Data Architecture

Data architecture is a discipline that documents an organization's data assets, maps how data flows through IT systems and provides a blueprint for managing data. Its goal is to ensure that data is managed properly and meets business requirements for information used to drive decision-making. While data architecture can support operational applications, it most prominently defines the underlying data environment for business intelligence (BI) and advanced analytics initiatives. Ideally, data architecture design is the first step in the data management process. But that often isn't the case, which creates inconsistent environments that need to be harmonized as part of a data architecture. Also, despite their foundational nature, data architectures aren't set in stone and must be updated as data and business needs change. That makes data architecture work an ongoing task for data management teams.

Data architecture goes hand in hand with data modeling, which creates diagrams of data structures, business rules and relationships between data elements. They're separate data management disciplines, though. Explaining at a high level how data modeling and data architecture differ, practitioners distinguish between modeling's microfocus on individual data assets and data architecture's broader macro perspective on all those assets.

Why are data architectures important?

A well-designed data architecture is a crucial part of the data management process. It supports data integration and data quality improvement efforts, as well as data engineering and data preparation. It also enables effective data governance and the development of internal data standards. Those two things, in turn, help organizations ensure that their data is accurate and consistent.

A data architecture is also the foundation of a comprehensive data strategy that supports business goals and priorities. Business strategies increasingly depend on data. As a result, data management and usage are too important to leave to individuals, according to Donald Farmer, principal of consultancy TreeHive Strategy. In addition to the data itself, he listed data catalogs, data management tools, various analytics techniques, collaboration capabilities and documented goals as key data strategy components. But that should all be underpinned by a strong data architecture.

If the technology elements are incorporated, a modern data architecture includes the ones previously mentioned in the evolution section plus some others, as listed here:

• Data warehouses, data lakes and data lake houses.
• Cloud systems, storage and applications.
• AI and machine learning tools.
• Data streaming and real-time analytics systems.
• Various data integration methods.
• API connectors to streamline data sharing between applications.
• Data pipelines that deliver needed data to users.
• Containerized and microservices applications.

Benefits of a data architecture?

A well-designed data architecture helps organizations develop effective data analytics platforms that deliver useful information and insights. Those insights improve strategic planning and operational decision-making, potentially leading to better business performance and competitive advantages. They also aid in other types of applications, such as scientific research, government programs and the diagnosis and treatment of medical conditions.

In addition, data architecture helps provide the following benefits in managing data:

• Improved data quality.
• Streamlined data integration.
• Reduced data storage costs.
• Increased data consistency across systems.
• More effective data governance.
• Better collaboration on data management and governance.