With the proliferation of analysis tools and skills, data has become increasingly common within companies. Consequently, Data Governance programs and tools must be seen as valuable elements for establishing the corporate Data Community. The “Data Governance by Design” approach entails:
- Continuous alignment of data management with strategic objectives.
- Sustainability of initiatives.
- Focus on value generation.
The design of the Data Governance framework should begin with goal identification, ensuring that organization, processes, procedures, and tools are functionally defined to implement the corporate strategy. To ensure alignment with objectives and sustainability, the Data Governance program should be designed as a continuous process, adopting an incremental and iterative approach. Effective implementation requires proper definition of roles, responsibilities, and the design of clear, distributed procedures, preferably automated in Data Management initiatives and Data Ops processes.
Examples of standards contributing to implementing Data Governance by Design include Metadata Management standards like naming conventions, object tagging, and controlled vocabularies.
Quantyca combines organizational know-how with deep knowledge of suitable technological tools to support Data Governance implementation and reduce operational complexity.
Key functionalities in Data Governance tools include Business Glossary, Data Catalog, Data Discovery, Data Quality, and Data Lineage. Additional modules or tools can be added based on organizational needs to extend Data Governance scope or enhance specific aspects.
In a holistic Data Governance approach, tools must be effectively integrated to provide a unified and comprehensive view. For example, linking physical data mapped in the Data (Product) Catalog with the Enterprise Ontology through Semantic Linking enables the construction of a corporate Knowledge Graph.
Data Governance brings various benefits, including risk reduction, support for automation and maintenance (operational efficiency), and democratization of data access (organizational scalability).
