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Data architecture and enterprise architecture: an explainer

Posted on: August 25, 2023
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In today’s digital age, data has become a valuable asset for organisations across virtually every industry – and efficiently managing and leveraging this data can be crucial for business success. 

This is where data architecture – and enterprise architecture – play a pivotal role. By implementing a modern data architecture, businesses can:

  • Streamline data management and workloads
  • Enable data-driven decision-making.
  • Gain a competitive edge.
  • Better support powerful tools that rely on data, such as automation, artificial intelligence, and machine learning.

But what is data architecture? How does it differ from enterprise data architecture? And, most importantly, how can organisations leverage these tools to unlock the full potential of data?

What is data architecture?

Data architecture refers to the design, structure, and organisation of data assets. It involves defining the rules, policies, and standards for managing and utilising data sets within an organisation. 

One common area of data architecture is data models, which represent how data is structured and related to each other. These models provide a blueprint or roadmap for organising data, and can serve as a guide for data integration and management efforts.

Data architecture also encompasses:

  • Data flows
  • Data storage
  • Data access methods.

Essentially, data architecture aims to establish a robust foundation for effective data management, and is typically overseen by data architects.

Examples of data architecture

One common example of data architecture is a data warehouse. Data warehousing is usually implemented by companies in order to centralise and consolidate their data from various sources – acting as a central repository – as well as to enable efficient reporting and analysis of data. 

There are many data warehouse providers, but some popular options include solutions such as Azure Synapse from Microsoft, Amazon Redshift, and Google BigQuery.

Another example of data architecture is a data lake. Data lakes are used by businesses such as large retail organisations to act as a centralised repository for all their data assets. This allows them to integrate real-time data from multiple sources and perform advanced analytics for better decision-making.

A data lake also enables organisations to store and process both structured and unstructured data, which supports a wide range of use cases, such as customer segmentation, personalised marketing, and supply chain optimisation.

What is enterprise architecture?

Enterprise architecture treats data architecture as an integrated piece of an organisation’s wider architecture. It takes a broader view, considering all of an organisation’s data assets, their strategic value, and their alignment with business goals.

This means that enterprise architecture extends beyond individual data systems, and instead looks at how data is shared, integrated, and used alongside other areas of the enterprise. 


By providing a complete, comprehensive view of data assets, enterprise architecture helps organisations:

  • Ensure data is accurate and relevant
  • Derive better insights from data
  • Make well-informed, data-driven decisions.

There are several types of frameworks available for enterprise architecture, such as The Open Group Architecture Framework (TOGAF) and the Zachman Framework.

Types and components of enterprise architecture

Enterprise architecture typically includes multiple domains, each addressing specific aspects of an organisation’s operations. Some common domains within enterprise architecture include:

  • Business architecture. Business architecture focuses on defining an organisation’s strategic goals, business processes, and stakeholders. 
  • Information architecture. Information architecture, which includes data architecture and the data strategy, deals with managing and utilising data assets effectively.
  • Application architecture. Application architecture focuses on designing and managing software applications to support business processes and systems. 
  • Technology architecture. Technology architecture covers the underlying technology infrastructure, including hardware, networks, and cloud platforms, that supports an organisation’s information systems.

Collectively, these domains work together to create a cohesive and aligned enterprise architecture.

Understanding the difference between data architecture and enterprise architecture

While data architecture is focused on the design and management of specific data assets, enterprise architecture takes a broader view. Its focus is on aligning various business areas, including data architecture, with an organisation’s overall business aims. 

This means that data architecture is essentially a subset of enterprise architecture, specifically addressing the design and management of data assets. Data architecture, as part of enterprise architecture, focuses on the specific challenges and requirements related to managing data assets effectively.


Enterprise architecture, meanwhile, provides a framework for integrating various aspects of an organisation. It ensures that these components work together cohesively to support the organisation’s goals, data pipelines, relational databases, and so on.

What does a data architect do?

Data architects design and implement data architecture schemas within organisations. For example, a data architect will typically:

  • Develop the conceptual and logical understanding of data within a particular organisation.
  • Consider factors such as data quality, data integrity, data usability, data scalability, and data security as part of their role.
  • Ensure that data architecture aligns with their organisation’s goals and requirements.

Data architecture and enterprise architecture: key areas for consideration

Data governance

It’s essential to implement effective data governance. This ensures data quality, privacy, and compliance. It involves defining data ownership, establishing data policies, and implementing processes for data stewardship and data lifecycle management.

Data integration

Efficient data integration enables seamless data flow between systems. There are different data platforms and interfaces that can help support integration, such as Extract, Transform, Load (ETL) processes, and data integration APIs, that ensure information is not restricted to inaccessible data silos.

Metadata management

Effectively managing metadata can provide a deeper understanding of data assets by enhancing their discoverability and usability. For example, organisations can establish metadata management processes to capture and maintain information about data sources.

Scalability and flexibility

A modern data architecture should be designed to scale and adapt to changing business needs, including handling big data, raw data, master data, and accommodating new data sources. Cloud platforms, with their scalable capabilities, can be leveraged to support the storage and data processing requirements of large and diverse data sets.

Security and privacy

Implementing robust data security measures protects sensitive information and can build trust with stakeholders. These measures can include encryption, access controls, and regular security audits to identify and mitigate potential vulnerabilities.

Collaboration and stakeholder engagement

Involving stakeholders from different departments ensures that data architecture aligns with everyone’s needs and requirements. Furthermore, collaboration between business users, data engineers, data scientists, business intelligence teams can help identify the most relevant data sources.

Become a data-led leader

Unlock the full potential of data by studying the 100% online MSc Management with Data Analytics at Keele University. This flexible, part-time programme has been designed for leaders and aspiring leaders who are aiming to progress into more senior roles, and who want to develop a firm understanding of the strategic and operational challenges in running an organisation, particularly through the lens of harnessing data for success.

You will develop your skill set and acquire a range of attributes that will stay with you for the rest of your working life – while setting yourself apart from other management graduates. Learn to evaluate and apply theories to various contemporary management problems, and study specialist modules in the high-demand field of data analytics.

Key areas of study include:

  • Visualisation for data analytics
  • Data analytics and databases
  • Leading and complexity
  • Operations and supply chain management
  • Financial statement analysis
  • Strategic marketing
  • Managing people and organisations
  • Systems design and programming.
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