Data asset management platform
Help enterprises build data asset value and ensure sustainable data operation.
Orbit provides users with visual data asset map, automated data asset classification, multi-dimensional data lineage analysis, and sustainable data standard implementation. Through the comprehensive planning and management of the stock and new data assets, metadata and business data, we will build a leading data operation system for enterprises, improve data visibility, data use efficiency and data asset value, and reduce data pollution and governance costs.
Automation of data asset map
Orbit can automatically build data asset maps of 18 types of entities, 44 types of relationships and 4 dimensions. Through the visualization of data asset lists, asset relationships and metadata, it can improve the visibility of data, so that enterprises can clearly understand their own data assets, as well as the relationships and dependencies between these assets.
Intelligent data asset portrait
Through algorithms, models and rules, Orbit automatically labels and classifies data assets to form a complete, accurate and understandable asset description for user management, query, analysis and use. The refined label and semantic description make data governance and application more intelligent and reduce the labor cost of operation and maintenance.
Multi-dimensional data consanguinity management
Orbit Orbit supports multi-dimensional blood relationship management at table level, field level and record level, and tracks and records blood relationship from data to data, data to application, and application to application, realizing the whole process of blood relationship management of data source, processing, transmission, and use. Let enterprises clearly understand where, why, by whom and how to use data.
Perfect data operation system
Provide a complete operational analysis system covering the collection, integration, standardization, governance, application and evaluation of data assets, including the monitoring of data content governance, data collection and data flow, to help enterprises accurately assess the status of data assets, improve data operation processes, and improve data operation efficiency.
All-round data quality inspection
Effectively identify the quality problems of business data and metadata, improve the readability and availability of data assets by correcting and resolving the missing and ambiguity of metadata, so as to further improve the quality of business data and enable enterprises to form high-value data assets in digital competition.
Continuous implementation of data standards
The traditional "sports" data governance first pollutes and then governs. It is neither systematic nor sustainable for enterprises to focus on data governance in a short time. Orbit breaks through this old model, not only supports the management of the stock data, but also continuously checks whether the new data and new table structure meet the data standards and gives rectification suggestions, so as to avoid the data pollution of enterprises from the root.