Data architecture and modelling

What do we mean by data architecture and modelling? 

The process of defining the structure, integration, storage, and management of data assets, including the design of data standards, systems, and database requirements to support business objectives. 

Data architecture is an essential foundation for building effective ICT and digital solutions that operate in a joined-up way. It sets the framework, standards, and whole organisation approach to data, to enable detailed data design. 

Data modelling is a sub-set of data architecture looking at the detailed design of structures and relationships and the flow of data across systems.  

Knowledge 

• Knowledge of database design, data warehousing and data modelling.
• Understanding of enterprise and data architecture best practice.
• Knowledge of database management including practices such as normalisation and de-normalisation.
• Knowledge of conceptual, logical, and physical data modelling, including entity relationship diagrams.
• Understanding of the importance of data standards and how to create and implement them.
• Understanding of extract, transform and load protocols (ETL).
• Understanding of data storage options including on premise and cloud.
• Understanding of data governance and data security.
• Understanding of emerging trends including big data and analytics, data mesh etc. 

Skills 

Able to:  

• Think strategically: Taking a whole organisation (and where appropriate, a place-based) approach to the definition of data, in the context of understanding Council needs and long-term priorities. 
• Influence decision-making in relation to technology and data: Ensuring data architecture is a key consideration in any technology or digital design decisions, to enable ‘joining up’ of services and insights. 
• Define data standards: Set out data standards for common entities to enable data standardisation, creating metadata standards to enable data to be managed effectively. 
• Design and implement master data management: In the context of organisational priorities and target outcomes, manage and harmonise core data entities so that there is a seamless and unified view of core data and a consistent and accurate flow of data across different systems.  
• Define data models: Including the structure and relationship of data entities such as tables and fields to support business needs. 
• Determine requirements: Including defining requirements for storage, access, and how data is held and organised across multiple solution and hosting types. 
• Manage metadata: Apply metadata appropriately, define related standards and implement effective metadata management. 
• Create conceptual, logical, and physical data models: Mapping data entities and relationships, specifying the structure of the data and then defining how the logical model will be implemented physically. 
• Collaborate and communicate: With technical and non-technical stakeholders to ensure data architecture is prioritised in transformational activity and to accurately reflect organisational objectives in data designs. 
• Manage risk: Understand risk and work to manage and mitigate data related risks as part of data architecture design.