows organizations to centrally enforce governance.

From an IT point of view, it is a lot easier to maintain a common Semantic Layer than to maintain thousands of individual reports.


Operational Data Models for real-time reporting

Business applications are built upon highly normalized transactional tables. Real-time reporting is achieved by designing data models directly on top of normalized transactional tables. These data models are not subjected to modeling constraints making it dynamic in nature. Orbit’s Data modeler is designed to address operational data reporting requirements.

Orbit’s data modeler’s ability to pre-configure user prompts is an essential feature for the enablement of self-service reporting.

Analytical Data Models help spot trends

The key to building analytical models is to ensure that raw data with billions of records is organized properly for data querying. Star and Snowflake schemas are the simplest forms of a dimensional data model, with data organized into facts and dimensions.

Analytics models require the denormalization of data, which essentially means eliminating expensive joins between tables to increase database query performance.


Advanced Data Models for real-time data mashing

Real-time data mashing across multiple data sources cannot be addressed by traditional ETL and data warehousing solutions. Orbit’s advanced data modeling features like data federation and data virtualization can help solve this complex problem.

Configurable data filters on the data model restrain users from building run-away SQL queries.

Subject Area Data Model are presentation objects

Logical data models (LDM) are presented as a set of Business Objects representing a particular subject area. The Business Objects are functionally secured by application roles, i.e. users having permissions to author content and having access to these business objects can build reports and analysis.


Webinar On Demand

Work Harder Multi Data + Mashing