Data aggregation or consolidation for accurate reporting and analytics is essential, and businesses have adapted many specialized business applications to stay competitive. These applications can be homegrown, on-premise, or cloud applications. Effective enterprise data management ensures that data is structured for seamless reporting, while Data Management and analytics enable businesses to derive insights from that data with precision.

Orbit’s data management functions allow business users and data engineers to use the drag-and-drop visual data modeler that’s incredibly easy to use and helps create ad-hoc data mashing models to complex data models. The data models can be further configured for data security and shared across the enterprise to promote self-service data-driven decision-making. This allows for streamlined Oracle ERP Data Management, integrating data from Oracle ERP systems into unified, actionable insights.

What Sets Orbit Data Management Apart?

Users log in with a single sign-on for data governance at an organization’s macro level, and individual business apps manage their permissions. With Orbit’s access controls, all permissions are managed from one point, and users get permission to menu functions based on specific application roles, making the system more efficient and user-friendly within enterprise Data Management systems.
Data security is the next layer of access controls. Data security rules can be defined and applied for user roles, restricting the user from accessing certain data. Orbit allows users access to data in your ERP and other corporate applications while enforcing data security rules defined in respective business applications. This ensures comprehensive Data Management and Analytics, promoting secure and controlled access across multiple platforms.
The Datasets feature is a data catalog promoting data stewardship by creating and maintaining trusted, reliable, and consistent data. The ad-hoc data mashing auto-modeler is a self-service ability for business users to create agile data by mashing corporate data with external datasets that might be in flat files like Excel, CSV, JSON, XML, or directly accessible as web service URLs. This functionality facilitates the integration of Oracle ERP Data Management into broader enterprise workflows.
In reporting and analytics, data models promote data stewardship and give the most lineage in empowering business users while enforcing data security in the process. Additionally, data models help reduce maintenance efforts, increase data lineage, and ensure data consistency by abstracting complex formulas across domains. This approach enhances the enterprise Data Management ecosystem by making it easier to govern large data sets and reduce complexities.
In modern businesses, data is often spread across various databases, both inside and outside the organization. Being able to run SQL queries across different types of data sources (relational and non-relational) and combine the results is crucial. For example, when we need information from both an Oracle database and an MSSQL server, combining them into one result set is called data federation, a key technique in Data Management and Analytics.
Data virtualization is a Data Management technique of hiding the data complexity under the hood. Business users do not need to know all the technical details about data sources, such as where and how data is stored. What they need is a single trusted view presented for consumption. For example, complex calculations like commissions can be abstracted to avoid mistakes in the Oracle ERP Data Management system.
You need to collect data (extract the data, transform the data, and load the data) from multiple sources so that you can make well-informed business decisions.

DataJump is Orbit’s solution for ETL (extract, transform, and load) and ELT (extract, load, and transform) requirements. DataJump gives you the ability to blend data from multiple sources into one single source, supporting your Enterprise Data Management strategy. DataJump allows you to synthesize data from multiple sources so that you can build a database such as a data hub, data warehouse, or data lake, all while ensuring the integrity of your Data Management and Analytics process.

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