TOGAF – Enterprise Architecture Framework has four types of Architectures elements or components namely – Business Architecture, Data, Application & Technology. [BDAT]
In this blog, we shall be describing second pillar – Data Architecture. ADM, the architecture development method [ADM], has three phases where the B (business), D (data), A (application) and T (technology) architectures are developed, both for the “as-is” situation and for the desired “to-be” goal.
Data architecture is a discipline that documents an organization’s data assets, maps how data flows through its systems/applications and provides a blueprint for managing data. The goal is to ensure that data is managed properly and meets business needs for information via Business Architecture or gets mapped to Business Architecture.
While data architecture can support operational applications, it most prominently defines the underlying data environment for business intelligence (BI)/Visualization and advanced analytics initiatives such as AI/ML. Its output includes a multilayer framework for data platforms and data management tools, as well as specifications and standards for collecting, integrating, transforming and storing data, thus enabling to convert data into Information. [DIKW]
Data architecture is a framework for how IT infrastructure supports your data strategy. The goal of any data architecture is to show the enterprise’s infrastructure how data is acquired, transported, stored, queried, and secured, thus enabling to convert data into Information. [DIKW]
One of the most important element or aspect of Data Architecture is Data Modelling which creates relationship between data structures, business rules and data elements. Data Modelling deals with data at Micro level while Data Architecture deals it at Macro level.
Modern day Data Architecture involves structured and unstructured data to be addressed. The adoption of big data technologies in businesses added unstructured and semi-structured forms of data to many architectures. The unstructured data or Semi-Structured data includes Stream Processing. This kind of complexity led to the deployment of data lakes/lake house, which often store raw data in its native format instead of filtering and transforming it for analysis upfront — a big change from the data warehousing process. [EDW] The new approach is driving wider use of ELT data integration, an alternative to ETL that inverts the load and transform steps.
Another emerging architecture concept is the data fabric, which aims to streamline data integration and management processes.
Example of Data Architecture
Common characteristics of well-designed data architectures
- Must be business-driven focused which in turn is aligned with organizational strategies and data requirements
- flexibility and scalability to enable various applications and meet new business needs for data
- Strong security protections to prevent unauthorized data access and improper use of data
Components of Data Architecture
A data architecture is a conceptual infrastructure that’s described by a set of diagrams and documents. Data management teams or Data Engineering Teams then use them to guide technology deployments and how data is managed.
- Data models, data definitions and common vocabularies for data elements
- Data flow diagrams (DFDs) that illustrate how data flows through systems and applications
- Documents that map data usage to business processes
- A high-level architectural blueprint, with different layers for processes like data ingestion, data integration and data storage
Benefits of a data architecture
- Enables to develop data oriented platform/s
- Helps improve data quality, streamline data integration and reduce data storage costs
- Enterprise View rather than domain-specific or departmental view
- Ease of Data Lifecycle Management / Information Lifecycle Management
TOGAF Certification @Zoc
TOGAF certification at Zoc Learnings will help Architects to comprehend importance of developing BDAT and following ADM.
Data Architecture enables to link it to Business Architecture thus enables to convert goals and objectives to actual implementation using components described above. As organizations build their roadmap for tomorrow’s applications – including AI, blockchain, and Internet of Things (IoT) workloads – they need a modern data architecture that can support the data requirements.