What is data architecture?

What is data architecture?

Data architecture is an overarching, structured framework and design of an organization's data systems. It serves as a blueprint for how data is collected, stored, processed, and accessed within an organization, ensuring that information is organized, secure, and accessible for improved decision-making and business operations.

Data architecture is related to data modeling, which focuses on structuring data in a way that aligns with an organization's needs so it’s easier to query, analyze, and maintain. An organization’s data architecture will help define its data model and the systems needed to support it, based on the business needs of the enterprise, such as data visualizations, reporting, or machine learning (ML).

Effective data architecture can help streamline data sources by reducing or eliminating data silos and facilitating centralized data storage. As an organization’s data needs grow, a well-designed data architecture will be able to adapt and scale accordingly to ensure data availability, integrity, security, and compliance while optimizing storage costs and resource utilization.

Advanced data architectures often manage scalability via cloud platforms, which eliminate costs associated with on-premises hardware and offer more data storage and processing flexibility. This can be especially important when organizations need to handle vast amounts of data, such as with AI/ML.
 

Data architecture components

Well-designed data architecture is comprised of several key components which all help define an organization’s data infrastructure. They include:

• Data sources. Identify the origins of data—from internal or external sources, such as databases, internet of things (IoT) devices, vendors, and user-generated content—to integrate it more efficiently into the organization's data infrastructure.
• Data storage. Determine where data is stored and maintained within an organization's information technology infrastructure, including databases, data warehouses, or cloud-based solutions. This helps define how data is organized, accessed, and managed to ensure data availability, reliability, and performance. Data storage also includes data redundancy, backup, and disaster recovery strategies.
• Data integration. Establish tools and processes to consolidate and aggregate data from various sources to create a unified and accurate view of information. This can include extract, transform, and load (ETL) tools that collect data from various sources and consolidate it in a single, centralized repository, like a data warehouse. It also encompasses data pipelines that govern how data is moved and delivered.
• Data processing. Outline how data is formatted, cleaned, and analyzed to derive meaningful insights, often involving data pipelines and analytics tools. This can include defining needs around real time, batch, or stream processing.
• Data governance. Define policies, rules, and standards for data quality, security, and compliance. This includes data reliability and protection and ensuring that data is managed, maintained, and used effectively and responsibly. Governance provides a structured approach to managing data throughout its lifecycle, from creation to storage, processing, and eventual disposal.
• Data access. Establish protocols and methods by which users and applications interact with, retrieve, and manipulate data—while adhering to security and privacy considerations. It includes the design and implementation of systems and interfaces, such as data APIs and reporting tools, that allow authorized parties to query, view, update, and analyze data according to specific needs and requirements.
• Metadata management. Document, organize, and catalog attributes and relationships to better understand data and how it can be used. It enables more efficient data discovery, analysis, and governance.
   

Data architecture patterns and examples

Data architecture patterns are used to address an organization’s specific data-related challenges through the use of a reusable, consistent data design. They provide a structured, efficient approach to solving everyday data management and processing problems, such as the implementation of data systems, databases, and data processing workflows.

By using established patterns, organizations can leverage proven solutions, avoid common pitfalls, and streamline the design and implementation of data-related components in their systems. They’re valuable tools for designing efficient, scalable, and secure data solutions.
Examples of data patterns include:

• Data lake. A data lake pattern includes the storage of large amounts of raw, unstructured, semi-structured, or structured data, often used for big data analytics and machine learning. They can also be used for data backup and recovery.
• Data fabric. A data fabric is a unified data integration and management layer. It's designed to eliminate standalone data silos by bringing all data together and enabling consistent distributed access, plus a full range of discovery, integration, and governance capabilities. As organizations spread data across locations like data lakes and data warehouses, a data fabric can connect them all, providing consistent, reliable, and flexible data querying.
• Data mesh. A data mesh is a relatively new concept designed to organize an enterprise’s data architecture by logical, business-oriented domains, with separate data producers or owners responsible for these domains. As organizations’ data needs become more complex, data meshes are seen as a way of managing that complexity by placing responsibility into the hands of those most familiar with each domain. This decentralized model contrasts with the traditional approach that organizes teams by layers within the technical architecture—such as ingestion, data management, or access—driven primarily by centralized coordination.
• Batch processing. Batch processing patterns involve collecting a group of data records, processing them together, and typically scheduling these jobs to run at specific intervals. In the context of data architecture, batch data patterns are used for purposes including ETL, data integration, and data backup.
• Real-time processing. In contrast to batch processing, real-time processing patterns involve interacting with data as it arrives or is generated, often with minimal delay. Real-time data patterns are used to facilitate real-time processing, streaming analytics, and event-driven architectures. They’re often used in applications where time-sensitive insights are critical.
• Lambda. A Lambda data pattern combines batch and stream processing methods to handle and analyze large volumes of data flexibly and efficiently. The Lambda architecture is particularly useful for handling real-time or near-real-time data processing requirements.
   

Data architecture best practices

Good data architecture helps ensure that data is organized, secure, accessible, and used efficiently to support business goals and objectives. It’s also a key component of master data management (MDM), which focuses on creating and maintaining a single, consistent, and accurate version of key data entities across an organization. 

To implement MDM effectively, organizations should consider several data architecture best practices:

• Data mapping. Define the relationships that can align data from different sources to a common, standardized format in the master data repository. This helps ensure that master data entities, such as customers or products, are consistent and accurate across the organization.
• Data integration. Implement integration tools to synchronize master data across different systems and applications within the organization.
• Data profiling and monitoring. Regularly profile master data to detect anomalies and data quality issues and implement monitoring and alerting systems to proactively address problems.
• Data cleansing. Identify, correct, and eliminate errors, inconsistencies, inaccuracies, and redundancies in master data. The goal is to make sure that the master data is high quality, accurate, and reliable to inform critical business operations and decision-making.
• Security and access controls. Set up strict security measures to protect master data, and implement access controls, authentication, and authorization to prevent unauthorized changes.
• Version control. Maintain version control for master data to track changes, understand historical values, and support auditing and compliance requirements.
• Data lifecycle management. Develop policies for managing the complete data lifecycle of master data, including creation, maintenance, archival, and deletion.

Organizations adhering to data architecture best practices like these can facilitate more accurate, consistent, and reliable master data and improve decision-making and business operations.
 

Risks of bad data architecture

While efficiently designed data architecture can give an organization a competitive edge, bad architecture can have the opposite effect, risking data quality, accessibility, security, and the ability to derive data-driven business insights.

Common causes of poor data architecture are insufficient planning and misaligned business requirements. These can lead to overly complex systems designed to serve a single purpose rather than the whole organization. As a result, these systems may not integrate well with each other, leading to inefficient data capabilities and increased technical debt over time.

This is often referred to as “spaghetti architecture,” where data silos are connected by a tangle of looping and overlapping lines in architecture visualizations.

Data inconsistency and a lack of standard data definitions can also lead to poor data architecture, as ineffective data modeling and storage structures can lead to inaccuracies in data, making it unreliable for decision-making and reporting.

Bad data architecture can also hamper data scalability within an enterprise, restricting how it handles increased data volumes and greater user demands, and ultimately affecting business competitiveness.
To limit these risks, organizations should take the time to plan their data architecture in alignment with their business goals, data governance, and compliance requirements.
 

Common data architecture frameworks

Organizations can choose from and leverage several different data architecture frameworks to help govern their data effectively. These frameworks provide structured approaches and best practices for data management. When considering a framework, companies should consider how it will align with their organizational structure and overall business strategy, as well as what software and hardware they will need to support the framework.
Here are some of the commonly used data architecture frameworks:

The Open Group Architecture Framework (TOGAF)

TOGAF is an overall framework for enterprise architecture, which includes data architecture. It gives organizations a standardized way to manage their data complexity and align their IT strategies with their business goals.
It’s structured around four key architectural domains or pillars, each of which represents a specific perspective or aspect of enterprise architecture.

• Business architecture. Understanding and defining the organization's business strategy, objectives, processes, and functions. Key elements include organizational structure, business goals and models, and business capabilities.
• Data architecture. The structuring and management of an organization's data assets, encompassing data modeling, data storage, data integration, and data quality and governance.
• Application architecture. The design and management of software applications and their interactions, including application components, software interfaces, and the overall technology stack.
• Technology architecture. Focusing on the infrastructure and technology components that support the organization's IT environment. This includes hardware, networks, servers, and technology standards. 

 
TOGAF is widely used in a variety of industries, including government, healthcare, finance, and telecommunications.

Zachman Framework

The Zachman Framework is an enterprise architecture framework that organizes architectural artifacts into a matrix. It helps organizations define, understand, and manage data, along with other aspects of the enterprise.

This framework features key questions that enterprises need to answer to gain a comprehensive understanding of their organizational and data needs. They cover issues around data sources and storage, process models, roles and responsibilities within the organization, and business motivations driving the enterprise.

The matrix provides a structured way to document and analyze an organization's architecture from multiple perspectives and levels of detail. It’s useful for enterprise architects and other stakeholders involved in planning, designing, and managing the architecture of complex organizations.

DAMA-DMBOK 2

DAMA-DMBOK (Data Management Body of Knowledge) is a framework that defines best practices for data management, including data architecture managed by DAMA International, a global organization dedicated to advancing the profession of data management. It provides guidance on data governance, data quality, data modeling, security, integration, and more.

Federal Enterprise Architecture Framework (FEAF)

FEAF is an enterprise architecture framework developed for use in U.S. government agencies. It includes data reference models and data architecture segments to support government operations.
 

Data architecture FAQs

How is data architecture different from database design?

While data architecture and database design both play critical roles in organizing, storing, and managing data, they focus on different aspects and stages of the data lifecycle. Data architecture determines the strategic direction and framework for managing data across an organization by establishing principles, standards, and guidelines on how data should be managed to support business goals. Database design is a more tactical practice that focuses on creating specific database structures and systems. They implement objectives at a more detailed level.

What are some common data architecture mistakes?

Poor planning is a key mistake that can hamper data architecture design and effectiveness. Failing to align the architecture with business goals, having insufficient data standards, neglecting data security and governance, and inadequate data documentation can all negatively affect data architecture. To prevent them, adhere to best practices and perform regular data audits and assessments to help identify and rectify issues in the data architecture as they occur.

How can I get started with data architecture?

Teradata has a wealth of resources for learning more about data architecture, along with how to design and build one that meets your organization’s needs.

What are the three most important things to consider with data architecture?

Many factors need to be considered when designing your organization’s data architecture. Three key concerns are defining what business problems you’re solving for, what existing systems can be integrated into the architecture, and how it can enable you to scale in the future and adapt as new technologies become available. 

Teradata VantageCloud helps organizations identify critical data architecture concerns. With a cloud-native deployment approach and streamlined data integration capabilities, our platform delivers architecture solutions that drive maximum business value and evolve as your data needs grow.