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One-stop Data Governance Platform
Demand pain points
Metadata Management
• Changes in metadata of the original system cannot be known about, making it difficult to make version control;
• Focus on the attributes of data dictionary without other types of attributes;
• Huge cost of data traceability, which is currently in the form of semi-automatic + manual entry of lineage information;
• Lack of data model design tools, and the difficulty of unifying the specifications at the development stage;
Data security
• Unsupport data desensitization and encryption;
• Lack of fine-grained permission control, such as for rows, columns, and cells.
• Lack of log and audit;
• No support for RBAC attribute-based permission control;
Data standards
• Standard organizing is time consuming, difficult and in need of intelligent means to improve efficiency;
• Lack of existing industry data standards;
• The current data standards are more for ex post facto instead of ex ante control;
• Lack of product means for data standards consistency, which currently are mostly manual weak checks.
Data quality
• Data standards cannot be automatically transformed into data quality rules checks;
• Lack of existing data quality rules;
• Lack of a closed-loop process for data quality improvement;
TDS data governance tools
◆ Product capability: Support data governance work and improve data management
◆ Product goal: to control, protect and improve the value of data assets
◆ core feature:
◆ Data standard management
◆ Data quality management
◆ Metadata management
◆ Data lineage
◆ Data security and permission management
◆ Intelligent data asset management
◆ Data model management
Product name
Data standards definition, inspection and rule scheduling
Definition, management, implementation and quality reporting of data governance rules
Permission control of tables and columns
Setting of rules for data flow, such as data desensitization and encryption
Metadata management of multiple heterogeneous data sources
Data lineage management and analysis
Recommended intelligent analysis and governance rule for data features, attributes and similarities
Data lifecycle analysis based on data mapping