Data management defined by enforced data policies, updated in real-time and reliant on validation and correct classification, should provide you with the active ability to realise results through the process of automation.
Efficiently validating, curating and optimising data at the enterprise level relies on systems architectures that facilitate real-time monitoring. Your architecture should enable visible data lineage and associated metadata while incorporating a comprehensive and searchable data catalogue that enables self-service data access and discovery.
Without the help of data engineering experts that are responsible for the ownership of your data estate, enterprise-level organisations cannot be assured of either quality or cleanliness within their data estate. The result: data swamps requiring costly, unsuccessful post-processing clean-up initiatives.
- Functional and accurate
- Defines the relationship between tables, primary and foreign keys, and stored procedures
- Enhances communication at an organisation level
- Documents data mappings in the ETL process
- Recognises correct data sources
- Ensures consistency in naming conventions, default values, semantics and security
- SLA-driven KPIs