What Is Data Architecture? (With Principles And Components)

By Indeed Editorial Team

Published 5 September 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

Data architecture is the structure of a company's data management processes. Building strong, user-friendly data tools and systems can help a business perform its obligations, reach its goals and gain financial success. Learning about data architecture and how it works can help you determine if a career in this field may suit your interests, talents and professional goals. In this article, we answer the question 'What is data architecture?,' explore the components, principles and types of data architecture frameworks, describe how it differs from data modelling and explore data architecture frameworks.

What Is Data Architecture?

Before you explore the details of data management, it is important to answer the question 'What is data architecture?'. Data architecture is the structure of an organisation's physical and digital data and the processes they use to manage it. Businesses rely on many types of data, such as client information and financial reports, to meet their needs, maintain operations and earn revenue. Professionals like data architects and data scientists manage databases to ensure their functionality and technologies are accessible, helpful and updated.

Data architecture allows companies to evaluate their needs and goals and translate them into usable data. Teams responsible for managing that data identify and establish system requirements that allow information to flow freely and securely throughout points of communication within the company. Organisations often pursue upgrades to their data architecture as technology becomes more advanced. Known as digital transformation, this involves modernising their architecture so they can make use of efficient technologies like artificial intelligence (AI).

Related: Data Architect Vs. Data Engineer (With Duties And Salaries)

Components Of Data Architecture

Here are the primary components of modern data architecture:

  • AI and machine learning (ML) models: AI is the simulation of human decisions by machines and computer systems, and ML is the capacity for machines to learn patterns that allow them to make these decisions.

  • Application programming interface (API): An API is a method of communication in which software components can communicate information with each other, such as data and functions.

  • Cloud storage: This is the storage of data on the internet that users can access from anywhere and at any time.

  • Cloud computing: This is the delivery of services through the internet, such as databases and software.

  • Data pipelines: These collect raw data from various sources and send them to a singular destination for analysis.

  • Data streaming: This is the capacity to deliver data continuously from a source to a destination in real-time.

  • Kubernetes: This is an open-source container orchestration platform that enables elastic web servers for deploying, managing and scaling applications.

  • Real-time analytics: This is the process of applying logic to data to perform analytics as the data arrives at its destination so you can make faster decisions.

Related: All You Need To Know About Data Warehouse Architecture

Principles Of Data Architecture

The proper management of data allows professionals to use it efficiently, which can benefit teams and organisations. Here are the essential principles of data architecture:

Collaboration

An essential element of modern data architecture is viewing data as a shared asset. Effective structures eliminate data silos, or divisions between organisational departments that prevent the flow and sharing of data. This allows teams and organisations to collaborate on projects, identify potential issues proactively and resolve concerns together.

Related: Collaboration Skills: Definition, Benefits And Examples

Accessibility

The goal of data architecture is to enable all members of an organisation, from entry-level professionals to executive teams, to access and store data that can help them perform efficiently in their roles. Data architects can accomplish this by ensuring the structures they implement are user-friendly and allow accessibility. This enables managers to distribute data-based tasks with the understanding that every team member has the appropriate level of access to the data.

Related: What Does A Database Manager Do? (With Skills And Salary)

Security

While it is important for each member of the organisation to have access to the data required for individual and collaborative tasks, it is also critical to maintain a high level of database security. Modern data architecture typically has advanced security features like encryption and tokenisation, which keep the database secure from hackers and unauthorised users. This can prevent potentially damaging breaches and protect the integrity of company data.

Related: How To Become A Security Manager (With Salary And FAQs)

Communication

Just as efficient data architecture structures allow for collaboration, they can also help streamline communication among individuals and teams within the organisation. Using a common vocabulary to refer to a structure, its elements and data management practices can prevent confusion during analysis and discussions. It can also improve workflow and help teams avoid making common errors that can delay goals.

Related: What Is A Medium In Communication? (With Types And Importance)

Organisation

Modern data architecture involves the intuitive organisation of data. This means the structure allows users to navigate and use its features easily. Organisation also involves data curation, which organises the data and ensures its value by conducting regular evaluations. Some key components data architects consider when ensuring structural organisation include product catalogues and fiscal calendar dimensions.

Related: Organisational Skills: Definition and Examples

Agility

Agility in data management is the ability to access data quickly and use it to perform a task efficiently. Agile data architecture systems are adaptable to change, and data architects optimise and update them regularly for optimal productivity. Moving data to another system or location within an organisation can compromise the accuracy and safety of the data. Agile data systems can prevent this by reducing data movement and ensuring data stays updated.

Related: 35 Data Architects Interview Questions (With Answers)

Data Architecture Vs. Data Modelling

The roles of a data architect and data modeller complement each other, but they also involve distinct differences. Data architects focus on data management on a large scale by developing strategies that influence a company's operations. They focus on implementing data systems and structures that enable an organisation to manage its data effectively. Data architects create, implement and manage the standards and practices a company uses for data management.

Data modellers work with data on a smaller scale by determining relationships between data points and how they affect each other and the organisation. Modelling involves applying analysis techniques that allow the modeller to understand and define the different types of data an organisation produces and collects. Essentially, it ensures the accuracy and quality of a company's data.

Related: 15 Examples Of Useful Open Source Data Modelling Tools

Data Architecture Frameworks

Various data architecture frameworks and models serve different purposes and needs, and businesses can choose one or multiple options that match their goals. Here are some types of these frameworks:

DMBOK 2

DMBOK 2, the Data Management Body of Knowledge 2nd edition, is a framework designed for data management that provides standard definitions and guiding principles for data management. The DMBOK 2 book includes over 2,000 data management vocabulary terms, which may be especially helpful for data architects, information technology professionals and business executives. It also covers the following topics:

  • Finance and accounting

  • Knowledge management

  • Architecture

  • Data modeling

  • XML

  • Analytics

Related: What Are the Different Database Types?

The Zachman Framework

The Zachman Framework is a structure for enterprise architecture that focuses on identification, definition, representation, specification, configuration and instantiation. This framework offers a logical approach to classifying and organising manual and automated systems. Its creators derived it from older disciplines of architecture, construction, engineering and manufacturing and processes of designing complex physical products. Layers include architectural standards, multiple models and databases.

TOGAF

The Open Group Architecture Framework (TOGAF) is a methodology for improving business productivity and performance. Its modular structure supports usability, incremental adoption of the TOGAF standard and practical application of the approach. It also provides a content framework to ensure consistency and guidance in establishing a hierarchy of architectural governance.

DCAM

The Data Management Capability Assessment Model (DCAM) is a framework that determines the scope of capabilities an organisation's data management processes require. DCAM ensures data can support digital transformation, business process design, data ethics and advanced analytics like artificial intelligence and machine learning. DCAM identifies the strategies, technology and practices an organisation can adopt for successful data management.

Please note that none of the companies, institutions or organizations mentioned in this article are affiliated with Indeed.

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