What is Data Architecture?

Data architecture refers to a framework for managing a business’ data infrastructure and strategy. It focuses on the acquisition, transportation, storage, security, and queries associated with data processes in a company or organization. Moreover, it formulates the steps of implementing a business intelligence (BI) model, thus providing the foundation of any data strategy. Effective data architecture will help ensure that data management is done correctly and business requirements are met promptly.

Data architecture is a discipline that manages the flow of data through the enterprise. One primary goal is to document the data assets of an organization and provide a path whereby the business can take full advantage of all of the information (data) they’ve acquired. In addition to that goal, there is also a need to develop a blueprint to deploy database management systems, data warehouses, data lakes, and other data platforms that have the required technical capabilities suitable for any specific application required by the business.

The key features of data architecture are:

Provides a governed data infrastructure.
Improves security and privacy of data throughout the entire organization.
Creates a framework for better comprehension of the company’s data.
Enables accurate and relevant data-driven decision-making within a company.

Data modeling and data architecture

Since businesses have access to massive volumes of data, data modeling and data architecture are vital concepts in BI. Data modeling generates a representation of an enterprise’s data in a model containing business concepts and their relations. On the other hand, data architecture is an infrastructure where data and models exist. The main goal is to store the data and make it more accessible safely. It creates the environment for a business to safely and securely utilize data tools, data solutions, and data platforms. Some of the key features are explained below:

Data Architecture	Data Modeling
Data assets are managed via a blueprint to meet strategic data requirements. The blueprint is defined by data architecture.	Data modeling is the process of discovering, communicating, analyzing, and representing data requirements in a precise form.
The macro view to comprehend the relationships between an organization's functions and data types.	Takes a more detailed and specific approach of specific systems with their business use cases.
Deals with infrastructure which contains the data of an organization.	Deals with the reliability and accuracy of the data of an organization.
Encompasses the data infrastructure of the entire organization.	Encompasses a limited set of business intelligence concepts and their relationship with each other.

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Both data architecture and data modeling bridge the gap between business goals and technology.

Data architecture works in parallel with data modeling.

Data architecture components

Data architecture can be formulated by various elements, which include:

‍Data pipelines define the movement of data between two points. It encompasses the entire data movement process, from its collection to refining, from storage to analysis.
‍Cloud storage refers to the storage and indexing programs and data employing the internet instead of using a computer hard drive.
‍APIs are used to communicate different information types to users, including data and functions. Transmission is done between a requester and a host through an IP address.
‍ML and AI models are different tools designed to make calculated decisions. It includes the prediction of outcomes, data collection, and provision of resources.
‍Data streaming refers to the continuous data flowing from its source to a destination. It is mainly used when real-time decision-making, streaming, and analytics are needed.
‍Cloud computing handles the infrastructure using third-party cloud vendors, thus empowering businesses and organizations of all sizes to shift their focus on innovation and product development.
‍Real-time analytics refers to the capability of a business to make informed decisions immediately employing data and tools.
Data marts: Subsets of warehouses designed to serve specific departments, making access more targeted and efficient.
Data lakehouses: Platforms that merge the flexibility of data lakes with the reliability of warehouses, supporting both structured and unstructured data.
Data catalogs: Metadata-driven inventories that make datasets discoverable and governable across the organization.
Lineage and observability tools: Capabilities that trace the journey of data and monitor quality, performance, and reliability.
Query engines and dashboards: Tools that let teams analyze data at scale and create visualizations to share insights widely.
Embedded products and AI/ML training: Features that push data into operational workflows or feed it directly into machine learning models.

Data architecture characteristics

State-of-the-art technologies can have a significant contribution to the effectiveness of modern data architectures in BI. Such data architectures can incorporate machine learning (ML), automation, the Internet of Things (IoT), and blockchain to enhance performance. Some of the key characteristics are:

Cloud-native. Developing and running applications in a distributed computing environment hosted in a cloud delivery model. Modern BI data architectures are compatible with end-to-end security and high data availability, with the added benefits of cost and performance scalability.

Scalable data pipelines. Transportation of data from a source to a destination should be compatible with growing volumes of data. Therefore, data architectures need to support instant data refresh capabilities.

Seamless data integration. It is a robust process where an application’s new module or feature is integrable without causing any noticeable complications. Usually, data architectures integrate with legacy applications using standard API interfaces. Modern data architectures must be capable of sharing data across systems and organizations.

Decoupled and extensible. The components of the system are not constrained on the same platform, build environment, and operating system. Modern data architectures are loosely coupled to perform minimal tasks irrespective of other services.

Real-time data enablement. This is the real-time ability to engage in active data management in compliance with enforced data policies. Modern data architectures should enable automated data validation and data governance.

Modern data architecture patterns

Two widely adopted patterns are shaping how organizations design and manage data today:

Data Fabric: A metadata-driven approach that automates integration across hybrid and multi-cloud environments. Data fabric connects sources seamlessly, enabling faster delivery of trusted data to teams.
Data Mesh: A decentralized approach where business domains take ownership of their own “data products.” This model encourages democratization, agility, and scalability by aligning data ownership with those who know it best.

Many organizations blend these patterns—using a data fabric to streamline integration while applying data mesh principles to empower business teams.

Data architecture frameworks

In order to develop a strong foundation for building a data architecture framework of an organization, there are a few enterprise frameworks which are explained in detail below:

DAMA-DMBOK (DAMA International’s Data Management Body of Knowledge) is developed explicitly for data management. It explains guiding principles for data management, also providing definitions for data management functions, deliverables, and roles.
Zachman Framework for Enterprise Architecture is an enterprise structural framework for organizing information created by John Zachman at IBM during the 1980s. The data column includes several layers. Additionally, it comprises architectural standards, an enterprise data model, a semantic model, a physical data model, and actual databases.
The Open Group Architecture Framework (TOGAF) is an enterprise architecture ontology that offers a high-level framework to develop enterprise software packages and applications. It follows a systematic approach to organizing the development process. This approach focuses on curtailing errors, managing timelines, ensuring cost-effectiveness, and aligning Information Technology with business units to produce desirable results.

Types of data architecture

Modern organizations typically adopt one of two approaches when shaping their data architecture:

Centralized: This approach consolidates data into unified platforms like data warehouses or lakes. It reduces redundancy, ensures consistent quality, and makes governance more straightforward.
Decentralized: This method distributes ownership across business domains, often supported by event-driven systems or NoSQL databases. It provides agility and real-time responsiveness, but requires strong governance practices to keep data consistent.

In practice, most companies adopt a hybrid model that combines the control of centralized systems with the flexibility of decentralized ones. This balance allows them to meet both enterprise-wide standards and team-specific needs.

Creation of data architecture

Developing a data architecture is not just a technical exercise—it’s a collaborative effort between business leaders, data teams, and end users. The goal is to ensure that the architecture supports strategic objectives, meets user needs, and maintains strong governance.

Key steps include:

Strategic alignment: Begin by meeting with executives to tie data architecture plans directly to business goals.
Requirement gathering: Engage with business users to understand the specific data they need for decision-making.
Governance and risk assessment: Evaluate risks related to privacy, compliance, and security, and establish clear governance policies.
Mapping data flows and lineage: Document where data comes from, how it moves, and how it will be transformed across systems.
Infrastructure evaluation: Assess the current technology stack, identifying gaps or inefficiencies that could hinder scalability.
Roadmap development: Create a phased plan for deploying the architecture, from foundational components like pipelines and storage to advanced layers such as analytics, governance, and AI integration.

When executed well, this process creates a flexible, business-driven framework that evolves with organizational needs while maintaining trust in data quality and compliance.

Principles of data architecture

Data architecture is a conceptual infrastructure described by a set of diagrams and documents. Data management teams utilize them to manage data and evaluate technical deployment. Such components include:

Data flow diagrams depicting the data flow through systems and applications.
Data models and data definitions.
Documents to map data usage of data to the processes of an organization. They also describe business goals, consumer needs, and core concepts for data management functions.
Standards and policies for data operations.
A high-level architectural blueprint, thus including different layers for diverse data processes.

Modern data architectures must comprise data governance policies, regulatory compliance processes, and the capability to support multi-cloud environments. If a data architecture is not designed around or supported by a company’s BI platform, it won’t provide value to the business decision-makers. Hence, its business impact potential will be wasted. A well-designed data architecture must possess a few valuable characteristics. Data requirements and business strategies should be aligned with the business-driven focus of effective data architecture. Moreover, it must be scalable and flexible to meet business requirements. Privacy is also a growing concern when it comes to handling data. Therefore, strong security precautions must be taken to prevent data misuse and unauthorized access.

Goals of data architecture

The purpose of data architecture is not just to organize data but to ensure that it actively drives business value. Key goals include:

Alignment with business objectives: Ensuring that data resources directly support the company’s strategy and growth.
Quality and consistency: Maintaining accuracy, completeness, and reliability across all datasets.
Scalability and agility: Designing a framework that adapts as data volumes grow and business needs evolve.
Governance and compliance: Upholding security, privacy, and regulatory standards while making data accessible.
Data-driven insights: Delivering trusted data that powers analytics, AI, and decision-making across the organization.

Importance of data architecture

Data architecture is the building block of a data strategy to support the business goals of an organization. A modern business strategy relies heavily on data, thus making data management and analytics the highest priority. As previously mentioned, an organization must develop a comprehensive data strategy with a robust data architecture for optimized and efficient data management to thrive in the competitive market.

Many BI platforms focus on one specific element of data architecture. However, there are numerous advantages of choosing a multi-faceted, feature-rich, and modern BI platform. Firstly, such BI platforms will integrate with and fully support the needs of the data architecture. It will align the entire enterprise around the objectives of the data architecture and become the mechanism of information delivery and insight. Subsequently, those insights improve operational decision-making and planning, which leads to better business performance and competitive advantages. Next, it implements enterprise view modeling, thus improving data quality and reducing data storage costs.

In the ever-advancing world, agility plays a crucial role in the performance of a company. It allows the company to accept changes in the business environment and industry. Modern BI tools can have the required agility to meet the analytical demands of business and consumer requirements outlined in a data architecture.

Challenges of data architecture

Despite the compelling advantages of data architecture, several drawbacks must be kept under consideration. One such challenge is that these architectures are too complex, referred to as ‘spaghetti architecture.’ Data flows are tangled into each other, resulting in a ramshackle data environment with incompatible modules and nodes. As a result, data integration becomes nearly impossible for analytical purposes.

Takeaways

Data architecture is essential for describing the organization’s logical and physical data assets and data management resources. It includes the models, standards, policies, and rules to monitor and control various companies’ acquisition, storage, arrangement, integration, and usage of data. Data architecture converts business needs into system requirements and data to manage its flow through the enterprise. Data architectures are not stagnant, they are constantly evolving in order to meet the growing analytical needs of the business. As it pertains to BI tools that support and enable effective data architecture, Domo stands alone in its ability to align an organization around their data architecture goals. Domo has pre-built, out-of-the-box features that organize, govern, and enforce an impactful data strategy.