Data architecture is the blueprint for achieving your business goals with data. Good data architecture is crucial to many data management systems. It ensures that all data is delivered correctly and to the right user.
Data architecture encompasses creating a reliable framework for data documentation, organization, transformation, and usage. For many companies, data is growing at a faster rate than ever before, and so is its complexity. This makes it increasingly important to understand what data architecture is.
Common use cases at many companies range from AI/ML to marketing analytics, and infrastructure varies widely, from spreadsheets to data warehouses.
In this complicated environment, data architecture is increasingly essential to managing, securing, and activating data throughout an organization.
This guide explores what data architecture is, its importance for today’s data-driven organizations, and how it supports business success.
What Is Data Architecture?
Data architecture is a framework that governs how an IT infrastructure upholds an organization’s data strategy, defining how data flows through IT systems and how it is managed.
It serves as a blueprint for ensuring data meets business needs and is used effectively for decision-making.
This framework translates business needs into data assets and manages organizational data flow. By structuring data architecture, organizations can enhance data management, governance, and utilization across various applications.
Data architecture also offers a blueprint for the database management systems, data warehouses, data lakes, BI tools, and other technical platforms that actuate a data strategy.
Data architecture delineates 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.
The framework converts business needs into system requirements to manage data flow through the company.
Data architecture is often described by a set of predefined policies and diagrams. These include:
- Data flow diagrams depicting the data flows through systems and applications
- Data models and data definitions
- Documents to map data usage to the processes of an organization
- Standards and policies for data operations
- High-level architectural blueprints
Data architecture documentation also describes business goals, consumer needs, and core concepts for data management functions.
The 6 Key Components of Data Architecture
Understanding the key components of a structured data architecture is the first step toward designing a scalable and resilient data framework that ensures a smooth data flow across systems while maintaining data quality, integrity, and accessibility.
1. Data Models
Data models are the fundamental concept that defines the logical structure for collecting, organizing, storing, and accessing data within databases. They outline the relationships between data entities and are often accompanied by visual elements representing the data flow through a system architecture.
2. Data Streaming
Data streaming encompasses sets of tools, components, and technologies designed to continuously capture and process large volumes of data from diverse sources, such as IoT devices, transactional systems, ERP tools, and more, enabling organizations to analyze data in real time rather than relying on batch processing, which requires waiting for data to be collected and processed at intervals.
3. Data Integration
Data integration refers to the methodology of merging data from different sources (local databases, cloud environments, and third-party applications) into a unified view to make it available and easier to analyze.
By ensuring data consistency and accessibility, integration supports analytics, business intelligence, and cross-functional collaboration. A well developed data integration infrastructure helps organizations make data driven decisions based on comprehensive, cohesive insights from diverse data sets.
Data integration provides the foundation for data governance by ensuring that data is accurate, consistent, and reliable.
4. Data Governance
A data governance framework is the combined configuration of policies, standards, and practices that ensure the proper collection, storage, and usage of data to maintain policy and regulatory compliance.
With the increasing volume and complexity of organizational data, a robust data governance scheme ensures accurate, consistent, and reliable data that helps organizations manage quality, security, and access control.
5. Data Security
A data security infrastructure involves protecting IT and digital assets, including hardware, software, storage devices, user devices, access, and administrative controls, from corruption, theft, or unauthorized access throughout their entire life cycle. This ensures an organization’s data confidentiality, authenticity, integrity, and availability are consistently maintained in alignment with its unique risk management profile and business goals.
6. Data Quality
Data quality addresses the overall usefulness of a dataset and its capacity to be quickly processed and analyzed for various applications. Methodologies for maintaining data quality ensure that a dataset meets several criteria: accuracy, completeness, validity, consistency, uniqueness, timeliness, and fitness for purpose.
Modern Data Architecture: Trends and Best Practices
Cloud-Based Data Architecture
A cloud-based data architecture supports scalable, flexible, and cost-effective processing and storing solutions for large volumes of data. AWS, Google Cloud, Azure, and others offer organizations the tools to easily adapt to growing data demands while benefiting from high availability and fault tolerance. Cloud-native technologies, like serverless computing and containerization, further enhance the efficiency of data processes in modern architectures.
Hybrid Models
A data architecture based on hybrid modules combines the benefits of an on-premises infrastructure with the flexibility associated with cloud solutions. This allows organizations to retain control over sensitive data while leveraging cloud capabilities for scale, agility, and innovation. Hybrid models are especially effective in environments where legacy systems need to coexist with modern, cloud-based technologies.
Data Lakes and Data Warehouses
Data lakes and data warehouses have distinct yet complementary roles within modern data architectures. While Data lakes are mostly utilizied to store vast amounts of raw, unstructured data from various sources to Provide a flexible environment for advanced analytics, machine learning, and exploratory data science, data warehouses focus on structured, processed data that is optimized for reporting, querying, and business intelligence tasks.
Modern data architecture notions combine the strengths of both to create a “Data Lakehouse” to seamlessly transition data between unstructured and structured states, supporting a broader range of use cases, from in-depth raw data analytics to high-performance querying and reporting, creating a platform that caters to diverse data needs.
AI and Machine Learning Powered Data Pipelines
Integrating Artificial Intelligence (AI) and machine learning (ML) technologies into data pipeline management allows engineers and analysts to focus on strategic initiatives while cutting the time spent on manual operations by automating processes such as data ingestion, analysis, and visualization.
With AI, data can be ingested from multiple sources, processed in real time, and analyzed for patterns or trends. Machine learning continuously improves data processing and analysis models over time, adapting to changes in the data. This means optimized data pipeline management and better performance with less manual intervention.
Data Architecture vs. Data Modeling
Data architecture and data modeling are two unique pieces of one gigantic puzzle – data management. Data modeling focuses on the micro side of data management; it’s a development of a visual representation of data elements’ attributes and their relationships. The data model should match the needs and nature of the organization.
Data architecture is the bigger picture. Data architects create frameworks that keep track of data assets and their flow, dictate their usage, store and integrate them, and so on—all to create a firm ground for data processing.
In this process, data architects use data models to create an appropriate design and choose the proper technology. In the same vein, good data architecture makes the process of creating data models easier.
Data Architecture vs. Information Architecture
Even though they sound like synonyms, data architecture and information architecture are two very different concepts. As mentioned, the first term refers to a discipline that documents data assets and flow, and it’s also a blueprint for data management.
On the other hand, information architecture is a discipline that organizes and labels data so that it becomes meaningful, i.e., so that it becomes information. Now, the difference between data and information is that the first is raw – meaningless units of facts, while information is data with context and meaning.
Without proper information architecture, users won’t be able to access the needed information. However, this type of architecture depends on the quality of data architecture. The reason is that the data architecture framework handles all data organization.
The Roles in Data Architecture Design and Development
Before making data architecture part of your business model, it’s important to learn more about the concept. For instance, learning to distinguish the different roles in data architecture design and development can improve communication within your team and increase efficiency. Below are the 4 most important ones:
Data Modelers
A data modeler’s primary role is figuring out how to represent the data. This person is also concerned with data accuracy. They create physical, conceptual, and logical models, so they must be proficient in relational and dimensional data modeling.
Data Scientists
Highly qualified professionals that gather and analyze all data, collecting it from various databases with the help of predictive models and algorithms. Their goals are identifying patterns, hypothesizing, analyzing market trends and customer behavior, and drawing accurate conclusions.
Data Architects
Data architects are responsible for designing database systems and other architectural elements. However, they also evaluate data and manage the purchase of required data technologies. Simply put, they need to determine which approach is best to store and analyze the data. They are also responsible for keeping the data safe.
Data Engineers
If architects are the visionaries, engineers are the builders. They create data pipelines, integrations, and platforms within plenty of databases. Their principal task is to figure out a way to deliver data from multiple sources and ensure it’s ready to be analyzed and processed.
The Technologies Behind Data Architecture
Technologies transform data architectures from documentation to existence, including components as diverse as machine learning, automation, the Internet of Things, and blockchain.
Some of the key technological features of modern data architectures include:
Cloud-native
Today’s data architectures build and maintain applications in a distributed computing environment hosted in a cloud delivery model. These frameworks are compatible with end-to-end security and high data availability, with the added functionality of cost and performance scalability.
Scalable data pipelines
The transportation of data from source to destination should be compatible with fast-growing volumes of data. For data-driven organizations, a modern data architecture has evolved from a competitive advantage to a foundational requirement.
Seamless data integration
An application’s new module or feature must be integrable without causing any noticeable complications. Usually, modern 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 regardless of other services.
Real-time data enablement
Modern data architectures must engage in active data management in compliance with enforced data policies in real-time. These frameworks must build and deploy automated data validation, management, classification, and governance.
Simple Solutions for Complex Data Pipelines
Boomi Enterprise Platform provides a unified solution for data pipelines, workflow orchestration, and data operations.
Some of Boomi’s features and capabilities:
- 200+ Native Connectors: Instantly connect to applications, databases, file storage options, and data warehouses with our fully-managed and always up-to-date connectors, including BigQuery, Redshift, Shopify, Snowflake, Amazon S3, Firebolt, Databricks, Salesforce, MySQL, PostgreSQL, and Rest API to name just a few.
- Python Support: Have a data source that requires custom code? With Boomi’s native Python support, you can pull data from any system, no matter how complex the need.
- 1-Click Data Apps: With Kits, deploy complete, production-level workflow templates in minutes with data models, pipelines, transformations, table schemas, and orchestration logic already defined for you based on best practices.
- Data Development Lifecycle Support: Separate walled-off environments for each stage of your development, from dev and staging to production, making it easier to move fast without breaking things. Get version control, API, & CLI included.
- Solution-Led Support: Consistently rated the best support by G2, receive engineering-led assistance from Boomi to facilitate all your data needs.
How to Develop a Data Architecture
Data management teams need to work in coherence with business executives and consumers to develop a data architecture. It ensures that business strategies, data requirements, and the architecture itself are in sync with each other.
Here are some sample steps for developing a data architecture:
- Meeting with senior executives for their support and requirements.
- Engaging with end-users to understand their data needs.
- Assess the risks and challenges associated with data based on data governance policies.
- Build and track data lineage, data lifecycle, and data flows.
- Evaluate the existing data management technology infrastructure for any discrepancies.
- Develop a roadmap for the data architecture deployment projects.
You can avoid the hassle of building, managing, and monitoring data pipelines by traditional methods with Boomi, which offers 180+ pre-built data connectors and pipeline templates. Develop workflows in minutes, saving precious time for your business and organization.
Popular Data Architecture Frameworks
DAMA-DMBOK
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
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
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.
Strong Data Architectures Make Strong Companies
As the volume and complexity of data continue to grow, strong companies need strong data architectures to thrive.
Modern data architectures allow companies to translate vast quantities of disparate data into manageable assets that can be leveraged to achieve business objectives.
That’s why, for a data-driven company, a modern data architecture is no longer just a competitive edge. A modern data architecture is the backbone of a data-first company.